The molecular characterization of tumors now informs clinical cancer care for many patients. This advent of molecular oncology has been driven by the expanding number of therapeutic biomarkers that can predict sensitivity to both approved agents and investigational agents. Beyond its role in driving clinical-trial enrollments and guiding therapy in individual patients, large-scale clinical genomics in oncology also represents a rapidly expanding research resource for translational scientific discovery. Here we review the progress, opportunities, and challenges of scientific and translational discovery from prospective clinical genomic screening programs now routinely conducted for patients with cancer.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Horak, P., Fröhling, S. & Glimm, H. Integrating next-generation sequencing into clinical oncology: strategies, promises and pitfalls. ESMO Open 1, e000094 (2016).
Hyman, D. M., Taylor, B. S. & Baselga, J. Implementing genome-driven oncology. Cell 168, 584–599 (2017).
Berger, M. F. & Mardis, E. R. The emerging clinical relevance of genomics in cancer medicine. Nat. Rev. Clin. Oncol. 15, 353–365 (2018).
Tamborero, D. et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).
Patterson, S. E. et al. The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies. Hum. Genomics 10, 4 (2016).
Huang, L. et al. The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations. J. Am. Med. Inform. Assoc. 24, 513–519 (2017).
Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).
Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170–174 (2017).
Chakravarty, D. et al. Oncokb: A precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017).
Ratner, M. First multi-gene NGS diagnostic kit approved. Nat. Biotechnol. 35, 699 (2017).
Allegretti, M. et al. Tearing down the walls: FDA approves next generation sequencing (NGS) assays for actionable cancer genomic aberrations. J. Exp. Clin. Cancer Res. 37, 47 (2018).
Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).
Drilon, A. et al. Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. N. Engl. J. Med. 378, 731–739 (2018).
Van Cutsem, E. et al. Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer. N. Engl. J. Med. 360, 1408–1417 (2009).
Damodaran, S., Berger, M.F. & Roychowdhury, S. Clinical tumor sequencing: opportunities and challenges for precision cancer medicine. in American Society of Clinical Oncology Educational Book 35 e175–e182 (American Society of Clinical Oncology, 2019).
Shevchenko, Y. & Bale, S. Clinical versus research sequencing. Cold Spring Harb. Perspect. Med. 6, a025809 (2016).
Shaw, K. R. M. & Maitra, A. The status and impact of clinical tumor genome sequencing. Annu. Rev. Genomics Hum. Genet. 20, 413–432 (2019).
Gagan, J. & Van Allen, E. M. Next-generation sequencing to guide cancer therapy. Genome Med. 7, 80 (2015).
Sholl, L. M. et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. JCI Insight 1, e87062 (2016).
Frampton, G. M. et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat. Biotechnol. 31, 1023–1031 (2013).
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
Gargis, A. S. et al. Good laboratory practice for clinical next-generation sequencing informatics pipelines. Nat. Biotechnol. 33, 689–693 (2015).
Gargis, A. S. et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat. Biotechnol. 30, 1033–1036 (2012).
Jennings, L. J. et al. Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists. J. Mol. Diagn. 19, 341–365 (2017).
Cescon, D. W., Bratman, S. V., Chan, S. M. & Siu, L. L. Circulating tumor DNA and liquid biopsy in oncology. Nat. Can. 1, 276–290 (2020).
Wagner, A. H. et al. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer. Nat. Genet. 52, 448–457 (2020).
Huang, F. W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).
Paik, P. K. et al. Response to MET inhibitors in patients with stage IV lung adenocarcinomas harboring MET mutations causing exon 14 skipping. Cancer Discov. 5, 842–849 (2015).
Cheng, D. T. et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).
Middha, S. et al. Reliable pan-cancer microsatellite instability assessment by using targeted next-generation sequencing data. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00084 (2017).
Jonsson, P. et al. Tumour lineage shapes BRCA-mediated phenotypes. Nature 571, 576–579 (2019).
Staaf, J. et al. Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study. Nat. Med. 25, 1526–1533 (2019).
Beltran, H. et al. Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol. 1, 466–474 (2015).
Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl. Med. 3, 111ra121 (2011).
Rusch, M. et al. Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat. Commun. 9, 3962 (2018).
Parsons, D. W. et al. Diagnostic yield of clinical tumor and germline whole-exome sequencing for children with solid tumors. JAMA Oncol. 2, 616–624 (2016).
Ghazani, A. A. et al. Assigning clinical meaning to somatic and germ-line whole-exome sequencing data in a prospective cancer precision medicine study. Genet. Med. 19, 787–795 (2017).
Berner, A. M., Morrissey, G. J. & Murugaesu, N. Clinical analysis of whole genome sequencing in cancer patients. Curr. Genet. Med. Rep. 7, 136–143 (2019).
