SUPPLEMENTARY INFORMATION

Transcription

1 SUPPLEMENTARY INFORMATION Systematic investigation of cancer-associated somatic point mutations in SNP databases HyunChul Jung 1,2, Thomas Bleazard 3, Jongkeun Lee 1 and Dongwan Hong 1 1. Cancer Genomics Branch, Division of Convergence Technology, National Cancer Center, Gyeonggi-do , Korea 2. Bioinformatics and Systems Biology Graduate Program, University of California San Diego, 95 Gilman Drive, La Jolla, CA 9293, USA 3. College of Natural Sciences, Seoul National University Graduate School, Seoul , Korea To whom correspondence should be addressed; dwhong@ncc.re.kr Nature Biotechnology: doi:1.138/nbt.2681

2 Table of Contents Materials and Methods... 3 Supplementary Notes... 6 Supplementary Figures Suppl. Figure 1. The number of overlapped positions supported by at least 1, 5 and 1 tumor samples... 8 Suppl. Figure 2. Mutually exclusive alteration pattern between PIK3CA and TP Suppl. Figure 3. For TP53, Kaplan-Meier survival curves for tumor samples with cancerassociated somatic mutations represented in dbsnp or other variants versus wild-type by logrank test... 1 Suppl. Figure 4. Workflow of the proposed comprehensive SNP filtering approach Supplementary Tables Suppl. Table 1. List of the compiled cancer genomics articles Suppl. Table 2. List of the cancer-associated somatic mutations represented in dbsnp Suppl. Table 3. Functional consequence of the cancer-associated somatic mutations represented in dbsnp Suppl. Table 4. High-confidence filtered cancer-associated somatic mutations represented in dbsnp shown in two example articles Suppl. Table 5. Analysis of mutually exclusive alteration patterns... 4 Suppl. Table 6. List of patients with cancer-associated somatic mutations represented in dbsnp and other variants in TP Suppl. Table 7 Cancer-associated somatic mutations represented in 1 Genomes Project. 42 References Nature Biotechnology: doi:1.138/nbt.2681

3 Materials and Methods Eligible cancer genomics articles We selected articles published in Nature, Nature Genetics, Genome Research, and PNAS between January 21 and June 212 that used next generation sequencing technology to study human cancer. In this survey, we focused on cancer genomics articles with whole genome sequencing (WGS) or whole exome sequencing (WES). We selected articles where the identification of point mutations was one of the main parts of the study and was mentioned in the abstract. We excluded articles which only investigated structural variations, copy number variations, or pathogen infections using sequencing data. We selected articles regardless of next generation sequencing platform used, number of samples, cancer type, and point mutation calling algorithm used. Articles were first identified in a PubMed and Google Scholar search. Then, we further searched for articles using the search engine of each journal. Several individuals independently read the methods, and supplementary methods of each paper to search for the SNP filtering approach used in the point mutation calling workflow. Following this inspection, we classified the articles into four categories: (A) Article with filtering; (B) Article with partial filtering; (C) Article with filtering-unknown; and (D) Other. The 'articles with filtering' were those that used the common SNP filtering approach which filtered identified point mutations against public SNP databases such as dbsnp 1 or the 1 Genomes Project database 2. According to descriptions of the SNP filtering, the 'articles with filtering did not use a subset of dbsnp databases such as common SNPs (SNPs with >= 1% minor allele frequency) or Flagged SNPs (Clinically associated SNPs), because most of the articles used old versions of dbsnp (e.g. dbsnp 13) that do not provide the subset. The 'articles with partial filtering' were those that filtered out point mutations using the public databases but which saved for analysis those in disease databases such as COSMIC 3 or OMIM 4. The 'articles with filtering-unknown' were those where we could not find any description of SNP filtering approach in any section of the article. Preprocessing of COSMIC and dbsnp database for extraction of overlapping SNPs We downloaded all SNPs listed in dbsnp135 from the UCSC genome browser 5. We extracted SNPs whose class was single on all chromosomes (n = 47,762,49). Out of the single SNPs, we selected SNPs whose function column contained missense, nonsense, stop-loss, or splice (n=514,7). We also downloaded COSMIC v6 data from the COSMIC web site. We selected point mutations Nature Biotechnology: doi:1.138/nbt.2681

