Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Feb 3;185(3):563-575.e11.
doi: 10.1016/j.cell.2022.01.003.

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients

Bastien Nguyen  1 , Christopher Fong  1 , Anisha Luthra  1 , Shaleigh A Smith  1 , Renzo G DiNatale  2 , Subhiksha Nandakumar  1 , Henry Walch  1 , Walid K Chatila  1 , Ramyasree Madupuri  1 , Ritika Kundra  1 , Craig M Bielski  3 , Brooke Mastrogiacomo  1 , Mark T A Donoghue  4 , Adrienne Boire  5 , Sarat Chandarlapaty  6 , Karuna Ganesh  7 , James J Harding  8 , Christine A Iacobuzio-Donahue  9 , Pedram Razavi  8 , Ed Reznik  1 , Charles M Rudin  7 , Dmitriy Zamarin  8 , Wassim Abida  10 , Ghassan K Abou-Alfa  10 , Carol Aghajanian  10 , Andrea Cercek  10 , Ping Chi  10 , Darren Feldman  10 , Alan L Ho  10 , Gopakumar Iyer  10 , Yelena Y Janjigian  10 , Michael Morris  10 , Robert J Motzer  10 , Eileen M O'Reilly  10 , Michael A Postow  10 , Nitya P Raj  10 , Gregory J Riely  10 , Mark E Robson  10 , Jonathan E Rosenberg  10 , Anton Safonov  10 , Alexander N Shoushtari  10 , William Tap  10 , Min Yuen Teo  10 , Anna M Varghese  10 , Martin Voss  10 , Rona Yaeger  10 , Marjorie G Zauderer  10 , Nadeem Abu-Rustum  11 , Julio Garcia-Aguilar  11 , Bernard Bochner  11 , Abraham Hakimi  11 , William R Jarnagin  11 , David R Jones  11 , Daniela Molena  11 , Luc Morris  11 , Eric Rios-Doria  11 , Paul Russo  11 , Samuel Singer  11 , Vivian E Strong  11 , Debyani Chakravarty  12 , Lora H Ellenson  12 , Anuradha Gopalan  12 , Jorge S Reis-Filho  12 , Britta Weigelt  12 , Marc Ladanyi  12 , Mithat Gonen  13 , Sohrab P Shah  13 , Joan Massague  14 , Jianjiong Gao  1 , Ahmet Zehir  12 , Michael F Berger  15 , David B Solit  16 , Samuel F Bakhoum  17 , Francisco Sanchez-Vega  18 , Nikolaus Schultz  19
Affiliations

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients

Bastien Nguyen et al. Cell. .

Abstract

Metastatic progression is the main cause of death in cancer patients, whereas the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we assembled MSK-MET, a pan-cancer cohort of over 25,000 patients with metastatic diseases. By analyzing genomic and clinical data from this cohort, we identified associations between genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma, and HR+/HER2+ breast ductal carcinoma, but not in others, including colorectal cancer and high-grade serous ovarian cancer, where copy-number alteration patterns may be established early in tumor development. We also identified somatic alterations associated with metastatic burden and specific target organs. Our data offer a valuable resource for the investigation of the biological basis for metastatic spread and highlight the complex role of chromosomal instability in cancer progression.

