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Integrative clinical genomics of metastatic cancer

Nature volume 548, pages 297–303 (2017)Cite this article

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Abstract

Metastasis is the primary cause of cancer-related deaths. Although The Cancer Genome Atlas has sequenced primary tumour types obtained from surgical resections, much less comprehensive molecular analysis is available from clinically acquired metastatic cancers. Here we perform whole-exome and -transcriptome sequencing of 500 adult patients with metastatic solid tumours of diverse lineage and biopsy site. The most prevalent genes somatically altered in metastatic cancer included TP53, CDKN2A, PTEN, PIK3CA, and RB1. Putative pathogenic germline variants were present in 12.2% of cases of which 75% were related to defects in DNA repair. RNA sequencing complemented DNA sequencing to identify gene fusions, pathway activation, and immune profiling. Our results show that integrative sequence analysis provides a clinically relevant, multi-dimensional view of the complex molecular landscape and microenvironment of metastatic cancers.

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Figure 1: Landscape of molecular alterations in metastatic cancer.
Figure 2: Putative pathogenic germline variants in metastatic cancers.
Figure 3: Diverse classes of gene fusions identified in metastatic cancers.
Figure 4: Diverse transcriptional profiles of metastatic cancers.
Figure 5: The immune microenvironment of metastatic cancers.

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Acknowledgements

This work was supported by a National Institutes of Health (NIH) Clinical Sequencing Exploratory Research Award NIH 1UM1HG006508. Other sources of support included the Prostate Cancer Foundation, Stand Up 2 Cancer (SU2C)-Prostate Cancer Foundation Prostate Dream Team Grant SU2C-AACR-DT0712, Early Detection Research Network grant U01 CA214170, and Prostate SPORE grant P50 CA186786. A.M.C. is a Howard Hughes Medical Institute Investigator, A. Alfred Taubman Scholar, and American Cancer Society Professor. M.C. is supported by a PCF Young Investigator Award. We acknowledge Y. Ning, R. Wang, X. Dang, M. Davis, L. Hodges, J. Griggs, J. Athanikar, C. Brennan, C. Betts, J. Chen, S. Kalyana-Sundaram, K. Giles, and R. Mehra for their contributions to this study. Over 100 physicians referred patients to this study and we acknowledge the following: K. Cooney, M. Hussain, S. Urba, N. Henry, V. Sahai, D. Simeone, C. Lao, J. Smerage, M. Caram, M. Burness, G. Kalemkerian, C. Van Poznak, M. Wicha, R. Buckanovich, J. Bufill, P. Grivas, P. Hu, A. Morikawa, P. Palmbos, B. Redman, F. Feng, G. Hammer, S. Merajver, and A. Pearson. We thank S. Roychowdhury and K. Pienta for help in protocol development for the MI-ONCOSEQ program. Most importantly, we recognize the generosity and kindness of the cancer patients and their families for participating in this study.

Author information

Author notes

  1. Dan R. Robinson, Yi-Mi Wu, Robert J. Lonigro and Marcin Cieślik: These authors contributed equally to this work.

Authors and Affiliations

  1. Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Dan R. Robinson, Yi-Mi Wu, Robert J. Lonigro, Pankaj Vats, Xuhong Cao, Erica Rabban, Chandan Kumar-Sinha, Javed Siddiqui, Scott A. Tomlins, Lakshmi P. Kunju, Marcin Cieślik & Arul M. Chinnaiyan

  2. Department of Pathology, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Dan R. Robinson, Yi-Mi Wu, Chandan Kumar-Sinha, Javed Siddiqui, Scott A. Tomlins, David Lucas, Lakshmi P. Kunju, Marcin Cieślik & Arul M. Chinnaiyan

  3. Department of Internal Medicine, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Erin Cobain, Jessica Everett, Victoria Raymond, Scott Schuetze, Ajjai Alva, Rashmi Chugh, Francis Worden, Mark M. Zalupski, Laurence H. Baker, Nithya Ramnath, Ann F. Schott, Daniel F. Hayes, Elena M. Stoffel & David C. Smith

