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1 Supplementary Materials for Clonal status of actionable driver events and the timing of mutational processes in cancer evolution Nicholas McGranahan, Francesco Favero, Elza C. de Bruin, Nicolai Juul Birkbak, Zoltan Szallasi, Charles Swanton* This PDF file includes: *Corresponding author. Published 15 April 2015, Sci. Transl. Med. 7, 283ra54 (2015) DOI: /scitranslmed.aaa1408 Materials and Methods Fig. S1. Tumor coverage and purity estimates. Fig. S2. Clonal heterogeneity of mutations in specific genes. Fig. S3. High-confidence clonal and subclonal mutations in nine cancer types. Fig. S4. TP53 and genome doubling. Fig. S5. Examples of parallel evolution. Fig. S6. Mutational spectra of clonal and subclonal mutations in cancer genes. Fig. S7. Overall survival and expression of NRXN3. Fig. S8. Clonality of actionable mutations. References (43 45) Other Supplementary Material for this manuscript includes the following: (available at Table S1. Driver genes within each cancer type (provided as a separate Excel file). Table S2. Mutational spectra of cancer genes (provided as a separate Excel file). Table S3. Cancer genes identified through clonality and temporal dissection analysis (provided as a separate Excel file). Table S4. Genes linked with targeted therapeutics (provided as a separate Excel file).

2 Supplementary Materials and Methods Estimating allele-specific integer copy numbers All data analysis was performed in the R statistical environment, version Affymetrix SNP6 data from paired tumor-normal samples were normalized and preprocessed using the Aroma Affymetrix CRMAv2 algorithm in the allele-specific setting to retrieve LogR and B-allele fractions (42). The BAF was further adjusted using the CalMaTe and TumorBoost algorithms (43, 44). Tumor copy number aberrations, ploidy and normal cell contamination were determined using ASCAT (45) with normal samples as references and hg19 coordinates. Sex chromosomes were excluded from the analysis. Samples that failed ASCAT processing due to poor model fit were discarded from the analysis. Estimating the cancer cell fraction and mutation copy number The mutation copy number and cancer cell fraction of each mutation were estimated by integrating ASCAT-derived integer copy number and tumor purity estimates with the variant allele frequency as outlined in Lohr et al (11) and Landau et al (17). For each variant, the variant allele frequency (VAF) depends on the local copy number of the tumor (CPN mut ), the purity (p), the local copy number of the normal sample (CPN norm ) and also the cancer cell fraction (CCF), defined as the proportion of cancer cells harboring the mutations. The expected VAF, given the CCF, can be calculated as follows: VAF (CCF) = p*ccf / CPN norm (1-p) + p*cpn mut. For a given mutation with a alternative reads, and a depth of N, the probability of a given CCF can be estimated using a binomial distribution P(CCF) = binom(a N, VAF(CCF)). CCF values can then be calculated over a uniform grid of 100 CCF values (0.01,1) and subsequently normalized to obtain a posterior distribution. Given that sex chromosomes were excluded from this analysis CPN norm was assumed to be 2. Similarly, the mutation copy number (the number of chromosomal alleles harboring the mutation) can be calculated as follows: Mutation copy number = (VAF/p)*((p*CNt)+CNn*(1-p)) In this analysis, mutations were classified as either clonal or subclonal based on the confidence interval of the CCF. Mutations were defined as clonal if the 95% confidence interval overlapped 1, and subclonal otherwise. Samples where over 95% of mutations were classified as subclonal were removed from further analysis. To explore whether results remained robust to alternative cut-offs, we implemented an additional approach to classify the clonal status of mutations. We adopted an alternative procedure whereby mutations with ambiguity in their clonal status were

3 excluded. Specifically, mutations were classified as clonal only if they met the following criteria: - 95% confidence interval of cancer cell fraction overlaps 1 - probability that mutation has a cancer cell fraction greater than 0.95 must be 0.75 (Prob(CCF>0.95) 0.75). Likewise, for mutations to be classified as subclonal, mutations had to meet the following criteria: - 95% confidence interval of cancer cell fraction cannot overlap with 1 - probability that mutation has a cancer cell fraction less than 0.95 must be 0.75 (Prob(CCF<0.95) 0.75). As can be seen in fig. S3, this modification means we do not force mutations in the twilight zone to be either clonal or subclonal; rather, these mutations remain unclassified. Nevertheless, using this modification, our results were consistent with our previous results. Temporal dissection of mutations Mutations were classified as either early or late based on their clonal status. When possible, we also timed mutations relative to copy number events (see (16)). In brief, for timing mutations relative to copy number events, we restricted our analysis to mutations occurring in regions with at least two copies of the major allele. For any such region, mutations at mutation copy number >1 were classified as before event and any mutations with a mutation copy number of 1 were classified as after event. Combining this with our cancer cell fraction estimates (see above), all clonal mutations that were not classified as after event were aggregated as early, and all subclonal or after event mutations were aggregated as late. Permutation testing to assess clonal enrichment To assess whether a specific gene had an enrichment or depletion of clonal/subclonal mutations a permutation test was devised. For example, a cancer gene may have 50 non-silent mutations (35 clonal and 15 subclonal) across 500 separate samples. To assess whether this gene exhibits an enrichment or depletion in clonal mutation we would randomly sample 50 non-silent mutations from 500 samples 10,000 times to obtain a background distribution representing the expected proportion of clonal/subclonal mutations. A p-value can then be obtained by comparing the observed proportion of clonal/subclonal mutations with this background distribution. MutSigCV implementation For each cancer type, MutSigCV was applied to all mutations as well as to early, late, clonal and subclonal mutations separately. A q-value threshold of 0.05 was adopted. For each run of MutSigCV only samples with at least 5 mutations within a given category were used and only genes with at least 10 non-silent mutations were considered. MutSigCV was implemented with default settings, using hg19 coordinates and the covariates and coverage tables provided (assuming full exome coverage).