Keywords: DNA sequencing; cancer; clinical sequencing; genomics; metastasis; molecular profiling; mutations; next-generation sequencing; organotropism; pan-cancer.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests S.C. receives consulting fees from Novartis, Lilly, and Sanofi and research funding from Daiichi-Sankyo and Paige.ai. J.J.H. receives consulting fees from Bristol Myers Squibb, Merck, Exelexis, Eisai, QED, Cytomx, Zymeworks, Adaptiimmune, and ImVax and research funding from Bristol Myers Squibb. C.A.I.-D. receives research funding from Bristol Myers Squibb. P. Razavi received consultation/Ad board/Honoraria from Novartis, Foundation Medicine, AstraZeneca, Epic Sciences, Inivata, Natera, and Tempus and institutional grant/funding from Grail, Illumina, Novartis, Epic Sciences, and ArcherDx. C.M.R. has consulted regarding oncology drug development with AbbVie, Amgen, Astra Zeneca, Epizyme, Genentech/Roche, Ipsen, Jazz, Lilly, and Syros and serves on the scientific advisory boards of Bridge Medicines, Earli, and Harpoon Therapeutics. D.Z. receives research funding from Astra Zeneca, Plexxikon, and Genentech and consulting fees from Merck, Synlogic Therapeutics, GSK, Bristol Myers Squibb, Genentech, Xencor, Memgen, Immunos, Agenus, Hookipa, Calidi, and Synthekine. B.W. has an ad hoc membership advisory board Repare Therapeutics. W.A. receives speaking honoraria from Roche, Medscape, Aptitude Health, and Clinical Education Alliance; consulting fees from Clovis Oncology, Janssen, ORIC pharmaceuticals, Daiichi Sankyo; and research funding from AstraZeneca, Zenith Epigenetics, Clovis Oncology, ORIC pharmaceuticals, and Epizyme. G.K.A.-A. receives research funding from Arcus, Agios, Astra Zeneca, Bayer, BioNtech, BMS, Celgene, Flatiron, Genentech/Roche, Genoscience, Incyte, Polaris, Puma, QED, Sillajen, and Yiviva and consulting fees from Adicet, Agios, Astra Zeneca, Alnylam, Autem, Bayer, Beigene, Berry Genomics, Cend, Celgene, CytomX, Eisai, Eli Lilly, Exelixis, Flatiron, Genentech/Roche, Genoscience, Helio, Incyte, Ipsen, Legend Biotech, Loxo, Merck, MINA, Nerviano, QED, Redhill, Rafael, Silenseed, Sillajen, Sobi, Surface Oncology, Therabionics, Twoxar, Vector, and Yiviva. E.M.O. receives research funding from Genentech/Roche, Celgene/BMS, BioNTech, BioAtla, AstraZeneca, Arcus, Elicio, Parker Institute, and AstraZeneca and consulting fees from Cytomx Therapeutics (DSMB), Rafael Therapeutics (DSMB), Sobi, Silenseed, Tyme, Seagen, Molecular Templates, Boehringer Ingelheim, BioNTech, Ipsen, Polaris, Merck, IDEAYA, Cend, AstraZeneca, Noxxon, BioSapien, Bayer (spouse), Genentech-Roche (spouse), Celgene-BMS (spouse), and Eisai (spouse). M.A.P. receive consulting fees from BMS, Merck, Array BioPharma, Novartis, Incyte, NewLink Genetics, Aduro, Eisai, and Pfizer; honoraria from BMS and Merck; research support from RGenix, Infinity, BMS, Merck, Array BioPharma, Novartis, and AstraZeneca. G.J.R. has institutional research funding from Mirait, Takeda, Merck, Roche, Novartis, and Pfizer. S.F.B. holds a patent related to some of the work described targeting CIN in advanced cancer. He owns equity in, receives compensation from, and serves as a consultant and the scientific advisory board and board of directors of Volastra Therapeutics Inc. A.N.S. has advisory board/personal fees from Bristol-Myers Squibb, Immunocore, and Castle Biosciences; research support from Bristol-Myers Squibb, Immunocore, Xcovery, Polaris, Novartis, Pfizer, and Checkmate Pharmaceuticals; and research funding from OBI-Pharma, GSK, Silenseed, BMS, and Lilly. R.Y. receives consulting fees from Array BioPharma/Pfizer, Mirati Therapeutics, and Natera and research funding from Pfizer and Boehringer Ingelheim. D.R.J. is member of the advisory council for Astra Zeneca and member of the Clinical Trial Steering Committee for Merck. D.M. reports disclosures from AstraZeneca, Johnson & Johnson, Boston Scientific, Bristol-Myers Squibb, and Merck. S.P.S. is shareholder and consultant for Canexia Health Inc. M.F.B receives consulting fees from Roche, Eli Lilly, and PetDx and research funding from Grail. D.B.S. has received consulted for and received honoraria from Pfizer, Lilly/Loxo Oncology, Vividion Therapeutics, Scorpion Therapetuics, and BridgeBio. B.N. is an employee of Loxo Oncology at Lilly.