  4. Department of Pediatrics, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Jeffrey Innis & Rajen J. Mody

  5. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA

    Joseph Vijai & Kenneth Offit

  6. Department of Health Behavior & Health Education, School of Public Health, University of Michigan, Ann Arbor, 48109, Michigan, USA

    J. Scott Roberts

  7. Comprehensive Cancer Center, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Moshe Talpaz & Arul M. Chinnaiyan

  8. Department of Urology, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Arul M. Chinnaiyan

  9. Howard Hughes Medical Institute, Chevy Chase, 20815, Maryland, USA

    Arul M. Chinnaiyan

Authors
  1. Dan R. Robinson
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  2. Yi-Mi Wu
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Contributions

D.R.R., Y.-M.W., and X.C. coordinated clinical sequencing. R.J.L., M.C., and P.V. developed the bioinformatics analysis. J.S. coordinated sample procurement, L.P.K., D.L., and S.A.T. led the histopathology analysis. D.C.S., S.S., M.M.Z., A.A., R.C., F.W., L.H.B., R.J.M., N.R., A.F.S., and D.F.H. coordinated patient recruitment. E.R. was the lead study coordinator. J.E., V.R., E.M.S., and J.I. provided genetic counselling and assessment of PPGMs, and J.V. and K.O. analysed relative risk assessment. J.S.R. coordinated the bioethics component. M.T. and A.M.C. coordinated IRB protocol development. D.R.R., Y.-M.W. and C.K.-S. prepared PMTBs. E.C., M.T., D.F.H., D.R.R., and Y.-M.W. implemented the clinical tiering of molecular aberrations. Y.-M.W., D.R.R., M.C., and A.M.C. developed the figures and tables. A.M.C., M.C., D.R.R., and Y.-M.W. wrote the manuscript with input from all authors. A.M.C. and M.T. designed and supervised the study.

Corresponding author

Correspondence to Arul M. Chinnaiyan.

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Competing interests

The authors declare no competing financial interests.

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Reviewer Information Nature thanks S. Bova, P. Robbins and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Demographics of the MET500 cohort and summary of common genetic aberrations.

a, Gender distribution of the MET500 cohort. b, Age distribution of the MET500 cohort. c, Bubble plot of clinically actionable genetic aberrations. Genes have been divided by putative gain-of-function (oncogene, red) or loss-of-function (tumour suppressor, blue) status. Common aberrations are defined as those observed in five or more MET500 analysis cohorts (Fig. 1c), restricted aberrations are found in fewer than five analysis cohorts. Bubble area is proportional to the observed frequency of the aberration across the MET500 cohort. d, Comparison of genetic aberration frequencies (SNVs, indels, amplifications, predicted homozygous deletions) between primary (TCGA) and metastatic (MET500) tumours for select tumour suppressors (left) and oncogenes (right). TCGA data for the primary cancer cohorts were obtained from the cBio portal. Nominal statistical significance is based on Fisher’s exact test. Statistically significant differences in frequencies after correction for multiple dependent tests using the Benjamini–Yekutieli procedure are indicated as circles, insignificant differences are shown as triangles.

Extended Data Figure 2 Analysis of pan-cancer metastatic transcriptomes.

