Integrative clinical genomics of metastatic cancer (original) (raw)

References

  1. Mehlen, P. & Puisieux, A. Metastasis: a question of life or death. Nat. Rev. Cancer 6, 449–458 (2006)
    Article CAS PubMed Google Scholar
  2. Steeg, P. S. Targeting metastasis. Nat. Rev. Cancer 16, 201–218 (2016)
    Article CAS PubMed PubMed Central Google Scholar
  3. Friedman, A. A., Letai, A., Fisher, D. E. & Flaherty, K. T. Precision medicine for cancer with next-generation functional diagnostics. Nat. Rev. Cancer 15, 747–756 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  4. Mauer, C. B., Pirzadeh-Miller, S. M., Robinson, L. D. & Euhus, D. M. The integration of next-generation sequencing panels in the clinical cancer genetics practice: an institutional experience. Genet. Med. 16, 407–412 (2014)
    Article PubMed Google Scholar
  5. Shen, T., Pajaro-Van de Stadt, S. H., Yeat, N. C. & Lin, J. C. Clinical applications of next generation sequencing in cancer: from panels, to exomes, to genomes. Front. Genet. 6, 215 (2015)
    Google Scholar
  6. Jones, S. et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci. Transl. Med. 7, 283ra53 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  7. Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl. Med. 3, 111ra121 (2011)
    Article CAS PubMed PubMed Central Google Scholar
  8. Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016)
    Article CAS PubMed PubMed Central Google Scholar
  9. Turajlic, S. & Swanton, C. Metastasis as an evolutionary process. Science 352, 169–175 (2016)
    Article ADS CAS PubMed Google Scholar
  10. Robinson, D. et al. Integrative clinical genomics of advanced prostate cancer. Cell 161, 1215–1228 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  11. The Cancer Genome Atlas Research Network, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013)
  12. Gagan, J. & Van Allen, E. M. Next-generation sequencing to guide cancer therapy. Genome Med. 7, 80 (2015)
    Article PubMed PubMed Central Google Scholar
  13. Mardis, E. R. The translation of cancer genomics: time for a revolution in clinical cancer care. Genome Med. 6, 22 (2014)
    Article PubMed PubMed Central Google Scholar
  14. Parsons, D. W. et al. Diagnostic yield of clinical tumor and germline whole-exome sequencing for children with solid tumors. J. Am. Med. Assoc. Oncol. 2, 616–624 (2016)
    Google Scholar
  15. Mody, R. J. et al. Integrative clinical sequencing in the management of refractory or relapsed cancer in youth. J. Am. Med. Assoc. 314, 913–925 (2015)
    Article CAS Google Scholar
  16. Wagle, N. et al. High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov. 2, 82–93 (2012)
    Article CAS PubMed Google Scholar
  17. Pritchard, C. C. et al. Inherited DNA-repair gene mutations in men with metastatic prostate cancer. N. Engl. J. Med. 375, 443–453 (2016)
    Article CAS PubMed PubMed Central Google Scholar
  18. Palanisamy, N. et al. Rearrangements of the RAF kinase pathway in prostate cancer, gastric cancer and melanoma. Nat. Med. 16, 793–798 (2010)
    Article CAS PubMed PubMed Central Google Scholar
  19. Robinson, D. R. et al. Functionally recurrent rearrangements of the MAST kinase and Notch gene families in breast cancer. Nat. Med. 17, 1646–1651 (2011)
    Article CAS PubMed PubMed Central Google Scholar
  20. Stransky, N., Cerami, E., Schalm, S., Kim, J. L. & Lengauer, C. The landscape of kinase fusions in cancer. Nat. Commun. 5, 4846 (2014)
    Article ADS CAS PubMed Google Scholar
  21. Agaram, N. P., Zhang, L., Sung, Y. S., Singer, S. & Antonescu, C. R. Extraskeletal myxoid chondrosarcoma with non-EWSR1_–_NR4A3 variant fusions correlate with rhabdoid phenotype and high-grade morphology. Hum. Pathol. 45, 1084–1091 (2014)
    Article CAS PubMed PubMed Central Google Scholar
  22. Weinreb, I. et al. Novel PRKD gene rearrangements and variant fusions in cribriform adenocarcinoma of salivary gland origin. Genes Chromosom. Cancer 53, 845–856 (2014)
    Article CAS PubMed Google Scholar
  23. Amir, A. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013)
    Article CAS PubMed Central Google Scholar
  24. Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013)
    Article CAS Google Scholar
  25. Liberzon, A. et al. The Molecular Signatures Database hallmark gene set collection. Cell Syst. 1, 417–425 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  26. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011)
    Article CAS PubMed PubMed Central Google Scholar
  27. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013)
    Article PubMed PubMed Central Google Scholar
  28. Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–i245 (2010)
    Article CAS PubMed PubMed Central Google Scholar
  29. López-Novoa, J. M. & Nieto, M. A. Inflammation and EMT: an alliance towards organ fibrosis and cancer progression. EMBO Mol. Med. 1, 303–314 (2009)
    Article CAS PubMed PubMed Central Google Scholar
  30. Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013)
    Article ADS CAS PubMed Google Scholar
  31. Attig, S. et al. Simultaneous infiltration of polyfunctional effector and suppressor T cells into renal cell carcinomas. Cancer Res. 69, 8412–8419 (2009)
    Article CAS PubMed Google Scholar
  32. Nakano, O. et al. Proliferative activity of intratumoral CD8+ T-lymphocytes as a prognostic factor in human renal cell carcinoma: clinicopathologic demonstration of antitumor immunity. Cancer Res. 61, 5132–5136 (2001)
    CAS PubMed Google Scholar
  33. Ruffini, E. et al. Clinical significance of tumor-infiltrating lymphocytes in lung neoplasms. Ann. Thorac. Surg. 87, 365–371 (2009)
    Article PubMed Google Scholar
  34. Boon, T., Coulie, P. G., Van den Eynde, B. J. & van der Bruggen, P. Human T cell responses against melanoma. Annu. Rev. Immunol. 24, 175–208 (2006)
    Article CAS PubMed Google Scholar
  35. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  36. Sica, A., Schioppa, T., Mantovani, A. & Allavena, P. Tumour-associated macrophages are a distinct M2 polarised population promoting tumour progression: potential targets of anti-cancer therapy. Eur. J. Cancer 42, 717–727 (2006)
    Article CAS PubMed Google Scholar
  37. Patel, S. P. & Kurzrock, R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol. Cancer Ther. 14, 847–856 (2015)
    Article CAS PubMed Google Scholar
  38. van Houdt, I. S. et al. Favorable outcome in clinically stage II melanoma patients is associated with the presence of activated tumor infiltrating T-lymphocytes and preserved MHC class I antigen expression. Int. J. Cancer 123, 609–615 (2008)
    Article CAS PubMed Google Scholar
  39. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006)
    Article ADS CAS PubMed Google Scholar
  40. Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015)
    Article ADS CAS PubMed PubMed Central Google Scholar
  41. Ulloa-Montoya, F. et al. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J. Clin. Oncol. 31, 2388–2395 (2013)
    Article CAS PubMed Google Scholar
  42. Mateo, J. et al. DNA-repair defects and olaparib in metastatic prostate cancer. N. Engl. J. Med. 373, 1697–1708 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  43. Helleday, T., Petermann, E., Lundin, C., Hodgson, B. & Sharma, R. A. DNA repair pathways as targets for cancer therapy. Nat. Rev. Cancer 8, 193–204 (2008)
    Article CAS PubMed Google Scholar
  44. Cieślik, M. et al. The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing. Genome Res. 25, 1372–1381 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  45. Leiserson, M. D. et al. MAGI: visualization and collaborative annotation of genomic aberrations. Nat. Methods 12, 483–484 (2015)
    Article CAS PubMed Google Scholar