4 Mutational Signature Analysis For each cancer type, mutational signature analysis was implemented separately on early and late mutations, as well as on all mutations. To ensure sufficient statistical power, we focused on mutations classified as early or late rather than simply clonal and subclonal. We used hierarchical clustering to ensure that the same signatures were identified in early, late, and all mutations. Each signature identified was then hierarchically clustered together with the signatures identified in Alexandrov et al. (4) and visually inspected to ensure a good fit. Tumors were classified as harboring a mutational signature if at least 25% of mutations or over 100 mutations were found to belong to a given signature (as in (4)). Finally, to compare the prevalence of mutational signatures in early and late mutations, a paired Wilcoxon test was used, comparing the proportion of early and late mutations that correspond to a given signature for each tumor. Only samples that harbored at least 30 mutations in total, with 10 early and 10 late mutations were considered. A small number of samples with outlier mutation signatures were identified and discarded so as to avoid biasing mutational signature interpretation (for example, one HNSC sample with a prevalent UV signature).

5 Figure S1 Figure S1 Tumor coverage and purity estimates A) Average (mean) coverage across called mutations for each tumor sample within each cancer type. Median and interquartile range is indicated. B) ASCAT tumor purity estimates for each tumor sample within each cancer type. C) ASCAT tumor purity in relation to subclonal fraction. Median and interquartile range is indicated.

6 Figure S2 Figure S2 Clonal heterogeneity of mutations in specific genes In the left panel, the proportion of mutations that are clonal (blue) and subclonal (red) are depicted for each gene. The significance is shown in the right panel, comparing the observed proportion of clonal mutations with the background distribution obtained from 10,000 randomizations, assuming the same mutation number. As can be seen, mutations in TP53 (across the pan-cancer cohort) and VHL (in KIRK tumors) are significantly more often clonal compared to background, whereas mutation in PTEN, PIK3R1, and MLL3 all show a significant tendency to be subclonal.

7 Figure S3 Figure S3 High-confidence clonal and subclonal mutations in nine cancer types A) The proportion of aggregated driver mutations vs. other mutations that are clonal/subclonal is indicated for each cancer type. Unclassified mutations were excluded. Red represents clonal mutations, and blue represents subclonal mutations. Notably, there is a higher proportion of clonal driver mutations compared to other clonal mutations. B) The cancer cell fraction of mutations in driver genes within each cancer type is depicted. Each symbol represents a somatic mutation in an individual tumor. Based on the probability distributions of the cancer cell fractions, mutations were determined to be either clonal (red circles, upper bound of confidence interval >=1 and prob(ccf>0.95) 0.75), subclonal (blue circles, upper band of confidence interval <1 and prob(ccf<0.95) 0.75), or else unclassified (gray). Error bars represent the 95% confidence interval.

8 Figure S4 Figure S4 TP53 and genome doubling A) The proportion of TP53 mutant and wild type tumors that exhibit genome doubling (GD) or do not exhibit genome doubling (ngd) is shown. Notably, there is a significant co-occurrence of genome doubling and mutations in TP53 (P<0.001, Fisher s exact test). B) Comparison between the observed proportion of early mutations with the background distribution obtained from 10,000 randomizations, assuming the same mutation number. As can be seen, mutations in TP53 are significantly more often early than late compared to background across the pan-cancer cohort.

9 Figure S5 Figure S5 Examples of parallel evolution A-B) Probability distributions over the cancer cell fraction for individual mutations are shown for specific tumors. In both cases, the cancer cell fractions are consistent with mutations occurring in independent tumor subclones. C) When multiple non-silent mutations were identified in the same cancer gene within one tumor sample, these mutations exhibited a significantly lower cancer cell fraction compared to mutations in cancer genes occurring only once in a cancer sample (P=7.151e-07, Wilcoxon rank sum test).

10 Figure S6 Figure S6 Mutational spectra of clonal and subclonal mutations in cancer genes A) Proportion of 8 distinct mutation types is shown for clonal and subclonal mutations within cancer genes in each tumor type. APOBEC refers to C>T or C>G mutations at TpCp (C or T) sites. For specific cancer genes, see Table S2. B) Proportion of mutation types for samples showing a significant enrichment of APOBEC-mediated mutagenesis. Notably, in HNSC, LUAD, and LUSC there is a clear enrichment for APOBEC mutations in subclonal cancer driver genes compared to clonal mutations. For specific cancer genes, see Table S2.

11 Figure S7 Figure S7 Overall survival and expression of NRXN3 High expression of NRXN3 is associated with significantly improved prognosis compared to low expression. High and low expression groups were defined relative to the median expression level. Survival plot was generated using KM plotter (

12 Figure S8 Figure S8 Clonality of actionable mutations For each known actionable site, the number of subclonal (numerator) and total mutations (denominator) is listed for each cancer type. Gray indicates absence of a mutation.