Evolutionary characterization of lung cancer metastasis https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13190308/ pmc Nature Nature 981 npgopen 0410462 Nature 0028-0836 1476-4687 pmc-is-collection-domain yes pmc-collection-title Nature Portfolio PMC13190308 PMC13190308.1 13190308 13190308 42056508 10.1038/s41586-026-10428-4 10428 1 Article Evolutionary characterization of lung cancer metastasis Hessey Sonya 1 2 3 4 http://orcid.org/0000-0002-7753-9421 Bunkum Abigail 1 2 3 http://orcid.org/0000-0001-8009-4999 Huebner Ariana 1 5 6 Haase Kerstin 1 2 Grigoriadis Kristiana 1 5 6 http://orcid.org/0000-0002-7097-0307 Naceur-Lombardelli Cristina 1 Liu Wing Kin 1 2 http://orcid.org/0000-0002-9243-9648 Harrigan Caitlin F. 7 8 9 Grieco Charlotte 1 6 http://orcid.org/0000-0002-6002-363X Marinelli Daniele 2 5 10 Ding Boyue 11 http://orcid.org/0000-0002-4817-0565 Martínez-Ruiz Carlos 1 6 Pawlik Piotr 1 5 Hill Mark S. 6 http://orcid.org/0000-0001-8169-7767 Lucas Olivia 1 3 4 6 Richard Corentin 1 2 http://orcid.org/0000-0002-1956-1882 Pich Oriol 6 http://orcid.org/0000-0002-4624-118X Thol Kerstin 1 5 http://orcid.org/0000-0001-6863-2360 Karasaki Takahiro 1 2 6 12 http://orcid.org/0000-0002-6777-5279 Ward Sophia 1 6 13 Athanasopoulou Foteini 1 6 13 Sivakumar Monica 1 Veeriah Selvaraju 1 Toncheva Antonia 1 Rowan Andrew J. 6 Prymas Paulina 1 Bridger Hayley 14 Mitchison Miriam 15 Borg Elaine 15 Falzon Mary 15 Proctor Ian 15 Mahadeva Ula 16 Green Anna 16 Forster Martin D. 1 4 Benafif Sarah 4 Ahmad Tanya 4 Lee Siow Ming 1 4 Papadatos-Pastos Dionysis 4 Naidu Babu 17 Langman Gerald 18 Krebs Matthew G. 19 Oliveira Pedro 20 Blackhall Fiona H. 19 Summers Yvonne 19 Weaver Jamie 19 http://orcid.org/0000-0003-3552-7446 Le Quesne John 21 22 23 Thomas Anne 24 Richards Cathy 25 26 Fennell Dean A. 26 27 Jogai Sanjay 28 Cave Judith 29 http://orcid.org/0000-0001-9869-591X Roxburgh Patricia 22 30 Fraser Sioban 21 23 http://orcid.org/0000-0001-7317-523X Kirk Alan 31 Blyth Kevin G. 21 22 32 Russell Peter 33 Hiley Crispin T. 1 6 http://orcid.org/0000-0002-5570-5070 Hackshaw Allan 14 TRACERx Consortium Hessey Sonya 1 2 3 4 http://orcid.org/0000-0002-7753-9421 Bunkum Abigail 1 2 3 http://orcid.org/0000-0001-8009-4999 Huebner Ariana 1 5 6 Haase Kerstin 1 2 Grigoriadis Kristiana 1 5 6 http://orcid.org/0000-0002-7097-0307 Naceur-Lombardelli Cristina 1 Liu Wing Kin 1 2 Grieco Charlotte 1 6 http://orcid.org/0000-0002-4817-0565 Martínez-Ruiz Carlos 1 6 Pawlik Piotr 1 5 http://orcid.org/0000-0001-8169-7767 Lucas Olivia 1 3 4 6 http://orcid.org/0000-0002-1956-1882 Pich Oriol 6 http://orcid.org/0000-0002-4624-118X Thol Kerstin 1 5 http://orcid.org/0000-0001-6863-2360 Karasaki Takahiro 1 2 6 12 http://orcid.org/0000-0002-6777-5279 Ward Sophia 1 6 13 Sivakumar Monica 1 Veeriah Selvaraju 1 Rowan Andrew J. 6 Borg Elaine 15 Falzon Mary 15 Forster Martin D. 1 4 Benafif Sarah 4 Ahmad Tanya 4 Lee Siow Ming 1 4 Papadatos-Pastos Dionysis 4 Naidu Babu 17 Langman Gerald 18 Krebs Matthew G. 19 Oliveira Pedro 20 Blackhall Fiona H. 19 Summers Yvonne 19 Fennell Dean A. 26 27 Cave Judith 29 http://orcid.org/0000-0001-7317-523X Kirk Alan 31 Blyth Kevin G. 21 22 32 Russell Peter 33 Hiley Crispin T. 1 6 http://orcid.org/0000-0002-5570-5070 Hackshaw Allan 14 Le Quesne John 21 22 23 Lester Jason F. 34 Bajaj Amrita 27 Nakas Apostolos 27 Sodha-Ramdeen Azmina 27 Wilson Claire 35 Scotland Molly 27 Boyles Rebecca 27 Dulloo Sean 26 27 Rathinam Sridhar 27 Matharu Gurdeep 36 Shaw Jacqui A. 36 Boleti Ekaterini 37 Cheyne Heather 38 Price Gillian 39 40 Kerr Keith M. 40 41 Khalil Mohammed 38 Richardson Shirley 38 Cruickshank Tracey 38 French Jack 42 Gilbert Kayleigh 42 Patel Akshay J. 43 Osman Aya 18 Middleton Gary 18 44 Shackleford Helen 18 Djearaman Madava 18 Sangha Mandeesh 18 Leek Angela 45 Atkin Adam 46 Chaturvedi Anshuman 47 48 Paiva-Correia Antonio 49 Lindsay Colin R. 19 48 Fontaine Eustace 46 Granato Felice 46 Davies Hodgkinson Jack 45 Novasio Juliette 46 Brown Katherine D. 47 48 Rammohan Kandadai 46 Joseph Leena 46 Carter Mathew 47 48 Totton Nicola 45 Bishop Paul 46 Crosbie Philip A. J. 46 48 50 Waplington Sara 46 Tugwood Jonathan 48 51 Dive Caroline 48 51 Aerts Hugo JWL 52 53 54 Wilson Gareth A. 6 Leppä Aino-Maija 6 Azizi Alexander A. 1 6 Liu Lydia Y. 1 6 Demeulemeester Jonas 55 56 57 Diossy Miklos 58 59 60 Birkbak Nicolai J. 1 6 61 62 63 Van Loo Peter 64 65 Rosenthal Rachel 6 Salgado Roberto 66 67 Schwarz Roland F. 68 69 Kaufmann Tom L. 69 70 Szallasi Zoltan 58 59 71 Frankell Alexander M. 72 Dwornik Angela 73 Karamani Angeliki 73 Grimes Karen 1 5 Chain Benny 73 Castignani Carla 74 75 Bailey Chris 6 Murphy Cian 6 Weeden Clare E. 76 Puttick Clare 1 5 6 Pearce David R. 73 Karagianni Despoina 1 77 Brempou Dimitria 73 Lim Emilia L. 78 Colliver Emma C. 6 Hazelwood Emma 72 Nye Emma 79 Sahai Erik 76 Grönroos Eva 76 Gimeno-Valiente Francisco 1 Foulds Gemma 80 Kassiotis George 76 81 Moth Georgia 1 Stavrou Georgia 73 Lowe Helen L. 73 Usaite Ieva 1 Mladenova Iva 1 3 5 13 Goldman Jacki 76 Reading James L. 1 82 Black James R. M. 1 6 Rane Jayant K. 6 73 Kittel Jeanette 1 2 Hartley John A. 73 Martin Arana Jorge 73 Peggs Karl S. 73 83 Enfield Katey S. S. 6 84 85 Honan Katherine 72 Selvaraju Kayalvizhi 73 Koh Kexin 1 2 Thakkar Krupa 1 Ensell Leah 73 Patruno Lucrezia 1 3 Al Bakir Maise 1 6 Shah Mansi 73 Litovchenko Maria 73 Zagorulya Maria 6 Magala Michalina 1 Leung Michelle M. 1 5 6 Escudero Mickael 76 Angelova Mihaela 6 Kanu Nnennaya 1 Shutkever Oliver 1 2 Hobson Philip 80 Stone Richard Kevin 79 Zaidi Rija 1 3 Bentham Robert 1 5 Goldstone Robert 13 Vendramin Roberto 1 6 86 Saghafinia Sadegh 1 Gamble Samuel 73 Ung Seng Kuong Anakin 73 Quezada Sergio A. 1 77 Vanloo Sharon 1 Harries Sian 1 6 13 Boeing Stefan 76 Beck Stephan 75 Bola Supreet Kaur 1 77 Marafioti Teresa 15 Visakan Theepan 1 Watkins Thomas B. K. 87 Jones Thomas Patrick 5 Spanswick Victoria 73 Barbè Vittorio 88 Lu Wei-Ting 76 89 Hill William 76 Zhang Woody Z. 72 Wu Yin 73 Naito Yutaka 76 Ramsden Zoe 76 Veiga Catarina 11 Collins-Fekete Charles-Antoine 11 Fraioli Francesco 90 Royle Gary 91 Ashford Paul 92 Procter Alexander James 93 Nair Arjun 93 94 Ahmed Asia 93 Lawrence David 95 Patrini Davide 95 Martinoni Hoogenboom Emilie 96 Monk Fleur 96 Holding James W. 96 Choudhary Junaid 96 Bhakhri Kunal 96 Taylor Magali N. 93 Pisciella Maria Chiara 96 Navani Neal 97 98 Gorman Pat 96 Khiroya Reena 15 Thakrar Ricky M. 97 98 Stephens Robert CM 96 Janes Sam M. 99 Bandula Steve 96 Kaplar Zoltan 100 101 Walker Aoife 14 Pilotti Camilla 14 Leslie Rachel 14 Kellett Salomey 14 Grapa Anca 102 Zhang Hanyun 103 AbdulJabbar Khalid 104 Pan Xiaoxi 105 Yuan Yinyin 105 Chuter David 106 MacKenzie Mairead 106 Alzetani Aiman 107 Georg Patricia 108 Chee Serena 108 Lim Eric 109 110 Rice Alexandra 110 Devaraj Anand 110 Nicholson Andrew G. 110 111 Proli Chiara 110 Kaniu Daniel 110 Bhayani Harshil 110 Chavan Hema 110 Raubenheimer Hilgardt 110 Ambrose Lyn 110 Malima Mpho 110 Fernandes Nadia 110 De Sousa Paulo 110 Shah Pratibha 110 Booth Sarah 110 Buderi Silviu I. 110 Jordan Simon 110 Begum Sofina 110 Hewish Madeleine 112 113 Danson Sarah 114 115 Shackcloth Michael J. 116 Robinson Lily 33 Kidd Andrew 117 Dick Craig 118 Whiteley Jennifer 31 Thomas Mathew 31 Asif Mohammed 31 Kostoulas Nikos 31 Bilancia Rocco 31 http://orcid.org/0000-0002-6296-1312 Moore David A. 1 6 15 http://orcid.org/0000-0002-5265-7392 Zaccaria Simone s.zaccaria@ucl.ac.uk 1 3 http://orcid.org/0000-0001-9537-4045 McGranahan Nicholas nicholas.mcgranahan.10@ucl.ac.uk 1 5 http://orcid.org/0000-0002-4299-3018 Swanton Charles charles.swanton@crick.ac.uk 1 4 6 http://orcid.org/0000-0003-1212-1259 Jamal-Hanjani Mariam m.jamal-hanjani@ucl.ac.uk 1 2 4 TRACERx EVO Consortium http://orcid.org/0000-0001-8009-4999 Huebner Ariana 1 5 6 http://orcid.org/0000-0002-7097-0307 Naceur-Lombardelli Cristina 1 http://orcid.org/0000-0002-4817-0565 Martínez-Ruiz Carlos 1 6 http://orcid.org/0000-0001-8169-7767 Lucas Olivia 1 3 4 6 http://orcid.org/0000-0002-6777-5279 Ward Sophia 1 6 13 Veeriah Selvaraju 1 Papadatos-Pastos Dionysis 4 Blackhall Fiona H. 19 Summers Yvonne 19 Hiley Crispin T. 1 6 http://orcid.org/0000-0002-5570-5070 Hackshaw Allan 14 Leek Angela 45 Atkin Adam 46 Lindsay Colin R. 19 48 Brown Katherine D. 47 48 Joseph Leena 46 Carter Mathew 47 48 Crosbie Philip A. J. 46 48 50 Moth Georgia 1 Goldman Jacki 76 Al Bakir Maise 1 6 Magala Michalina 1 Vanloo Sharon 1 Harries Sian 1 6 13 Walker Aoife 14 Kellett Salomey 14 Robinson Lily 33 Kalokoh Zainab 14 Keene Elizabeth 1 Vikasan Theepan 1 Ambrose Karen 76 Gavrielides Mike 76 Rosenfeld Nitzan 119 Roshan Amrit 119 Agergaard Soerensen Cecilie 120 Solomon Ben 67 Tan Lavinia 67 Parreira Ana 47 48 Faivre-Finn Corinne 47 48 Gomes Fabio 47 48 Gomez-Randulfe Igor 47 48 Webster Jack 47 48 Cove-Smith Laura 47 48 Maroa Pamela 46 Taylor Paul 47 48 Califano Raffaele 19 48 Tenconi Sara 46 Peryt Adam 120 121 Coonar Aman 120 121 Stone Amanda 120 121 Sanganee Caroline 120 121 Goddard Martin 120 121 Preston Stephen 121 Aresu Giuseppe 120 121 Lichfield Jane 120 121 Knight Julia 120 121 DSA Lauren 120 121 Urda Maria Manuela 120 121 Nizami Maria 120 121 Rintoul Robert 120 121 Armstrong Zoe 120 121 Mathew Abiya 122 Namwanja Damalie 122 Thomson Nicky 122 Earwaker Philip 122 http://orcid.org/0000-0002-6296-1312 Moore David A. 1 6 15 http://orcid.org/0000-0001-9537-4045 McGranahan Nicholas nicholas.mcgranahan.10@ucl.ac.uk 1 5 http://orcid.org/0000-0002-4299-3018 Swanton Charles charles.swanton@crick.ac.uk 1 4 6 http://orcid.org/0000-0003-1212-1259 Jamal-Hanjani Mariam m.jamal-hanjani@ucl.ac.uk 1 2 4 PEACE consortium Hessey Sonya 1 2 3 4 http://orcid.org/0000-0002-7753-9421 Bunkum Abigail 1 2 3 http://orcid.org/0000-0001-8009-4999 Huebner Ariana 1 5 6 Haase Kerstin 1 2 Grigoriadis Kristiana 1 5 6 http://orcid.org/0000-0002-7097-0307 Naceur-Lombardelli Cristina 1 Liu Wing Kin 1 2 http://orcid.org/0000-0002-4817-0565 Martínez-Ruiz Carlos 1 6 Pawlik Piotr 1 5 http://orcid.org/0000-0001-8169-7767 Lucas Olivia 1 3 4 6 http://orcid.org/0000-0002-1956-1882 Pich Oriol 6 http://orcid.org/0000-0002-4624-118X Thol Kerstin 1 5 http://orcid.org/0000-0001-6863-2360 Karasaki Takahiro 1 2 6 12 http://orcid.org/0000-0002-6777-5279 Ward Sophia 1 6 13 Veeriah Selvaraju 1 Rowan Andrew J. 6 Bridger Hayley 14 Mitchison Miriam 15 Falzon Mary 15 Green Anna 16 Forster Martin D. 1 4 Benafif Sarah 4 Ahmad Tanya 4 Lee Siow Ming 1 4 Papadatos-Pastos Dionysis 4 Naidu Babu 17 Langman Gerald 18 Krebs Matthew G. 19 Oliveira Pedro 20 Blackhall Fiona H. 19 Summers Yvonne 19 Richards Cathy 25 26 Fennell Dean A. 26 27 Jogai Sanjay 28 Cave Judith 29 http://orcid.org/0000-0001-9869-591X Roxburgh Patricia 22 30 Fraser Sioban 21 23 Blyth Kevin G. 21 22 32 Hiley Crispin T. 1 6 http://orcid.org/0000-0002-5570-5070 Hackshaw Allan 14 Le Quesne John 21 22 23 Wilson Claire 35 Shaw Jacqui A. 36 Osman Aya 18 Middleton Gary 18 44 Dive Caroline 48 51 Van Loo Peter 64 65 Salgado Roberto 66 67 Frankell Alexander M. 72 Dwornik Angela 73 Chain Benny 73 Pearce David R. 73 Brempou Dimitria 73 Lim Emilia L. 78 Colliver Emma C. 6 Nye Emma 79 Reading James L. 1 82 Black James R. M. 1 6 Rane Jayant K. 6 73 Kittel Jeanette 1 2 Martin Arana Jorge 73 Enfield Katey S. S. 6 84 85 Patruno Lucrezia 1 3 Al Bakir Maise 1 6 Angelova Mihaela 6 Shutkever Oliver 1 2 Zaidi Rija 1 3 Vendramin Roberto 1 6 86 Gamble Samuel 73 Ung Seng Kuong Anakin 73 Quezada Sergio A. 1 77 Harries Sian 1 6 13 Beck Stephan 75 Bola Supreet Kaur 1 77 Marafioti Teresa 15 Barbè Vittorio 88 Hill William 76 Janes Sam M. 99 Walker Aoife 14 Leslie Rachel 14 Chuter David 106 Gomes Fabio 47 48 Shiu Kai-Keen 96 Bridgewater John 96 Hochhauser Daniel 73 96 Enver Tariq 73 Sinclair Ron 96 Rhodes Zoe 96 Linch Mark 73 Brandner Sebastian 123 Shaw Heather 96 124 Attard Gerhardt 73 96 Gishen Faye 6 42 125 Kanu Nnennaya 1 Gimeno Valiente Francisco 1 Flanagan Adrienne 73 Vainauskas Osvaldas 73 Wingate Anna 73 Wetterskog Daniel 73 Hasan A. M. Mahedi 73 Lise Stefano 73 Leone Gianmarco 73 Jayaram Anuradha 73 96 Alifrangis Constantine 96 McGovern Ursula 96 Bowles Kerry 96 Vargiamiou Athanasia 96 Chamberlain Christopher Aled 1 77 Robinson Welles 1 77 McNeish Iain 126 Ojeda Mosquera Nataly 1 77 Liu Jiali 1 77 O’Farrell Felix 1 77 Marcel Chenelle 96 Turajlic Samra 102 127 128 Larkin James 102 127 Pickering Lisa 127 Furness Andrew 127 Young Kate 127 Drake Will 119 129 Edmonds Kim 127 Hunter Nikki 127 Mangwende Mary 127 Grostate Lauren 127 Spain Lavinia 76 127 Shepherd Scott 76 127 Yan Haixi 76 127 Shum Benjamin 127 128 Tippu Zayd 76 127 Hanley Brian 76 102 127 Spencer Charlotte 76 127 Emmerich Max 76 127 Gerard Camille 76 127 Carlyle Eleanor 127 Hazell Steve 127 Mudhar Hardeep 115 Messiou Christina 102 127 Latifoltojar Arash 102 127 Fendler Annika 76 Byrne Fiona 76 Pallikonda Husayn 76 Lobon Irene 76 Coulton Alexander 76 Cattin Anne-Laure 76 Deng Daqi 76 Feng Hugang 76 Yousaf Nadia 127 Popat Sanjay 127 Milner-Watts Charlotte 127 Murra Aida 127 Korteweg Justine 127 Terry Lauren 127 Biano Jennifer 127 Peat Kema 127 Turay Emma 127 Hill Peter 127 130 Miletic Marija 127 Javaid Anadil 127 Thomas Jennifer 127 Kudic Bakir 127 McGowan Orla 127 Ramesh Dharmista 127 Saka Oznur 127 Arslan Sinem 127 Marandino Laura 127 Ammar Reina 127 Kapur Gurneet 127 Kabir Dilruba 127 McMahon David 127 John Alexius 127 Kalofonou Foteini 127 Josephs Debra 43 Irshad Sheeba 43 Spicer James 131 Stewart Ruby 43 Wright Natasha 43 Mitu Ruxandra 43 Enting Deborah 43 Rudman Sarah 43 Ghosh Sharmistha 43 Karapanagiotou Eleni Lena 43 Pintus Elias 43 Tutt Andrew 43 Brenton James D. 132 Thompson Nicola 133 Fitzgerald Rebecca 134 Jimenez-Linan Merche 133 Provenzano Elena 133 Paterson Anna 133 Allinson Kieren 133 Stewart Grant D. 135 McDermott Ultan 133 136 Maughan Tim 137 Ansorge Olaf 136 Campbell Peter 136 Carter Mat 47 Poile Charlotte 26 Kutywayo Kudazyi H. 27 Dungey Maurice R. 26 Hahne Jens Claus 138 Baijal Shobhit 18 Ferris Charlotte 18 Bancroft Hollie 18 Kerr Amy 18 Webb Joanne 18 Kadiri Salma 18 Olisemeke Bernard 18 Wilson Rodelaine 18 Tomlinson Ian 18 Nolan Luke 28 Holden Samantha 28 Fernandes Tania 28 McKenzie Mairead 106 Patel Shivani 14 http://orcid.org/0000-0002-6296-1312 Moore David A. 1 6 15 http://orcid.org/0000-0002-5265-7392 Zaccaria Simone s.zaccaria@ucl.ac.uk 1 3 http://orcid.org/0000-0001-9537-4045 McGranahan Nicholas nicholas.mcgranahan.10@ucl.ac.uk 1 5 http://orcid.org/0000-0002-4299-3018 Swanton Charles charles.swanton@crick.ac.uk 1 4 6 http://orcid.org/0000-0003-1212-1259 Jamal-Hanjani Mariam m.jamal-hanjani@ucl.ac.uk 1 2 4 http://orcid.org/0000-0002-6296-1312 Moore David A. 1 6 15 http://orcid.org/0000-0002-5265-7392 Zaccaria Simone s.zaccaria@ucl.ac.uk 1 3 http://orcid.org/0000-0001-9537-4045 McGranahan Nicholas nicholas.mcgranahan.10@ucl.ac.uk 1 5 http://orcid.org/0000-0002-4299-3018 Swanton Charles charles.swanton@crick.ac.uk 1 4 6 http://orcid.org/0000-0003-1212-1259 Jamal-Hanjani Mariam m.jamal-hanjani@ucl.ac.uk 1 2 4 1 https://ror.org/02jx3x895 grid.83440.3b 0000000121901201 Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK 2 https://ror.org/02jx3x895 grid.83440.3b 0000000121901201 Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK 3 https://ror.org/02jx3x895 grid.83440.3b 0000000121901201 Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK 4 https://ror.org/00wrevg56 grid.439749.4 0000 0004 0612 2754 Department of Oncology, University College London Hospitals, London, UK 5 https://ror.org/02jx3x895 grid.83440.3b 0000000121901201 Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK 6 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, UK 7 https://ror.org/03dbr7087 grid.17063.33 0000 0001 2157 2938 Department of Computer Science, University of Toronto, Toronto, Canada 8 https://ror.org/03kqdja62 grid.494618.6 0000 0005 0272 1351 Vector Institute for Artificial Intelligence, Toronto, Canada 9 https://ror.org/043q8yx54 grid.419890.d 0000 0004 0626 690X Ontario Institute for Cancer Research, Toronto, Canada 10 https://ror.org/02be6w209 grid.7841.a Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy 11 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Department of Medical Physics and Biomedical Engineering, University College London, London, UK 12 https://ror.org/057zh3y96 grid.26999.3d 0000 0001 2169 1048 Department of Thoracic Surgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan 13 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Genomics Science Technology Platform, Francis Crick Institute, London, UK 14 https://ror.org/054225q67 grid.11485.39 0000 0004 0422 0975 Cancer Research UK and UCL Cancer Trials Centre, London, UK 15 https://ror.org/00wrevg56 grid.439749.4 0000 0004 0612 2754 Department of Cellular Pathology, University College London Hospitals, London, UK 16 https://ror.org/00j161312 grid.420545.2 Department of Cellular Pathology, Guy’s and St Thomas’ NHS Foundation Trust, London, UK 17 https://ror.org/03angcq70 grid.6572.6 0000 0004 1936 7486 Birmingham Acute Care Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK 18 https://ror.org/014ja3n03 grid.412563.7 0000 0004 0376 6589 University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK 19 https://ror.org/027m9bs27 grid.5379.8 0000 0001 2166 2407 Division of Cancer Sciences, University of Manchester and The Christie NHS Foundation Trust, Manchester, UK 20 https://ror.org/03v9efr22 grid.412917.8 0000 0004 0430 9259 Department of Pathology, The Christie NHS Foundation Trust, Manchester, UK 21 https://ror.org/03pv69j64 grid.23636.32 0000 0000 8821 5196 Cancer Research UK Scotland Institute, Glasgow, UK 22 https://ror.org/00vtgdb53 grid.8756.c 0000 0001 2193 314X School of Cancer Sciences, University of Glasgow, Glasgow, UK 23 https://ror.org/05kdz4d87 grid.413301.4 0000 0001 0523 9342 Pathology Department, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, UK 24 https://ror.org/04h699437 grid.9918.9 0000 0004 1936 8411 Leicester Cancer Research Centre, College of Life Sciences, University of Leicester, Leicester, UK 25 https://ror.org/02fha3693 grid.269014.8 0000 0001 0435 9078 Department of Histopathology, University Hospitals of Leicester NHS Trust, Leicester, UK 26 https://ror.org/04h699437 grid.9918.9 0000 0004 1936 8411 University of Leicester, Leicester, UK 27 https://ror.org/02fha3693 grid.269014.8 0000 0001 0435 9078 University Hospitals of Leicester NHS Trust, Leicester, UK 28 https://ror.org/0485axj58 grid.430506.4 University Hospital Southampton NHS Trust, Southampton, UK 29 https://ror.org/0485axj58 grid.430506.4 Department of Oncology, University Hospital Southampton NHS Foundation Trust, Southampton, UK 30 https://ror.org/05kdz4d87 grid.413301.4 0000 0001 0523 9342 Beatson West of Scotland Cancer Centre, NHS Greater Glasgow and Clyde, Glasgow, UK 31 https://ror.org/0103jbm17 grid.413157.5 0000 0004 0590 2070 Golden Jubilee National Hospital, Clydebank, UK 32 https://ror.org/05kdz4d87 grid.413301.4 0000 0001 0523 9342 Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, UK 33 https://ror.org/04kpzy923 grid.437503.6 0000 0000 9219 2564 Princess Alexandra Hospital, Princess Alexandra Hospital NHS Trust, Harlow, UK 34 https://ror.org/04zet5t12 grid.419728.1 0000 0000 8959 0182 Singleton Hospital, Swansea Bay University Health Board, Swansea, UK 35 https://ror.org/04h699437 