Download references

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
    You can also search for this author inPubMed Google Scholar
  2. Yi-Mi Wu
    You can also search for this author inPubMed Google Scholar
  3. Robert J. Lonigro
    You can also search for this author inPubMed Google Scholar
  4. Pankaj Vats
    You can also search for this author inPubMed Google Scholar
  5. Erin Cobain
    You can also search for this author inPubMed Google Scholar
  6. Jessica Everett
    You can also search for this author inPubMed Google Scholar
  7. Xuhong Cao
    You can also search for this author inPubMed Google Scholar
  8. Erica Rabban
    You can also search for this author inPubMed Google Scholar
  9. Chandan Kumar-Sinha
    You can also search for this author inPubMed Google Scholar
  10. Victoria Raymond
    You can also search for this author inPubMed Google Scholar
  11. Scott Schuetze
    You can also search for this author inPubMed Google Scholar
  12. Ajjai Alva
    You can also search for this author inPubMed Google Scholar
  13. Javed Siddiqui
    You can also search for this author inPubMed Google Scholar
  14. Rashmi Chugh
    You can also search for this author inPubMed Google Scholar
  15. Francis Worden
    You can also search for this author inPubMed Google Scholar
  16. Mark M. Zalupski
    You can also search for this author inPubMed Google Scholar
  17. Jeffrey Innis
    You can also search for this author inPubMed Google Scholar
  18. Rajen J. Mody
    You can also search for this author inPubMed Google Scholar
  19. Scott A. Tomlins
    You can also search for this author inPubMed Google Scholar
  20. David Lucas
    You can also search for this author inPubMed Google Scholar
  21. Laurence H. Baker
    You can also search for this author inPubMed Google Scholar
  22. Nithya Ramnath
    You can also search for this author inPubMed Google Scholar
  23. Ann F. Schott
    You can also search for this author inPubMed Google Scholar
  24. Daniel F. Hayes
    You can also search for this author inPubMed Google Scholar
  25. Joseph Vijai
    You can also search for this author inPubMed Google Scholar
  26. Kenneth Offit
    You can also search for this author inPubMed Google Scholar
  27. Elena M. Stoffel
    You can also search for this author inPubMed Google Scholar
  28. J. Scott Roberts
    You can also search for this author inPubMed Google Scholar
  29. David C. Smith
    You can also search for this author inPubMed Google Scholar
  30. Lakshmi P. Kunju
    You can also search for this author inPubMed Google Scholar
  31. Moshe Talpaz
    You can also search for this author inPubMed Google Scholar
  32. Marcin Cieślik
    You can also search for this author inPubMed Google Scholar
  33. Arul M. Chinnaiyan
    You can also search for this author inPubMed Google Scholar

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 toArul M. Chinnaiyan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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 _χ_2 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 (_χ_2 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).

Reporting summary

Supplementary Table 3

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

PowerPoint slides

Rights and permissions

About this article

Cite this article

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

Download citation