Integrative clinical genomics of metastatic cancer
Open accessAbstract
Metastasis is the primary cause of cancer-related deaths. Although The Cancer Genome Atlas has sequenced primary tumour types obtained from surgical resections, much less comprehensive molecular analysis is available from clinically acquired metastatic cancers. Here we perform whole-exome and -transcriptome sequencing of 500 adult patients with metastatic solid tumours of diverse lineage and biopsy site. The most prevalent genes somatically altered in metastatic cancer included TP53, CDKN2A, PTEN, PIK3CA, and RB1. Putative pathogenic germline variants were present in 12.2% of cases of which 75% were related to defects in DNA repair. RNA sequencing complemented DNA sequencing to identify gene fusions, pathway activation, and immune profiling. Our results show that integrative sequence analysis provides a clinically relevant, multi-dimensional view of the complex molecular landscape and microenvironment of metastatic cancers.
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These two mutually exclusive states likely account for the reversibility in chromosome missegregation rates seen in primary tumors and metastases.
“These two mutually exclusive states, which were recently observed in a pan-cancer genomic analysis of metastatic tumors41, likely account for the reversibility in chromosome missegregation rates seen in primary tumors and metastases”
From Chromosomal instability drives metastasis through a cytosolic DNA response (2018) · cites this paper as [41]
CIN drives the subset of human metastases characterized by EMT and inflammation.
“It also leads us to suggest that CIN drives the subset of human metastases characterized by EMT and inflammation41.”
From Chromosomal instability drives metastasis through a cytosolic DNA response (2018) · cites this paper as [41]
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Cites
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440 · see citing works
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · see citing works
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802 · see citing works
Integrative Clinical Genomics of Advanced Prostate Cancer
Robinson, D., Van Allen, E. M., Wu, Y.-M., Schultz, N., Lonigro, R. J., Mosquera, J.-M., Montgomery, B., Taplin, M.-E., Pritchard, C. C., Attard, G., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.001 · Cited by 3,645 · see citing works
Self-citationPD-L1 expression as a predictive biomarker in cancer immunotherapy
Patel, S. P., & Kurzrock, R. (2015) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-14-0983 · Cited by 2,291 · see citing works
Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550 · see citing works
Precision medicine for cancer with next-generation functional diagnostics
Friedman, A. A., Letai, A., Fisher, D. E., & Flaherty, K. T. (2015) · Nature Reviews Cancer · doi:10.1038/nrc4015 · Cited by 570 · see citing works
Flagged first, then by citations
Research works printed but not linked: 38
- [1]Mehlen P Puisieux A Metastasis: a question of life or death Nature reviews. Cancer 6 449 458 10.1038/nrc1886 2006 16723991 · DOI
- [2]Steeg PS Targeting metastasis Nature reviews. Cancer 16 201 218 10.1038/nrc.2016.25 2016 27009393 PMC7055530 · DOI · Paper page
- [4]Mauer CB Pirzadeh-Miller SM Robinson LD Euhus DM The integration of next-generation sequencing panels in the clinical cancer genetics practice: an institutional experience Genetics in medicine: official journal of the American College of Medical Genetics 16 407 412 10.1038/gim.2013.160 2014 24113346 · DOI
- [5]Shen T Pajaro-Van de Stadt SH Yeat NC Lin JC Clinical applications of next generation sequencing in cancer: from panels, to exomes, to genomes Frontiers in genetics 6 215 10.3389/fgene.2015.00215 2015 26136771 PMC4469892 · DOI
- [6]Jones S Personalized genomic analyses for cancer mutation discovery and interpretation Science translational medicine 7 283ra253 10.1126/scitranslmed.aaa7161 2015 PMC4442685 25877891 · DOI
- [7]Roychowdhury S Personalized oncology through integrative high-throughput sequencing: a pilot study Science translational medicine 3 111ra121 10.1126/scitranslmed.3003161 2011 PMC3476478 22133722 · DOI