Pleasance, E. et al. Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat. Cancer 1, 452–468 (2020).
Nangalia, J. & Campbell, P. J. Genome sequencing during a patient’s journey through cancer. N. Engl. J. Med. 381, 2145–2156 (2019).
Touat, M. et al. Mechanisms and therapeutic implications of hypermutation in gliomas. Nature 580, 517–523 (2020).
Chang, M. T. et al. Accelerating discovery of functional mutant alleles in cancer. Cancer Discov. 8, 174–183 (2018).
Razanamahery, J. et al. Erdheim-Chester disease with concomitant Rosai-Dorfman like lesions: a distinct entity mainly driven by MAP2K1. Haematologica 105, e5–e8 (2020).
Durham, B. H. et al. Activating mutations in CSF1R and additional receptor tyrosine kinases in histiocytic neoplasms. Nat. Med. 25, 1839–1842 (2019).
Hyman, D. M. et al. HER kinase inhibition in patients with HER2- and HER3-mutant cancers. Nature 554, 189–194 (2018).
Diamond, E. L. et al. Efficacy of MEK inhibition in patients with histiocytic neoplasms. Nature 567, 521–524 (2019).
Smyth, L. M. et al. Efficacy and determinants of response to HER kinase inhibition in HER2-mutant metastatic breast cancer. Cancer Discov. 10, 198–213 (2020).
Slotkin, E. K. et al. Patient-driven discovery, therapeutic targeting, and post-clinical validation of a novel AKT1 fusion-driven cancer. Cancer Discov. 9, 605–616 (2019).
Drilon, A. et al. A next-generation TRK kinase inhibitor overcomes acquired resistance to prior TRK kinase inhibition in patients with TRK fusion-positive solid tumors. Cancer Discov. 7, 963–972 (2017).
Saito, Y. et al. Landscape and function of multiple mutations within individual oncogenes. Nature 582, 95–99 (2020).
Gorelick, A. N. et al. Phase and context shape the function of composite oncogenic mutations. Nature 582, 100–103 (2020).
Brenan, L. et al. Phenotypic characterization of a comprehensive set of MAPK1/ERK2 missense mutants. Cell Rep. 17, 1171–1183 (2016).
Yao, Z. et al. BRAF mutants evade ERK-dependent feedback by different mechanisms that determine their sensitivity to pharmacologic inhibition. Cancer Cell 28, 370–383 (2015).
Yao, Z. et al. Tumours with class 3 BRAF mutants are sensitive to the inhibition of activated RAS. Nature 548, 234–238 (2017).
Gao, Y. et al. Allele-specific mechanisms of activation of MEK1 mutants determine their properties. Cancer Discov. 8, 648–661 (2018).
Mateo, J. et al. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT). Ann. Oncol. 29, 1895–1902 (2018).
Li, M. M. et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: A joint consensus recommendation of the Association For Molecular Pathology, American Society Of Clinical Oncology, and College Of American Pathologists. J. Mol. Diagn. 19, 4–23 (2017).
Madsen, R. R. et al. Oncogenic PIK3CA promotes cellular stemness in an allele dose-dependent manner. Proc. Natl Acad. Sci. USA 116, 8380–8389 (2019).
Burgess, M. R. et al. KRAS allelic imbalance enhances fitness and modulates MAP kinase dependence in cancer. Cell 168, 817–829.e15 (2017).
Mueller, S. et al. Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes. Nature 554, 62–68 (2018).
Bielski, C. M. et al. Widespread selection for oncogenic mutant allele imbalance in cancer. Cancer Cell 34, 852–862.e4 (2018).
Hyman, D. M. et al. AKT inhibition in solid tumors with AKT1 mutations. J. Clin. Oncol. 35, 2251–2259 (2017).
Vasan, N. et al. Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kα inhibitors. Science 366, 714–723 (2019).
Canon, J. et al. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217–223 (2019).
André, F. et al. Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 380, 1929–1940 (2019).
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).
Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).
Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).
Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50, 1189–1195 (2018).
Dewhurst, S. M. et al. Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov. 4, 175–185 (2014).
López, S. et al. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat. Genet. 52, 283–293 (2020).
Kuznetsova, A. Y. et al. Chromosomal instability, tolerance of mitotic errors and multidrug resistance are promoted by tetraploidization in human cells. Cell Cycle 14, 2810–2820 (2015).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Latham, A. et al. Microsatellite instability is associated with the presence of Lynch syndrome pan-cancer. J. Clin. Oncol. 37, 286–295 (2019).
Van Hoeck, A., Tjoonk, N. H., van Boxtel, R. & Cuppen, E. Portrait of a cancer: mutational signature analyses for cancer diagnostics. BMC Cancer 19, 457 (2019).