4 where a hg19 coordinate was available and whose mutation description column had Nonstop extension, Substitution Nonsense, or Substitution Missense. To select only somatic point mutations, we chose point mutations where the mutation somatic status was confirmed somatic variant or reported in another cancer sample as somatic and discarded point mutations of which the status was variant of unknown origin, reported in another sample as germline, not specified, and confirmed germline variant. We removed duplicate mutation entries with the same sample ID and retained just one representative of each. We focused on overlapping non-silent SNPs supported by at least five tumor samples in the main analyses. Prediction of functional consequence of the cancer-associated somatic mutations represented in dbsnp We used three in-silico methods, SIFT 6, PolyPhen2 7, and MutationAssessor 8 to assess the functional impact of the cancer-associated somatic mutations represented in dbsnp database. In cases where positions had several reported variant alleles, we ran the three tools with all reported nucleotide changes. The prediction results can be found in supplementary figure 4 and table 4. Next, we classified each mutation position into functional and non-functional groups. The positions predicted to be functional with relatively low confidence were also classified into the functional group. The positions having multiple prediction results due to several reported variant alleles were classified into the functional group, if one of the nucleotide changes was predicted to be functional. We used PhyloP 9 to assess the degree of conservation. Mutation positions for which the PhyloP score was greater than 1.3 (P <.5) were classified into the functional group. Identification of the high-confidence filtered mutations For the bladder cancer article 1, we first downloaded publicly available raw sequencing data (SRA38181) from the NCBI Sequence Read Archive (SRA) 11. We followed the same alignment and variant calling approach to replicate their variant calling results. We first aligned reads against NCBI reference genome (hg18) using Burrows-Wheeler Alignment (BWA) tool 12 and performed local realignment of the BWA-aligned reads using Genome Analysis Toolkit (GATK) 13. After removing PCR duplicates using Picard, somatic point mutations were called by VarScan 14. We first aligned wholeexome sequencing of 9 bladder tumor samples used in the discovery step through the pipeline Nature Biotechnology: doi:1.138/nbt.2681

5 described above. To confirm our results, we contacted authors to ask for their point mutation calling results (VarScan output file). The consistency between the results was very high. For example, there was little difference in the number of reads supporting variant alleles and variant allele frequencies. Based on the high consistency in the variant calling results of 9 tumor samples, we decided to analyze the 88 tumor samples using their variant calling results. To select high-confidence filtered cancerassociated somatic mutations represented in dbsnp, we used a list of the validated point mutations in their supplementary materials with the variant calling results. For each tumor sample, we selected the filtered mutations for which the number of reads supporting variant allele and variant allele frequencies were greater than those of at least one confirmed point mutation from the same sample. In cases where tumor samples had too few confirmed mutations for setting the two cutoff values, we did not include the mutations from these tumor samples. For the prostate cancer article 15, we downloaded sequencing data (SRA37395) from NCBI SRA and processed the data with the pipeline described above to detect variants. To select high-confidence filtered cancer-associated somatic mutations represented in dbsnp, we only focused on the detection of homozygous mutations. There were two reasons for this. First, we did not have any reliable cutoff values such as the number of reads supporting variant allele and variant allele frequency from validated mutations. Second, we did not take the same variant calling approaches used in the article. Thus, we selected only homozygous mutations of which variant allele frequency was higher than 95%. Moreover, we manually inspected the identified homozygous mutations with the Integrative Genomics Viewer (IGV) browser 16. Finally, we contacted authors to ask for confirmation of the identified homozygous mutations and they confirmed them. Evaluation of clinical significance of the cancer-associated somatic mutations represented in dbsnp in TP53 We obtained patient survival information in Supplementary Table 1 of the article concerned 17. The 48 patients provided information such as survival (in months) after the diagnosis, first hormone therapy, and first chemotherapy. We first searched for high-confidence non-silent somatic mutations and highlevel copy number alterations in TP53. According to TP53 mutants, we classified the patients into those with the cancer-associated somatic mutations represented in dbsnp; those with other variants such as non-silent point mutations (excluding the cancer-associated somatic mutations represented in Nature Biotechnology: doi:1.138/nbt.2681

6 dbsnp), frameshift indels, and structural variations (high-level amplifications or deletions); and those without variants (wild-type). The patient (WA1) having both the cancer-associated somatic mutation represented in dbsnp and high-level deletion was excluded in this analysis. Patients with cancerassociated somatic mutations represented in dbsnp or other variants in TP53 did not show significant prognostic difference for survival after the diagnosis and first chemotherapy. Supplementary Notes Investigation of cancer-associated somatic mutations represented in 1 Genomes Project database dbsnp135, which we used in this study, includes 1 Genomes Project Pilot 1,2,3 and Phase 1 data, which are the most recent to be released. Therefore, we searched for mutations reported by 1 Genomes Project data among the cancer-associated somatic mutations represented in dbsnp database (n=257). We found that 9 of the 257 mutations were reported by them and almost half (n=4) of the 9 mutations were predicted to have a functional consequence by at least three out of the four methods. 4 of the 9 mutations were common germline SNPs with MAF of at least 1%. 2 of them, rs (MAF=7.9%) in ATM and rs (MAF=1.2%) in STK11, were found to be a melanoma susceptibility locus by GWAS and be related to a cancer prone syndrome by OMIM database, respectively. The other two common SNPs might be passenger mutations or their association with cancer might not be revealed yet. In addition, 4 of the remaining 5 SNPs with MAF of less than 1% were flagged as clinically-associated (Supplementary Table 7). For example, rs generates one of the six well-known hot-spot codons in TP53. In addition, rs in APC and rs in STK11 are related to multiple colorectal adenomas and cancer prone disorder by OMIM database. Furthermore, we don t exclude the possibility that some of the overlapping SNPs reported by 1 Genomes Project data were erroneously entered into the COSMIC database. Description of the SNP filtering pipeline Our proposed comprehensive SNP filtering approach was implemented in a web-based tool called CSTAR (Cancer genome Sequencing Tool to Acquire Reliable somatic point mutations; which takes non-silent point mutations in SNP databases as an input (VCF, MAF or tab Nature Biotechnology: doi:1.138/nbt.2681