- [8]Byron SA Van Keuren-Jensen KR Engelthaler DM Carpten JD Craig DW Translating RNA sequencing into clinical diagnostics: opportunities and challenges Nature reviews. Genetics 17 257 271 10.1038/nrg.2016.10 2016 PMC7097555 26996076 · DOI
- [9]Turajlic S Swanton C Metastasis as an evolutionary process Science 352 169 175 10.1126/science.aaf2784 2016 27124450 · DOI · Paper page
- [11]Cancer Genome Atlas Research, N The Cancer Genome Atlas Pan-Cancer analysis project Nature genetics 45 1113 1120 10.1038/ng.2764 2013 24071849 PMC3919969 · DOI · Paper page
- [12]Gagan J Van Allen EM Next-generation sequencing to guide cancer therapy Genome medicine 7 80 10.1186/s13073-015-0203-x 2015 26221189 PMC4517547 · DOI
- [13]Mardis ER The translation of cancer genomics: time for a revolution in clinical cancer care Genome medicine 6 22 10.1186/gm539 2014 25031616 PMC4062062 · DOI
- [14]Parsons DW Diagnostic Yield of Clinical Tumor and Germline Whole-Exome Sequencing for Children With Solid Tumors JAMA oncology 10.1001/jamaoncol.2015.5699 2016 PMC5471125 26822237 · DOI
- [15]Mody RJ Integrative Clinical Sequencing in the Management of Refractory or Relapsed Cancer in Youth Jama 314 913 925 10.1001/jama.2015.10080 2015 26325560 PMC4758114 · DOI
- [16]Wagle N High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing Cancer discovery 2 82 93 10.1158/2159-8290.CD-11-0184 2012 22585170 PMC3353152 · DOI
- [17]Pritchard CC Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer The New England journal of medicine 375 443 453 10.1056/NEJMoa1603144 2016 27433846 PMC4986616 · DOI · Paper page
- [18]Palanisamy N Rearrangements of the RAF kinase pathway in prostate cancer, gastric cancer and melanoma Nature medicine 16 793 798 10.1038/nm.2166 2010 PMC2903732 20526349 · DOI
- [19]Robinson DR Functionally recurrent rearrangements of the MAST kinase and Notch gene families in breast cancer Nature medicine 17 1646 1651 10.1038/nm.2580 2011 PMC3233654 22101766 · DOI
- [20]Stransky N Cerami E Schalm S Kim JL Lengauer C The landscape of kinase fusions in cancer Nature communications 5 4846 10.1038/ncomms5846 2014 PMC4175590 25204415 · DOI · Paper page
- [21]Agaram NP Zhang L Sung YS Singer S Antonescu CR Extraskeletal myxoid chondrosarcoma with non-EWSR1-NR4A3 variant fusions correlate with rhabdoid phenotype and high-grade morphology Human pathology 45 1084 1091 10.1016/j.humpath.2014.01.007 2014 24746215 PMC4015728 · DOI
- [22]Weinreb I Novel PRKD gene rearrangements and variant fusions in cribriform adenocarcinoma of salivary gland origin Genes, chromosomes & cancer 53 845 856 10.1002/gcc.22195 2014 24942367 · DOI
- [24]Lonsdale J The Genotype-Tissue Expression (GTEx) project Nature genetics 45 580 585 10.1038/ng.2653 2013 23715323 PMC4010069 · DOI · Paper page
- [26]Liberzon A Molecular signatures database (MSigDB) 3.0 Bioinformatics 27 1739 1740 10.1093/bioinformatics/btr260 2011 21546393 PMC3106198 · DOI · Paper page
- [28]Vaske CJ Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM Bioinformatics 26 i237 i245 10.1093/bioinformatics/btq182 2010 20529912 PMC2881367 · DOI · Paper page
- [29]López-Novoa JM Nieto MA Inflammation and EMT: an alliance towards organ fibrosis and cancer progression EMBO Molecular Medicine 1 303 314 10.1002/emmm.200900043 2009 20049734 PMC3378143 · DOI
- [30]Yoshihara K Inferring tumour purity and stromal and immune cell admixture from expression data Nature communications 4 10.1038/ncomms3612 2013 PMC3826632 24113773 · DOI · Paper page
- [31]Attig S Simultaneous infiltration of polyfunctional effector and suppressor T cells into renal cell carcinomas Cancer Res 69 8412 8419 10.1158/0008-5472.CAN-09-0852 2009 19843860 · DOI
- [32]Nakano O Proliferative activity of intratumoral CD8(+) T-lymphocytes as a prognostic factor in human renal cell carcinoma: clinicopathologic demonstration of antitumor immunity Cancer Res 61 5132 5136 2001 11431351
- [33]Ruffini E Clinical significance of tumor-infiltrating lymphocytes in lung neoplasms Ann Thorac Surg 87 365 371 discussion 371-362 10.1016/j.athoracsur.2008.10.067 2009 19161739 · DOI
- [34]Boon T Coulie PG Van den Eynde BJ van der Bruggen P Human T cell responses against melanoma Annu Rev Immunol 24 175 208 10.1146/annurev.immunol.24.021605.090733 2006 16551247 · DOI
