Cancers develop as a result of driver mutations1,2 that lead to clonal outgrowth and the evolution of disease3,4. The discovery and functional characterization of individual driver mutations are central aims of cancer research, and have elucidated myriad phenotypes5 and therapeutic vulnerabilities6. However, the serial genetic evolution of mutant cancer genes7,8 and the allelic context in which they arise is poorly understood in both common and rare cancer genes and tumour types. Here we find that nearly one in four human tumours contains a composite mutation of a cancer-associated gene, defined as two or more nonsynonymous somatic mutations in the same gene and tumour. Composite mutations are enriched in specific genes, have an elevated rate of use of less-common hotspot mutations acquired in a chronology driven in part by oncogenic fitness, and arise in an allelic configuration that reflects context-specific selective pressures. cis-acting composite mutations are hypermorphic in some genes in which dosage effects predominate (such as TERT), whereas they lead to selection of function in other genes (such as TP53). Collectively, composite mutations are driver alterations that arise from context- and allele-specific selective pressures that are dependent in part on gene and mutation function, and which lead to complex—often neomorphic—functions of biological and therapeutic importance.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 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.
All mutational data from the prospective sequencing cohort are available at http://download.cbioportal.org/composite_mutations_maf.txt.gz. Mutational data from The Cancer Genome Atlas were acquired from https://gdc.cancer.gov/about-data/publications/pancanatlas. RNA sequencing data have been deposited in the Gene Expression Omnibus with accession number GSE136295. All other genomic and clinical data accompany the Article, and are available in the Extended Data and Supplementary Information. All other materials are available upon request from the corresponding authors.
Source code for these analyses is available at https://github.com/taylor-lab/composite-mutations.
Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).
Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17–37 (2013).
Cairns, J. Mutation selection and the natural history of cancer. Nature 255, 197–200 (1975).
Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Hyman, D. M., Taylor, B. S. & Baselga, J. Implementing genome-driven oncology. Cell 168, 584–599 (2017).
Knudson, A. G., Jr. Mutation and cancer: statistical study of retinoblastoma. Proc. Natl Acad. Sci. USA 68, 820–823 (1971).
Bielski, C. M. et al. Widespread selection for oncogenic mutant allele imbalance in cancer. Cancer Cell 34, 852–862.e4 (2018).
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).
Jin, G. et al. Disruption of wild-type IDH1 suppresses d-2-hydroxyglutarate production in IDH1-mutated gliomas. Cancer Res. 73, 496–501 (2013).
Mueller, S. et al. Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes. Nature 554, 62–68 (2018).
Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).
Chang, M. T. et al. Accelerating discovery of functional mutant alleles in cancer. Cancer Discov. 8, 174–183 (2018).
Intlekofer, A. M. et al. Acquired resistance to IDH inhibition through trans or cis dimer-interface mutations. Nature 559, 125–129 (2018).
Hidaka, N. et al. Most T790M mutations are present on the same EGFR allele as activating mutations in patients with non-small cell lung cancer. Lung Cancer 108, 75–82 (2017).
Gainor, J. F. et al. Molecular mechanisms of resistance to first- and second-generation ALK inhibitors in ALK-rearranged lung cancer. Cancer Discov. 6, 1118–1133 (2016).
Kobayashi, S. et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 352, 786–792 (2005).
Vasan, N. et al. Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kα inhibitors. Science 366, 714–723 (2019).
Chen, Z. et al. EGFR somatic doublets in lung cancer are frequent and generally arise from a pair of driver mutations uncommonly seen as singlet mutations: one-third of doublets occur at five pairs of amino acids. Oncogene 27, 4336–4343 (2008).
Huang, F. W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).
Bell, R. J. A. et al. The transcription factor GABP selectively binds and activates the mutant TERT promoter in cancer. Science 348, 1036–1039 (2015).
Berenjeno, I. M. et al. Oncogenic PIK3CA induces centrosome amplification and tolerance to genome doubling. Nat. Commun. 8, 1773 (2017).
Kinross, K. M. et al. An activating Pik3ca mutation coupled with Pten loss is sufficient to initiate ovarian tumorigenesis in mice. J. Clin. Invest. 122, 553–557 (2012).
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).
Hyman, D. M. et al. Precision medicine at Memorial Sloan Kettering Cancer Center: clinical next-generation sequencing enabling next-generation targeted therapy trials. Drug Discov. Today 20, 1422–1428 (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).
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).
Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 1, 1–16 (2017).
Campbell, B. B. et al. Comprehensive analysis of hypermutation in human cancer. Cell 171, 1042–1056.e10 (2017).
Niu, B. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).
Middha, S. et al. Reliable pan-cancer microsatellite instability assessment by using targeted next-generation sequencing data. JCO Precis. Oncol. 1, 1–17 (2017).
Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Smedley, D. et al. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res. 43, W589–W598 (2015).
