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Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes

Abstract

Advanced and metastatic tumors with complex treatment histories drive cancer mortality. Here we describe the POG570 cohort, a comprehensive whole-genome, transcriptome and clinical dataset, amenable for exploration of the impacts of therapies on genomic landscapes. Previous exposure to DNA-damaging chemotherapies and mutations affecting DNA repair genes, including POLQ and genes encoding Polζ, were associated with genome-wide, therapy-induced mutagenesis. Exposure to platinum therapies coincided with signatures SBS31 and DSB5 and, when combined with DNA synthesis inhibitors, signature SBS17b. Alterations in ESR1, EGFR, CTNNB1, FGFR1, VEGFA and DPYD were consistent with drug resistance and sensitivity. Recurrent noncoding events were found in regulatory region hotspots of genes including TERT, PLEKHS1, AP2A1 and ADGRG6. Mutation burden and immune signatures corresponded with overall survival and response to immunotherapy. Our data offer a rich resource for investigation of advanced cancers and interpretation of whole-genome and transcriptome sequencing in the context of a cancer clinic.

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Fig. 1: POG570 cohort description.
Fig. 2: Tumor genomic landscape and frequent alterations.
Fig. 3: Treatment-associated recurrent alterations.
Fig. 4: Mutation signatures identified in metastatic tumors.
Fig. 5: Previous therapy shapes the tumor genomic landscape.
Fig. 6: Germline alterations and effects on the genomic landscape.
Fig. 7: Immune landscapes of metastatic cancers.

Data availability

Genomic and transcriptomic sequence datasets, including metadata with library construction and sequencing approaches have been deposited at the European Genome–phenome Archive (EGA, http://www.ebi.ac.uk/ega/) as part of the study EGAS00001001159 with accession numbers as listed in Supplementary Table 1. Data on mutations, copy changes and expression from tumor samples in the POG program organized by OncoTree classification (http://oncotree.mskcc.org) are also accessible from https://www.personalizedoncogenomics.org/cbioportal/. The complete small mutation catalog and gene expression TPMs are available for download from http://bcgsc.ca/downloads/POG570/. Previously published TCGA and PCAWG data that were re-analyzed here are available from data portals (https://portal.gdc.cancer.gov/ and https://dcc.icgc.org/) with sample barcodes as listed in Supplementary Table 9. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The bioinformatics analyses were performed using open-source software, including Burrows–Wheeler alignment tool (v.0.5.7 for up to 125 bp reads and v.0.7.6a for 150 bp reads), CNAseq91 (v.0.0.6), APOLLOH92 (v.0.1.1), SAMtools93 (v.0.1.17), MutationSeq94 (v.1.0.2 and v.4.3.5), Strelka95 (v.1.0.6), SNPEff96 (v.3.2 for somatic and v.4.1 for germline), ABySS97 (v.1.3.4), TransABySS97,98 (v.1.4.10), Chimerascan99 (v.0.4.5), DeFuse100 (v.0.6.2), Manta101 (v.1.0.0), Delly102 (v.0.7.3), MAVIS71 (v.2.1.1), STAR73 (v.2.5.2b), RSEM74 (v.1.3.0), MSIsensor76 (v.0.2), HRDtools16 (v.0.0.0.9), BioBloomTools78 (v.2.0.11b), EXPANDS84 (v.2.1.1), SignIT (https://github.com/eyzhao/SignIT), samtools93 (v.0.1.17), ClinVar103 (v.20180905), InterVar88, ControlFREEC104 (v.5), CIBERSORT89 (v.1.04), Jaguar105 (v.2.0.3), MiXCR90 (Java, v.2.1.2) and VDJtools106 (v.1.1.9). Additional packages used for meta-analyses include R packages ClusteredMutations (v.1.0.1), vegan (v.2.5.3), ConsensusClusterPlus (v.1.44.0), ComplexHeatmap (v.1.18.1), survival (v.2.42.3), survminer (v.0.4.2) and Python package scikit-learn86 (Python, v.0.20). Additional processing involved in-house scripts that are available upon request.