Gulhan, D. C., Lee, J. J.-K., Melloni, G. E. M., Cortés-Ciriano, I. & Park, P. J. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nat. Genet. 51, 912–919 (2019).
Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23, 517–525 (2017).
Degasperi, A. et al. A practical framework and online tool for mutational signature analyses show inter-tissue variation and driver dependencies. Nat. Can. 1, 249–263 (2020).
Ma, J., Setton, J., Lee, N. Y., Riaz, N. & Powell, S. N. The therapeutic significance of mutational signatures from DNA repair deficiency in cancer. Nat. Commun. 9, 3292 (2018).
Swisher, E. M. et al. Rucaparib in relapsed, platinum-sensitive high-grade ovarian carcinoma (ARIEL2 Part 1): an international, multicentre, open-label, phase 2 trial. Lancet Oncol. 18, 75–87 (2017).
Timms, K. M. et al. Association of BRCA1/2 defects with genomic scores predictive of DNA damage repair deficiency among breast cancer subtypes. Breast Cancer Res. 16, 475 (2014).
Meric-Bernstam, F. et al. Incidental germline variants in 1000 advanced cancers on a prospective somatic genomic profiling protocol. Ann. Oncol. 27, 795–800 (2016).
Schrader, K. A. et al. Germline variants in targeted tumor sequencing using matched normal DNA. JAMA Oncol. 2, 104–111 (2016).
Huang, K.-L. et al. Pathogenic germline variants in 10,389 adult cancers. Cell 173, 355–370.e14 (2018).
Lu, C. et al. Patterns and functional implications of rare germline variants across 12 cancer types. Nat. Commun. 6, 10086 (2015).
Mandelker, D. et al. Mutation detection in patients with advanced cancer by universal sequencing of cancer-related genes in tumor and normal DNA vs guideline-based germline testing. J. Am. Med. Assoc. 318, 825–835 (2017).
Mandelker, D. & Ceyhan-Birsoy, O. Evolving significance of tumor-normal sequencing in cancer care. Trends Cancer 6, 31–39 (2019).
Green, R. C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013).
Gray, S. W. et al. Oncologists’ and cancer patients’ views on whole-exome sequencing and incidental findings: results from the CanSeq study. Genet. Med. 18, 1011–1019 (2016).
Ganguli, I. et al. Cascades of care after incidental findings in a US national survey of physicians. JAMA Netw. Open 2, e1913325 (2019).
Johns, A. L. et al. Lost in translation: returning germline genetic results in genome-scale cancer research. Genome Med. 9, 41 (2017).
Walsh, M. F. et al. Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes. Hum. Mutat. 39, 1542–1552 (2018).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).
Schrock, A. B. et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSI-high metastatic colorectal cancer. Ann. Oncol. 30, 1096–1103 (2019).
Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).
Anagnostou, V. et al. Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer. Nat. Can. 1, 99–111 (2020).
McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).
Liu, D. et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat. Med. 25, 1916–1927 (2019).
Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
Miao, D. et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors. Nat. Genet. 50, 1271–1281 (2018).
Wang, S., Jia, M., He, Z. & Liu, X.-S. APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer. Oncogene 37, 3924–3936 (2018).
Chalmers, Z. R. et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 9, 34 (2017).
Fancello, L., Gandini, S., Pelicci, P. G. & Mazzarella, L. Tumor mutational burden quantification from targeted gene panels: major advancements and challenges. J. Immunother. Cancer 7, 183 (2019).
Vokes, N. I. et al. Harmonization of tumor mutational burden quantification and association with response to immune checkpoint blockade in non-small-cell lung cancer. JCO Precis. Oncol. https://doi.org/10.1200/PO.19.00171 (2019).
Chan, T. A. et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann. Oncol. 30, 44–56 (2019).
Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).
Chowell, D. et al. Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat. Med. 25, 1715–1720 (2019).
Giannakis, M. et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 15, 857–865 (2016).
McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271.e11 (2017).
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).
Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).
Szolek, A. et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30, 3310–3316 (2014).
Chandran, S. S. & Klebanoff, C. A. T cell receptor-based cancer immunotherapy: Emerging efficacy and pathways of resistance. Immunol. Rev. 290, 127–147 (2019).
Hollingsworth, R. E. & Jansen, K. Turning the corner on therapeutic cancer vaccines. Vaccines 4, 7 (2019).
Jurtz, V. et al. NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).
Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017).
Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).
Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 (2017).
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
Hilf, N. et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240–245 (2019).
Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).
Klebanoff, C. A. & Wolchok, J. D. Shared cancer neoantigens: making private matters public. J. Exp. Med. 215, 5–7 (2018).
Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).
Wang, Q. J. et al. Identification of T-cell receptors targeting KRAS-mutated human tumors. Cancer Immunol. Res. 4, 204–214 (2016).