7 delimited text file). The pipeline compares the input SNP list to a knowledgebase that is comprised of overlapping SNPs between dbsnp and COSMIC databases. Those point mutations not present in SNP databases are immediately forwarded as candidate mutations. For mutations present in dbsnp, the program references functional consequences predicted by in-silico prediction tools SIFT, Polyphen, Mutation Assessor and Phylop, clinical associations flagged by dbsnp, and disease susceptibility information from the GWAS catalog ( The first filtering module allows users to create customized cancer-associated variant lists by selecting 1 the required minimum number of tumor samples supporting each mutation in COSMIC, 2 the number of mutations occurred in gene, and 3 the required number of tools predicting damage. The second parameter in particular is designed to aid in identifying cancer driver genes by rescuing mutations that are either clustered in hot spots or scattered along the entire gene. The second module then rescues clinicallyassociated or disease susceptibility SNPs. Finally, the rescued SNP list from the two modules is provided as an output. Supplementary Figure 4 shows the workflow of the proposed comprehensive SNP filtering approach. Nature Biotechnology: doi:1.138/nbt.2681

8 Supplementary Figures Supplementary Figure 1. The number of overlapped positions supported by at least 1, 5 and 1 tumor samples 514,587 Nature Biotechnology: doi:1.138/nbt.2681

9 Supplementary Figure 2. Mutually exclusive alteration pattern between PIK3CA and TP53 (P =.3). Tumor samples with or without mutations are labeled in red or blue, respectively. For PIK3CA and TP53, newly identified tumor samples with filtered mutations were marked with asterisks. P values were calculated by two-tailed Fisher exact test Nature Biotechnology: doi:1.138/nbt.2681

10 Supplementary Figure 3. For TP53, Kaplan-Meier survival curves for tumor samples with cancerassociated somatic mutations represented in dbsnp or other variants versus wild-type by log-rank test. The tumor sample (WA1) having both the cancer-associated somatic mutation represented in dbsnp and other variant was excluded from survival analysis Nature Biotechnology: doi:1.138/nbt.2681

11 Supplementary Figure 4. Workflow of the proposed comprehensive SNP filtering approach Nature Biotechnology: doi:1.138/nbt.2681

12 Supplementary Tables Supplementary Table 1. List of the compiled cancer genomics articles Journal Year Category Title Evidence sentences Nature 21 Filtering A comprehensive catalogue of somatic mutations from a human cancer genome In order to allow for any under-called positions in the germline, no observations of that allele were permitted in the germ line, although one call was permitted if the depth was 3. Substitutions corresponding to known SNP positions (dbsnp 129) were excluded. Substitutions were annotated using Ensembl version 52. Nature 21 Filtering A small-cell lung cancer genome with complex signatures of tobacco exposure We used the optimal thresholds defined in point 5 of the power calculations above (based on a mutation prevalence of 8 per Mb, as estimated from capillary sequence data in COSMIC) to determine whether there was sufficient evidence for calling a somatic substitution or not at each base in this preliminary list. Resulting tumour-specific substitutions were further filtered to remove (1) those residing in regions of loss of heterozygosity (LOH) in the normal cell line; (2) those potentially due to misalignment in segmental duplications and near sequence gaps; (3) those corresponding to polymorphic positions in dbsnp; (4) those potentially due to misalignment or miscalls as they are adjacent to SNPs or within 5 bp of insertions and deletions; and (5) those where all supporting reads contained the putative variant in the first or last 5 bp of the read (to reduce effects of misalignment across indels). Substitutions were annotated using Ensembl version 52. Nature 21 Filtering Nature 21 Filtering Genome remodelling in a basallike breast cancer metastasis and xenograft. The mutation spectrum revealed by paired genome sequences from a lung cancer patient We again followed the same procedure as described in Mardis et al(1). Predicted SNVs and Indels were compared to dbsnp 129. For SNVs, we require a position match for determining concordance between the variant and dbsnp 129. In addition, we compared (by position) predicted SNVs with SNPs found in the CEU and YRI trios as determined from the 1, Genomes project. This suggests that excluding SNVs that are only partially called in the normal would have increased the overall validation rate to 78% without a large impact on sensitivity. Further, excluding such loci that are only partially called in the normal would yield only 8,732 tumor-specific SNVs that are also described in dbsnp (i.e. likely false negative calls in the normal genome assembly). Nature Genetics 211 Filtering Frequent somatic mutations in MAP3K5 and MAP3K9 in metastatic melanoma identified by exome sequencing The pileup file of all variations detected in each sample was first compared to all variations annotated in dbsnp132 along with data from the 1 Genomes Project. After this analysis, all newly identified variations were fully annotated. Nature Genetics 211 Filtering Exome sequencing identifies GRIN2A as frequently mutated in melanoma To eliminate common germline mutations from consideration, alterations observed in dbsnp13 or in the 1 Genomes Project 11_21 data release project were removed. Nature Biotechnology: doi:1.138/nbt.2681