grid.9918.9 0000 0004 1936 8411 Leicester Medical School, University of Leicester, Leicester, UK 36 https://ror.org/04h699437 grid.9918.9 0000 0004 1936 8411 Cancer Research Centre, University of Leicester, Leicester, UK 37 https://ror.org/04rtdp853 grid.437485.9 0000 0001 0439 3380 Royal Free London NHS Foundation Trust, London, UK 38 https://ror.org/02q49af68 grid.417581.e 0000 0000 8678 4766 Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK 39 https://ror.org/02q49af68 grid.417581.e 0000 0000 8678 4766 Department of Medical Oncology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK 40 https://ror.org/016476m91 grid.7107.1 0000 0004 1936 7291 University of Aberdeen, Aberdeen, UK 41 https://ror.org/02q49af68 grid.417581.e 0000 0000 8678 4766 Department of Pathology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK 42 https://ror.org/02vg92y09 grid.507529.c 0000 0000 8610 0651 Whittington Hospital NHS Trust, London, UK 43 https://ror.org/00j161312 grid.420545.2 Guy’s and St Thomas’ NHS Foundation Trust, London, UK 44 https://ror.org/03angcq70 grid.6572.6 0000 0004 1936 7486 Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK 45 grid.521475.0 0000 0004 0612 4047 Manchester Cancer Research Centre Biobank, Manchester, UK 46 https://ror.org/00he80998 grid.498924.a 0000 0004 0430 9101 Wythenshawe Hospital, Manchester University NHS Foundation Trust, Wythenshawe, UK 47 https://ror.org/03v9efr22 grid.412917.8 0000 0004 0430 9259 The Christie NHS Foundation Trust, Manchester, UK 48 https://ror.org/027m9bs27 grid.5379.8 0000 0001 2166 2407 Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK 49 https://ror.org/00he80998 grid.498924.a 0000 0004 0430 9101 Cellular Pathology Department, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Wythenshawe, UK 50 https://ror.org/027m9bs27 grid.5379.8 0000 0001 2166 2407 Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK 51 https://ror.org/027m9bs27 grid.5379.8 0000000121662407 Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK 52 https://ror.org/03vek6s52 grid.38142.3c 000000041936754X Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA USA 53 https://ror.org/03vek6s52 grid.38142.3c 000000041936754X Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA USA 54 https://ror.org/02jz4aj89 grid.5012.6 0000 0001 0481 6099 Radiology and Nuclear Medicine, CARIM and GROW, Maastricht University, Maastricht, The Netherlands 55 https://ror.org/00eyng893 grid.511459.d Integrative Cancer Genomics Laboratory, VIB Center for Cancer Biology, Leuven, Belgium 56 https://ror.org/03fds3g42 VIB Center for AI and Computational Biology, Leuven, Belgium 57 https://ror.org/05f950310 grid.5596.f 0000 0001 0668 7884 Department of Oncology, KU Leuven, Leuven, Belgium 58 Danish Cancer Institute, Copenhagen, Denmark 59 https://ror.org/00dvg7y05 grid.2515.3 0000 0004 0378 8438 Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA 60 https://ror.org/01jsq2704 grid.5591.8 0000 0001 2294 6276 Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary 61 https://ror.org/040r8fr65 grid.154185.c 0000 0004 0512 597X Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark 62 https://ror.org/01aj84f44 grid.7048.b 0000 0001 1956 2722 Department of Clinical Medicine, Aarhus University, Aarhus, Denmark 63 https://ror.org/01aj84f44 grid.7048.b 0000 0001 1956 2722 Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark 64 https://ror.org/04twxam07 grid.240145.6 0000 0001 2291 4776 Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, TX USA 65 https://ror.org/04twxam07 grid.240145.6 0000 0001 2291 4776 Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX USA 66 https://ror.org/008x57b05 grid.5284.b 0000 0001 0790 3681 Department of Pathology, ZAS Hospitals, Antwerp, Belgium 67 https://ror.org/02a8bt934 grid.1055.1 0000 0004 0397 8434 Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia 68 https://ror.org/00rcxh774 grid.6190.e 0000 0000 8580 3777 Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany 69 https://ror.org/05dsfb086 0000 0005 1089 7074 Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany 70 https://ror.org/00rcxh774 grid.6190.e 0000 0000 8580 3777 Institute for Computational Cancer Biology (ICCB), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany 71 https://ror.org/01g9ty582 grid.11804.3c 0000 0001 0942 9821 Department of Bioinformatics, Semmelweis University, Budapest, Hungary 72 https://ror.org/013meh722 grid.5335.0 0000 0001 2188 5934 Somatic Evolution Monitoring Lab, Early Cancer Institute, CRUK Cambridge Centre, University of Cambridge, Cambridge, UK 73 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 University College London Cancer Institute, London, UK 74 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Cancer Genomics Laboratory, Francis Crick Institute, London, UK 75 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Medical Genomics, University College London Cancer Institute, London, UK 76 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Francis Crick Institute, London, UK 77 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Immune Regulation and Tumour Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK 78 https://ror.org/03rmrcq20 grid.17091.3e 0000 0001 2288 9830 Department of Biochemistry and Molecular Biology and Edwin S.H. Leong Centre for Healthy Aging, Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia Canada 79 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Experimental Histopathology, Francis Crick Institute, London, UK 80 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Flow Cytometry STP, Francis Crick Institute, London, UK 81 https://ror.org/041kmwe10 grid.7445.2 0000 0001 2113 8111 Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK 82 https://ror.org/02jx3x895 grid.83440.3b 0000000121901201 Pre-Cancer Immunology Laboratory, Department of Haematology, University College London Cancer Institute, London, UK 83 https://ror.org/00wrevg56 grid.439749.4 0000 0004 0612 2754 Department of Haematology, University College London Hospitals, London, UK 84 Basic and Translational Research, BC Cancer Research Institute, Vancouver, British Columbia Canada 85 https://ror.org/03rmrcq20 grid.17091.3e 0000 0001 2288 9830 Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada 86 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK 87 https://ror.org/00f54p054 grid.168010.e 0000000419368956 Department of Pathology, Stanford University School of Medicine, Stanford, CA USA 88 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Stem Cells and Organoids Platform, Francis Crick Institute, London, UK 89 https://ror.org/052gg0110 grid.4991.5 0000 0004 1936 8948 Department of Oncology, University of Oxford, Oxford, UK 90 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Institute of Nuclear Medicine, Division of Medicine, University College London, London, UK 91 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Department of Medical Physics and Bioengineering, University College London Cancer Institute, London, UK 92 https://ror.org/02jx3x895 grid.83440.3b 0000000121901201 Institute of Structural and Molecular Biology, University College London, London, UK 93 https://ror.org/00wrevg56 grid.439749.4 0000 0004 0612 2754 Department of Radiology, University College London Hospitals, London, UK 94 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 UCL Respiratory, Department of Medicine, University College London, London, UK 95 https://ror.org/042fqyp44 grid.52996.31 0000 0000 8937 2257 Department of Thoracic Surgery, University College London Hospitals NHS Trust, London, UK 96 https://ror.org/00wrevg56 grid.439749.4 0000 0004 0612 2754 University College London Hospitals, London, UK 97 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK 98 https://ror.org/00wrevg56 grid.439749.4 0000 0004 0612 2754 Department of Thoracic Medicine, University College London Hospitals, London, UK 99 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 Lungs for Living Research Centre, UCL Respiratory, Department of Medicine, University College London, London, UK 100 Integrated Radiology Department, North-Buda St John’s Central Hospital, Budapest, Hungary 101 https://ror.org/00wrevg56 grid.439749.4 0000 0004 0612 2754 Institute of Nuclear Medicine, University College London Hospitals, London, UK 102 https://ror.org/043jzw605 grid.18886.3f 0000 0001 1499 0189 Institute of Cancer Research, London, UK 103 https://ror.org/01b3dvp57 grid.415306.5 0000 0000 9983 6924 Garvan Institute of Medical Research, Sydney, New South Wales Australia 104 Case45, London, UK 105 https://ror.org/04twxam07 grid.240145.6 0000 0001 2291 4776 University of Texas MD Anderson Cancer Center, Houston, TX USA 106 Independent Cancer Patient’s Voice, London, UK 107 https://ror.org/0485axj58 grid.430506.4 0000 0004 0465 4079 Department of Thoracic Surgery, NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK 108 https://ror.org/0485axj58 grid.430506.4 University Hospital Southampton NHS Foundation Trust, Southampton, UK 109 https://ror.org/041kmwe10 grid.7445.2 0000 0001 2113 8111 Academic Division of Thoracic Surgery, Imperial College London, London, UK 110 https://ror.org/00j161312 grid.420545.2 Royal Brompton and Harefield Hospitals, part of Guy’s and St Thomas’ NHS Foundation Trust, London, UK 111 https://ror.org/041kmwe10 grid.7445.2 0000 0001 2113 8111 National Heart and Lung Institute, Imperial College, London, UK 112 https://ror.org/02wnqcb97 grid.451052.7 0000 0004 0581 2008 Royal Surrey Hospital, Royal Surrey Hospitals NHS Foundation Trust, Guildford, UK 113 https://ror.org/00ks66431 grid.5475.3 0000 0004 0407 4824 University of Surrey, Guildford, UK 114 https://ror.org/05krs5044 grid.11835.3e 0000 0004 1936 9262 University of Sheffield, Sheffield, UK 115 https://ror.org/018hjpz25 grid.31410.37 0000 0000 9422 8284 Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK 116 https://ror.org/000849h34 grid.415992.2 0000 0004 0398 7066 Liverpool Heart and Chest Hospital, Liverpool, UK 117 https://ror.org/00vtgdb53 grid.8756.c 0000 0001 2193 314X Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, UK 118 https://ror.org/05kdz4d87 grid.413301.4 0000 0001 0523 9342 NHS Greater Glasgow and Clyde, Glasgow, UK 119 https://ror.org/026zzn846 grid.4868.2 0000 0001 2171 1133 Queen Mary University of London, London, UK 120 https://ror.org/013meh722 grid.5335.0 0000 0001 2188 5934 University of Cambridge, Cambridge, UK 121 https://ror.org/01qbebb31 grid.412939.4 0000 0004 0383 5994 Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK 122 https://ror.org/04v54gj93 grid.24029.3d 0000 0004 0383 8386 Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK 123 https://ror.org/0370htr03 grid.72163.31 0000 0004 0632 8656 University College London Queen Square Institute of Neurology, London, UK 124 https://ror.org/01wwv4x50 grid.477623.3 0000 0004 0400 1422 Mount Vernon Cancer Centre, Northwood, UK 125 https://ror.org/02jx3x895 grid.83440.3b 0000 0001 2190 1201 UCL Medical School, University College London, London, UK 126 https://ror.org/041kmwe10 grid.7445.2 0000 0001 2113 8111 Imperial College London, London, UK 127 https://ror.org/034vb5t35 grid.424926.f 0000 0004 0417 0461 The Royal Marsden Hospital, London, UK 128 https://ror.org/04tnbqb63 grid.451388.3 0000 0004 1795 1830 Cancer Dynamics Laboratory, Francis Crick Institute, London, UK 129 https://ror.org/00b31g692 grid.139534.9 0000 0001 0372 5777 St Bartholomew’s Hospital, Barts Health NHS Trust, London, UK 130 https://ror.org/041kmwe10 grid.7445.2 0000 0001 2113 8111 Imperial College London NHS Foundation Trust, London, UK 131 https://ror.org/0220mzb33 grid.13097.3c 0000 0001 2322 6764 King’s College London, London, UK 132 https://ror.org/013meh722 grid.5335.0 0000000121885934 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK 133 https://ror.org/04v54gj93 grid.24029.3d 0000 0004 0383 8386 Addenbrooke’s Hospital, Cambridge University Hospitals, Cambridge, UK 134 https://ror.org/013meh722 grid.5335.0 0000 0001 2188 5934 Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge, UK 135 https://ror.org/013meh722 grid.5335.0 0000 0001 2188 5934 Department of Surgery, University of Cambridge, Cambridge, UK 136 https://ror.org/05cy4wa09 grid.10306.34 0000 0004 0606 5382 Wellcome Sanger Institute, Hinxton, UK 137 https://ror.org/052gg0110 grid.4991.5 0000 0004 1936 8948 MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK 138 https://ror.org/04h699437 grid.9918.9 0000 0004 1936 8411 National Institute for Health Research Biomedical Research Centre and Cancer Research UK Experimental Cancer Medicine Centre, University of Leicester, Leicester, UK 29 4 2026 2026 653 8115 514029 911 922 29 6 2025 17 3 2026 29 04 2026 22 05 2026 22 05 2026 © The Author(s) 2026 2026 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . Limited understanding of the biological processes that govern metastatic dissemination hinders its prevention and treatment 1 . Here, using 501 longitudinally collected primary and metastatic tumour samples from 24 patients with non-small cell lung cancer (NSCLC) enrolled in the TRACERx lung study and PEACE autopsy programme, we infer tumour evolution from diagnosis to death. With DNA-sequencing data encompassing 70% of the metastases that were radiologically detected before death and paired multi-region sampled primary tumours, we show that the genomes of metastases diverge markedly from those of their ancestral primary tumour, with additional driver alterations and genome doubling events occurring after metastatic dissemination. In 62.5% of patients, multiple primary tumour subclones disseminated, each founding a distinct metastasis. These metastases served as sources of onward spread: more than half of the metastases sampled were seeded by other metastases. The duration that metastases existed in situ influenced their likelihood of seeding further metastases. Most metastatic migrations started and ended in the same anatomical cavity. The few subclones that exited the thorax to seed metastases disseminated widely and were enriched for somatic copy-number alterations, suggesting that chromosomal instability may facilitate extrathoracic spread. This spatial and temporal evolutionary analysis sheds light on the extent of metastatic diversity and seeding in advanced NSCLC—which tends to be underestimated in single metastasis biopsies—and identifies genomic and clinical mediators of metastatic progression. DNA-sequencing data from primary tumours and paired metastases from participants in the TRACERx lung study and PEACE autopsy programme are used to analyse the metastatic diversity of advanced non-small cell lung cancer and the seeding patterns that underpin it. Subject terms Cancer genomics Metastasis Non-small-cell lung cancer Genome informatics pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement yes pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY issue-copyright-statement © Springer Nature Limited 2026 Main Metastasis, the process by which cancer cells spread from their site of origin to a secondary location, is the leading cause of cancer-related mortality 2 . Lung cancer accounts for the largest share of metastatic cancer cases 3 , owing to its high incidence 4 , frequent presentation as de novo metastatic disease 5 and high rate of relapse after curative-intent surgery for localized disease 6 . Understanding the genetic basis for the mechanisms that enable cancer cells to migrate from the primary site into local tissues, air spaces, lymphatics and/or circulation, and to ‘seed’ foreign ‘soils’ 7 , could inform strategies to prevent and treat this lethal condition 1 . The migration of cancer cells is a transient event that is difficult to observe in patients in real time; however, a retrospective view of this process can be gleaned by tracking the evolutionary history of subpopulations of cancer cells, or subclones, in primary tumours and metastases using DNA-sequencing data 8 – 12 . With this approach, studies of ovarian 9 , breast 13 , 14 and prostate cancer 15 have revealed complex metastatic migration patterns that involve multiple tumour subclones migrating bidirectionally between the primary tumour and metastases, and between anatomically distinct metastatic sites. The accuracy and completeness of this view of metastasis is contingent on the extent to which the sampled metastases are representative of a patient’s disease burden, and the availability of the primary tumour for comparison 16 . Despite its importance, such comprehensive and longitudinal sampling is rarely performed because of its clinical infeasibility in living patients, but it can be achieved by integrating research autopsy programmes 16 , 17 with prospective clinical studies such as TRACERx (Tracking Non-small Cell Lung Cancer Evolution through Therapy (Rx); ClinicalTrials.gov identifier NCT01888601 ) 18 , 19 , which performs multi-region profiling of early-stage, operable primary NSCLC. To address this need, the national, multi-centre, pan-cancer research autopsy programme PEACE (Posthumous Evaluation of Advanced Cancer Environment; NCT03004755 ) was strategically embedded in centres that recruited patients to TRACERx to enable co-enrolment to both studies and the generation of a clinically annotated tumour tissue resource that spans the complete disease course, from diagnosis to death. In this study, we reconstructed detailed tumour evolutionary histories and metastatic migration patterns using high-depth whole-exome sequencing (WES) data from longitudinally collected primary tumour, pre-mortem and post-mortem metastasis samples from 24 patients enrolled in both TRACERx and PEACE (Supplementary Fig. 1 ), to investigate the genetic properties that endow cancer cells with the capacity to metastasize. By integrating migration patterns with serial radiological imaging performed before death, we uncover tumour-intrinsic, temporal and anatomical properties that govern NSCLC metastasis. The TRACERx–PEACE cohort The clinical characteristics of the patients in this cohort, including age (median [range]: 70 [47–87] years), smoking status (19 ex-smokers, 3 current smokers and 2 never-smokers), disease-free survival (DFS; median [interquartile range; IQR]: 11 [6−18] months) and overall survival (OS; median [IQR]: 29 [16–46] months), were broadly representative of patients with comparable stages of operable NSCLC (TNM version 8: 4 stage I, 8 stage II and 12 stage III) 19 . Common NSCLC histological subtypes were represented (9 lung adenocarcinomas (LUAD) and 10 squamous cell carcinomas (LUSC)), and 5 other subtypes, including 2 large cell carcinomas, 2 pleomorphic carcinomas and 1 carcinosarcoma, were present (Supplementary Table 1 ). In total, 108 regions from 24 resected primary tumours (median regions per primary [range]: 4 [2–8]), 41 regions from 35 metastases sampled pre-mortem (12 lymph node metastases resected during primary surgery from 7 patients, 17 metastases sampled at relapse from 15 patients and 6 at disease progression from 4 patients) and 352 regions from 233 anatomically distinct metastases collected at autopsy were subjected to WES (median depth: 401.2, IQR: 360.2–441.5, Fig. 1a ) and passed quality control (Supplementary Fig. 1 and Methods ). At autopsy, metastatic