Forbes, S. A. et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Alexandrov, L. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Pich, O. et al. Somatic and germline mutation periodicity follow the orientation of the DNA minor groove around nucleosomes. Cell 175, 1074–1087.e18 (2018).
Sabarinathan, R., Mularoni, L., Deu-Pons, J., Gonzalez-Perez, A. & López-Bigas, N. Nucleotide excision repair is impaired by binding of transcription factors to DNA. Nature 532, 264–267 (2016).
Buisson, R. et al. Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale genomic features. Science 364, eaaw2872 (2019).
Hess, J. M. et al. Passenger hotspot mutations in cancer. Cancer Cell 36, 288–301.e14 (2019).
Needleman, S. B. & Wunsch, C. D. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443–453 (1970).
McGranahan, N. et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl. Med. 7, 283ra54 (2015).
Dimitrova, N. et al. Stromal expression of miR-143/145 promotes neoangiogenesis in lung cancer development. Cancer Discov. 6, 188–201 (2016).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Bult, C. J., Blake, J. A., Smith, C. L., Kadin, J. A. & Richardson, J. E. Mouse genome database (MGD) 2019. Nucleic Acids Res. 47, D801–D806 (2019).
Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).
Tan, G. & Lenhard, B. TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555–1556 (2016).
Touzet, H. & Varré, J.-S. Efficient and accurate P-value computation for position weight matrices. Algorithms Mol. Biol. 2, 15 (2007).
Supek, F. & Lehner, B. Clustered mutation signatures reveal that error-prone DNA repair targets mutations to active genes. Cell 170, 534–547.e23 (2017).
Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).
We thank the members of the E.R. and B.S.T. laboratories for discussion and support. This work was supported by National Institutes of Health awards P30 CA008748, P01 CA087497 (S.W.L.), U54 OD020355 (S.W.L. and B.S.T.), R01 CA207244 (B.S.T.), R01 CA204749 (B.S.T.), R01 CA245069 (B.S.T.); Brown Performance Group ICI Fund (N.V. and E.R.), Society of MSK (N.V. and E.R.), American Cancer Society, Anna Fuller Fund and the Josie Robertson Foundation (B.S.T.). F.J.S.-R. is an HHMI Hanna Gray Fellow supported in part by an MSKCC Translational Research Oncology Training Fellowship (T32-CA160001). S.W.L. is an investigator of the Howard Hughes Medical Institute.
N.V. reports advisory board activities for Novartis and consulting activities for Petra Pharmaceuticals. M.S. has received research funding from Puma Biotechnology, Daiichi-Sankio, Immunomedics, Targimmune and Menarini Ricerche; is a cofounder of Medendi.org, and is on the advisory boards of the Bioscience Institute and Menarini Ricerche. S.W.L. is a founder and scientific advisory board member of Oric Pharmaceuticals, Mirimus, Inc. and Blueprint Medicines; and is on the scientific advisory boards of Constellation Pharmaceuticals, Petra Pharmaceuticals and PMV Pharmaceuticals. 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, Inc. All stated activities were outside of the work described here. The other authors declare no competing interests.
Peer review information Nature thanks Moritz Gerstung, Mark Lackner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Distribution of cancer types in the study cohort. b, The rate of composite mutations (22.7% of all tumours) compared to a simulated background rate (black, P = 10−5 from one-sided permutation test for enrichment with 100,000 random permutation-based simulations (no permutation exceeded observed value)). c, The observed rate of composite mutations in the primary untreated cancers of the TCGA cohort (n = 10,908 solid tumours) when controlling for gene content for consistency with the targeted sequencing panel of the prospective cohort studied here. The null distribution from sampling (Methods) is shown in black. d, The observed and expected rate of composite mutations in tumours of the indicated tumour mutational burden (as in Fig. 1b, n = 30,505 biologically independent tumour samples with tumour mutational burden ≤ 40, P = 1 × 10−9 from two-sided Wilcoxon signed-rank test).
a, The number of composite mutations comprising two or more constituent variants (top) and the distribution of likely causative mutational signatures among them (bottom). Composite mutants comprising greater than three mutations were increasingly produced by APOBEC-associated mutagenesis, indicative of localized hypermutation53,54, but accounted for a minority of events cohort-wide. b, Left, the somatic mutational data in the study cohort reflect the elevated mutation rates previously observed at both the positions closest to the nucleosome dyad as well as DNA bound to active transcription-factor binding sites38,39. However, mutations arising in composite events were proportionally less often proximal to such sites (defined here as within the full width at half maximum of the peak of mutation rate (red)) than were singleton mutations (right, P = 10−27 and 10−47, respectively; two-sided two-sample Z-test, n = 323,883 single-nucleotide substitutions arising in 471 biologically distinct melanoma samples).
a, The number and percentage of cases in the study cohort containing composite mutations in the indicated genes (right) juxtaposed to their overall mutation rate (left). Genes with a significant enrichment of composite mutations are shown (Q < 0.01, FDR-adjusted P values from one-sided binomial test for enrichment, n = 26,997 as in Fig. 2b), limited to the top 10 genes by significance in each category of gene function, unless fewer. b, The significance of enrichment for composite mutations (n and statistical tests as described in a and Fig. 2b) limited to 168 oncogenes.