13 Nature Genetics 211 Filtering Exome sequencing identifies somatic mutations of DNA methyltransferase gene DNMT3A in acute monocytic leukemia We used an in-house software system to identify somatic mutations by comparing variants identified in bone marrow exome data set against dbsnp and germline variants present in peripheral blood control samples. Nature Genetics 211 Filtering Frequent mutations of chromatin remodeling genes in transitional cell carcinoma of the bladder To eliminate any previously described germline variants, we cross-referenced potential somatic mutations against the dbsnp13 and SNP datasets of Han Chinese in Beijing (CHB) and Japanese in Toyko (JPT) from the three pilot studies in the 1 Genomes Project. Nature Genetics 211 Filtering Analysis of the coding genome of diffuse large B-cell lymphoma For the tumor samples, only 'high confidence' variants (that is, variants supported by at least one read in one direction and two non-duplicate reads in the opposite direction) were retained, according to the GS Reference Mapper Software algorithm. For the normal samples, a less stringent criterion was applied in that all variants detected in at least one read were considered to be present in the sample. Candidate somatic (that is, tumorspecific) variants were then obtained by removing known population polymorphisms present in the NCBI dbsnp database (Build 132) as well as variants present in the corresponding paired normal DNA. Nature Genetics 211 Filtering Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer Candidate somatic (that is, tumor-specific) variants were then obtained by removing known population polymorphisms present in the NCBI dbsnp database (Build 132) as well as variants present in the corresponding paired normal DNA. Nature Genetics 211 Filtering Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma In order to eliminate any previously described germline variants, the somatic mutations were cross-referenced against the dbsnp (version 13) and SNP data sets of Han Chinese in Beijing (CHB) and Japanese in Toyko (JPT) from the three pilot studies in the 1 genomes project ( Any mutations present in above data sets were filtered out and the remaining mutations were subjected to subsequent analyses. Nature 211 Filtering Nature 211 Filtering PNAS 211 Filtering Frequent mutation of histonemodifying genes in non-hodgkin lymphoma Frequent pathway mutations of splicing machinery in myelodysplasia Whole-exome sequencing of neoplastic cysts of the pancreas reveals recurrent mutations in components of ubiquitindependent pathways Any SNV near gapped alignments or exactly overlapping sites assessed as being polymorphisms (SNPs) were disregarded, including variants matching a position in dbsnp or the sequenced personal genomes of Venter58, Watson59 or the anonymous Asian6 and Yoruban61 individuals. Synonymous variants, polymorphisms registered in the dbsnp131 and 1 genome database, and variants on the intron region except splicing sites were filtered. Duplicate tags were removed, and a mismatched base was identified as a mutation only when (i) it was identified by more than five distinct tags, (ii) the number of distinct tags containing a particular mismatched base was at least 2% of the total distinct tags, (iii) it was not present in >.1% of the tags in the matched normal sample, and (iv) it was not present in SNP databases (dbsnp Build 134 Release, and genomes.org/index.html). Nature Biotechnology: doi:1.138/nbt.2681