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Acknowledgements

This work would not be possible without the participation of our patients and families, the POG team, Canada’s Michael Smith Genome Sciences Centre technical platforms, the generous support of the BC Cancer Foundation and their donors and Genome British Columbia (project B20POG). We acknowledge contributions from Genome Canada and Genome BC (projects 202SEQ M.A.M. and S.M.J., 212SEQ M.A.M. and S.M.J., 12002 GBC M.A.M., S.M.J. and J.L.), Canada Foundation for Innovation (projects 20070 M.A.M. and S.M.J., 30981 M.A.M., S.M.J. and J.L., 30198 M.A.M., 33408 M.A.M. and S.M.J.) including the CGEn platform (35444 S.M.J.) and the BC Knowledge Development Fund. We acknowledge the generous support of the Canadian Institutes of Health Research Foundation Grants program (FDN 143288, M.A.M.), University of British Columbia Clinician Investigator Program (M.L.T.) and the Canadian Institutes of Health Research Vanier Canada Graduate Scholarship (E.Y.Z.). The results published here are in part based upon analyses of data generated by the following projects and obtained from dbGaP (http://www.ncbi.nlm.nih.gov/gap): TCGA managed by the National Cancer Institute and National Human Genome Research Institute (http://cancergenome.nih.gov); and the Genotype-Tissue Expression (GTEx) Project, supported by the Common Fund of the Office of the Director of the National Institutes of Health (https://commonfund.nih.gov/GTEx). Data from PCAWG managed by the International Cancer Genome Consortium was retrieved from https://dcc.icgc.org/pcawg.

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M.A.M., J.L., M.R.J., Y.S. and E.P. conceptualized the study. C.R., E.Y.Z., K.L.M., E.C., A.D., M.W., S.K.C., S.Z., S.B., A.M., D.D., R.D.C., D.M., M.C., C.C., D.B., S. Sadeghi, W.Z., T.W., D.C, Y.M. and S.D.B. contributed to software development and implementation. Analyses were performed by E.T., E.P., L.W., E.Y.Z., H.K., K.F., R.B., K.D., L.C., J.K.G., J.A., K.W., C.J.G., M.L.T., M.R.J., Z.B., H.P., T.V. and R.S. Data were collected and experiments were performed by A.J.M., R.A.M., Y.Z., M.R.J., Y.S., M.B., G.A.T., E.M., V.C., K.S., S.Y., D.A.R., D.W. and R.A.H. Provision of patient samples and curation of data was conducted by K.G., A.T., S. Sun, H.L., D.J.R., S.C., D.F.S., J.L., M.K.C.L., J.M.L., B.D., A. Fisic, J.N. and S.M. The original draft was written by E.P., E.T., L.W., E.Y.Z., H.K., K.D., K.W., M.R.J. and Y.S. M.A.M., J.L. and S.J.M.J. reviewed and edited the manuscript. Data visualization was conducted by E.T., H.K., R.B., E.Y.Z., L.C., K.D., E.P., K.W., K.F., Z.B. and J.K.G. Project management and co-ordination was performed by J.N., A. Fok and J.M.K. Funding was acquired by M.A.M., J.L. and S.J.M.J.

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Correspondence to Marco A. Marra.

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Extended data

Extended Data Fig. 1 Cohort demographics.

a, Drug class and frequency of prior treatment across the POG570 cohort. Drugs are grouped into classes based on mechanism (see Supplementary Table 2). b, Drug co-occurrence by patient in SARC, PANC, OV and LUNG tumor types. Darker circles indicate drugs used more frequently, and darker lines show drugs more frequently used in combination.

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Extended Data Fig. 2 Genomic alterations and mutations in advanced and metastatic cancers.

a, Small mutation rates, measured in mutations/Mb, in exonic and intronic regions in comparison to intergenic regions for the largest tumor groups in the cohort. Both boxplots for each tumor type have the same n value. b, Genomic location and frequency of mutation clusters found in at least five patients across the cohort. c, Proportion of mutations that are subclonal for individual samples by tumor type (see Methods). d, Comparison of heterogeneity for subpopulation detection between EXPANDS runs of differing inputs: 100 iterations of a fixed input of 1000 mutations, and a variable input (up to a maximum of 8000, see Methods). e, Correlation of heterogeneity between EXPANDS runs of differing inputs. R and P were calculated using a Spearman correlation (n=501). f, Comparison of heterogeneity between samples with a high (>=20%) and low (<20%) proportion of subclonal mutations. The P value was calculated using a Wilcoxon rank sum test. g, Size (frequency) of subpopulations containing driver genes (see Methods) across the largest tumor types in the POG570 cohort. Higher frequencies refer to more dominant, clonal sub-populations. h, Cox proportional hazards model demonstrating the influence of tumor type, mutation burden and heterogeneity of subpopulations on overall survival from advanced disease diagnosis for all patients (n=570). n values for individual groups are indicated by the N column. Horizontal lines indicate the 95% confidence interval. The central line on the violin plots in c-d represent the median and the tips extend to the minimum and maximum values of the distribution. Boxplots in a, d and f represent the median, upper and lower quartiles of the distribution, and whiskers represent the limits of the distribution (1.5 * interquartile range). Heterogeneity is defined as the Shannon Index for the sample (see Methods). All statistical tests are two-sided. In all instances, n=number of patients in each corresponding group.