sampling was guided by radiological imaging performed before death and macroscopic examination by the attending pathologist. For most metastases collected pre-mortem or at autopsy, a single metastasis region was sampled (75%, 200/268), but in 25% (68/268), multiple regions from the same metastasis were sampled. The mean number of metastasis regions collected per patient was 16 (range: 3–39) and the mean number of anatomically distinct metastases sampled per patient was 11 (range: 2–37). These encompassed 19 anatomical locations, including common metastasis sites observed in NSCLC 20 : lung (125 regions), lymph node (80 regions), liver (33 regions), musculoskeletal soft tissues (23 regions: 13 chest wall, 3 diaphragm, 2 abdominal wall, 5 other), brain (22 regions), adrenal gland (17 regions) and bone (7 regions) (Fig. 1a ). For the 23 patients with available radiological imaging, quality-controlled WES data were available for 70% (112/160) of the metastases that were detected with imaging performed before death (Fig. 1b ). Fig. 1 Clinical and sample characteristics of the TRACERx–PEACE cohort. a , Longitudinal timelines for 24 patients showing clinical events and sampling time points, ordered by overall survival. Tumour histology, stage and smoking status are annotated. The body map summarizes the total number of samples obtained per organ across the cohort. LN, lymph node. b , Number of metastases per patient that were imaged only (white), sampled only (peach) or both imaged and sampled (red) among 23 patients with radiological imaging available. c , Mean genetic diversity (mutations and SCNAs; Methods ) per patient within primary tumours (intra-primary, dark blue; n  = 23 patients, 100 regions), within multi-region sampled metastases (intra-metastasis, dark green; n  = 21 patients, 191 regions), between anatomically distinct metastases (inter-metastasis, green; n  = 24 patients, 258 metastases) and between primary and metastatic samples (primary–metastasis, blue; n  = 24 patients, 24 primaries–258 metastases). Lines connect patients. Wilcoxon signed-rank test. The box plots show the median and IQR with whiskers denoting values within 1.5 times the IQR from the first and third quartiles. Body map illustration in a by J. Brock adapted from ref. 11 under a Creative Commons licence CC BY 4.0 . Intra- and inter-metastasis genetic heterogeneity To resolve the extent of metastatic heterogeneity and the degree to which metastases resemble the primary tumour from which they originate, we performed a detailed genomic analysis of somatic mutations, somatic copy-number alterations (SCNAs) and whole-genome doubling (WGD) in primary and metastasis regions, and reconstructed the subclonal architecture and phylogenetic history of each patient’s disease (Supplementary Fig. 2 and Methods ). Subclones were classified into four groups to delineate when they arose relative to metastatic dissemination: truncal (the most recent common ancestor (MRCA) of all sequenced cancer cells); primary-unique (non-truncal subclones present in the primary tumour and undetected in any metastasis); metastasis-unique (non-truncal subclones present in one or more metastases and undetected in the primary tumour); and shared subclonal (non-truncal subclones present in both the primary and one or more metastases). We detected subclonal diversity within individual metastases (median [range]: 7 [3–37] subclones per metastasis) and between anatomically distinct metastases (79% of metastases contained a subclone that was not detected in any other metastasis). The number of subclones detected per metastasis increased with the number of metastasis regions sampled (Pearson’s R : 0.6, P  = 7.64 × 10 −27 ; Extended Data Fig. 1a ), implying that, as with primary NSCLC tumours 18 , 19 , 21 , single-region sampling can underestimate the subclonal diversity in a metastasis. The number of metastasis-unique subclones identified increased with the number of anatomically distinct metastases sampled per patient (Pearson’s R : 0.52, P  = 0.01; Extended Data Fig. 1a ), highlighting that samples from anatomically distinct metastases are needed to detect the subclones variably distributed across metastatic sites. The number of metastasis-unique subclones identified per patient in this cohort (median [range]: 28 [4–58]) was 11-fold greater than it was in our previous analysis of metastases sampled at the time of primary surgery or relapse in 126 patients enrolled in TRACERx 11 (median [range]: 2.5 [0–13]); the mean number of metastasis regions sampled per patient was 1.7 (range: 1–6) in that study, compared with 16 (range: 3–39) in this study (Supplementary Fig. 3 ). We used two metrics based on either somatic mutations or SCNAs to quantify the genetic diversity between anatomically distinct metastases (inter-metastasis heterogeneity), within individual metastases (intra-metastasis heterogeneity) and between the primary tumour and metastases from the same patient (primary–metastasis heterogeneity; Methods ). Metastases from the same patient were more similar to each other than they were to the paired primary (mean inter-metastasis versus mean primary–metastasis heterogeneity per patient: mutation diversity P  = 0.004, SCNA diversity P  = 6.0 × 10 −5 , Wilcoxon signed-rank test; Fig. 1c ), as observed in prostate cancer 15 . Primary–metastasis heterogeneity was positively associated with both intra-primary heterogeneity (mean per patient: mutation diversity Pearson’s R : 0.57, P  = 0.005; SCNA diversity Pearson’s R : 0.51, P  = 0.013; Extended Data Fig. 1b ) and intra-metastasis heterogeneity (mean per patient: mutation diversity Pearson’s R : 0.55, P  = 0.01; SCNA diversity Pearson’s R : 0.33, P  = 0.15; Extended Data Fig. 1b ), suggesting that somatic evolution in the primary tumour, metastases, or both after metastatic dissemination contributes to this genetic divergence. The degree of primary–metastasis divergence did not differ according to the treatment patients received (Supplementary Fig. 4 ), although such inferences might be limited owing to cohort size. Individual multi-region sampled metastases were less heterogeneous than their paired multi-region sampled primary (mean intra-metastasis versus mean intra-primary heterogeneity per patient: mutation diversity P  = 0.033, SCNA diversity P  = 0.004, Wilcoxon signed-rank test; Fig. 1c ), but when all metastases sampled from a patient were considered, the heterogeneity among them was not significantly different to that within the paired primary tumour (mean inter-metastasis versus mean intra-primary heterogeneity per patient: mutation diversity P  = 0.443, SCNA diversity P  = 0.656, Wilcoxon signed-rank test; Fig. 1c ). These data are consistent with primary tumours and metastases continuing to evolve after they diverge. As such, resected primary NSCLC tumours are unlikely to be representative of metastases detected during clinical follow up 22 , and, as in other cancer types 9 , 15 , 23 , the full extent of metastatic heterogeneity is likely to be underestimated when metastatic sampling is limited. Mutational processes change over time Somatic mutations arise from a variety of mutational processes. To assess their contribution to temporal and spatial genomic heterogeneity, we evaluated mutational signatures known to be active in NSCLC across truncal, shared subclonal, primary-unique and metastasis-unique subclones ( Methods ). Smoking and APOBEC signatures were more prevalent in primary than in metastasis-unique subclones. This was evident when considering the majority aetiology of each subclone (the mutational process that constituted more than 50% of mutations; Extended Data Fig. 1c and Methods ), or the number of subclones in which each mutational process was detected (average percentage of subclones with smoking signature: primary 21% versus metastasis 7%, P  = 0.02; average percentage of subclones with APOBEC signature: primary 54% versus metastasis 34%, P  = 0.0045; Wilcoxon signed-rank test; Extended Data Fig. 1d and Methods ). In ever-smokers (patients who had smoked 100 or more cigarettes during their lifetime), 83.4% of all mutations attributed to the smoking signature SBS4 occurred in the trunk, consistent with SBS4 being an early process in NSCLC evolution 21 . The lower prevalence of SBS4 in metastases might also reflect a lack of exposure owing to smoking cessation (80% of patients were ex-smokers) or that not all organs are equally exposed 24 . APOBEC activity was highest in shared subclonal and primary-unique subclones (Extended Data Fig. 1c,d ). Episodic APOBEC activity was observed throughout tumour evolution 25 , including in metastases, characterized by fluctuating levels of APOBEC mutagenesis along phylogenetic branches in 39% (9/23) of patients with APOBEC activity (Extended Data Fig. 1e ). Among metastasis-unique subclones, the majority aetiologies, in order of prevalence, were clock-like (SBS1 and SBS5), APOBEC (SBS2 and SBS13), platinum (SBS31 and SBS35) and smoking (SBS4) (Extended Data Fig. 1c ). Platinum-related signatures were detected in metastasis-unique subclones in 64% (9/14) of patients treated with platinum chemotherapy. None of these mutational processes were site-specific: each occurred in multiple metastatic sites (Extended Data Fig. 1f and Supplementary Fig. 5 ). Mutational signature profiles of metastasis-unique subclones were more similar to each other than they were to ancestral primary subclones ( P  = 0.0043, Wilcoxon signed-rank test; Extended Data Fig. 1g ), suggesting that the observed temporal shifts in mutational process activity contribute to primary–metastasis divergence. Putative driver alterations occur in metastases To investigate genetic alterations that might underpin somatic evolution in metastases, we assessed the frequency of known genetic drivers of tumorigenesis. Annotating somatic mutations ( Methods ), 196 driver mutations were identified: 174 single-nucleotide variants (SNVs), 4 dinucleotide variants (DNVs), and 18 insertion–deletions (indels), of which 70% affected tumour suppressor genes (TSGs) (Fig. 2a ). Overall, 49% (97/196) were truncal and thus shared between all primary and metastasis regions, consistent with studies in other cancer types 23 , 26 – 29 (Fig. 2b ). KRAS was the most frequently mutated oncogene (21% (5/24) of patients) and was always truncal. Of the driver mutations, 27% (53/196) were metastasis-unique (detected in metastases, but not in the primary tumour). Most patients (83%, 20/24) had at least one metastasis-unique driver mutation (median [range]: 2 [0–6] per patient; Fig. 2a ). The number of metastasis-unique drivers per patient correlated with the total number of metastasis-unique mutations (Pearson’s R  = 0.8, P  = 3.2 × 10 −6 ; Fig. 2c ), and both were associated with the duration of chemotherapy treatment (Extended Data Fig. 2a ). Metastasis-unique subclones with platinum signature activity were more likely to contain a metastasis-unique driver than were their counterparts without ( P  = 0.016, chi-squared test; Fig. 2d ), suggesting that in addition to the greater number of drivers that result from treatment-related mutagenesis, treatment may select for or induce these mutations. Fig. 2 Evolutionary timing of putative driver alterations. a , Summary of the focal copy-number amplifications (Amp) affecting oncogenes (top), focal LOH affecting TSGs (middle) and putative driver mutations (Mut: SNVs, indels and DNVs; bottom) across patients. Only mutations occurring in at least two patients or showing biallelic inactivation ( Methods ) in one patient are shown. Alterations are classified as truncal (grey), shared subclonal (light blue), primary-unique (dark blue) or metastasis-unique (green). Mutations that co-occur with LOH are marked with circles. Horizontal bars indicate total occurrences per gene and class. b , Alteration class distribution for events in a . Clinically actionable events are highlighted in boxes; bold font denotes biomarkers for approved lung cancer therapies (OncoKB level 1) and regular font denotes biomarkers with compelling clinical evidence (OncoKB level 3A). n  = 24 patients. c , Correlation between the number of metastasis-unique mutations and the number of metastasis-unique driver mutations per patient; points coloured by the relapse treatment status of each patient. d , Fraction of metastasis-unique subclones in platinum-treated patients ( n  = 14) with detectable platinum-associated mutational signatures, stratified by the presence or absence of a driver mutation. Chi-squared test. Next, we used OncoKB 30 , 31 to determine whether actionable drivers occur uniquely in metastases (Fig. 2b ). No level 1 actionable mutations, which predict response to a drug licensed for NSCLC management, were detected in metastasis-unique subclones. Only one metastasis-unique mutation was annotated as potentially actionable: SMARCA4 (p.E1083K), identified in a lung metastasis in patient CRUKP3358, was classified as a level 3A mutation (a biomarker for an unlicensed drug with efficacy in clinical trials 32 ). We also examined the timing of focal amplifications and loss of heterozygosity (LOH) affecting cancer-related oncogenes and TSGs, respectively ( Methods ). Among TSG driver mutations, 46% (63/138) had biallelic inactivation, defined as a mutation affecting one allele with LOH affecting the other ( Methods and Fig. 2a ). In 76% (48/63) of these cases, both events were detected in the primary tumour; 21% (13/63) occurred sequentially, with the first hit in the primary and the second in a metastasis; and in 3% (2/63) of cases, both events occurred in a metastasis. For example, CRUKP2037 had a truncal driver mutation affecting ARID1A (N942S), followed by LOH of the ARID1A locus within a lymph node metastasis, as well as a truncal LOH affecting B2M , followed by a subclonal B2M driver mutation, both in the primary tumour (Extended Data Fig. 2b ). The most frequently mutated TSG was TP53 (75% (18/24) of patients), which is associated with metastatic seeding 11 , 33 . It was biallelically inactivated in 89% (16/18) of patients: in most cases both hits were truncal (88% (14/16)), whereas in the others, subclonal LOH followed a truncal driver mutation, including on parallel phylogenetic branches (CRUKP8172). WGD, which is often associated with TP53 disruption 34 and can mitigate against deleterious alterations 35 , occurred in 92% (22/24) of patients. These events occurred throughout tumour evolution (76% (31/41) occurred in the primary—11 truncal, 11 shared subclonal and 9 primary-unique—and 24% (10/41) occurred in metastases; Extended Data Fig. 2c ), and were associated with increased primary–metastasis (Pearson’s R : 0.57, P  = 0.0034) and inter-metastasis SCNA heterogeneity (Pearson’s R : 0.67, P  = 0.00051; Extended Data Fig. 2d ). Primary subclonal and metastasis-unique WGD events occurred on parallel phylogenetic branches in 29% (7/24) of patients, suggesting that they confer a fitness advantage in late-stage disease. Overall, although most driver alterations, including those that are clinically actionable, occurred in primary tumours, metastases accrued additional—often treatment-associated—genetic alterations of potential biological consequence. Pervasive metastasis-to-metastasis seeding In addition to ongoing evolution in metastases, cancer cells migrating between anatomical sites will influence the subclonal landscape of metastatic disease. To elucidate the metastatic migration patterns that underpin advanced NSCLC, we applied the MACHINA algorithm 8 to the phylogenies inferred for each patient to identify the subclones that seeded metastases and their corresponding migration routes (Extended Data Fig. 3 ). In 3 patients, seeding subclones originated exclusively in the primary tumour. In the remaining 88% (21/24), seeding subclones were identified in both the primary tumour (mean per patient [range]: 2.8 [1–8]) and one or more metastases (mean per patient [range]: 6.0 [1–16]), herein referred to as primary-to-metastasis seeding subclones and metastasis-to-metastasis seeding subclones, respectively (Fig. 3a ). Metastasis-to-primary reseeding was not considered because no patients had radiologically detectable metastatic disease at the time of primary tumour resection. On average, 4 anatomically distinct metastases were seeded by the primary tumour per patient (range: 1–22). In 62.5% (15/24) of patients, these primary-seeded metastases were seeded by distinct primary-to-metastasis seeding subclones, in contrast with previous studies that detected only a single primary seeding subclone in most patients 9 , 11 , 36 . The number of seeding subclones identified per patient correlated with the number of anatomically distinct metastases (Pearson R : 0.48, P  = 0.018) and the number of metastasis regions sampled (Pearson R : 0.52, P  = 0.009; Extended Data Fig. 5a ), indicating that seeding subclone prevalence can be underestimated when metastasis sampling is limited. Overall, however, a greater number of metastases were seeded by other metastases than by the primary tumour: 60% (156/258) of metastases were seeded by metastasis-to-metastasis seeding subclones, 38% (98/258) were seeded by primary-to-metastasis seeding subclones and 2% (4/258) were seeded by both (10 low-purity metastases were excluded from tree building and migration analyses; Methods and Fig. 3b ). For example, in CRUKP2037, only one of the six thoracic lymph node metastases sampled at autopsy was seeded by the primary; the remaining five were seeded by subclones from other established thoracic lymph node metastases (Extended Data Fig. 3 ). Fig. 3 Metastatic seeding patterns. a , Number of patients whose metastases were seeded exclusively by the primary tumour (Primary->Met seeding only, black) or by both the primary tumour and other metastases (Primary->Met and Met->Met seeding, red). b , Number of metastases seeded by the primary tumour (black), by another metastasis (grey) or by both sources (yellow). c , Proportion of metastases seeded by other metastases (grey) or by the primary tumour (black) across increasing migration probability thresholds ( Methods ). d , Number of primary-to-metastasis (black outline) and metastasis-to-metastasis (grey outline) seeding subclones per patient. Each primary-to-metastasis seeding subclone is assigned a distinct colour (concordant between bars and phylogenetic tree nodes). Metastasis-to-metastasis seeding subclone colours match the colour of their primary-to-metastasis seeding ancestor, with lighter shades assigned to each new metastasis-to-metastasis seeding subclone in that lineage. Pie wedges show the fraction of metastases seeded by each correspondingly coloured seeding subclone. Metastases on body maps are coloured by their seeding subclone, with arrows indicating migration routes. Subclones contributing to both primary-to-metastasis and metastasis-to-metastasis spread are marked with an asterisk. P, primary-to-metastasis seeding subclone; M, metastasis-to-metastasis seeding subclone. Body map illustration in d by J. Brock adapted from ref. 11 under a Creative Commons licence CC BY 4.0 . Consistent with studies in other cancer types 9 , 15 , 36 , most metastases were seeded by a single migrating subclone (72.5%, 187/258), as opposed to multiple subclones that migrated together or in sequence (Extended Data Fig. 4 ). Primary-to-metastasis migrations involved a single subclone more frequently than metastasis-to-metastasis migrations ( P  = 7.73 × 10 −4 , chi-squared test; Supplementary Fig. 6a ). Migrations that started and ended in the same organ involved multiple subclones more often (43.6%, 41/94) than migrations between organs (26.8%, 49/183, P  = 0.007, chi-squared test; Supplementary Fig. 6b ), implying that a greater number of subclones were capable of migrating within, as opposed to between, organs. Three independent analyses provide orthogonal evidence for the inferred metastasis-to-metastasis migrations. First, we developed a probabilistic approach to assign a confidence level to each metastatic migration ( Methods and Extended Data Fig. 5b ). Metastasis-to-metastasis migrations remained predominant when considering only the migrations inferred with highest confidence (Fig. 3c ). Second, we used standard-of-care radiological imaging to determine the first time each metastasis was radiologically detected. Metastases seeded by the primary were detected earlier than metastases seeded by other metastases (median 272 versus 745 days from surgery, respectively, P  = 2.41 × 10 −5 , Mann–Whitney U test; Extended Data Fig. 5c ). Third, we evaluated migration directionality by quantifying LOH events present in the seeding but not in the seeded metastasis—an implausible scenario given LOH irreversibility ( Methods ). Metastases linked by a metastasis-to-metastasis migration had significantly more conserved LOH events (which are not an input to MACHINA 8 ) compared with alternative seeding sources (inferred metastasis source versus primary source P  = 3.29 × 10 −28 ; inferred metastasis source versus alternative metastasis source P  = 0.011; Mann–Whitney U test; Extended Data Fig. 5d ). Furthermore, the metastatic migration patterns inferred were highly consistent with those obtained using different combinations of algorithms for phylogenetic 37 and metastatic migration inference 38 (Supplementary Fig. 7 ). These patterns reveal that in NSCLC, multiple primary subclones seed metastases, initiating a cascade of metastasis-to-metastasis seeding that promotes metastatic progression. Seeding capacity across subclone lineages To test whether metastatic capacity differs among seeding subclones, we used the number of metastases seeded by each seeding subclone as a surrogate measure of its seeding capacity ( Methods ). In 93% (14/15) of the patients with multiple primary-to-metastasis seeding subclones, no significant differences were observed in the number of metastases seeded by each subclone (Extended Data Fig. 5e ). In the patient in which a difference in seeding capacity was detected, CRUKP8172, one of the 2 primary seeding subclones seeded 21 of the 30 sampled metastases (Monte Carlo likelihood ratio test P  = 0.00001; Fig. 3d ). Despite the fact that most of the primary-to-metastasis seeding subclones had a similar metastatic capacity, only 45% (30/67) of them produced descendants that seeded additional metastases, raising the possibility that the capacity to seed metastases from the primary tumour differs from that required for metastasis-to-metastasis seeding. We therefore assessed whether subclones from the same primary tumour, after establishing metastases, had an equal likelihood of further spread. In 67% (10/15) of patients, significant differences were observed in the number of metastasis-to-metastasis migrations that descended from each primary-to-metastasis seeding subclone (Fig. 3c and Extended Data Fig. 5e ). For example, in CRUKP9198, three primary subclones each seeded one or two metastases (Monte Carlo likelihood ratio test P  = 1.0), but their subsequent spread differed: one did not give rise to any metastasis-to-metastasis migrations; another had a descendant that seeded one metastasis; and the third had descendants that seeded twelve metastases via metastasis-to-metastasis migrations (Extended Data Fig. 5f ). Thus, in this small cohort, the capacity to seed further metastases is not uniformly inherited by descendants of primary-to-metastasis seeding subclones. Duration in situ associates with seeding To investigate determinants of metastatic capacity, we examined patient, metastasis and subclone characteristics associated with metastasis-to-metastasis seeding. Patients in whom metastasis-to-metastasis seeding was predominant (more than 50% of all migrations) relapsed later (median DFS 14 versus 5 months, P  = 0.036, Mann–Whitney U test) and had significantly longer OS (median 32 versus 15 months, P  = 0.019, Mann–Whitney U test), compared with patients in whom it was not (less than 50%; Extended Data Fig. 6a ). Their clinical demographics and treatment histories were otherwise similar (Extended Data Fig. 6a ). In fact, OS exhibited a positive linear association with the proportion of migrations that were metastasis-to-metastasis (Pearson’s R : 0.46, P  = 0.025; Fig. 4a ), raising the possibility that metastasis-to-metastasis seeding is related to the duration metastases are in situ. Fig. 4 Metastasis-to-metastasis seeding associates with duration in situ and anatomical site. a , Correlation between the percentage of migrations that constitute metastasis-to-metastasis per patient and overall survival (days). b , Proportion of seeding (dark teal) and non-seeding (light teal) metastases stratified by time of first detection on imaging: relapse scan; scan in first half of the post-relapse period; scan in second half; or after the last scan before death (at autopsy). Metastases without known timing of first appearance or seeding status were excluded. n  = 23 patients; Fisher’s exact test comparing earliest and latest categories. c , Burden of SNVs and SCNAs accumulated after seeding (that is, in descendants of the seeding subclone) for seeding (dark teal) and non-seeding (light teal) metastases. Dots indicate median per patient; lines connect patients. n = 20 patients with both metastasis types; Wilcoxon signed-rank test. d , Maximum radiological volume measured on any clinical scan for seeding versus non-seeding metastases. Dots indicate median per organ site; lines connect organ sites. n  = 23 patients, 129 metastases; Wilcoxon signed-rank test. e , Proportion of seeding and non-seeding metastases with branched (grey) versus linear (white) metastasis-unique subclone phylogenies. n  = 24 patients; Fisher’s exact test. f , Prevalence of metastasis-to-metastasis seeding across anatomical locations. Intra, intrathoracic; extra, extrathoracic. g , Correlation between the proportion of metastases in each anatomical site with ‘sufficient’ duration in situ ( Methods ) and the prevalence of seeding metastases from that site. n  = 24 patients, 258 metastases. h , Proportion of non-seeding (light teal) or seeding (dark teal) metastases in intrathoracic (purple edge) and extrathoracic organs (orange edge). n  = 24 patients, 258 metastases; Fisher’s exact test. i , Origin of seeding for intrathoracic (purple) and extrathoracic (orange) metastases, stratified by intrathoracic versus extrathoracic source. n  = 12 patients with both metastasis types; Fisher’s exact test. The box plots show the median and IQR with whiskers denoting values within 1.5 times the IQR from the first and third quartiles. Body map illustration in h by J. Brock adapted from ref. 11 under a Creative Commons licence CC BY 4.0 . To investigate this, we evaluated the seeding capacity of each metastasis with respect to when it arose during the disease course. Metastases that seeded other metastases were identified significantly earlier on radiological imaging: 36.8% (14/38) of metastases detected on the first relapse scan seeded metastases, compared with 16.4% (10/61) detected only after the last scan, at autopsy ( P  = 0.03, Fisher’s exact test; Fig. 4b ). Consistent with having a longer duration in situ, these metastases also accrued more somatic alterations than metastases that did not seed (mutations P  = 5.7 × 10 −5 , SCNAs P  = 0.002, Wilcoxon signed-rank test; Fig. 4c and Methods ). Likewise, primary-to-metastasis seeding subclones with metastasis-to-metastasis seeding descendants emerged earlier in tumour evolution than those without metastasis-to-metastasis seeding descendants (mutation distance from the trunk P  = 0.053, SCNA distance from the trunk P  = 0.025, one-sided Wilcoxon signed-rank test; Extended Data Fig. 6b ). In fact, 68% (141/207) of the metastases that did not seed other metastases contained fewer mutations than the mutational burden typically observed at the point when metastasis-to-metastasis seeding subclones emerged in the same patient (Extended Data Fig. 6c–e and Methods ). This suggests that many non-seeding metastases could potentially seed metastases with longer time in situ. Two explanations could underlie this finding. First, metastases with a longer duration in situ might reach larger sizes, such that an increased number of cancer cells have the potential to seed. Indeed, metastases that seeded other metastases had significantly greater maximum radiological volumes than those that did not ( P  = 0.025, Wilcoxon signed-rank test; Fig. 4d and Methods ). Second, subclones capable of seeding metastases might emerge from the reservoir of subclonal diversity that accrues over time. In keeping with this, metastases that seeded other metastases had more subclones ( P  = 0.028, Mann–Whitney U test; Extended Data Fig. 6f ) and were more likely to have branched phylogenies than were metastases that did not ( P  = 9.8 × 10 −8 , Fisher’s exact test; Fig. 4e ). These data imply that the greater number of cancer cells and/or subclones afforded by a longer duration in situ is associated with the likelihood of metastasis-to-metastasis seeding. Anatomical constraints of seeding Anatomical location may also influence the likelihood of metastases seeding further metastases; for example, owing to differences in organ vascularity or lymphatic drainage 39 . Metastasis-to-metastasis seeding varied across anatomical locations: 28.4% of lung and 30.6% of intrathoracic lymph node metastases seeded other metastases, compared with only 8.3% of peritoneal and 10.7% of liver metastases (Fig. 4f ). These differences mostly reflected the temporal order in which metastases arose. The prevalence of metastasis-to-metastasis seeding from each site strongly correlated with the proportion of metastases at that site with ‘sufficient’ time in situ to seed others, defined by comparing the mutation burden of each metastasis with the number of mutations accrued before the emergence of metastasis-to-metastasis seeding subclones in the same patient (Pearson’s R : 0.80, P  = 0.01; Fig. 4g and Methods ). In particular, intrathoracic metastases (encompassing mediastinal lymph node and lung metastases; Methods ), which were detected significantly earlier on radiological imaging than were extrathoracic metastases ( P  = 3.94 × 10 −4 , Mann–Whitney U test; Extended Data Fig. 7a ), seeded other metastases more frequently (intrathoracic versus extrathoracic metastases that seeded other metastases: 26.4% (32/121) versus 13.9% (19/137), P  = 0.013, Fisher’s exact test; Fig. 4h ). Thus, the tendency we observe for NSCLC metastases to emerge in an anatomical sequence (intrathoracic early; extrathoracic late) is reflected in metastatic seeding patterns. Next, we investigated whether the anatomical location in which seeding originates influences the location of the resulting metastases. Overall, 71.3% (92/129) of metastasis-to-metastasis migrations remained within the anatomical cavity in which they originated ( P  = 2.40 × 10 −5 , Fisher’s exact test; Fig. 4i ); that is, intrathoracic metastases predominantly seeded intrathoracic metastases, and extrathoracic metastases predominantly seeded extrathoracic metastases. This observation was consistent across patients (Extended Data Fig. 7b ), maintained when restricting to the highest confidence migrations ( Methods and Extended Data Fig. 7c ) and not explained by a propensity for within-organ spread (59% (54/92) of migrations within the same anatomical cavity were not within the same organ). Primary-to-metastasis seeding also differed with respect to anatomical cavities. Only one-third (16/48) of primary seeding subclones seeded extrathoracic metastases; however, those that did were more likely to seed multiple metastases than were subclones that seeded intrathoracic metastases ( P  = 0.007, Fisher’s exact test; Extended Data Fig. 7d ). For example, in CRUKP1584, the primary tumour seeded 11 metastases—7 intrathoracic and 4 extrathoracic. The primary subclone capable of exiting the thorax seeded eight metastases, whereas the subclone that remained within the thorax seeded three (Fig. 3d ). The same pattern was evident among metastasis-to-metastasis seeding subclones that originated within intrathoracic metastases (Extended Data Fig. 7e ), suggesting that the process of cancer cells migrating between anatomical cavities differs from the process of migrating within them. Extrathoracic spread tracks chromosomal instability Despite sufficient time in situ (as defined in Extended Data Fig. 6c,d and Methods ), more than half of metastases did not seed further metastases, implying that time is necessary but not sufficient for metastatic seeding. Genetic alterations that affect cancer cell phenotypes might influence their ability to seed metastases 40 . Previously, we found that primary tumour chromosomal instability (CIN) is associated with the likelihood of metastatic relapse 18 , 19 , the detection of multiple primary seeding subclones 11 and extrathoracic spread 19 , 41 . In this cohort, in which extensive metastatic sampling has increased the detection of seeding subclones, primary-to-metastasis seeding subclones similarly contained more SCNAs ( P  = 9.6 × 10 −5 , linear mixed effects (LME) model; Fig. 5a,b ) but not SNVs (Supplementary Fig. 8a ) than non-seeding subclones from the same primary. In addition, the percentage of primary subclones that seeded metastases was strongly associated with the median SCNA burden (Pearson’s R : 0.62, P  = 0.0013) and rate of SCNA acquisition (per mutation) across all primary subclones (Pearson’s R : 0.72, P  = 7.5 × 10 −5 ; Fig. 5c ), suggesting the degree of CIN in primary subclones correlates with their likelihood to seed. Fig. 5 Extrathoracic seeding subclones are enriched for chromosomal instability. a , Example patient (CRUKP8780), depicting anatomical sites of subclones (top right tree), their migration patterns (body map) and the related classification of seeding and non-seeding (bottom left tree). b , SCNA burden per primary-to-metastasis seeding subclone (dark blue) and non-seeding primary subclone (light blue). LME model with subclone mutation burden as covariate and patient as random effect. n  = 21 patients, 274 subclones. c , Correlation between the percentage of primary subclones that seed metastases and the median subclone SCNA burden (left) or the SCNA/SNV ratio (right). n = 24 patients. d , SCNA burden per metastasis-to-metastasis seeding subclone (dark green) and non-seeding metastasis subclone (light green). LME model as in b . n  = 21 patients, 625 subclones. e , Primary subclone SCNA burden stratified by subclone seeding status: non-seeding (blue), seeded intrathoracic metastasis (purple) or seeded extrathoracic metastasis (orange), shown for patients with intrathoracic only (left; n  = 12 patients, 129 subclones), extrathoracic only (middle; n  = 5 patients, 70 subclones) or both intrathoracic and extrathoracic (right; n  = 4 patients, 75 subclones) metastases seeded by the primary. Dots, median per patient; Lines, connect primary tumours. LME model as for b . f , Median SCNA burden per subclone in intrathoracic metastases that seed intrathoracic metastases (purple) and intrathoracic metastases that seed extrathoracic metastases (orange). LME model as in b . n  = 15 patients, 32 metastases. The box plots show the median and IQR with whiskers denoting values within 1.5 times the IQR from the first and third quartiles. Body map illustration in a by J. Brock adapted from ref. 11 under a Creative Commons licence CC BY 4.0 . This association extended to late-stage disease: metastasis-to-metastasis seeding subclones had significantly more SCNAs (p-value = 3.0 × 10 −7 , LME model; Fig. 5d ) and had a higher rate of SCNA acquisition (Supplementary Fig. 8b ) than non-seeding metastasis subclones from the same patient. Although some subclones had SCNAs affecting genes that are implicated in metastasis 11 , 41 —such as the focal amplification of CCND1 in the primary subclone that seeded a chest wall metastasis in CRUKP3207, and in a left frontal lobe brain metastasis that seeded a right frontal lobe metastasis in CRUKP8433—no significant enrichment in the rate of SCNAs affecting driver genes or driver mutations was detected in seeding compared with non-seeding subclones (Supplementary Fig. 9 ). This cohort might be underpowered to detect such differences, or this finding could suggest that CIN supports metastasis through alternative means, such as by generating subclonal diversity or by altering the tumour microenvironment 42 . Given the distinct seeding patterns observed between intrathoracic and extrathoracic spread, we examined the characteristics of subclones that seeded metastases in each location. Primary subclones that seeded extrathoracic metastases, but not those that seeded intrathoracic metastases, had significantly more SCNAs, compared to non-seeding primary subclones (Fig. 5e ). We confirmed this observation in the published cohort of TRACERx patients 11 for whom paired primary and metastasis data and extrathoracic relapse status based on imaging were available (Extended Data Fig. 7f and Supplementary Fig. 10 ). Metastasis-to-metastasis seeding followed the same pattern: intrathoracic metastases that seeded extrathoracic metastases contained subclones with higher SCNA burdens, compared with intrathoracic metastases that seeded intrathoracic metastases ( P  = 7.2 × 10 −4 , LME model; Fig. 5f ). These data correlate subclone SCNA burden with extrathoracic seeding capacity, suggesting that CIN supports an aspect of the metastatic process that is specific to this route of spread. Discussion Previous genomic studies vary considerably in their conclusions about metastatic heterogeneity 9 , 43 – 46 and seeding 11 , 15 , 47 . Sampling-related variation could account for some of these differences, given that data generated from limited samples might fail to accurately represent often-widespread metastatic disease 16 . Here, DNA-sequencing data from 108 primary tumour regions paired with 393 metastasis regions, encompassing the majority of metastases radiologically detected before death in 24 patients, enabled in-depth characterization of the subclonal landscape of NSCLC metastases and the cellular migrations that founded them. The predominant seeding pattern involved the dissemination of multiple primary subclones before surgery or from postoperative residual