14 PNAS 211 Filtering Nature Genetics 212 Filtering Nature Genetics 212 Filtering Nature 212 Filtering Nature 212 Filtering PNAS 211 Others Nature Genetics 211 Nature Genetics 212 Partial filtering Partial filtering Exome sequencing identifies a spectrum of mutation frequencies in advanced and lethal prostate cancers Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma Loss-of-function mutations in Notch receptors in cutaneous and lung squamous cell carcinoma Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes Exome sequencing of liver flukeassociated cholangiocarcinoma A majority of the variants identified by exome sequencing were present within dbsnp. After removing from consideration all variants that were observed in the pilot dataset of the 1 Genomes Project (11, 12) as well as any variants present in any of ~2, additional exomes sequenced at the University of Washington, the number of variants remaining in 2/23 samples was reduced to ~35. Variants were filtered for their coding localization, annotation in dbsnp131 or 1 genomes, somatic and functionally impairment. If a base with consensus quality lower than 2 occurs within 3bp on either side of the target SNV, we discarded the SNVs. After SNV calling in the tumor samples, candidate SNVs were filtered based on the lymphocyte sequence of the same patient; (1) candidate SNV alleles with a frequency.3 after removing reads with base quality < 15, and mapping quality < 2, (2) depth of coverage in lymphocyte 5, (3) depth of coverage in lymphocyte 1 and candidate SNV allele was represented in the dbsnp database v131 ( Variants, which are reported in dbsnp13, that were found in any of the normal blood samples or that were found within the public genomes from Complete Genomics were removed from the data set. A variant called in a tumour was considered to be a candidate somatic mutation if the matched normal sample had at least 1 reads covering this position and had zero variant reads, and the variant was not reported in dbsnp131 or the 1 Genomes data set (October 211). Tumor samples withouth matched normal samples : To eliminate common germline polymorphisms from consideration, variants that had the same position as variants present in pilot data from the 1, Genomes Project or in 2, exomes corresponding to normal (nontumor, nonxenografted) tissues sequenced at the University of Washington were removed from consideration. ; Tumor samples with matached normal samples : All mutations known in dbsnp were subtracted unless present in COSMIC. To identify somatic mutations, we excluded from our analysis all germline variants found in the dbsnp131 or 1 Genomes Project (4th August 21 release) databases and then subtracted the sequence variants of the normal exomes from the tumor exomes. Any sequence variants found in COSMIC v47, a database of cancer somatic mutations, were retained. We compared our variants against the common polymorphisms present in dbsnp131 and in the 1 Genomes Project databases, in order to discard any common SNPs. Several cancer somatic mutations are also present in dbsnp, and we retained any common variants also found to be present in COSMIC v47. Nature 212 Partial filtering The genetic basis of early T-cell precursor acute lymphoblastic leukaemia High-confidence germline variants that were not found in dbsnp were retained as novel variants. In addition, variants in dbsnp that were also present in OMIM or COSMIC were retained as these variants are likely to be of biologic importance. Nature 212 Partial filtering Novel mutations target distinct subgroups of medulloblastoma Since only tumor samples were sequenced, known germline variations in dbsnp (excluding validated mutations in COSMIS, OMIMSNP and ClinicalVar), NHLBI Exome Sequencing Project ( on ) and germline variations identified by PCGP were removed. Nature Biotechnology: doi:1.138/nbt.2681

15 Nature Genetics 211 Unknown High-resolution characterization of a hepatocellular carcinoma genome Nature Genetics 211 Unknown Inactivating mutations of the chromatin remodeling gene ARID2 in hepatocellular carcinoma Nature Genetics 211 Unknown Nature Genetics 211 Unknown Nature Genetics 211 Unknown Nature Genetics 211 Unknown Nature 211 Unknown Nature 211 Unknown Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A- TCF7L2 fusion Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia A novel recurrent mutation in MITF predisposes to familial and sporadic melanoma Initial genome sequencing and analysis of multiple myeloma Nature 211 Unknown Nature 211 Unknown The genomic complexity of primary human prostate cancer Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia Nature Biotechnology: doi:1.138/nbt.2681

16 Genome Research 211 Unknown Nature Genetics 212 Unknown Nature Genetics 212 Unknown Nature Genetics 212 Unknown Nature 212 Unknown Nature 212 Unknown Nature 212 Unknown Nature 212 Unknown Nature 212 Unknown Nature 212 Unknown Nature 212 Unknown Whole-exome sequencing of human pancreatic cancers and characterization of genomic instability caused by MLH1 haploinsufficiency and complete deficiency Exome sequencing identifies recurrent somatic MAP2K1 and MAP2K2 mutations in melanoma Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer A novel retinoblastoma therapy from genomic and epigenetic analyses Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing Exome sequencing identifies frequent mutation of the SWI_SNF complex gene PBRM1 in renal carcinoma Melanoma genome sequencing reveals frequent PREX2 mutations Sequence analysis of mutations and translocations across breast cancer subtypes Whole-genome analysis informs breast cancer response to aromatase inhibition The landscape of cancer genes and mutational processes in breast cancer Nature Biotechnology: doi:1.138/nbt.2681