Coombs, C. C. et al. Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes. Cell Stem Cell 21, 374–382.e4 (2017).
Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).
Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).
Abelson, S. et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature 559, 400–404 (2018).
Desai, P. et al. Somatic mutations precede acute myeloid leukemia years before diagnosis. Nat. Med. 24, 1015–1023 (2018).
Penson, A. et al. Development of genome-derived tumor type prediction to inform clinical cancer care. JAMA Oncol. 6, 84–91 (2020).
Dalton, W. S. & Friend, S. H. Cancer biomarkers—an invitation to the table. Science 312, 1165–1168 (2006).
Merker, J. D. et al. Proficiency testing of standardized samples shows very high interlaboratory agreement for clinical next-generation sequencing-based oncology assays. Arch. Pathol. Lab. Med. 143, 463–471 (2019).
Tricoli, J. V. et al. Design and development of the molecular analysis for therapy choice (NCI-MATCH) designated laboratory network. J. Clin. Oncol. 37, 3016–3016 (2019).
Rehm, H. L. et al. ACMG clinical laboratory standards for next-generation sequencing. Genet. Med. 15, 733–747 (2013).
US Food and Drug Administration. FDA Recognition of Public Human Genetic Variant Databases. https://www.fda.gov/medical-devices/precision-medicine/fda-recognition-public-human-genetic-variant-databases (2018).
Sherman, R. E. et al. Real-world evidence - what is it and what can it tell us? N. Engl. J. Med. 375, 2293–2297 (2016).
Booth, C. M., Karim, S. & Mackillop, W. J. Real-world data: towards achieving the achievable in cancer care. Nat. Rev. Clin. Oncol. 16, 312–325 (2019).
Bolton, K. L. et al. Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer. J. Am. Med. Assoc. 307, 382–390 (2012).
US Food and Drug Administration. Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics Guidance for Industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-trial-endpoints-approval-cancer-drugs-and-biologics (2018).
Korn, E. L., Freidlin, B. & Abrams, J. S. Overall survival as the outcome for randomized clinical trials with effective subsequent therapies. J. Clin. Oncol. 29, 2439–2442 (2011).
Saad, E. D. & Buyse, M. Overall survival: patient outcome, therapeutic objective, clinical trial end point, or public health measure? J. Clin. Oncol. 30, 1750–1754 (2012).
AACR Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).
Smyth, L.M. et al. Characteristics and outcome of AKT1E17K-mutant breast cancer defined through AACR Project GENIE, a clinicogenomic registry. Cancer Discov. https://doi.org/10.1158/2159-8290.CD-19-1209 (2020).
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Rossi, G. & Ignatiadis, M. Promises and pitfalls of using liquid biopsy for precision medicine. Cancer Res. 79, 2798–2804 (2019).
Pantel, K. & Alix-Panabières, C. Liquid biopsy and minimal residual disease - latest advances and implications for cure. Nat. Rev. Clin. Oncol. 16, 409–424 (2019).
Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997.e24 (2018).
Kim, C. et al. Chemoresistance Evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893.e13 (2018).
Holohan, C., Van Schaeybroeck, S., Longley, D. B. & Johnston, P. G. Cancer drug resistance: an evolving paradigm. Nat. Rev. Cancer 13, 714–726 (2013).
Mandelker, D. et al. Germline-focussed analysis of tumour-only sequencing: recommendations from the ESMO Precision Medicine Working Group. Ann. Oncol. 30, 1221–1231 (2019).
Ptashkin, R. N. et al. Prevalence of clonal hematopoiesis mutations in tumor-only clinical genomic profiling of solid tumors. JAMA Oncol. 4, 1589–1593 (2018).
We thank M.F. Berger and D.B. Solit for discussions. This work was supported by US National Institutes of Health awards P30 CA008748, U54 OD020355 (B.S.T.), R01 CA207244 (D.M.H., B.S.T.), R01 CA204749 (B.S.T.), and R01 CA245069 (B.S.T.).
D.M.H. reports receiving research funding from AstraZeneca, Puma Biotechnology, and Loxo Oncology, and personal fees from Atara Biotherapeutics, Chugai Pharma, Boehringer Ingelheim, AstraZeneca, Pfizer, Bayer, Debiopharm Group, and Genentech. B.S.T. reports receiving honoria and research funding from Genentech and Illumina and advisory board activities for Boehringer Ingelheim and Loxo Oncology, a wholly owned subsidiary of Eli Lilly.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Donoghue, M.T.A., Schram, A.M., Hyman, D.M. et al. Discovery through clinical sequencing in oncology. Nat Cancer 1, 774–783 (2020). https://doi.org/10.1038/s43018-020-0100-0