disease (62.5% of patients), each giving rise to a distinct metastasis (72.5% of metastases were founded by a single subclone). Metastasis-to-metastasis seeding from the resultant metastases, however, were inferred to account for most sampled metastases (60%), suggesting that clinical interventions aimed at minimizing existing metastatic disease could prevent further metastatic progression. Moreover, the latency period associated with metastasis-to-metastasis seeding indicates that there could be a window of opportunity for such interventions. Consistent with this hypothesis, local consolidative therapy (LCT) with radiotherapy or surgery for metastases that persist after systemic therapy improves the outcomes of patients with metastatic NSCLC 48 – 50 and other cancer types 51 in phase II trials. However, in a 2024 study, LCT did not produce any survival benefit in patients with NSCLC who were treated predominantly with immunotherapy 52 , highlighting the unresolved challenge of identifying patients who will truly benefit from this approach. Our data offer a biological rationale for this treatment strategy in appropriately selected patients, and, potentially, in selected metastases, such as those with high CIN. Corroborating insights from lineage-tracing models of metastasis 53 – 55 , the cellular attributes required to seed metastases, and the likelihood of doing so, varied according to the route of spread. Within-cavity seeding—in which direct invasion, spread through airspaces 56 , 57 and lymphatic and circulatory migration are all possible routes of spread 58 —was more frequent and feasible than was seeding between cavities, which requires transit in the circulation. In colorectal cancer, local lymph node metastases develop through evolutionary mechanisms that are fundamentally different from those of distant metastases 36 , 59 , 60 . Here, CIN, which we 18 , 19 , 41 and others 61 have previously found to be associated with metastatic capacity, was a distinguishing feature of subclones that seeded extrathoracic metastases. This raises the possibility that the metastatic advantage conferred by CIN in NSCLC relates to a process specific to extrathoracic seeding, such as circulatory spread or adaptation to a non-thoracic microenvironment, and highlights the need for further functional studies that are designed to elucidate the mechanisms of spread along different anatomical routes. The generalizability of these results to untreated patients (88% received systemic therapies in this cohort), patients who present with de novo metastatic disease and other cancer types is unknown. The size of the cohort limited our ability to assess the effects of treatments on metastasis evolution, seeding and recurrent genomic events. Furthermore, bulk WES, although performed at high depth, can underestimate subclonal diversity, when compared with whole-genome and/or single-cell sequencing technologies, and precludes investigation of the roles of structural variants, extrachromosomal DNA, the tumour microenvironment and other non-genetic processes in metastasis. Our follow-on study, TRACERx EVO ( NCT05628376 ), endeavours to address these limitations. It aims to recruit 600 patients with NSCLC, small cell lung cancer or pleural mesothelioma across the spectrum of cancer stages (I–IV), and to carry out up to 100 research autopsies using the PEACE study infrastructure, performing high-depth whole-genome sequencing on the collected samples. This work demonstrates the extensive genetic diversity that constitutes metastatic disease, revealing that NSCLC progression is propagated by a multitude of primary and metastasis subclones with seeding capacity. It thus highlights the value of this longitudinal, clinically annotated dataset that, by facilitating further interrogation of this complexity, will foster greater understanding of the metastatic process, and inform strategies to curtail it. Methods Patient cohort The PEACE study PEACE is a pan-cancer, UK-wide research autopsy programme ( https://clinicaltrials.gov/study/NCT03004755 ) designed to investigate the biology of metastatic disease and drug resistance. The study was sponsored by the University College London (UCL) Clinical Trials Centre and approved by the Health Research Authority National Research Ethics Service Committee London–Dulwich on 15 August 2013, in accordance with the UK Human Tissue Act 2004, with research ethics committee reference 13/LO/0972. Informed consent was provided by patients during life or by a person in a qualifying relationship after death. Eligibility was defined by the following inclusion criteria: (1) age 18 years or over; (2) confirmed solid malignancy with metastatic disease (where the site of origin is known or unknown), with the exception of primary brain tumours, in which there might not be evidence of metastatic disease; and (3) oral and written informed consent from patient to enter the study and to undergo tissue collection after death or from a nominated representative or a person in a qualifying relationship after the patient has died. Exclusion criteria were: (1) medical or psychiatric condition that would preclude informed consent; (2) history of intravenous drug abuse within the past five years; or (3) confirmed diagnosis of known high-risk infections (for example, HIV/AIDS-positive, hepatitis B or C, tuberculosis and Creutzfeldt–Jacob disease), unless the patient case is of particular scientific interest and was agreed in advance with local mortuary staff and pathologist. The TRACERx–PEACE lung cohort The TRACERx study ( https://clinicaltrials.gov/ct2/show/NCT01888601 ) is a prospective observational cohort study approved by an independent research ethics committee (13/LO/1546). The inclusion and exclusion criteria, clinical data acquisition and tissue and plasma sampling procedures have been described 18 , 19 . In brief, the TRACERx study includes patients with histopathologically confirmed early-stage I–IIIB NSCLC who underwent primary surgery. Patients are followed up after surgery, during which longitudinal clinical data, plasma and, in the case of disease relapse or progression, tissue samples are collected. Forty-nine patients with NSCLC were enrolled in both TRACERx and PEACE; 41 died and 33 underwent a research autopsy (Supplementary Fig. 1 ). Research autopsies were not performed owing to lack of death notification ( n  = 6), post-mortem withdrawal of consent ( n  = 1) or COVID-19 restrictions ( n  = 1). No tumour was identified in four patients who underwent an autopsy after pathological assessment. In three patients, WES data from all primary or all autopsy samples failed quality control. WES data were unavailable for two patients at data lock. The final cohort comprised 24 patients. Patients were assigned study identifiers that were subsequently converted to linked identifiers (CRUKP prefix) to maintain anonymity. Tissue and blood samples were barcoded and tracked in a centralized database overseen by the sponsor (UCL Clinical Trials Centre). Research autopsy sample procurement Research autopsies were performed as soon as possible after death (median [IQR]: 79 h [56.0–157.3]; median time to refrigeration: 3.7 h [3.2–5.4]) at the recruiting-site affiliated mortuary (University College London Hospitals (UCLH), Guy’s and St Thomas’ Hospital, Birmingham Heartlands Hospital, Leicester Royal Infirmary Hospital or the Christie Hospital). Tissue sampling was led by a pathologist who was provided with the patient’s pre-mortem clinical history and imaging. None of the patients (or persons in a qualifying relationship) in this cohort elected to restrict research autopsy sampling. All macroscopically visible metastases were sampled where feasible, and multiple regions of individual metastases were sampled where feasible. Sample annotations that distinguished regions from an individual metastasis (that is, a multi-region sampled metastasis) from macroscopically distinct metastases were assigned to allow intra- and inter-metastasis analyses. Labelled specimens were photographed where feasible. Where possible, metastases were bisected longitudinally: one half was snap-frozen in liquid nitrogen and stored at −80 °C, and the other half was formalin-fixed and paraffin-embedded (FFPE). Fresh sterile instruments were used for each sample. Body fluids (pleural, peritoneal and cerebrospinal) were centrifuged and cell pellets snap-frozen separately. Peripheral blood was collected pre-mortem or at autopsy from the femoral vein or cardiac ventricle. Central histopathological review Diagnostic histopathological slides from the primary tumours were centrally reviewed as previously described 18 . Haematoxylin and eosin (H&E)-stained slides created from the metastasis FFPE blocks were digitally archived and assessed for tumour content, necrosis, autolysis and lymphocyte content by a pathologist. Intrathoracic and extrathoracic classification Metastases were classified as intrathoracic or extrathoracic on the basis of the anatomical site recorded on the pre-mortem sample histopathology reports or by the autopsy pathologist. Where anatomical origin was uncertain, radiological imaging was reviewed (see  Supplementary Note ). Intrathoracic metastases Intrathoracic metastases included mediastinal lymph nodes, mediastinal soft tissue, lung, lung surgical bed, pleura and chest wall if the metastasis radiologically arose from within the pleural boundary. Extrathoracic metastases Extrathoracic metastases included axillary, cervical, supraclavicular and abdominopelvic lymph nodes, chest wall if the metastasis radiologically arose from outside the pleural boundary, other subcutaneous or soft tissue musculoskeletal masses, cardiac (pericardium and myocardium), diaphragm (unless radiological evidence of direct pleural or intrapulmonary extension), bone, liver, brain, gastric, adrenals, kidney, peritoneum and bladder. Clinical outcome data DFS was defined as the period from the date of registration to the time of radiological confirmation of the recurrence of the primary tumour registered for TRACERx or the time of death by any cause. OS was defined as the period from the date of registration to the time of death by any cause. Radiological data curation and analysis Anonymized clinical imaging scans and reports (CT, PET–CT and MRI) were available for 23 out of 24 patients under the TRACERx study protocol, spanning baseline primary imaging, relapse, up to the last scan before death. Radiologically visible, measurable metastases were contoured using ITK-SNAP v.4.2.0 by a clinical oncologist to produce three-dimensional tumour volumes, a selection of which were reviewed by a second, senior clinical oncologist. Radiological lesions were manually mapped to sequenced metastasis samples where possible. Where multiple sequenced samples mapped to one radiological lesion, the genomic feature predominant to the set (mode) was used in analyses. Time to first detection Time to first detection was defined as days from primary surgery to the first detection on imaging. Metastases that were not detected on imaging but were sampled at autopsy were assigned a time to first detection halfway between the last scan performed and the date of death. Scan period Scan dates were normalized per patient by the number of days between relapse imaging and death. Scans after the relapse scan and before the last scan before death were assigned to the first half (≤50%) or second half (>50%) of the metastatic period. Maximum tumour volume Maximum tumour volume was defined as the largest volume recorded for a metastasis on any longitudinal scan. DNA extraction and WES DNA extraction and WES were performed as previously described 18 for both primary tumour and metastasis samples. Paired germline DNA was resequenced in the same run as subsequently sequenced metastases. All primary tumour regions and pre-mortem metastasis biopsies and metastatectomies collected underwent WES. Research-autopsy metastasis samples with adequate histopathological tumour content and DNA integrity number (DIN) > 4, as measured using an Agilent TapeStation system, underwent WES. In patients with a large number of suitable samples, samples were selected to capture the anatomical distribution of metastatic disease while maximizing tissue quality. Overall, 376 out of 601 research autopsy metastases were selected for WES (Supplementary Fig. 1 ). Post-sequencing quality control Primary and metastasis samples that failed copy-number calling and variant calling are summarized in Supplementary Fig. 1 . Metastases that passed variant but not copy-number calling ( n  = 10) were included in analyses that do not involve phylogenies (which require both copy-number and mutation calls). Bioinformatic pipeline WES data were analysed using the previously described bioinformatic pipeline to perform alignment, somatic mutation calling, copy-number detection and signature artefact quality control with the following modifications: (i) DNVs were defined by two criteria: First, a proportion test was performed to determine whether the frequencies of two SNVs were significantly similar. For all putative DNVs with a significant test, reads were extracted to calculate the proportion of reads overlapping between the bases. DNVs were called when ≥90% of reads contained both variants in at least one sample from the patient. (ii) Refphase 62 was used to infer haplotype-specific copy-number alterations, and to rescue low-purity tumour regions, using the multi-region data. (iii) Driver mutation annotation was updated as detailed in ‘Detection of driver alterations’. WGD detection WGD events were identified and assigned to phylogenies using ParallelGDDetect as described previously 19 . Genomically independent tumours Sequenced primary and metastasis regions were deemed genomically related or independent to the other samples collected from the patient as previously described 19 . Multiple genomically independent tumours were detected for three patients. In CRUKP1584, a synchronous lung tumour resected at the time of primary surgery was genomically independent from the other tumour resected at the same time and all metastases subsequently sampled, consistent with a second primary that did not metastasize. In CRUKP8172 and CRUKP7741, metastases sampled at autopsy (a lung and an oesophageal sample, respectively) were genomically independent from the corresponding primary and metastasis samples, consistent with second primaries or metastases from undetected second primaries. Because paired primary–metastasis samples were available for these three genomically independent tumours, they were excluded from the final cohort. Subclone and phylogenetic tree reconstruction Mutation clustering and tree building CONIPHER 63 was used to identify clusters of somatic mutations that occurred in the same tumour subclone and to reconstruct tumour phylogenetic trees. Two functionalities of CONIPHER were important in the context of the number of samples available per patient. First, mutations were pre-clustered by presence (more than one mutant read) or absence across the tumour regions available per patient and PyClone 64 was applied to each mutation group independently, making the mutation clustering step scalable 11 , 63 . Second, CONIPHER enumerated all possible phylogenetic tree topologies compatible with the pigeonhole principle and the crossing rule 65 for each set of mutation clusters. The sum condition error (SCE) was computed for each solution to quantify the extent to which the evolutionary constraints imposed by the topology were violated. The tree topology with the lowest SCE was selected for further analysis and the multiple solutions were used to assign metastatic migration probabilities (see ‘Metastatic migration probabilities’). Subclone clonality Phylogenetic trees were used to classify mutation clusters as truncal or subclonal. The truncal cluster corresponded to the mutation cluster ancestral to all others, or the MRCA. Remaining clusters were classified as subclonal and their presence or absence in primary and metastasis regions was further subclassified: primary-unique (detected in the primary, undetected in any metastasis), metastasis-unique (detected in one or more metastases, undetected in the primary) or shared subclonal (detected in the primary tumour and in one or more metastases). Somatic mutations, SCNAs and WGDs were classified as truncal, shared subclonal, primary-unique or metastasis-unique on the basis of clusters they were assigned to by CONIPHER 63 , ALPACA 41 and ParallelGDDetect 19 , respectively. Inference of subclone proportions Subclone proportions per sample were inferred from the mutation cluster cancer cell fraction (CCF) and the phylogenetic tree topology. Leaf node CCFs represent the terminal subclone proportions. Internal node proportions were calculated by subtracting the summed CCFs of the descendant clusters from the parent cluster CCF iteratively from the leaf nodes to the trunk. Subclones with proportions ≤5% were considered extinct (>5% were termed extant). Inference of subclone copy-number profiles ALPACA 41 uses the subclonal and phylogenetic structure of tumours derived from SNV frequencies to infer subclone-specific copy-number profiles. ALPACA was run with default settings, using as input the phylogenetic tree and subclone proportions derived from CONIPHER, the allele-specific fractional copy-number estimates from Refphase 62 , and estimated confidence intervals. The burden of SCNAs per subclone was computed as the total number of break points detected per subclone and the ratio of SNVs to SCNAs was used to quantify the rate of SCNA acquisition per subclone. Detection of driver alterations Somatic mutations were annotated using OncoKB 30 , 31 , openCRAVAT 66 ( https://www.opencravat.org/ ) and the Ensembl Variant Effect Predictor (v.114) 67 . Mutations were classified as putative drivers if they fulfilled any of the following criteria: (1) classified as a loss-of-function event by LOFTEE 68 in a gene annotated as a TSG in the COSMIC Cancer Gene Census (v.102) 69 ( https://cancer.sanger.ac.uk ); (2) called by SpliceAI 70 (using a threshold of 0.8) in a gene listed in COSMIC (v102); (3) predicted to be a driver mutation by BoostDM 71 ; (4) classified as a driver by CHASMplus 72 at a false discovery rate < 0.05, using the histology-specific models for LUAD and LUSC tumours; or (5) annotated as oncogenic in OncoKB 30 , 31 . OncoKB was further used to assign therapeutic levels of clinical actionability (levels 1–3B) 30 , 31 . Putative SCNA drivers were defined by intersecting loci of significant amplification or deletion identified using GISTIC (v.2.0) 73 in a study of 1,000 lung cancer tumours 74 with COSMIC Cancer Gene Census genes associated with mutation types A (amplification) or D (deletion). The oncogenes ALK , TERT and FGFR1 and TSGs MAP2K4 and TSC1 were also considered. Analyses were restricted to alterations most likely to have a functional consequence: focal amplifications in oncogenes and LOH affecting TSGs. Biallelic hits affecting TSGs Potential biallelic inactivation of TSGs was assessed for each driver mutation. Subclone-specific copy numbers inferred by