17 Nature 212 Unknown Clonal selection drives genetic divergence of metastatic medulloblastoma Genome Research 212 Unknown Whole genome sequencing of matched primary and metastatic acral melanomas PNAS 212 Unknown Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing Nature 212 Unknwon The clonal and mutational evolution spectrum of primary triple-negative breast cancers Nature Biotechnology: doi:1.138/nbt.2681

18 Supplementary Table 2. List of the cancer-associated somatic mutations represented in dbsnp Gene Chromosome & Position (Hg19) Number of supporting tumor samples rs ID Gene Chromosome & Position (Hg19) Number of supporting tumor samples JAK2 9: rs TP53 17: rs KRAS 12: rs PTPN11 12: rs : rs CDKN2A 9: rs KRAS 12: rs TP53 17: rs KRAS 12: rs PDGFRA 4: rs IDH1 2: rs EGFR 7: rs PIK3CA 3: rs PTEN 1: rs EGFR 7: rs APC 5: rs FGFR3 4: rs EGFR 7: rs NRAS 1: rs : rs PIK3CA 3: rs TP53 17: rs TP53 17: rs CDKN2A 9: rs KIT 4: rs HRAS 11: rs IDH1 2: rs TP53 17: rs TP53 17: rs APC 5: rs TP53 17: rs TP53 17: rs NRAS 1: rs NF2 22: rs PIK3CA 3: rs APC 5: rs : rs PTEN 1: rs TP53 17: rs PIK3CA 3: rs NRAS 1: rs TSHR 14: rs TP53 17: rs HRAS 11: rs : rs : rs TP53 17: rs VHL 3: rs53826 FGFR3 4: rs PIK3CA 3: rs TP53 17: rs FGFR2 1: rs : rs KIT 4: rs TP53 17: rs VHL 3: rs5387 HRAS 11: rs : rs KRAS 12: rs CDKN2A 9: rs : rs CDKN2A 9: rs IDH2 15: rs CDKN2A 9: rs RET 1: rs TP53 17: rs NRAS 1: rs KRAS 12: rs FGFR3 4: rs PIK3CA 3: rs GNAS 2: rs EGFR 7: rs TP53 17: rs VHL 3: rs FLT3 13: rs TP53 17: rs : rs CSF1R 5: rs HRAS 11: rs STK11 19: rs NRAS 1: rs TP53 17: rs MPL 1: rs APC 5: rs rs ID Nature Biotechnology: doi:1.138/nbt.2681

19 DNMT3A 2: rs FGFR3 4: rs : rs NF2 22: rs PDGFRA 4: rs : rs PIK3CA 3: rs VHL 3: rs53818 TP53 17: rs SRC 2: rs TP53 17: rs KIT 4: rs KRAS 12: rs TP53 17: rs GNAQ 9: rs TSHR 14: rs APC 5: rs STK11 19: rs : rs CDKN2A 9: rs PTEN 1: rs VHL 3: rs IDH2 15: rs GNAS 2: rs KRAS 12: rs APC 5: rs TP53 17: rs KRAS 12: rs TP53 17: rs VHL 3: rs53811 TP53 17: rs TSHR 14: rs EGFR 7: rs MPL 1: rs FGFR3 4: rs SMO 7: rs : rs ATM 11: rs TP53 17: rs PTPN11 12: rs TP53 17: rs WT1 11: rs NRAS 1: rs PTEN 1: rs HRAS 11: rs : rs AKT1 14: rs TSHR 14: rs NRAS 1: rs TP53 17: rs KIT 4: rs WT1 11: rs PTPN11 12: rs KRAS 12: rs PIK3CA 3: rs ERBB2 17: rs TP53 17: rs ABL1 9: rs GNAS 2: rs EGFR 7: rs KIT 4: rs RB1 13: rs CDKN2A 9: rs CDKN2A 9: rs : rs APC 5: rs TP53 17: rs STK11 19: rs : rs RET 1: rs KRAS 12: rs IDH1 2: rs TP53 17: rs FGFR2 1: rs PTEN 1: rs VHL 3: rs5384 3: rs APC 5: rs PTEN 1: rs TP53 17: rs CDKN2A 9: rs : rs HRAS 11: rs MET 7: rs KIT 4: rs TP53 17: rs KIT 4: rs WT1 11: rs TP53 17: rs VHL 3: rs5382 EGFR 7: rs DNMT3A 2: rs HRAS 11: rs TSHR 14: rs PIK3CA 3: rs VHL 3: rs Nature Biotechnology: doi:1.138/nbt.2681