a, Structural rearrangements in metastatic genomes. Distribution of the number of fusions per case is plotted across the MET500 by analysis cohort (see Fig. 1c for cancer abbreviations). The y axis is truncated at 100 fusions. Error bars show the range from Q1 − 1.5 × IQR to Q3 + 1.5 × IQR, where Q1 is the first quartile, Q3 is the third quartile, and IQR is the interquartile range. b, Summary Circos diagrams of predicted inactivating fusions for select tumour-suppressor genes across the cohort. Arc end positions indicate the chimaeric junctions; colours indicate type of rearrangement. Black, tandem duplication; blue, translocation; red, inversion; grey, signifies that multiple close junctions were detected. c, The t-SNE plot for the TCGA pan-cancer meta-cohort (a random selection of cases from each primary tumour type) on the basis of the expression of tumour-type-specific marker genes (same genes as in Fig. 4a). d, The t-SNE plot for the MET500 samples coloured by biopsy site (same samples as in Fig. 4a, there coloured by cancer type). e, Average percentile expression of tissue-specific genes in normal tissues, primary cancers, and metastases. Error bars, s.d. Significance tests were done for all normal–primary and primary–metastasis pairs of samples; all comparisons were significant (P < 0.01) according to a two-tailed t-test, with the exception of those indicated with NS. ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, oesophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukaemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectal adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumours; THYM, thymoma; THCA, thyroid carcinoma; UCS, uterine carcinosarcoma; UCEC, uterine corpus endometrial carcinoma; UVM, uveal melanoma.

Extended Data Figure 3 Global activity of oncogenic signatures.

a, Activity of signatures is calculated relative to a normal tissue baseline: that is, activity scores are compared with a compendium of 36 normal tissues. Therefore, this plot represents a comparison of pathway activities between metastatic tissues and normal tissues. Increased activity (positive difference, red) or decreased activity (negative difference, blue) indicates that the signature genes are on average more (or less) expressed in a metastatic tumour sample relative to the baseline (in average percentile point difference labelled ‘% diff activity’). Samples (columns) are ordered from left to right by decreasing average activity difference (column averages: that is, the aggregate score in b). b, Box plots summarizing the aggregate scores (column averages of ‘% diff activity’) in a. Analysis cohorts are ordered left to right by median aggregate scores. Error bars show the range from Q1 − 1.5 × IQR to Q3 + 1.5 × IQR.

Extended Data Figure 4 Relative activity of oncogenic signatures.

Hierarchically clustered heatmap of activity scores for the most variable oncogenic signatures. In contrast to Supplementary Figure 7, here activity scores are computed intrinsically: that is, relative to other samples in the MET500 (like ssGSEA or GSVA), which represents a relative comparison between different patients/samples. Red indicates that a signature is more active (in percentile points) for a given sample relative to the median activity; blue indicates that a signature is less active for a given sample.

Extended Data Figure 5 Activity of cancer hallmarks in metastatic cancers.

Clustered heatmaps of activity scores for the 50 MSigDB cancer hallmarks are shown. a, Gene expression patterns of cancer hallmark pathways. Average increase (red) or decrease (blue) in the relative expression levels (percentiles) of transcriptional signatures associated with the hallmarks of cancers. b, Activity scores are calculated relative to a compendium of 36 normal tissues, which represent a comparison of hallmark activities between metastatic tissues and normal tissues (analogous to Supplementary Figure 7 but for a different gene set). Red indicates that a signature is more active (in percentile points) for a given sample relative to the median activity; blue indicates that a signature is less active for a given sample.

Extended Data Figure 6 Discovery of oncogenic meta-signatures.

Relative activity scores were computed for all experimental signatures in the MSigDB database across the MET500 cohort. The signatures were clustered into 25 meta-signatures on the basis of their activity profiles across the MET500. For each of the 25 meta-signature clusters, the 5 most variable signatures were selected. Red indicates that a signature is more active (in percentile points) for a given sample relative to the median activity across the MET500; blue indicates that a signature is less active for a given sample.

Extended Data Figure 7 Activity of the oncogenic meta-signatures.

a, Relative activity of EMT and proliferation signatures across the TCGA analysis meta-cohort. b, Relative activity of the 25 meta-signatures across MET500 samples from different biopsy sites. Red indicates that a signature is more active for a given biopsy site relative to the median activity; blue indicates that a signature is less active for a given biopsy site. c, Relative activity of the 25 meta-signatures across samples from different normal tissues. Red indicates that a signature is more active (in percentile points) for a given tissue relative to the median activity; blue indicates that a signature is less active for a given tissue. d, Correlations between the 25 meta-signatures. Correlation heatmap and hierarchical clustering showed similarities (red) and dissimilarities (blue) in the transcriptional activity of computationally derived aggregate sets of MSigDB signatures: that is, ‘meta-signatures’ across samples from the MET500 stratified by the most common primary tumour type (left) and biopsy site (right).