The cis composite mutations classified as arising in post-treatment specimens due to acquired resistance to one of several molecularly targeted therapies in the study cohort.
a, TP53R280T/E287D mutant lung adenocarcinoma. Left, mutant allele fractions of clonal TP53 mutations consistent with loss of wild-type TP53 (error bars, 95% binomial confidence intervals). Expected mutant allele fractions of different copy number states are shown as horizontal lines. Mutant KEAP1 in the same tumour (with LOH) is shown for reference. Right, spanning reads indicating cis mutations. b, Right and left, Trp53 and Cdkn1a mRNA expression in KrasG12D/+Trp53Mut mouse lung cancer cells expressing distinct Trp53 genotypes. Bars, average of three replicates, error bars are 95% confidence intervals. c, The aggregate Z-score per replicate for the mRNA expression of canonical p53-target genes (n = 3 replicates per allele; box centre is median, edges are 25% and 75% quartiles, whiskers are minimum and maximum of the most extreme values). d, Principal component analysis of the transcriptomes of Trp53 genotypes (n = 3 replicates shown per condition). e, Dendrogram as in Fig. 3f, indicating the genes of interest (effectors of the AP-1 transcription factor network (PID_AP1_PATHWAY; Q = 1.4 × 10−7 based on computed overlap (using mSigDB) with n = 5,501 gene sets from the curated C2 collection)). f, The prevalence of TP53R280T and TP53E287D mutations (top), and the fraction arising as composite mutants (bottom). The corresponding mouse alleles are given in parentheses. g, Principal component analysis of the transcriptomes of the Trp53R277K/E282K composite mutation genotypes (as in d). n = 3 replicates per allele. h, The percentage of GFP+ FACS-purified KrasG12D/+Trp53−/− lung adenocarcinoma cells stably transduced with pMIG-empty or pMIG-p53-R277T-E284D, and cultured in vitro for 10 days in a 60:40 mixture with untransduced parental cells. Bar indicates mean, error bars are s.d., n = 3 independent infections. i, Overall survival of immunocompromised mice bearing lung tumours of the indicated Trp53 genotypes generated by tail vein injection of stably transduced and FACS-purified KrasG12D/+Trp53−/− lung adenocarcinoma cells (n = 100,000 cells).
Down-sampling indicates the number of residues identified as enriched for arising in composite mutations in each of four genes (Q < 0.1, FDR-adjusted one-sided Fisher’s exact tests as in Fig. 4a; n = 1,000–26,997 patients per down-sample) as a function of the number of tumours sequenced (LOESS fit is shown with 95% confidence interval). Four genes that accounted for the greatest proportion of all enriched residues detected are shown (Fig. 4a). EGFR appears to reach saturation for discovery of residues enriched for arising in composite, whereas the other genes have not yet reached saturation for discovery at the current cohort size.
a, The fraction of all composite mutations identified here in which one or both individual mutations could be unambiguously attributed to an established mutational signature. The majority of composite variants could not be directly attributed to APOBEC, ultraviolet, smoking or other known mutational signatures. b, The fraction of composite mutations per gene in which one or both variants could be attributed to an established mutational signature.
a, The number of affected cases containing each of the indicated somatic mutations in TERT, EGFR or PIK3CA as either individual mutations (top) or as part of composite mutants (bottom). Conditional mutations were defined as those statistically enriched for arising as part of composite mutations, but seldom as individual hotspot mutations in cancer (predominantly accompanied by a second somatic mutation). b, The incidence of TERT promoter mutations and the fraction arising as composite mutations (orange). Bottom, the co-occurrence and mutual exclusivity of composite mutations in the TERT promoter (The P values for n = 5 and 6 co-occurring mutations are 0.002 and 3 × 10−7, respectively, and for 0 mutually exclusive mutations is 1 × 10−25; two-sided Fisher’s exact test, n = 29,507 patients). c, Transcription factor GABPA binding affinity for mutant and wild-type TERT promoter sequences at the 228G>A, 250G>A and the conditional 205G>A allele.
About this article
Cite this article
Gorelick, A.N., Sánchez-Rivera, F.J., Cai, Y. et al. Phase and context shape the function of composite oncogenic mutations. Nature 582, 100–103 (2020). https://doi.org/10.1038/s41586-020-2315-8
Cancer Science (2021)
Nature Cancer (2020)
Cancer Science (2020)