20 FGFR3 4: rs PTPN11 12: rs FGFR3 4: rs APC 5: rs TP53 17: rs VHL 3: rs KIT 4: rs KRAS 12: rs EGFR 7: rs GNAS 2: rs FGFR3 4: rs RB1 13: rs FLT3 13: rs MET 7: rs PTPN11 12: rs CBL 11: rs ALK 2: rs NF2 22: rs FGFR3 4: rs VHL 3: rs5382 APC 5: rs FKBP9 7: rs : rs KIT 4: rs PTPN11 12: rs VHL 3: rs53828 FBXW7 4: rs JAK3 19: rs KIT 4: rs STK11 19: rs : rs WT1 11: rs PIK3CA 3: rs TP53 17: rs PIK3CA 3: rs VHL 3: rs KRAS 12: rs : rs : rs ERBB2 17: rs APC 5: rs KRAS 12: rs APC 5: rs PTEN 1: rs FGFR3 4: rs RET 1: rs KIT 4: rs WT1 11: rs PIK3CA 3: rs CDC73 1: rs PTEN 1: rs RB1 13: rs PIK3CA 3: rs VHL 3: rs53823 APC 5: rs : rs CDKN2A 9: rs TRRAP 7: rs CDKN2A 9: rs : rs EGFR 7: rs KIT 4: rs PIK3CA 3: rs ZDHHC11 5: rs PTPN11 12: rs RB1 13: rs KIT 4: rs RB1 13: rs APC 5: rs NF1 17: rs NF2 22: rs : rs STK11 19: rs CDKN2A 9: rs TSHR 14: rs APC 5: rs KIT 4: rs Nature Biotechnology: doi:1.138/nbt.2681

21 Supplementary Table 3. Functional consequence of the cancer-associated somatic mutations represented in dbsnp Gene rsid Chromosome & Position (Hg19) Number of supporting tumor samples Ref Var SIFT PolyPhen2 MutationAssessor Phylop Prediction Score Prediction Score Prediction Score Score ABL1 rs : C G DAMAGING low AKT1 rs : C T DAMAGING high ALK rs : C T Not scored N/A medium APC rs : G T Not scored N/A nonsense Nonsense 2.94 APC rs : C T Not scored N/A nonsense Nonsense 2.86 APC rs : C T Not scored N/A nonsense Nonsense 1.57 APC rs : C T Not scored N/A nonsense Nonsense 2.94 APC rs : C T Not scored N/A nonsense Nonsense 2.83 APC rs : C T Nonsense N/A nonsense Nonsense 1.39 APC rs : C T Not scored N/A nonsense Nonsense 1.51 APC rs : C T Nonsense N/A nonsense Nonsense.78 APC rs : G A Not scored N/A benign.1 low APC rs : G T Not scored N/A benign.1 Nonsense 2.86 APC rs : C T Nonsense N/A nonsense Nonsense 2.52 APC rs : C T Nonsense N/A nonsense Nonsense -.3 APC rs : C T Nonsense N/A nonsense Nonsense -.3 APC rs : C T Nonsense N/A nonsense Nonsense 1.46 APC rs : G C Not scored N/A benign. neutral APC rs : G T Not scored N/A benign. Nonsense 1.53 APC rs : C T Nonsense N/A nonsense Nonsense 1.28 ATM rs : G A TOLERATED.23 benign.4 medium rs : A C DAMAGING.82 high Nature Biotechnology: doi:1.138/nbt.2681

22 rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs : A T DAMAGING medium : A G TOLERATED medium : T C DAMAGING high : T A DAMAGING high : A C DAMAGING high : C T DAMAGING high : C A DAMAGING high : C A DAMAGING high : C T DAMAGING high : C G DAMAGING high : C T DAMAGING high : C A DAMAGING high : C G DAMAGING medium : C G DAMAGING high : C T DAMAGING high : C G DAMAGING high : T C DAMAGING medium : T A DAMAGING medium : T G DAMAGING medium : A C DAMAGING high : A T DAMAGING high : G C DAMAGING medium : T C DAMAGING medium : C A DAMAGING.1 benign.4 low Nature Biotechnology: doi:1.138/nbt.2681

23 rs : C T DAMAGING benign.4 medium CBL rs : G A DAMAGING medium CDC73 rs : C A Nonsense N/A nonsense Nonsense 1.37 CDC73 rs : C G Nonsense N/A nonsense Nonsense 1.37 CDKN2A rs : C T TOLERATED medium CDKN2A rs : C G TOLERATED medium CDKN2A rs : C A TOLERATED medium CDKN2A rs : synonymous in 2 G T DAMAGING Uniprot 2.75 CDKN2A rs : synonymous in 7 G A DAMAGING Uniprot 2.75 CDKN2A rs : C A DAMAGING medium CDKN2A rs : C G DAMAGING medium CDKN2A rs : C T TOLERATED medium CDKN2A rs : C A Nonsense N/A benign.9 low CDKN2A rs : C T DAMAGING low CDKN2A rs : C A Nonsense N/A low CDKN2A rs : C T TOLERATED medium CDKN2A rs : C A Nonsense N/A medium CDKN2A rs : G T DAMAGING low CDKN2A rs : G A DAMAGING low CDKN2A rs : G A DAMAGING Nonsense 2.81 CDKN2A rs : G A Nonsense N/A low CDKN2A rs : G A Nonsense N/A medium CDKN2A rs : C G TOLERATED medium CDKN2A rs : C T Nonsense N/A medium CDKN2A rs : G T DAMAGING 1. synonymous in 2.75 Nature Biotechnology: doi:1.138/nbt.2681