ALPACA 41 were used to determine whether LOH occurred in the same, ancestral or descendant subclone relative to the SNV, allowing LOH→SNV, SNV→LOH or coincident events. Tumour samples containing subclones with two or more events affecting a TSG were further evaluated with a binomial test of the null hypothesis that all remaining allele copies were mutated. The expected variant allele frequency (VAF) of the SNV under the null hypothesis was calculated as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\rm{VAF}}}_{{\rm{expected}}}=\frac{m\times {\rm{CCF}}\times \rho }{2(1-\rho )\,+\,{{\rm{C}}{\rm{N}}}_{{\rm{T}}}\rho },$$\end{document} VAF expected = m × CCF × ρ 2 ( 1 − ρ ) + C N T ρ , where ρ denotes tumour purity, CCF the cancer cell fraction, CN T the tumour copy number and m the mutation multiplicity; m  = CN T under the null hypothesis in LOH regions. The cumulative distribution function (CDF) of the binomial distribution was obtained using the total coverage at the gene locus as the number of trials and the VAF expected as success probability. Finally, the P  value was taken as 2 × CDF when CDF < 0.5, or 2 × (1 − CDF) when CDF > 0.5, respectively, and multiple testing correction was applied using the Holm–Sidak method. A biallelic event was called at P  < 0.05 in any sample. Subclone-specific mutational signature activity COSMIC signature activity was estimated using SigProfilerAssignment 75 for mutation clusters defined by CONIPHER. Signatures were fitted directly to clusters with 50 or more mutations. For each cluster with fewer than 50 mutations, signatures were fitted to mutations resampled (1,000 iterations) from the index cluster (60% probability), a neighbouring cluster on the phylogenetic tree (20% probability) and a cluster matching the index cluster clonality class (primary-unique, shared subclonal or metastasis-unique)(20% probability). Signature activities for these clusters were defined as the mean across bootstrap estimates. Mutation clusters with high uncertainty (standard deviation > 0.1 on the estimated activity of two or more signatures) were excluded. Signature prevalence and majority aetiology Signatures used for this analysis were in line with previously identified signatures in NSCLC 19 . Signature activity was grouped by aetiology: clock-like (SBS1 and SBS5), smoking (SBS4), APOBEC (SBS2 and SBS13), other (SBS17b) and platinum chemotherapy (SBS31 and SBS35). SBS31 and SBS35 were evaluated only in patients treated with platinum chemotherapy. Aetiology-level activity was defined as the summed activity of constituent signatures per subclone. Aetiology prevalence refers to the proportion of subclones in which an aetiology is detected (activity ≥ 0.06). An aetiology was designated the majority aetiology of a subclone if activity exceeded 0.5; otherwise, the subclone was classified as having no majority aetiology. Episodic APOBEC activity A signature was considered to have emerged if it was inactive in the parent and active in the child subclone. Along each phylogenetic trunk-to-leaf lineage, episodic APOBEC activity was defined as either (i) activity present in the trunk and emerging at least once thereafter, or (ii) emergence occurring two or more times. This stringent definition required activity transitions from inactive to active. Mutational signature distance Mean pairwise cosine distances between signature activity vectors were calculated for each patient to compare metastasis-unique subclones with their ancestral primary subclones (trunk, shared subclones or primary-unique). Mutation and SCNA diversity measurement Two genomic distance metrics were calculated per patient using either mutations (SNVs, DNVs and indels) or SCNAs. SCNA diversity was defined as the segment-length-weighted L1 norm between allele-specific fractional copy number for each pair of tumour regions, normalized by ploidy to account for WGD. SNV diversity was defined as the L1 norm between the SNV CCF values for each pair of tumour regions. Truncal events were included only if the MRCA was inferred as the seeding subclone. Truncal SCNAs comprised segments with a gain or loss relative to ploidy present in all samples; truncal SNVs were those assigned to the MRCA by CONIPHER 63 . Intra-primary and intra-metastasis heterogeneity were quantified as the mean of the pairwise SNV or SCNA diversity measurements between regions of the same primary tumour or regions from a single metastasis, respectively. Inter-metastasis heterogeneity was quantified as the mean of the pairwise SNV or SCNA diversity measurements between all anatomically distinct metastases per patient. Primary–metastasis heterogeneity was quantified as the mean of the pairwise SNV or SCNA diversity measurements between all primary and metastasis regions per patient. Inferring metastatic migration patterns Metastatic migrations were reconstructed by applying MACHINA 8 to CONIPHER 63 -derived phylogenies in the polytomy-resolution mode. Three seeding models of increasing complexity were evaluated: primary-only seeding, single-source metastasis-to-metastasis seeding and multi-source metastasis-to-metastasis seeding. The most parsimonious solution was selected per patient. Primary reseeding was not considered, because primaries were resected before detectable metastatic relapse. Inferred migrations specified the tumour in which the migration originated, the tumour in which the migration terminated and the subclones that migrated. Where MACHINA inferred a metastasis-unique subclone (detected in metastases and undetected in the primary tumour according to sample CCFs) to be present in the primary (reflecting the most parsimonious solution), these subclones were reclassified as shared subclonal for downstream analyses. Ten regions from ten metastases failed copy-number quality control and were excluded from tree building and migration analyses. Metastatic migration probabilities To account for phylogenetic uncertainty, MACHINA was run (as above) on the 100 lowest-SCE phylogenies per patient enumerated by CONIPHER. Migration probabilities were defined as the proportion of solutions in which a given migration was observed. Conserved LOH LOH events (which are not input to MACHINA) were used to assess the accuracy of migration inferences. Assuming that LOH events are irreversible, clonal LOH (that is, present in all cancer cells of a sample) detected in samples A and B (conserved LOH) was considered compatible with a migration occurring from A to B, whereas clonal LOH detected in A but not in B was not (non-conserved LOH). For all possible primary–metastasis and metastasis–metastasis pairs, allele-specific clonal LOH segments were identified using a purity-adjusted fractional copy-number threshold < 0.1, and the fraction of LOH segments conserved across the genome was calculated, normalized by the total number of segments with LOH. Pairs were categorized according to whether or not a migration was inferred between them. Assessment of duration in situ The number of mutations and SCNAs private to a metastasis that descended from the subclone(s) that seeded that metastasis were used as a surrogate for its duration in situ. ‘Sufficient’ duration for metastasis-to-metastasis seeding The number of mutations accumulated before the emergence of metastasis-to-metastasis seeding subclones was used a molecular proxy for ‘sufficient’ duration in situ for metastasis-to-metastasis seeding. For each patient, the 90th percentile of the number of mutations accumulated before the emergence of metastasis-to-metastasis seeding subclones was calculated. The mean of this distribution was used as a patient-specific threshold to indicate ‘sufficient’ time in situ for metastasis-to-metastasis seeding. Metastases with total mutation counts below this threshold were classified as having ‘insufficient’ time in situ for metastasis-to-metastasis seeding (Extended Data Fig. 6 ). Comparisons of seeding and non-seeding subclones Subclones inferred by MACHINA 8 to migrate were defined as seeding subclones. These were classified as primary-to-metastasis or metastasis-to-metastasis seeding subclones according to the migration source. All other subclones were defined as non-seeding subclones (Fig. 5a ). Truncal clones (both seeding and non-seeding) were excluded from seeding versus non-seeding subclone comparisons. Subclone metastatic capacity Metastatic capacity was approximated by the number of metastases seeded per subclone, reasoning that seeding several metastases might be indicative of greater metastatic capacity than seeding one or few. For each patient, we tested whether primary-to-metastasis subclones seeded a comparable number of metastases under a multinomial model using Monte Carlo testing ( P  > 0.05 indicating compatibility with equal probabilities). An analogous analysis assessed whether descendants of primary-to-metastasis subclones were equally likely to give rise to metastasis-to-metastasis seeding. Statistical information Statistical analyses were performed in R or Python. Two-tailed Wilcoxon tests were used for distribution comparisons (paired (most commonly within patient comparisons): Wilcoxon signed-rank; unpaired: Mann–Whitney U ), unless otherwise specified. Group comparisons used two-tailed Fisher’s exact tests, binomial tests, Monte Carlo likelihood ratio tests or LME models, as appropriate. Sample sizes are depicted and/or stated for all analyses. Reporting summary Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article. Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-026-10428-4. Supplementary information Supplementary Information This file contains Supplementary Figs. 1–10, Supplementary Table 1 and Supplementary Note. Reporting Summary Supplementary Information PEACE protocol Peer Review File Extended data figures and tables Extended Data Fig. 1 Mutational processes evolve during metastatic progression. a , Correlation between the number of regions sampled and the number of subclones detected per metastasis (left), and between the number of anatomically distinct metastases sampled and the number of metastasis-unique subclones detected per patient (right). b , Correlation of primary–metastasis heterogeneity (x-axis) with intra-primary (dark blue) and intra-metastasis (dark green) heterogeneity based on mutation or SCNA diversity. c , Prevalence of mutational processes over time, measured as the percentage of truncal, primary-unique, shared subclonal and metastasis-unique subclones in which a given signature accounts for >50% of mutations (ie. the majority aetiology). d , Percentage of primary (truncal, shared subclonal and primary-unique) and metastasis-unique subclones per patient in which each signature aetiology was detected. Dashed lines connect patients. Wilcoxon signed-rank test. e , The phylogenetic tree and estimated signature activities for each subclone (node) expressed as the fraction of mutations assigned to each signature (pie wedges) for CRUKP4761. Branches coloured based on the presence (bold red) or absence (black) of episodic APOBEC mutagenesis (Methods). f , The majority aetiology of metastasis-unique subclones that are unique to a single anatomical location. g , Mean cosine distance per patient between signature compositions of metastasis-unique subclones and their ancestral primary subclones (shared subclonal). Dashed lines connect patients. Wilcoxon signed-rank test. The box plots show the median and IQR with whiskers denoting values within 1.5 times the IQR from the first and third quartiles. Extended Data Fig. 2 Effects of treatment and genome doubling on metastases. a , Association between cumulative chemotherapy duration (sum of days across all adjuvant and metastatic regimens) and the number of metastasis-unique mutations (top) and metastasis-unique driver mutations (bottom). 24 patients. b , CRUKP2037 body map and phylogenetic tree showing the evolutionary timing and anatomical distribution of driver mutations (SNVs, indels and DNVs, teal), focal LOH affecting TSGs and arm-level LOH events, culminating in biallelic disruption of MAP3K1 (5q), PTEN (10q), TP53 (17p) and B2M (15q) (blue) and genome doubling (WGD, black) events. c , Number and evolutionary timing of WGD events per patient. d , Correlation between the number of WGD events and inter-metastasis heterogeneity (green) or primary–metastasis SCNA diversity (blue) per patient. Body map illustration in b by J. Brock adapted from ref. 11 under a Creative Commons licence CC BY 4.0 . Extended Data Fig. 3 Metastatic migration patterns. Body maps depicting the anatomical site of origin and the site of the metastasis seeded for each inferred metastatic migration (arrows, coloured according to the migration starting site). Body map illustration by J. Brock adapted from ref. 11 under a Creative Commons licence CC BY 4.0 . Extended Data Fig. 4 Origin and number of seeding subclones per migration. a – c , The number of subclones (indicated by the number of small circles), their anatomical site of origin and the site of the metastasis seeded (indicated by the colour of the small circle and large circle respectively) are displayed for migrations (arrow) involving a single subclone only ( a ), multiple subclones that all originated from the same site ( b ) and multiple subclones that originated from different anatomical sites ( c ). Migrations are distinguished based on the site of origin of the subclones involved (primary-to-metastasis migrations; black arrow, metastasis-to-metastasis; grey arrow, migrations involving subclones from both the primary and a metastasis; yellow) and are ordered according to whether the seeding subclones originated in the same organ as the metastasis and by the number of seeding subclones. Subclones linked by a line originated from the same metastasis. Extended Data Fig. 5 Orthogonal evidence for metastasis-to-metastasis seeding. a , Correlation between sampling extent (number of primary regions, individual metastases, and metastasis regions per patient) and the number of seeding subclones identified per patient. b , Schematic of metastatic migration probability estimation. For each patient, MACHINA was run on the 100 most plausible phylogenetic tree topologies. The frequency of solutions supporting each migration was used to derive migration probabilities. c , Days from surgery to first radiological detection of metastases seeded by the primary tumour (black) or another metastasis (grey). 23 patients. Mann–Whitney U test. d , Fraction of LOH events conserved between each metastasis and its inferred seeding source (grey), an alternative metastasis (yellow), or the primary tumour (black). 24 patients. Mann–Whitney U test. e , Monte Carlo likelihood ratio tests of the null hypotheses that primary-to-metastasis seeding subclones from the same primary tumour seed an equal number of metastases (left) and give rise to an equal number of metastasis-to-metastasis migrations (right). f , Phylogenetic tree and metastatic migration patterns for CRUKP9198 showing 3 primary-to-metastasis seeding subclones (lineages A–C). Although each primary lineage seeded a similar number of metastases (top), lineage C generated substantially more metastasis-to-metastasis migrations than A or B (bottom). The box plots show the median and IQR with whiskers denoting values within 1.5 times the IQR from the first and third quartiles. Body map illustrations in b , f by J. Brock adapted from ref. 11 under a Creative Commons licence CC BY 4.0 . Extended Data Fig. 6 Metastasis-to-metastasis seeding associates with time in situ. a , Clinical features of patients with predominant (>50% of total migrations, n = 14) or non-predominant (<50%, n = 10) metastasis-to-metastasis migrations. b , Number of SNVs and SCNAs accrued between the trunk and primary-to-metastasis seeding subclones that did (grey) or did not (white) give rise to subsequent metastasis-to-metastasis seeding, normalized by the total number of subclonal SNVs or SCNAs in the primary tumour, 12 patients with both types of subclones, one-sided Wilcoxon signed-rank test. c , Strategy to estimate the patient-specific threshold for sufficient time in situ for metastasis-to-metastasis seeding (example: patient CRUKP0294). The 90th percentile of mutations (numbers) accrued in the interval (dashed lines) between metastasis founding and metastasis-to-metastasis seeding subclone emergence (grey outlined nodes) was resampled to generate a distribution of the expected mutation burden accumulated before onward seeding (histogram). that represents the likely number of mutations that would be accrued in a metastasis prior to it seeding another metastasis. The post-seeding mutation burden of each metastasis was compared with the patient-specific distribution mean (dark red) to classify each metastasis as having sufficient or insufficient time in situ to seed another metastasis. d , Classification of seeding (circles) and non-seeding (diamonds) metastases as having sufficient (red) or insufficient (peach) time in situ based on the patient-specific threshold (dark red line). e , Proportion of non-seeding and seeding metastases with sufficient or insufficient duration in situ for metastasis-to-metastasis seeding. f , Mean number of extant subclones per patient in the primary tumour, non-seeding metastases, or seeding metastases. Mann–Whitney U test. The box plots show the median and IQR with whiskers denoting values within 1.5 times the IQR from the first and third quartiles. Extended Data Fig. 7 Features of intrathoracic and extrathoracic metastases. a , Days from surgery to first detection of metastasis on imaging, normalized by survival from surgery, for intrathoracic (n = 73) and extrathoracic (n = 71) metastases from 23 patients. Mann–Whitney U test. b , Observed Fisher’s exact test odds ratio (OR) between metastasis location (intrathoracic or extrathoracic) and seeding source (intrathoracic or extrathoracic) compared with a null distribution from 10,000 within-patient permutations of seeding source. The observed OR (purple) exceeds what would be expected by chance (grey). Only patients with both intrathoracic and extrathoracic metastases included (n = 12). c , Proportion of intrathoracic (top) and extrathoracic (bottom) metastases seeded by intrathoracic (purple) or extrathoracic (orange) metastases across increasing migration probability thresholds (0–1). d , e , Proportion of subclones that seeded one or multiple (>1) metastases stratified by location of the metastasis seeded (intrathoracic, purple; extrathoracic, orange) for primary-to-metastasis seeding subclones (n = 24 patients, d ) and metastasis-to-metastasis seeding subclones from intrathoracic metastases (n = 20 patients, e ). Fisher’s exact test. f , Site of relapse based on radiological imaging was known for 62 of the 126 patients in the published TRACERx 421 relapse cohort. SCNA burden of seeding and non-seeding primary subclones was compared for patients with intrathoracic-only relapse (n = 22, left) and patients with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} ≥ 1 extrathoracic metastasis at relapse (n = 40, right). LME model with subclone mutation count as covariate and patient as random effect. The box plots show the median and IQR with whiskers denoting values within 1.5 times the IQR from the first and third quartiles. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. These authors contributed equally: Sonya Hessey, Abigail Bunkum, Ariana Huebner, Kerstin Haase, Kristiana Grigoriadis, Cristina Naceur-Lombardelli These authors jointly supervised this work: Simone Zaccaria, Nicholas McGranahan, Charles Swanton, Mariam Jamal-Hanjani Lists of authors and their affiliations appear online Extended data is available for this paper at 10.1038/s41586-026-10428-4. Supplementary information The online version contains supplementary material available at 10.1038/s41586-026-10428-4. Acknowledgements S.H. is supported by the National Institute for Health and Care Research (NIHR) UCLH Biomedical Research Centre and a NIHR Clinical Lectureship, was previously supported by a Cancer Research UK (CRUK) City of London Clinical Research Training Fellowship and received funds from the Rosetrees Trust. A.B. is supported by a CRUK UCL Centre Non-Clinical Training Award (CANTAC721\100022). C.F.H. is supported by the NSERC doctoral postgraduate scholarship, the Data Sciences Institute at the University of Toronto doctoral fellowship and Mitacs Globalink Research Internship funding. C.G. is a CRUK Clinical Research Training Fellow, supported by the CRUK City of London Centre Award (SEBCATP-2024/100003). C.M.-R. is supported by the Rosetrees Trust (M630) and the Wellcome Trust. P. Pawlik is supported by CRUK. M.S.H. is supported by CRUK (TRACERx: C11496/A17786). O.L. is supported by a Clinical Lectureship at UCL funded by the CRUK Lung Cancer Centre of Excellence and previously by a Wellcome Trust Clinical Research Fellowship (225491/Z/22/Z). T.K. is supported by the Japan Society for the Promotion of Science (JSPS) Overseas Research Fellowships programme (202060447). A.J.R. is supported by the Francis Crick Institute. M.D.F. is supported by the NIHR UCL–UCLH Biomedical Research Centre and runs early-phase studies in the NIHR UCLH Clinical Research Facility supported by the UCL Experimental Cancer Medicine Centre (ECMC). S.M.L. is partially supported by the NIHR UCL–UCLH Biomedical Research Centre. M.G.K. is supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). The study was supported by the NIHR Manchester Clinical Research Facility (NIHR203956). F.H.B. is supported by the NIHR Manchester Clinical Research Facility. C.T.H. is supported by the NIHR UCLH Biomedical Research Centre. D.A.M. is supported by the CRUK Lung Cancer Centre of Excellence (C11496/A30025). S.Z. is a CRUK Career Development Fellow (RCCCDF-Nov21\100005) and is further supported by the Rosetrees Trust (M917). N.M. receives funding from CRUK (DRCPFA-Nov23/100003) and has received funding from the Wellcome Trust and the Royal Society (211179/Z/18/Z) relevant to this work. N.M. also receives funding from the CRUK Lung Cancer Centre of Excellence, the Rosetrees Trust and the NIHR UCLH Biomedical Research Centre. C.S. is a Royal Society Napier Research Professor (RSRP\R\210001). His work is supported by the Francis Crick Institute, which receives its core funding from CRUK (CC2041), the UK Medical Research Council (CC2041) and the Wellcome Trust (CC2041). C.S. is funded by CRUK (TRACERx (C11496/A17786), PEACE (C416/A21999) and the CRUK Cancer Immunotherapy Catalyst Network); the CRUK Lung Cancer Centre of Excellence (C11496/A30025); the Rosetrees Trust; the Butterfield and Stoneygate Trusts; the Novo Nordisk Foundation (ID16584); a Royal Society Professorship Enhancement Award (RP/EA/180007 and RF\ERE\231118); the NIHR UCLH Biomedical Research Centre; the UCL ECMC; the Breast Cancer Research Foundation (US) (BCRF-23-157); a CRUK Early Detection and Diagnosis Primer Award (grant EDDPMA-Nov21/100034); and a Mark Foundation for Cancer Research ASPIRE Award (grant 21-029-ASP) and ASPIRE Phase II award (grant 23-034-ASP). C.S. is in receipt of an ERC Advanced Grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement 835297). M.J.-H. received a CRUK Career Establishment Award and has received funding from CRUK, the IASLC International Lung Cancer Foundation, the Lung Cancer Research Foundation, the Rosetrees Trust, the UK and Ireland Neuroendocrine Tumour Society (UKI NETS) and the NIHR UCLH Biomedical Research Centre. The TRACERx study ( NCT01888601 ) is sponsored by UCL (UCL/12/0279). TRACERx is funded by CRUK (C11496/A17786) and coordinated through the CRUK and UCL Cancer Trials Centre, which has a core grant from CRUK (C444/A15953). The PEACE study ( NCT03004755 ) is sponsored by UCL (UCL/13/0165). PEACE is funded by CRUK (C416/A21999) and coordinated through the CRUK and UCL Cancer Trials Centre. This work was supported by the CRUK Lung Cancer Centre of Excellence and the CRUK City of London Centre, as well as the UCL ECMC and the NIHR UCLH Biomedical Research Centre. We acknowledge the patients and relatives who participated in the TRACERx study and the PEACE national autopsy programme. We also thank the members of the TRACERx, TRACERx EVO and PEACE consortia for participating in these studies, especially all site personnel, investigators and funders who supported the generation of the data in these studies. We acknowledge the support of staff at the Scientific Computing and Genomics science technology platforms at the Francis Crick Institute, and thank J. Brock from Research Illustration for his help. Author contributions S.H., A.B., A. Huebner, K.H., K.G. and C.F.H. performed bioinformatic analyses. S.H., W.K.L., B.D. and C.T.H. analysed radiological imaging data. C.G., D.M., C.M.-R., C. Richard, O.L., O.P., K.T. and P. Pawlik assisted with bioinformatic analyses. A. Huebner, C.M.-R. and M.S.H. developed the bioinformatic pipeline to process raw data. S.H., W.K.L., T.K., C.T.H., C.N.-L., P. Prymas and A. Toncheva assisted with clinical data annotation. D.A.F., Y.S., F.H.B., J.C., K.G.B., M.D.F., S.B., D.P.-P., S.M.L., T.A., O.L., B.N., M.G.K., P. Russell, A.K. and C.T.H. coordinated clinical aspects of the TRACERx study, patient recruitment and follow-up. M.D.F., S.B., D.P.-P., S.M.L., T.A., B.N., M.G.K., J.W., J.L.Q., A. Thomas, C. Richards., S.J., P. Roxburgh and O.L. coordinated clinical aspects of the PEACE study, patient recruitment and follow-up. D.A.M. and M.S. reviewed pathology. C.N.-L., S.V., S.W., F.A., P. Prymas, A. Toncheva and A.J.R. assisted with sample collection and sample processing. D.A.M., M.M., E.B., M.F., I.P., U.M., A.G., G.L., P.O., C. Richards, S.J. and S.F. performed research autopsies. A. Hackshaw and H.B. helped oversee the running of the TRACERx and PEACE studies. S.Z., N.M., C.S. and M.J.-H. jointly designed and supervised the study. S.H., A.B., A. Huebner, K.H., K.G., C.N.-L., C.F.H., C.G., S.Z., N.M., C.S and M.J.-H. wrote the manuscript. Peer review Peer review information Nature thanks Edwin Cuppen and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Data availability The WES data derived from the TRACERx and PEACE studies and analysed in this work have been deposited in the European Genome–Phenome Archive (EGA), which is hosted by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG), under accession code EGAS00001008217 and the associated dataset EGAD00001015763 ; access is controlled by the PEACE data access committee. Details on how to apply for access are available on the linked pages. All processed data used to create the figures are available via Zenodo at 10.5281/zenodo.15755949 (ref. 76 ). Code availability The code for the alignment pipeline and the subsequent processing pipeline can be found on GitHub ( https://github.com/FrancisCrickInstitute/peace-alignment and https://github.com/FrancisCrickInstitute/peace-pipeline , respectively). All code to reproduce figures is available via Zenodo at 10.5281/zenodo.15755949 (ref. 76 ). Competing interests S.V. is a co-inventor on a patent of methods for detecting molecules in a sample (US patent no. 10578620; Methods for detecting molecules in a sample). P. Roxburgh has received grant support from CRUK, Beatson Cancer Charity, Wellbeing of Women, AstraZeneca, Atrios and Tesaro; consultancy from Roche, Abbvie and MSD; and speaker fees from AstraZeneca, GSK and pharma&. M.D.F. acknowledges grant support from CRUK, AstraZeneca, Boehringer Ingelheim, MSD, Merck and NIHR; holds appointments with DRA and HCA; serves in unpaid leadership roles at the Ruth Strauss Foundation; is an advisory board member for Transgene; and has consulted for Achilles, Amgen, AstraZeneca, Bayer, BeiOne, Boxer, BMS, Celgene, EQRx, Guardant Health, GSK, Immutep, Janssen, Merck, MSD, Oxford VacMedix, Pharmamar, Regeneron, Roche, Societe Generale, Syncorp, Takeda and UltraHuman. S.B. acknowledges educational grant support from Roche. T.A. acknowledges educational grant support from Boehringer Ingelheim, Roche, Takeda and Johnson & Johnson, and has consulted for AstraZeneca, Daichii Sanko, Johnson & Johnson, Merck, Pfizer, Roche and Takeda. M.G.K. has received funding grants from BerGenBio, Novartis and Roche; advisory board or consultancy fees from Astellas, Bayer, Guardant Health, Johnson & Johnson, Roche, Seattle Genetics and Zai Lab; speaker fees from BMS, Johnson & Johnson and Roche; and support for attending meetings and/or travel from BMS, Eisai, Johnson & Johnson, Roche and Zai Lab. C. Richards. Eli Lilly (Advisory Board), Pfizer, AstraZeneca, Servier laboratories and Dako (talks), Ipsen (conference funding). D.A.F. reports grants from Aldeyra, Boehringer Ingelheim, Astex Therapeutics, Bayer, BMS, GSK, RS Oncology, Clovis, Eli Lilly, BMS, MSD and GSK, and personal fees from Atara, BMS, Boehringer Ingelheim, Cambridge Clinical Laboratories, Targovax, Roche and RS Oncology. C.T.H. has received speaker fees from AstraZeneca and Merck and research funding from Roche and AstraZeneca, and has a paid advisory role for Genesiscare UK. A. Hackshaw has received fees for being a member of independent data monitoring committees for Roche-sponsored clinical trials, and academic projects coordinated by Roche. D.A.M. reports speaker fees from AstraZeneca and Takeda; consultancy fees from AstraZeneca, Thermo Fisher Scientific, Takeda, Amgen, Janssen, MIM Software, Bristol Myers Squibb and Eli Lilly; and has received educational support from Takeda and Amgen. N.M. holds patents related to determining HLA LOH (PCT/GB2018/052004), determining B cell fraction in mixed samples (PCT/EP2024/062999), determining lymphocyte abundance in mixed samples (PCT/EP2022/070694), identifying responders to cancer treatment (PCT/GB2018/051912), targeting neoantigens (PCT/EP2016/059401), identifying patient response to immune checkpoint blockade (PCT/EP2016/071471) and predicting survival rates of patients with cancer (PCT/GB2020/050221), and has a patent pending in determining HLA disruption (PCT/EP2023/059039). C.S. acknowledges grant support from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Pfizer, Invitae (previously ArcherDX; collaboration in minimal residual disease sequencing technologies), Ono Pharmaceutical, Personalis and Roche–Ventana. He is chief investigator for the AstraZeneca MeRmaiD 1 and 2 clinical trials and is the steering committee chair. He is also co-chief investigator of the NHS Galleri trial funded by GRAIL and a paid member of GRAIL’s scientific advisory board. He has received consultancy fees from Achilles, Bicycle Therapeutics (paid board member, and chair of clinical advisory group), Genentech, Medicxi, China Innovation Centre of Roche (CICoR; formerly Roche Innovation Centre – Shanghai), Metabomed (until July 2022), Relay Therapeutics (scientific advisory board member), Saga Diagnostics (scientific advisory board member) and the Sarah Cannon Research Institute. C.S. has received honoraria from Amgen, AstraZeneca, Bristol Myers Squibb, GlaxoSmithKline, Illumina, MSD, Novartis, Pfizer and Roche–Ventana. C.S. has previously held stock or options in GRAIL, currently has stock or options in Bicycle Therapeutics and Relay Therapeutics and previously held stock and was co-founder of Achilles Therapeutics. C.S. declares patent applications relating to methods for lung cancer detection (PCT/US2017/028013 and US20190106751A1); targeting neoantigens (PCT/EP2016/059401); identifying patent response to immune checkpoint blockade (PCT/EP2016/071471); identifying patients who respond to cancer treatment (PCT/GB2018/051912); determining HLA LOH (PCT/GB2018/052004); predicting survival rates of patients with cancer (PCT/GB2020/050221); methods and systems for tumour monitoring (PCT/EP2022/077987); and analysis of HLA allele transcriptional deregulation (PCT/EP2023/059039). C.S. is an inventor on a European patent application (PCT/GB2017/053289) relating to assay technology to detect tumour recurrence. This patent has been licensed to a commercial entity and under their terms of employment, C.S. is due a revenue share of any revenue generated from such licence(s). M.J.-H. has received funding from CRUK, the National Institutes of Health (NIH) National Cancer Institute, International Association for the Study of Lung Cancer (IASLC) Foundation, Lung Cancer Research Foundation, Rosetrees Trust, UKI NETs and NIHR; has consulted for Astex Pharmaceutical and Achilles Therapeutics; was a member of the Achilles Therapeutics scientific advisory board and steering committee; and has received speaker honoraria from Pfizer, Astex Pharmaceuticals, Oslo Cancer Cluster, Bristol Myers Squibb and Genentech. M.J.-H. is listed as a co-inventor on a European patent application relating to methods to detect lung cancer (PCT/ US2017/028013); this patent has been licensed to commercial entities, and, under terms of employment, M.J.-H. is due a share of any revenue generated from such licence(s). M.J.-H. is also listed as a co-inventor on the GB priority patent application (GB2400424.4) titled ‘Treatment and prevention of lung cancer’. References 1. Pich O The translational challenges of precision oncology Cancer Cell 2022 40 458 478 10.1016/j.ccell.2022.04.002 35487215 Pich, O. et al. The translational challenges of precision oncology. Cancer Cell 40 , 458–478 (2022). 35487215 10.1016/j.ccell.2022.04.002 2. Dillekas H Rogers MS Straume O Are 90% of deaths from cancer caused by metastases? Cancer Med. 2019 8 5574 5576 10.1002/cam4.2474 31397113 PMC6745820 Dillekas, H., Rogers, M. S. & Straume, O. Are 90% of deaths from cancer caused by metastases?. Cancer Med. 8 , 5574–5576 (2019). 31397113 10.1002/cam4.2474 PMC6745820 3. Hudock NL Future trends in incidence and long-term survival of metastatic cancer in the United States Commun. Med. 2023 3 76 10.1038/s43856-023-00304-x 37244961 PMC10224927 Hudock, N. L. et al. Future trends in incidence and long-term survival of metastatic cancer in the United States. Commun. Med. 3 , 76 (2023). 37244961 10.1038/s43856-023-00304-x PMC10224927 4. Public Health England. Cancer survival in England for patients diagnosed between 2014 and 2018, and followed up to 2019 (PHE, 2021). 5. Sung H Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J. Clin. 2021 71 209 249 33538338 10.3322/caac.21660 Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71 , 209–249 (2021). 33538338 10.3322/caac.21660 6. Ganti AK Klein AB Cotarla I Seal B Chou E Update of incidence, prevalence, survival, and initial treatment in patients with non-small cell lung cancer in the US JAMA Oncol. 2021 7 1824 1832 10.1001/jamaoncol.2021.4932 34673888 PMC8532041 Ganti, A. K., Klein, A. B., Cotarla, I., Seal, B. & Chou, E. Update of incidence, prevalence, survival, and initial treatment in patients with non-small cell lung cancer in the US. JAMA Oncol. 7 , 1824–1832 (2021). 34673888 10.1001/jamaoncol.2021.4932 PMC8532041 7. Fidler IJ The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited Nat. Rev. Cancer 2003 3 453 458 10.1038/nrc1098 12778135 Fidler, I. J. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nat. Rev. Cancer 3 , 453–458 (2003). 12778135 10.1038/nrc1098 8. El-Kebir M Satas G Raphael BJ Inferring parsimonious migration histories for metastatic cancers Nat. Genet. 2018 50 718 726 10.1038/s41588-018-0106-z 29700472 PMC6103651 El-Kebir, M., Satas, G. & Raphael, B. J. Inferring parsimonious migration histories for metastatic cancers. Nat. Genet. 50 , 718–726 (2018). 29700472 10.1038/s41588-018-0106-z PMC6103651 9. McPherson A Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer Nat. Genet. 2016 48 758 767 10.1038/ng.3573 27182968 McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48 , 758–767 (2016). 27182968 10.1038/ng.3573 10. Reiter JG Reconstructing metastatic seeding patterns of human cancers Nat. Commun. 2017 8 14114 10.1038/ncomms14114 28139641 PMC5290319 Reiter, J. G. et al. Reconstructing metastatic seeding patterns of human cancers. Nat. Commun. 8 , 14114 (2017). 28139641 10.1038/ncomms14114 PMC5290319 11. Al Bakir M The evolution of non-small cell lung cancer metastases in TRACERx Nature 2023 616 534 542 10.1038/s41586-023-05729-x 37046095 PMC10115651 Al Bakir, M. et al. The evolution of non-small cell lung cancer metastases in TRACERx. Nature 616 , 534–542 (2023). 37046095 10.1038/s41586-023-05729-x PMC10115651 12. Naxerova K Evolutionary paths towards metastasis Nat. Rev. Cancer 2025 25 545 560 10.1038/s41568-025-00814-x 40263543 PMC13022989 Naxerova, K. Evolutionary paths towards metastasis. Nat. Rev. Cancer 25 , 545–560 (2025). 40263543 10.1038/s41568-025-00814-x PMC13022989 13. Brown D Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations Nat. Commun. 2017 8 14944 10.1038/ncomms14944 28429735 PMC5474888 Brown, D. et al. Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations. Nat. Commun. 8 , 14944 (2017). 28429735 10.1038/ncomms14944 PMC5474888 14. Hoadley KA Tumor evolution in two patients with basal-like breast cancer: a retrospective genomics study of multiple metastases PLoS Med. 2016 13 e1002174 10.1371/journal.pmed.1002174 27923045 PMC5140046 Hoadley, K. A. et al. Tumor evolution in two patients with basal-like breast cancer: a retrospective genomics study of multiple metastases. PLoS Med. 13 , e1002174 (2016). 27923045 10.1371/journal.pmed.1002174 PMC5140046 15. Gundem G The evolutionary history of lethal metastatic prostate cancer Nature 2015 520 353 357 10.1038/nature14347 25830880 PMC4413032 Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520 , 353–357 (2015). 25830880 10.1038/nature14347 PMC4413032 16. Hessey S Fessas P Zaccaria S Jamal-Hanjani M Swanton C Insights into the metastatic cascade through research autopsies Trends Cancer 2023 9 490 502 10.1016/j.trecan.2023.03.002 37059687 Hessey, S., Fessas, P., Zaccaria, S., Jamal-Hanjani, M. & Swanton, C. Insights into the metastatic cascade through research autopsies. Trends Cancer 9 , 490–502 (2023). 37059687 10.1016/j.trecan.2023.03.002 17. Iacobuzio-Donahue CA Cancer biology as revealed by the research autopsy Nat. Rev. Cancer 2019 19 686 697 10.1038/s41568-019-0199-4 31519982 PMC7453489 Iacobuzio-Donahue, C. A. et al. Cancer biology as revealed by the research autopsy. Nat. Rev. Cancer 19 , 686–697 (2019). 31519982 10.1038/s41568-019-0199-4 PMC7453489 18. Jamal-Hanjani M Tracking the evolution of non-small-cell lung cancer N. Engl. J. Med. 2017 376 2109 2121 10.1056/NEJMoa1616288 28445112 Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376 , 2109–2121 (2017). 