Figures

Figure 1.
Figure 1.. Overview of the MSK-MET cohort.
Metastatic patterns of 50 tumor types. For each tumor type, the following attributes are shown from left to right: tumor type abbreviation, number of patients, distribution of age at sequencing (red vertical line indicates the median), overall survival in years from time of sequencing (red vertical line indicates the median OS), sex ratio (female = gold, male = grey), distribution of metastatic burden across all patients (ranging from 0 to ≥6 distinct metastatic sites), and a heatmap with the percentage of metastatic patients with metastases at specific metastatic sites (the entire clinical course was taken into consideration). The number in each cell indicates the frequency of patients having at least one reported metastasis at that given site. For each tumor type, the distribution of all metastasis events by 21 organ sites is shown as a stacked bar chart to the right of the heatmap. For each metastatic site, the distribution of all 50 tumor types is shown as a stacked bar chart below the heatmap. For each metastatic site, the number of patients with at least one metastasis is indicated in parentheses. Frequencies for sex-specific target organs (female genital, ovary and male genital) were calculated using patients of the corresponding sex. See also Table S1A–C and Figure S1.
Figure 2.
Figure 2.. Genomic differences between primary tumors and metastases.
Comparisons of the median fraction genome altered (FGA), median whole-genome duplication (WGD) frequency, median tumor mutation burden (TMB), and median clonal fraction for each tumor type in metastatic vs. primary tumors. Tumor types with statistically significant differences are labeled. For TMB both axes were limited to 10mut/Mb. (A) The following clinical and genomic features are shown side-by-side for primary (top row within each cancer type) and metastatic (bottom row) sequenced samples using a combination of bar charts and violin plots; from left to right: sample counts, FGA, fraction of samples with WGD, TMB, clonality, fraction of samples with high TMB, and distribution of the highest actionable alteration levels. The black vertical line in each violin plots represents the median. The heatmap shows the frequency of individual arm level alterations in primary tumors and metastases (only the frequency of the more frequent event, gain or loss, is shown). Tumor types are ordered from top to bottom by decreasing FGA in metastasis and grouped by organ systems. * indicates q-value < 0.05. WGD and clonality were available for a subset of 17,224 samples with FACETS data. (B) Statistically significant differences in the frequency of oncogenic alterations and pathways between primary tumors and metastases in individual tumor types. Triangles summarize oncogenic alteration frequencies in primary tumors vs. metastases and are colored according to alteration type. Gene names in italics refer to specific genes, those in regular font refer to pathways. See also Table S2A–C and Figure S2 and S3.
Figure 3.
Figure 3.. Genomic features associated with metastatic burden.
(A) Spearman’s correlation coefficient between FGA (circle) and TMB (diamond) with metastatic burden. Associations without a significant trend are shown in grey, and the lines indicate 95% CI. (B) Correlation between FGA and TMB with metastatic burden in the entire data set, prostate adenocarcinoma, hypermutated uterine cancer, and MSS colorectal cancer. Boxplots display median point, IQR boxes and 1.5 × IQR whiskers for all samples. Split violin plots show the distribution of FGA and TMB in primary tumors (left, not filled) and metastases (right, filled). (C) Statistically significant oncogenic alterations and pathways associated with metastatic burden in individual tumor types. Spearman’s correlation coefficient is shown for each event, and the lines indicate 95% CI. Gene names in italics refer to specific genes, those in regular font refer to pathways. See also Table S3A–B.
Figure 4.
Figure 4.. Genomic features associated with metastasis to specific target organs.
(A) Statistically significant oncogenic alterations and pathways associated with organ-specific patterns of metastatic spread. Gene names in italics refer to specific genes, those in regular font refer to pathways. (B) Schematic drawing summarizing the main findings from (A). See also Table S4A–B and Figure S4
See this image and copyright information in PMC

Comment in

References

    1. AACR Project GENIE Consortium (2017). AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov. 7, 818–831. - PMC - PubMed
    1. Abida W, Armenia J, Gopalan A, Brennan R, Walsh M, Barron D, Danila D, Rathkopf D, Morris M, Slovin S, et al. (2017). Prospective Genomic Profiling of Prostate Cancer Across Disease States Reveals Germline and Somatic Alterations That May Affect Clinical Decision Making. JCO Precis Oncol 2017. - PMC - PubMed
    1. Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, Chatila WK, Chakravarty D, Han GC, Coleman I, et al. (2018). The long tail of oncogenic drivers in prostate cancer. Nat. Genet. 50, 645–651. - PMC - PubMed
    1. Bakhoum SF, Ngo B, Laughney AM, Cavallo J-A, Murphy CJ, Ly P, Shah P, Sriram RK, Watkins TBK, Taunk NK, et al. (2018). Chromosomal instability drives metastasis through a cytosolic DNA response. Nature 553, 467–472. - PMC - PubMed
    1. Ben-David U, and Amon A (2020). Context is everything: aneuploidy in cancer. Nat. Rev. Genet. 21, 44–62. - PubMed

Publication types