24 CDKN2A CSF1R rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs rs Uniprot 9: synonymous in 3 G A DAMAGING Uniprot : T C DAMAGING neutral : T A DAMAGING medium : T G DAMAGING medium : T C DAMAGING medium : A G DAMAGING medium : A T DAMAGING medium : A C DAMAGING medium : G A DAMAGING medium : G C DAMAGING medium : C G DAMAGING medium : C T DAMAGING medium : C A DAMAGING medium : C T DAMAGING medium : C G DAMAGING medium : C A DAMAGING medium : T A DAMAGING.3 benign.13 medium : T G DAMAGING benign.13 medium : T C DAMAGING benign.13 medium : C A DAMAGING medium : C G DAMAGING medium : C T DAMAGING medium : A T TOLERATED medium : A C DAMAGING medium Nature Biotechnology: doi:1.138/nbt.2681

25 rs : A G DAMAGING medium rs : C G TOLERATED medium rs : C T DAMAGING medium rs : C A DAMAGING medium rs : G T DAMAGING medium rs : G C DAMAGING medium rs : G A DAMAGING medium rs : G A DAMAGING medium rs : G T DAMAGING medium DNMT3A rs : G A DAMAGING high DNMT3A rs : C T DAMAGING medium EGFR rs : T G DAMAGING high EGFR rs : T A DAMAGING high EGFR rs : C T DAMAGING low EGFR rs : G C DAMAGING high EGFR rs : G A DAMAGING high EGFR rs : T G DAMAGING high EGFR rs : T A DAMAGING medium EGFR rs : G A DAMAGING medium EGFR rs : G T DAMAGING medium EGFR rs : G T DAMAGING medium EGFR rs : C T DAMAGING high EGFR rs : C A DAMAGING medium EGFR rs : C T DAMAGING medium EGFR rs : G A DAMAGING 1. high Nature Biotechnology: doi:1.138/nbt.2681

26 EGFR rs : G T DAMAGING high ERBB2 rs : T C DAMAGING high ERBB2 rs : G T TOLERATED low ERBB2 rs : G A TOLERATED low FBXW7 rs : G C DAMAGING high FBXW7 rs : G A DAMAGING high FGFR2 rs : A T medium FGFR2 rs : A C medium FGFR2 rs : G C high FGFR3 rs : A C 1. high FGFR3 rs : A T 1. high FGFR3 rs : G T.1 benign.1 medium FGFR3 rs : G T 1. high FGFR3 rs : C T 1. high FGFR3 rs : C G.1 1. high FGFR3 rs : A T.96 medium FGFR3 rs : A G.1.99 medium FGFR3 rs : G A.3.96 medium FGFR3 rs : C A TOLERATED.6.63 medium FGFR3 rs : A C 1. high FGFR3 rs : A G 1. high FKBP9 rs : G A DAMAGING high FLT3 rs : T A DAMAGING high FLT3 rs : C T DAMAGING medium Nature Biotechnology: doi:1.138/nbt.2681

27 FLT3 rs : C A DAMAGING medium FLT3 rs : C G DAMAGING medium GNAQ rs : T C high GNAQ rs : T A high GNAQ rs : T G high GNAS rs : C A DAMAGING high GNAS rs : C T DAMAGING high GNAS rs : A T DAMAGING high GNAS rs : G T DAMAGING high GNAS rs : G A DAMAGING high GNAS rs : G T DAMAGING high HRAS rs : C T benign.29 high HRAS rs : C A benign.29 high HRAS rs : C A.5 high HRAS rs : C G.5 high HRAS rs : C G.2.53 medium HRAS rs : C A.1.53 medium HRAS rs : C T.1.53 medium HRAS rs : C G.86 high HRAS rs : C T.86 high HRAS rs : C A.1.86 medium HRAS rs : T A.1 benign.1 high HRAS rs : T C.2 benign.1 high HRAS rs : C G.1 benign.3 high HRAS rs : C A.1 benign.3 high Nature Biotechnology: doi:1.138/nbt.2681