- [35]Newman AM Robust enumeration of cell subsets from tissue expression profiles Nature Methods advance online publication 10.1038/nmeth.3337 2015 PMC4739640 25822800 · DOI · Paper page
- [36]Sica A Schioppa T Mantovani A Allavena P Tumour-associated macrophages are a distinct M2 polarised population promoting tumour progression: potential targets of anti-cancer therapy Eur J Cancer 42 717 727 10.1016/j.ejca.2006.01.003 2006 16520032 · DOI
- [38]van Houdt IS Favorable outcome in clinically stage II melanoma patients is associated with the presence of activated tumor infiltrating T-lymphocytes and preserved MHC class I antigen expression Int J Cancer 123 609 615 10.1002/ijc.23543 2008 18498132 · DOI
- [39]Galon J Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome Science 313 1960 1964 10.1126/science.1129139 2006 17008531 · DOI · Paper page
- [41]Ulloa-Montoya F Predictive Gene Signature in MAGE-A3 Antigen-Specific Cancer Immunotherapy Journal of Clinical Oncology 31 2388 2395 10.1200/JCO.2012.44.3762 2013 23715562 · DOI · Paper page
- [42]Mateo J DNA-Repair Defects and Olaparib in Metastatic Prostate Cancer The New England journal of medicine 373 1697 1708 10.1056/NEJMoa1506859 2015 26510020 PMC5228595 · DOI · Paper page
- [43]Helleday T Petermann E Lundin C Hodgson B Sharma RA DNA repair pathways as targets for cancer therapy Nature reviews. Cancer 8 193 204 10.1038/nrc2342 2008 18256616 · DOI · Paper page
- [44]Cieslik M The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing Genome research 25 1372 1381 10.1101/gr.189621.115 2015 26253700 PMC4561495 · DOI
- [45]Leiserson MD MAGI: visualization and collaborative annotation of genomic aberrations Nat Methods 12 483 484 10.1038/nmeth.3412 2015 26020500 · DOI
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- Authors
- Robinson, D. R., Wu, Y.-M., Lonigro, R. J., Vats, P., Cobain, E., Everett, J., Cao, X., Rabban, E., Kumar-Sinha, C., Raymond, V., Schuetze, S., Alva, A., Siddiqui, J., Chugh, R., Worden, F., Zalupski, M. M., Innis, J., Mody, R. J., Tomlins, S. A., Lucas, D., Baker, L. H., Ramnath, N., Schott, A. F., Hayes, D. F., Vijai, J., Offit, K., Stoffel, E. M., Roberts, J. S., Smith, D. C., Kunju, L. P., Talpaz, M., Cieślik, M., & Chinnaiyan, A. M.
- Affiliations
- University of Michigan (US)
- Memorial Sloan Kettering Cancer Center (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Chinnaiyan, Arul M.
- ORCID
- Robinson, Dan R.
- Vats, Pankaj
- Cobain, Erin
- Everett, Jessica
- Cao, Xuhong
- Raymond, Victoria
- Schuetze, Scott
- Alva, Ajjai
- Siddiqui, Javed
- Chugh, Rashmi
- Worden, Francis
- Zalupski, Mark M.
- Innis, Jeffrey
- Mody, Rajen J.
- Tomlins, Scott A.
- Lucas, David
- Baker, Laurence H.
- Ramnath, Nithya
- Hayes, Daniel F.
- Vijai, Joseph
- Offit, Kenneth
- Stoffel, Elena M.
- Smith, David C.
- Kunju, Lakshmi P.
- Talpaz, Moshe
- Chinnaiyan, Arul M.
- Venue
- Nature
- Published
- 2017-08
- Volume
- 548
- Issue
- 7667
- Pages
- 297-303
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC5995337
- OpenAlex
- W2741363425
- Cited by
- 863
- References
- 7
- Citations by year
- 2026: 37 · 2025: 64 · 2024: 89 · 2023: 120 · 2022: 109 · 2021: 123 · 2020: 105 · 2019: 108 · 2018: 89 · 2017: 17 · 2014: 1
- FWCI
- 29.40
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institute for Health and Care Research
- National Institutes of Health
- National Human Genome Research Institute (UM1HG006508)
- National Cancer Institute (U01CA214170, P30CA046592, P50CA186786)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Keywords
- metastatic cancer, solid tumors, somatic mutations, TP53, CDKN2A, PTEN, PIK3CA, RB1, germline mutations, DNA repair deficiency, gene fusion, immune microenvironment, tumor transcriptome, precision oncology
- MeSH terms
- Exome Sequencing, Adult, DNA Repair, Female, Genetics, Medical, Humans, Male, Neoplasm Metastasis, Tumor Suppressor Protein p53, Germ-Line Mutation, Cyclin-Dependent Kinase Inhibitor p16, Genomics, Ubiquitin-Protein Ligases, Cyclin-Dependent Kinase Inhibitor p18, PTEN Phosphohydrolase, Retinoblastoma Binding Proteins, Class I Phosphatidylinositol 3-Kinases, Tumor Microenvironment, Transcriptome
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