Extended Data Figure 8 Prediction of immune infiltration in cancer tissues.

a, Correlation between the MImmScore, a measurement of absolute immune infiltration in a tumour sample, with tumour content estimated from exome DNA-seq using CNVs and SNVs. b, Correlation between MImmScores and an analogous score for tumour-stromal infiltration. c, Correlation between a T-cell expression score summarizing the expression levels (RNA-seq-based) of marker genes CD3D, CD3E, CD3G, CD6, SH2D1A, TRAT1 and the estimated number of T cells based on T-cell repertoire profiling (DNA-based). d, Number of T cells based on T-cell repertoire profiling for index cases stratified into MImmScore low (<0) or MImmScore high (>0). Error bars show the range from Q1 − 1.5 × IQR to Q3 + 1.5 × IQR. Significance levels of Spearman’s rank correlation coefficient test: *P = 0.05–0.001, **P = 0.001–10−6, ***P < 10−6.

Extended Data Figure 9 Differential immune infiltration in various cancer types.

a, Distribution of MImmScores, a measurement of the magnitude of immune infiltration in a tumour sample, for MET500 samples/patients grouped by tumour biopsy site. b, Distribution of MImmScores across the TCGA meta-cohort, grouped by primary cancer designation. Haematological malignancies (DLBC, LAML) are included as positive control. Error bars show the range from Q1 − 1.5 × IQR to Q3 + 1.5 × IQR. c, Percentage of patients in each of the MET500 analysis cohorts with a high MImmScore, defined here as >80th percentile across the whole MET500. The total number of cases with high MImmScore is indicated above each bar. d, Same as c but for the TCGA meta-cohort. e, Correlation between the total number of T cells (templates) based on T-cell repertoire (DNA-seq) of the TCR CDR3 sequence and the number of expanded clones (an expanded T-cell clone is defined as having more than 30 cells with the same CDR3 sequence). f, Ratio of expression levels for markers of CD8+ T cells (CD8A, CD8B) and regulatory T cells (FOXP3) as a function of the total number of T cells. Significance levels of Spearman’s rank correlation coefficient: *P = 0.05–0.001, **P = 0.001–10−6, ***P < 10−6.

Extended Data Figure 10 Genomic correlates of immune infiltration.

a, Association between the MImmScore and mutation status (hypermutated samples have been defined here as having >250 non-synonymous mutations). Statistical significance of this association was determined using logistic regression. b, c, A χ2 test for independence is used to determine whether the clusterings of samples based on T-cell and APC markers are independent. Enrichment or depletion is calculated as the Pearson’s residual. Red indicates (positive enrichment) that the clusters overlap significantly. Blue indicates (depletion) that clusters tend to be mutually exclusive. Clustered heatmap of enrichment levels (χ2 table cell residuals) is shown in b. Enrichment levels for clusters for the active Tcell-1 and Tcell-4 clusters and all APC clusters (APC-1, -4 active) are shown in c.

Supplementary information

Supplementary Tables

This file contains Supplementary Table 1 (Demographics and clinical details), Supplementary Table 2 (Sequencing statistics), Supplementary Table 4 (Pathogenic germline variants in the MET500 cohort), Supplementary Table 5 (Germline mutations in metastatic cancer), Supplementary Table 6 (Pathogenic fusions in the MET500 cohort) and Supplementary Table 7 (Immune cell infiltration analyses).

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Supplementary Table 3

This file contains Supplementary Table 3 (Recurrent molecular aberrations in the MET500 cohort).

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Robinson, D., Wu, YM., Lonigro, R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017). https://doi.org/10.1038/nature23306

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