Abstract
Mutational hotspots indicate selective pressure across a population of tumor samples, but their prevalence within and across cancer types is incompletely characterized. An approach to detect significantly mutated residues, rather than methods that identify recurrently mutated genes, may uncover new biologically and therapeutically relevant driver mutations. Here, we developed a statistical algorithm to identify recurrently mutated residues in tumor samples. We applied the algorithm to 11,119 human tumors, spanning 41 cancer types, and identified 470 somatic substitution hotspots in 275 genes. We find that half of all human tumors possess one or more mutational hotspots with widespread lineage-, position- and mutant allele–specific differences, many of which are likely functional. In total, 243 hotspots were novel and appeared to affect a broad spectrum of molecular function, including hotspots at paralogous residues of Ras-related small GTPases RAC1 and RRAS2. Redefining hotspots at mutant amino acid resolution will help elucidate the allele-specific differences in their function and could have important therapeutic implications.
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Acknowledgements
We would like to thank the members of the Taylor and Schultz laboratories and members of the Marie-Jos��e and Henry R. Kravis Center for Molecular Oncology for useful discussions throughout this work. M.T.C. was supported by the US National Institutes of Health (NIH) training grant T32 GM007175 and A.B.O. was supported by NIH grant P30 CA82103. This work has been further supported by an NIH Core Grant (P30 CA008748, PI: Thompson); the Josie Robertson and Prostate Cancer Foundations (N.S. and B.S.T.), the Sontag Foundation and Cycle for Survival (B.S.T.).
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M.T.C., S.A. and B.S.T. conceived the study. N.S. and B.S.T. supervised analyses. M.T.C., S.A., N.D.S., A.B.O. and B.S.T. developed methods and algorithms. M.T.C., J.S.C., J.G., C.K. and N.S. acquired data. M.T.C., S.A., J.S.C., S.P.G., B.H.L., J.G. and D.B.S. performed experiments and analyses. M.T.C., N.S. and B.S.T. wrote the manuscript with input from all authors.
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Integrated supplementary information
Supplementary Figure 1 Hotspot detection components and workflow.
a) A schematic of the hotspot detection methodology employed here is shown. b) The steps involved in filtering mutation calls, samples, and genes, as well as generating and curating the final hotspot list.
Supplementary Figure 2 Facets of underlying mutational processes incorporated into hotspot discovery model.
a) We incorporated the intrinsic mutability of individual trinucleotides genome-wide, as this varies widely and is driven by diverse endogenous and exogenous sources. Shown are all mutations in the study cohort, the subset of mutations detected in two mutagen-associated tumor types (tobacco-associated lung adenocarcinoma and UV-associated cutaneous melanoma), and two subsets of tumors driven by aberrant DNA repair processes (microsatellite instability and POLE exonuclease domain mutations). b) We truncate the standard binomial model based on a priori understanding of the ways in which hotspots can emerge in candidate cancer genes (Supplementary Fig. 1a) to allow for the identification of warmspot mutations, most often in genes with one or a few hotspots of exceptional frequency that would otherwise suppress other less common alleles. Shown are all hotspots that achieved significance only after model truncation (x vs. y-axis significance values). The color reflects increasing truncated significance. Inset, the pattern of significance among all hotspots found after truncation.
Supplementary Figure 3 Global features of significant hotspots.
a) The number of hotspots across the genes identified here (inset: distribution of hotspot type). b) The frequency of specific hotspots across the 41 tumor types analyzed here. c) The number of mutant alleles distributed among the hotspots detected (inset: number of mutant alleles at a given hotspot increases with the number of tumor types affected, dot is the average number of mutant alleles across the hotspots identified in each of the indicated number of tumor types, bars are the 95% confidence interval). d) The fraction of total mutational burden present in the hotspot (positional specificity) of each affected gene.
Supplementary Figure 4 RNA expression in tumors with known oncogenic hotspots.
The mRNA expression of the indicated gene is shown for all tumors (gray bars) across cancer types in which one or more tumor harbors the indicated hotspot (count of tumor types plotted is indicated, expression is a Z-score of log2 RSEM normalized count data inferred from level-3 TCGA RNA sequencing data). Tumors harboring the oncogenic hotspot are indicated with red tick marks (x-axis) the density distribution of which is shown in blue. The top row indicates genes with no association between the level of expression and the presence of the hotspot. Middle row are those genes whose expression is elevated in tumors bearing the hotspot. The bottom row indicates genes and hotspots of variable patterns of expression. Multiple hotspots in the same gene with different patterns of expression (ERBB2 and PIK3CA) are shown for reference.