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Extended Data Fig. 3 Treatment-associated recurrent mutations.

a, Frequency and occurrence of mutations in breast and lung cancer patients treated with therapies described in Fig. 3c. b, tSNE of site of origin genes (see Methods) demonstrates that gene expression separates samples by tumor type within the POG570 cohort (n=570 patients, all samples).

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Extended Data Fig. 4 De novo mutation signature cosine similarities.

a-c, Cosine similarities for all de novo SBS (c), Indel (d) and DBS (e) signatures detected in the POG570 cohort with COSMIC signatures.

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Extended Data Fig. 5 Additional mutation signatures.

a-b, Additional SBS (a) and indel (b) signature patterns detected in the POG570 cohort that do not strongly match any COSMIC signatures.

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Extended Data Fig. 6 Mutation signature spearman correlation.

a, Pairwise spearman correlations of exposures between all signatures detected in the POG570 cohort (n=482 samples). Sample sizes are limited for each correlation (tile) by the number of samples that have evidence of both signatures, as displayed in Fig. 4a.

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Extended Data Fig. 7 Prior therapy shapes the tumor genomic landscape.

a, DNA repair genes most frequently mutated across the POG570 cohort (more than 1% of samples), and the number of mutated DNA repair genes per sample. b, Genomic instability (HRD) in patients with mutations in all DNA repair pathways. c, Genomic instability and therapy duration in tumors with prior genotoxic treatment (Wilcoxon rank sum test). d, Exposure of signature SBS31 in samples pre-treated with cis- or carboplatin, separated by tumor type. P values are calculated by two-sided Wilcoxon rank sum tests, comparing the treated and non-treated samples, with no correction. e-f, Prior treatment with platinum (for 2 months-1 year) and HRD status in samples with DBS5 (e) and ID6 (f) signature exposure. Samples were defined as HR deficient if they had a somatic or germline variant in an HR gene (Supplementary Table 7) and exhibited an HRD score > 35, corresponding to the 70th percentile of this cohort. g, Signature SBS2 exposure in BRCA tumors by ER (estrogen receptor) status. h, APOBEC3a expression (TPM) and prior tamoxifen therapy duration in BRCA tumors. DNA repair genes and pathways are defined in Supplementary Table 6: SSA, Single strand annealing: NHEJ, Non-homologous end joining; HDR, Homology directed repair; MMEJ, microhomology mediated end joining; NER, nucleotide excision repair; CCR, cell cycle regulation; DSB, DSB chromatin signaling; ISCR, Interstrand crosslink repair; TS, translesion synthesis; SSBR, single strand break repair; MMR, Mismatch repair; BER, Base excision repair. Boxplots in c, e-h represent the median, upper and lower quartiles of the distribution, and whiskers represent the limits of the distribution (1.5 * interquartile range). All P values presented on boxplots are determined by a two-sided Wilcoxon rank sum test, unless otherwise specified.

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Extended Data Fig. 8 Germline events.

a, Type and frequency of germline variants detected in the POG570 cohort (variants found in the 27 genes with germline events shown in Fig. 5a. b, Expression of genes in patients with germline events, compared to the POG570 cohort. A percentile less than 25th (0.25) indicates low expression. c, Exposure of signatures SBS30 and SBS18 in patients with germline, and germline and somatic variants, compared with the POG570 cohort.

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Extended Data Fig. 9 Tumor microenvironment.

a, Relationship between tumor content and total predicted immune cell content. b, Cox proportional hazards model of overall survival from advanced disease diagnosis for all patients with CIBERSORT scores (n=568), taking tumor type, immune cluster, and tumor content into consideration. n values for individual groups are shown by the N column. Horizontal lines indicate the 95% confidence interval. c, Shared TCRβ sequences. Samples are displayed around the perimeter, where width represents the frequency of the shared clone in each sample, and joining lines indicate clones shared between samples. The two samples sharing the dominant CSARESTSDPKNEQFF clonotype are identifiable as the two dominant light blue arcs. d, Summary of tumor types and immune checkpoint inhibitors patients received after their POG biopsy. Drug targets and combinations are listed in Methods. e, Probability of continued therapy for the immunotherapy-treated cohort based on exonic mutation burden (n=76) and combined T cell scores (n=57) (see Methods). The p value for e was determined using a log-rank test, and n for each group is displayed in the table under the curves. Tumor types and biopsy sites as described in Fig. 1. All statistical tests are two-sided.

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Supplementary information

Supplementary Information

Supplementary Methods.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–9.

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Pleasance, E., Titmuss, E., Williamson, L. et al. Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat Cancer 1, 452–468 (2020). https://doi.org/10.1038/s43018-020-0050-6

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