28445112 10.1056/NEJMoa1616288 19. Frankell AM The evolution of lung cancer and impact of subclonal selection in TRACERx Nature 2023 616 525 533 10.1038/s41586-023-05783-5 37046096 PMC10115649 Frankell, A. M. et al. The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature 616 , 525–533 (2023). 37046096 10.1038/s41586-023-05783-5 PMC10115649 20. Riihimaki M Metastatic sites and survival in lung cancer Lung Cancer 2014 86 78 84 10.1016/j.lungcan.2014.07.020 25130083 Riihimaki, M. et al. Metastatic sites and survival in lung cancer. Lung Cancer 86 , 78–84 (2014). 25130083 10.1016/j.lungcan.2014.07.020 21. de Bruin EC Spatial and temporal diversity in genomic instability processes defines lung cancer evolution Science 2014 346 251 256 10.1126/science.1253462 25301630 PMC4636050 de Bruin, E. C. et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346 , 251–256 (2014). 25301630 10.1126/science.1253462 PMC4636050 22. Zhao ZM Early and multiple origins of metastatic lineages within primary tumors Proc. Natl Acad. Sci. USA 2016 113 2140 2145 10.1073/pnas.1525677113 26858460 PMC4776530 Zhao, Z. M. et al. Early and multiple origins of metastatic lineages within primary tumors. Proc. Natl Acad. Sci. USA 113 , 2140–2145 (2016). 26858460 10.1073/pnas.1525677113 PMC4776530 23. Yates LR Genomic evolution of breast cancer metastasis and relapse Cancer Cell 2017 32 169 184 10.1016/j.ccell.2017.07.005 28810143 PMC5559645 Yates, L. R. et al. Genomic evolution of breast cancer metastasis and relapse. Cancer Cell 32 , 169–184 (2017). 28810143 10.1016/j.ccell.2017.07.005 PMC5559645 24. Pich, O. et al. Somatic evolution following cancer treatment in normal tissue. Nature 10.1038/s41586-025-09792-4 (2025). 10.1038/s41586-025-09792-4 PMC13190248 41372419 25. Petljak M Characterizing mutational signatures in human cancer cell lines reveals episodic APOBEC mutagenesis Cell 2019 176 1282 1294 10.1016/j.cell.2019.02.012 30849372 PMC6424819 Petljak, M. et al. Characterizing mutational signatures in human cancer cell lines reveals episodic APOBEC mutagenesis. Cell 176 , 1282–1294 (2019). 30849372 10.1016/j.cell.2019.02.012 PMC6424819 26. Siegel MB Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer J. Clin. Invest. 2018 128 1371 1383 10.1172/JCI96153 29480819 PMC5873890 Siegel, M. B. et al. Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer. J. Clin. Invest. 128 , 1371–1383 (2018). 29480819 10.1172/JCI96153 PMC5873890 27. Makohon-Moore AP Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer Nat. Genet. 2017 49 358 366 10.1038/ng.3764 28092682 PMC5663439 Makohon-Moore, A. P. et al. Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer. Nat. Genet. 49 , 358–366 (2017). 28092682 10.1038/ng.3764 PMC5663439 28. Reiter JG Minimal functional driver gene heterogeneity among untreated metastases Science 2018 361 1033 1037 10.1126/science.aat7171 30190408 PMC6329287 Reiter, J. G. et al. Minimal functional driver gene heterogeneity among untreated metastases. Science 361 , 1033–1037 (2018). 30190408 10.1126/science.aat7171 PMC6329287 29. Turajlic S Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal Cell 2018 173 581 594 10.1016/j.cell.2018.03.057 29656895 PMC5938365 Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx Renal. Cell 173 , 581–594 (2018). 29656895 10.1016/j.cell.2018.03.057 PMC5938365 30. Chakravarty D OncoKB: a precision oncology knowledge base JCO Precis. Oncol. 2017 1 1 16 10.1200/PO.17.00011 PMC5586540 28890946 Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1 , 1–16 (2017). 10.1200/PO.17.00011 PMC5586540 28890946 31. Suehnholz SP Quantifying the expanding landscape of clinical actionability for patients with cancer Cancer Discov. 2024 14 49 65 10.1158/2159-8290.CD-23-0467 37849038 PMC10784742 Suehnholz, S. P. et al. Quantifying the expanding landscape of clinical actionability for patients with cancer. Cancer Discov. 14 , 49–65 (2024). 37849038 10.1158/2159-8290.CD-23-0467 PMC10784742 32. Hulse M PRT3789 is a first-in-human SMARCA2-selective degrader that induces synthetic lethality in SMARCA4 -mutated cancers Cancer Res. 2026 86 213 235 10.1158/0008-5472.CAN-25-1141 40991405 Hulse, M. et al. PRT3789 is a first-in-human SMARCA2-selective degrader that induces synthetic lethality in SMARCA4 -mutated cancers. Cancer Res. 86 , 213–235 (2026). 40991405 10.1158/0008-5472.CAN-25-1141 33. Van Egeren D Genomic analysis of early-stage lung cancer reveals a role for TP53 mutations in distant metastasis Sci. Rep. 2022 12 19055 10.1038/s41598-022-21448-1 36351964 PMC9646734 Van Egeren, D. et al. Genomic analysis of early-stage lung cancer reveals a role for TP53 mutations in distant metastasis. Sci. Rep. 12 , 19055 (2022). 36351964 10.1038/s41598-022-21448-1 PMC9646734 34. Bielski CM Genome doubling shapes the evolution and prognosis of advanced cancers Nat. Genet. 2018 50 1189 1195 10.1038/s41588-018-0165-1 30013179 PMC6072608 Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50 , 1189–1195 (2018). 30013179 10.1038/s41588-018-0165-1 PMC6072608 35. Lopez S Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution Nat. Genet. 2020 52 283 293 10.1038/s41588-020-0584-7 32139907 PMC7116784 Lopez, S. et al. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat. Genet. 52 , 283–293 (2020). 32139907 10.1038/s41588-020-0584-7 PMC7116784 36. Hu Z Li Z Ma Z Curtis C Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases Nat. Genet. 2020 52 701 708 10.1038/s41588-020-0628-z 32424352 PMC7343625 Hu, Z., Li, Z., Ma, Z. & Curtis, C. Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nat. Genet. 52 , 701–708 (2020). 32424352 10.1038/s41588-020-0628-z PMC7343625 37. Leshchiner I Inferring early genetic progression in cancers with unobtainable premalignant disease Nat. Cancer 2023 4 550 563 10.1038/s43018-023-00533-y 37081260 PMC10132986 Leshchiner, I. et al. Inferring early genetic progression in cancers with unobtainable premalignant disease. Nat. Cancer 4 , 550–563 (2023). 37081260 10.1038/s43018-023-00533-y PMC10132986 38. Roddur, M. S. et al. Characterizing the solution space of migration histories of metastatic cancers with MACH2. Preprint at bioRxiv 10.1101/2024.11.19.624301 (2025). 39. Massague J Obenauf AC Metastatic colonization by circulating tumour cells Nature 2016 529 298 306 10.1038/nature17038 26791720 PMC5029466 Massague, J. & Obenauf, A. C. Metastatic colonization by circulating tumour cells. Nature 529 , 298–306 (2016). 26791720 10.1038/nature17038 PMC5029466 40. Lambert AW Pattabiraman DR Weinberg RA Emerging biological principles of metastasis Cell 2017 168 670 691 10.1016/j.cell.2016.11.037 28187288 PMC5308465 Lambert, A. W., Pattabiraman, D. R. & Weinberg, R. A. Emerging biological principles of metastasis. Cell 168 , 670–691 (2017). 28187288 10.1016/j.cell.2016.11.037 PMC5308465 41. Pawlik P Clone copy number diversity is linked to survival in lung cancer Nature 2025 646 190 197 10.1038/s41586-025-09398-w 40804524 PMC12488491 Pawlik, P. et al. Clone copy number diversity is linked to survival in lung cancer. Nature 646 , 190–197 (2025). 40804524 10.1038/s41586-025-09398-w PMC12488491 42. Li J Non-cell-autonomous cancer progression from chromosomal instability Nature 2023 620 1080 1088 10.1038/s41586-023-06464-z 37612508 PMC10468402 Li, J. et al. Non-cell-autonomous cancer progression from chromosomal instability. Nature 620 , 1080–1088 (2023). 37612508 10.1038/s41586-023-06464-z PMC10468402 43. Choi YJ Intraindividual genomic heterogeneity of high-grade serous carcinoma of the ovary and clinical utility of ascitic cancer cells for mutation profiling J. Pathol. 2017 241 57 66 10.1002/path.4819 27741368 Choi, Y. J. et al. Intraindividual genomic heterogeneity of high-grade serous carcinoma of the ovary and clinical utility of ascitic cancer cells for mutation profiling. J. Pathol. 241 , 57–66 (2017). 27741368 10.1002/path.4819 44. Sanborn JZ Phylogenetic analyses of melanoma reveal complex patterns of metastatic dissemination Proc. Natl Acad. Sci. USA 2015 112 10995 11000 10.1073/pnas.1508074112 26286987 PMC4568214 Sanborn, J. Z. et al. Phylogenetic analyses of melanoma reveal complex patterns of metastatic dissemination. Proc. Natl Acad. Sci. USA 112 , 10995–11000 (2015). 26286987 10.1073/pnas.1508074112 PMC4568214 45. Gibson WJ The genomic landscape and evolution of endometrial carcinoma progression and abdominopelvic metastasis Nat. Genet. 2016 48 848 855 10.1038/ng.3602 27348297 PMC4963271 Gibson, W. J. et al. The genomic landscape and evolution of endometrial carcinoma progression and abdominopelvic metastasis. Nat. Genet. 48 , 848–855 (2016). 27348297 10.1038/ng.3602 PMC4963271 46. Kumar A Substantial interindividual and limited intraindividual genomic diversity among tumors from men with metastatic prostate cancer Nat. Med. 2016 22 369 378 10.1038/nm.4053 26928463 PMC5045679 Kumar, A. et al. Substantial interindividual and limited intraindividual genomic diversity among tumors from men with metastatic prostate cancer. Nat. Med. 22 , 369–378 (2016). 26928463 10.1038/nm.4053 PMC5045679 47. Noorani A Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma Nat. Genet. 2020 52 74 83 10.1038/s41588-019-0551-3 31907488 PMC7100916 Noorani, A. et al. Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma. Nat. Genet. 52 , 74–83 (2020). 31907488 10.1038/s41588-019-0551-3 PMC7100916 48. Iyengar P Consolidative radiotherapy for limited metastatic non-small-cell lung cancer: a phase 2 randomized clinical trial JAMA Oncol. 2018 4 e173501 10.1001/jamaoncol.2017.3501 28973074 PMC5833648 Iyengar, P. et al. Consolidative radiotherapy for limited metastatic non-small-cell lung cancer: a phase 2 randomized clinical trial. JAMA Oncol. 4 , e173501 (2018). 28973074 10.1001/jamaoncol.2017.3501 PMC5833648 49. Gomez DR Local consolidative therapy vs. maintenance therapy or observation for patients with oligometastatic non-small-cell lung cancer: long-term results of a multi-institutional, phase ii, randomized study J Clin. Oncol. 2019 37 1558 1565 10.1200/JCO.19.00201 31067138 PMC6599408 Gomez, D. R. et al. Local consolidative therapy vs. maintenance therapy or observation for patients with oligometastatic non-small-cell lung cancer: long-term results of a multi-institutional, phase ii, randomized study. J Clin. Oncol. 37 , 1558–1565 (2019). 31067138 10.1200/JCO.19.00201 PMC6599408 50. Peng P EGFR-TKIs plus stereotactic body radiation therapy (SBRT) for stage IV non-small cell lung cancer (NSCLC): a prospective, multicenter, randomized, controlled phase II study Radiother. Oncol. 2023 184 109681 10.1016/j.radonc.2023.109681 37105304 Peng, P. et al. EGFR-TKIs plus stereotactic body radiation therapy (SBRT) for stage IV non-small cell lung cancer (NSCLC): a prospective, multicenter, randomized, controlled phase II study. Radiother. Oncol. 184 , 109681 (2023). 37105304 10.1016/j.radonc.2023.109681 51. Palma DA Stereotactic ablative radiotherapy for the comprehensive treatment of oligometastatic cancers: long-term results of the SABR-COMET phase II randomized trial J. Clin. Oncol. 2020 38 2830 2838 10.1200/JCO.20.00818 32484754 PMC7460150 Palma, D. A. et al. Stereotactic ablative radiotherapy for the comprehensive treatment of oligometastatic cancers: long-term results of the SABR-COMET phase II randomized trial. J. Clin. Oncol. 38 , 2830–2838 (2020). 32484754 10.1200/JCO.20.00818 PMC7460150 52. Iyengar P NRG-LU002: randomized phase II/III trial of maintenance systemic therapy versus local consolidative therapy (LCT) plus maintenance systemic therapy for limited metastatic non-small cell lung cancer (NSCLC) J. Clin. Oncol. 2024 42 8506 10.1200/JCO.2024.42.16_suppl.8506 Iyengar, P. et al. NRG-LU002: randomized phase II/III trial of maintenance systemic therapy versus local consolidative therapy (LCT) plus maintenance systemic therapy for limited metastatic non-small cell lung cancer (NSCLC). J. Clin. Oncol. 42 , 8506 (2024). 53. Quinn JJ Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts Science 2021 371 eabc1944 10.1126/science.abc1944 33479121 PMC7983364 Quinn, J. J. et al. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 371 , eabc1944 (2021). 33479121 10.1126/science.abc1944 PMC7983364 54. Berthelet J The site of breast cancer metastases dictates their clonal composition and reversible transcriptomic profile Sci. Adv. 2021 7 eabf4408 10.1126/sciadv.abf4408 34233875 PMC8262813 Berthelet, J. et al. The site of breast cancer metastases dictates their clonal composition and reversible transcriptomic profile. Sci. Adv. 7 , eabf4408 (2021). 34233875 10.1126/sciadv.abf4408 PMC8262813 55. Merino D Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer Nat. Commun. 2019 10 766 10.1038/s41467-019-08595-2 30770823 PMC6377663 Merino, D. et al. Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer. Nat. Commun. 10 , 766 (2019). 30770823 10.1038/s41467-019-08595-2 PMC6377663 56. Jia M Comprehensive analysis of spread through air spaces in lung adenocarcinoma and squamous cell carcinoma using the 8th edition AJCC/UICC staging system BMC Cancer 2020 20 705 10.1186/s12885-020-07200-w 32727513 PMC7391600 Jia, M. et al. Comprehensive analysis of spread through air spaces in lung adenocarcinoma and squamous cell carcinoma using the 8th edition AJCC/UICC staging system. BMC Cancer 20 , 705 (2020). 32727513 10.1186/s12885-020-07200-w PMC7391600 57. Karasaki T Evolutionary characterization of lung adenocarcinoma morphology in TRACERx Nat. Med. 2023 29 833 845 10.1038/s41591-023-02230-w 37045996 PMC7614478 Karasaki, T. et al. Evolutionary characterization of lung adenocarcinoma morphology in TRACERx. Nat. Med. 29 , 833–845 (2023). 37045996 10.1038/s41591-023-02230-w PMC7614478 58. Franquet T Uncommon thoracic manifestations from extrapulmonary tumors: computed tomography evaluation—pictorial review Respir. Med. 2020 168 105986 10.1016/j.rmed.2020.105986 32469707 Franquet, T. et al. Uncommon thoracic manifestations from extrapulmonary tumors: computed tomography evaluation—pictorial review. Respir. Med. 168 , 105986 (2020). 32469707 10.1016/j.rmed.2020.105986 59. Naxerova K Origins of lymphatic and distant metastases in human colorectal cancer Science 2017 357 55 60 10.1126/science.aai8515 28684519 PMC5536201 Naxerova, K. et al. Origins of lymphatic and distant metastases in human colorectal cancer. Science 357 , 55–60 (2017). 28684519 10.1126/science.aai8515 PMC5536201 60. Reiter JG Lymph node metastases develop through a wider evolutionary bottleneck than distant metastases Nat. Genet. 2020 52 692 700 10.1038/s41588-020-0633-2 32451459 PMC7343611 Reiter, J. G. et al. Lymph node metastases develop through a wider evolutionary bottleneck than distant metastases. Nat. Genet. 52 , 692–700 (2020). 32451459 10.1038/s41588-020-0633-2 PMC7343611 61. Bakhoum SF Chromosomal instability drives metastasis through a cytosolic DNA response Nature 2018 553 467 472 10.1038/nature25432 29342134 PMC5785464 Bakhoum, S. F. et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature 553 , 467–472 (2018). 29342134 10.1038/nature25432 PMC5785464 62. Watkins TBK Refphase: multi-sample phasing reveals haplotype-specific copy number heterogeneity PLoS Comput. Biol. 2023 19 e1011379 10.1371/journal.pcbi.1011379 37871126 PMC10621967 Watkins, T. B. K. et al. Refphase: multi-sample phasing reveals haplotype-specific copy number heterogeneity. PLoS Comput. Biol. 19 , e1011379 (2023). 37871126 10.1371/journal.pcbi.1011379 PMC10621967 63. Grigoriadis K CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction Nat. Protoc. 2024 19 159 183 10.1038/s41596-023-00913-9 38017136 Grigoriadis, K. et al. CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction. Nat. Protoc. 19 , 159–183 (2024). 38017136 10.1038/s41596-023-00913-9 64. Roth A PyClone: statistical inference of clonal population structure in cancer Nat. Methods 2014 11 396 398 10.1038/nmeth.2883 24633410 PMC4864026 Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11 , 396–398 (2014). 24633410 10.1038/nmeth.2883 PMC4864026 65. Deshwar AG PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors Genome Biol. 2015 16 35 10.1186/s13059-015-0602-8 25786235 PMC4359439 Deshwar, A. G. et al. PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol. 16 , 35 (2015). 25786235 10.1186/s13059-015-0602-8 PMC4359439 66. Pagel KA Integrated informatics analysis of cancer-related variants JCO Clin. Cancer Inform. 2020 4 310 317 10.1200/CCI.19.00132 32228266 PMC7113103 Pagel, K. A. et al. Integrated informatics analysis of cancer-related variants. JCO Clin. Cancer Inform. 4 , 310–317 (2020). 32228266 10.1200/CCI.19.00132 PMC7113103 67. McLaren W The Ensembl Variant Effect Predictor Genome Biol. 2016 17 122 10.1186/s13059-016-0974-4 27268795 PMC4893825 McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17 , 122 (2016). 27268795 10.1186/s13059-016-0974-4 PMC4893825 68. Karczewski KJ The mutational constraint spectrum quantified from variation in 141,456 humans Nature 2020 581 434 443 10.1038/s41586-020-2308-7 32461654 PMC7334197 Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581 , 434–443 (2020). 32461654 10.1038/s41586-020-2308-7 PMC7334197 69. Tate JG COSMIC: the Catalogue Of Somatic Mutations In Cancer Nucleic Acids Res. 2019 47 D941 D947 10.1093/nar/gky1015 30371878 PMC6323903 Tate, J. G. et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 47 , D941–D947 (2019). 30371878 10.1093/nar/gky1015 PMC6323903 70. Jaganathan K Predicting splicing from primary sequence with deep learning Cell 2019 176 535 548 10.1016/j.cell.2018.12.015 30661751 Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176 , 535–548 (2019). 30661751 10.1016/j.cell.2018.12.015 71. Muinos F Martinez-Jimenez F Pich O Gonzalez-Perez A Lopez-Bigas N In silico saturation mutagenesis of cancer genes Nature 2021 596 428 432 10.1038/s41586-021-03771-1 34321661 Muinos, F., Martinez-Jimenez, F., Pich, O., Gonzalez-Perez, A. & Lopez-Bigas, N. In silico saturation mutagenesis of cancer genes. Nature 596 , 428–432 (2021). 34321661 10.1038/s41586-021-03771-1 72. Tokheim C Karchin R CHASMplus reveals the scope of somatic missense mutations driving human cancers Cell Syst. 2019 9 9 23 10.1016/j.cels.2019.05.005 31202631 PMC6857794 Tokheim, C. & Karchin, R. CHASMplus reveals the scope of somatic missense mutations driving human cancers. Cell Syst. 9 , 9–23 (2019). 31202631 10.1016/j.cels.2019.05.005 PMC6857794 73. Mermel CH GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers Genome Biol. 2011 12 R41 10.1186/gb-2011-12-4-r41 21527027 PMC3218867 Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12 , R41 (2011). 21527027 10.1186/gb-2011-12-4-r41 PMC3218867 74. Campbell, J. et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat. Genet. 48 , 607–616 (2016). 10.1038/ng.3564 PMC4884143 27158780 75. Diaz-Gay M Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment Bioinformatics 2023 39 btad756 10.1093/bioinformatics/btad756 38096571 PMC10746860 Diaz-Gay, M. et al. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. Bioinformatics 39 , btad756 (2023). 38096571 10.1093/bioinformatics/btad756 PMC10746860 76. Hessey, S. et al. Evolutionary characterisation of lung cancer metastasis. Zenodo 10.5281/zenodo.15755949 (2026).