Supplementary Figure 5 Lineage map of hotspots in common tumor suppressors.
As in main text Fig. 2A, shown here are all hotspots detected in excluded tumor suppressor genes that harbor at least one hotspot affecting >5% of tumors of one or more tumor types are shown. Frequencies are indicated and genes, hotspots, and tumor types are ordered as described in main text Fig. 2A. These included 14 hotspots from Arg213-1450 of the N-terminal of APC, the mutational cluster region (MCR), affecting between 6 and 37 tumors nearly all of which were colorectal cancers.
Supplementary Figure 6 Squamous cell type-specific hotspots.
a) The enrichment of hotspots in squamous cell tumors (by frequency and significance, as indicated). b) The distribution of tumor types among cases mutated for either MAPK1 E322 (top) or EP300 D1399 (bottom).
Supplementary Figure 7 Impact of unconventional hotspots.
a) Significant splice site hotspots are shown and have diverse affect on transcript sequence and structure. In blue is the coverage and splicing pattern inferred from RNA sequencing of a representative tumor harboring each hotspot. The impact of each is summarized (rightmost column) and include in- frame and frame-shift events resulting from exon skipping, intron retention, and deletions. Highlighted in yellow are splice site hotspots at opposite ends of the same intron with both similar and dissimilar impact on transcript structure. SMTNL2 was not assessable due to little detectable expression in E244 (e3+1)-mutant tumors. b) The spectrum of nonsense mutations in hotspots indicate a subset are comprised exclusively of nonsense mutations. c) Shown is the impact of nonsense hotspots on transcript expression in CDKN2A, TP53, and APC, three genes affected by the greatest number of nonsense mutations. As expected, the expression (inferred from RNA sequencing of affected cases in TCGA cohorts) of all three genes was significantly decreased between tumors with missense mutations versus those with candidate loss-of- function (LOF) mutations of any kind, including nonsense hotspots. Nevertheless, where no difference in TP53 or APC expression existed between tumors with non-hotspot LOF mutations (labeled Misc. LOF) and those carrying nonsense hotspots, the tumors bearing a nonsense hotspot in CDKN2A expressed significantly less transcript levels p16INK4A mRNA than did tumors with non-hotspot LOF mutations.
Supplementary Figure 8 Additional candidate long tail hotspots.
a) Two hotspot mutations were detected in the N-terminal of NUP93 (E14K and Q15*), a constitutively essential gene. The E14K hotspot was recurrently mutated in breast cancer (55% of all E14K mutants), making it among the most common hotspots in breast cancers (right). b) Two somatic missense hotspots H28R and R60Q affect the bHLHz domain of MAX in a diversity of tumor types (indicated at bottom of panel d). These hotspots are different in type and position to the germline nonsense mutations present in sporadic pheochromocytomas and paragangliomas (bottom). c) The MYC:MAX heterodimer bound to DNA in which the DNA binding domain of MAX is highlighted (in blue) indicates the position of R60Q and H28R hotspots in highly conserved residues at the 5’ and 3’ end of the canonical E-box CACGTG motif respectively. A H374R mutation in MYC (annotated), also present in a uterine endometrial like MAX H28R mutations, is at a site equivalent to MAX H28R, extending the affected subset of cases in this tumor type. d) MAX hotspot mutations are mutually exclusive with mutations and amplification of MYC in affected tumor types, irrespective of hypermutation status.
Supplementary Figure 9 GQ60GK dinucleotide mutations are a single genomic event.
Shown are aligned reads from whole-exome sequencing of the tumor and matched normal DNA and RNA sequencing of the tumor from representative affected cases indicating that the GQ60GK dinucleotide mutation is a single event expressed in cis.
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Chang, M., Asthana, S., Gao, S. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat Biotechnol 34, 155–163 (2016). https://doi.org/10.1038/nbt.3391
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DOI: https://doi.org/10.1038/nbt.3391
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