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Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data


Autoři: Judith Abécassis aff001;  Anne-Sophie Hamy aff001;  Cécile Laurent aff001;  Benjamin Sadacca aff001;  Hélène Bonsang-Kitzis aff001;  Fabien Reyal aff001;  Jean-Philippe Vert aff002
Působiště autorů: Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France aff001;  MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France aff002;  Institut Curie, PSL Research University, INSERM, U900, Paris, France aff003;  Institut de Mathématiques de Toulouse, UMR5219 Université de Toulouse, CNRS UPS IMT, Toulouse, France aff004;  Department of Surgery, Institut Curie, Paris, France aff005;  Google Brain, Paris, France aff006
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0224143

Souhrn

Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

Klíčová slova:

Carcinomas – Computational pipelines – Genetic causes of cancer – Head and neck squamous cell carcinoma – Mutation detection – Prognosis – Somatic mutation


Zdroje

1. Nowell PC. The clonal evolution of tumor cell populations. Science. 1976;194(4260):23–28.

2. Gerstung M, Jolly C, Leshchiner I, Dentro SC, Yu K, Tarabichi M, et al. The evolutionary history of 2,658 cancers. bioRxiv. 2017.

3. Dentro SC, Wedge DC, Van Loo P. Principles of Reconstructing the Subclonal Architecture of Cancers. Cold Spring Harbor perspectives in medicine. 2017;7(8):a026625. doi: 10.1101/cshperspect.a026625 28270531

4. Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F. Cancer evolution: Mathematical models and computational inference. Systematic Biology. 2015;64(1):e1–e25. doi: 10.1093/sysbio/syu081 25293804

5. Schwartz R, Schäffer AA. The evolution of tumour phylogenetics: Principles and practice. Nature Reviews Genetics. 2017;18(4):213–229. doi: 10.1038/nrg.2016.170 28190876

6. Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, et al. PyClone: Statistical inference of clonal population structure in cancer. Nature Methods. 2014;11(4):396–398. doi: 10.1038/nmeth.2883 24633410

7. Miller CA, White BS, Dees ND, Griffith M, Welch JS, Griffith OL, et al. SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution. PLoS Computational Biology. 2014;10(8):e1003665. doi: 10.1371/journal.pcbi.1003665 25102416

8. Deshwar AG, Vembu S, Yung CK, Jang GH, Stein L, Morris Q. PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biology. 2015;16(1):1–20. doi: 10.1186/s13059-015-0602-8

9. Andor N, Harness JV, Müller S, Mewes HW, Petritsch C. Expands: Expanding ploidy and allele frequency on nested subpopulations. Bioinformatics. 2014;30(1):50–60. doi: 10.1093/bioinformatics/btt622 24177718

10. Jahn K, Kuipers J, Beerenwinkel N. Tree inference for single-cell data. Genome Biology. 2016;17(1):86. doi: 10.1186/s13059-016-0936-x 27149953

11. Davis A, Navin NE. Computing tumor trees from single cells. Genome Biology. 2016;17(1):1–4. doi: 10.1186/s13059-016-0987-z

12. Ciccolella S, Soto Gomez M, Patterson M, Della Vedova G, Hajirasouliha I, Bonizzoni P. Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses. bioRxiv. 2018; p. 268243.

13. Morris LGT, Riaz N, Desrichard A, Şenbabaoğlu Y, Hakimi AA, Makarov V, et al. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget. 2016;7(9). doi: 10.18632/oncotarget.7067

14. Andor N, Graham TA, Jansen M, Xia LC, Aktipis CA, Petritsch C, et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nature Medicine. 2016;22(1):105–113. doi: 10.1038/nm.3984 26618723

15. McGranahan N, Swanton C. Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell. 2017;168(4):613–628. doi: 10.1016/j.cell.2017.01.018 28187284

16. Dentro SC, Leshchiner I, Haase K, Tarabichi M, Wintersinger J, Deshwar AG, et al. Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types. bioRxiv. 2018; p. 312041.

17. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nature Reviews Clinical Oncology. 2018;15(2):81–94. doi: 10.1038/nrclinonc.2017.166 29115304

18. Nik-Zainal S, Van Loo P, Wedge DC, Alexandrov LB, Greenman CD, Lau KW, et al. The life history of 21 breast cancers. Cell. 2012;149(5):994–1007. doi: 10.1016/j.cell.2012.04.023 22608083

19. Gerlinger M, Horswell S, Larkin J, Rowan AJ, Salm MP, Varela I, et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nature Genetics. 2014;46(3):225–233. doi: 10.1038/ng.2891 24487277

20. Navin NE. Tumor evolution in response to chemotherapy: Phenotype versus genotype. Cell Reports. 2014;6(3):417–419. doi: 10.1016/j.celrep.2014.01.035 24529750

21. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341):90–95. doi: 10.1038/nature09807 21399628

22. Noorbakhsh J, Kim H, Namburi S, Chuang JH. Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power. Scientific Reports. 2018;8(1):1–12. doi: 10.1038/s41598-018-29154-7

23. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: An R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Research. 2016;44(8):e71. doi: 10.1093/nar/gkv1507 26704973

24. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signaling. 2013;6(269). doi: 10.1126/scisignal.2004088

25. Martincorena I, Raine KM, Gerstung M, Dawson KJ, Haase K, Van Loo P, et al. Universal Patterns of Selection in Cancer and Somatic Tissues. Cell. 2017;171(5):1029–1041.e21. doi: 10.1016/j.cell.2017.09.042 29056346

26. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, et al. COSMIC: Somatic cancer genetics at high-resolution. Nucleic Acids Research. 2017;45(D1):D777–D783. doi: 10.1093/nar/gkw1121 27899578

27. Gao B, Huang Q, Baudis M. segment_liftover: a Python tool to convert segments between genome assemblies. F1000Research. 2018;7:319. doi: 10.12688/f1000research.14148.1 29946440

28. Aran D, Sirota M, Butte AJ. Systematic pan-cancer analysis of tumour purity. Nature communications. 2015;6:8971. doi: 10.1038/ncomms9971 26634437

29. Carter SL, Cibulskis K, Helman E, McKenna A, Shen H, Zack T, et al. Absolute quantification of somatic DNA alterations in human cancer. Nature Biotechnology. 2012;30(5):413–421. doi: 10.1038/nbt.2203 22544022

30. Mroz EA, Rocco JW. MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma. Oral Oncology. 2013;49(3):211–215. doi: 10.1016/j.oraloncology.2012.09.007 23079694

31. Davidson-Pilon C, Kalderstam J, Zivich P, Kuhn B, Fiore-Gartland A, Moneda L, et al. CamDavidsonPilon/lifelines: v0.20.0; 2019. Available from: https://doi.org/10.5281/zenodo.2584900.

32. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. 1995;57(1):289–300.

33. Van Belle V, Pelckmans K, Van Huffel S, Suykens JAK. Support vector methods for survival analysis: A comparison between ranking and regression approaches. Artificial Intelligence in Medicine. 2011;53(2):107–118. doi: 10.1016/j.artmed.2011.06.006 21821401

34. Pölsterl S, Gupta P, Wang L, Conjeti S, Katouzian A, Navab N. Heterogeneous ensembles for predicting survival of metastatic, castrate-resistant prostate cancer patients. F1000Research. 2017;5:2676. doi: 10.12688/f1000research.8231.2

35. Schröder MS, Culhane AC, Quackenbush J, Haibe-Kains B. survcomp: An R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics. 2011;27(22):3206–3208. doi: 10.1093/bioinformatics/btr511 21903630

36. Pencina MJ, D’Agostino RB. OverallC as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Statistics in Medicine. 2004;23(13):2109–2123. doi: 10.1002/sim.1802 15211606

37. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15(12). doi: 10.1186/s13059-014-0550-8

38. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39(4):782–795. doi: 10.1016/j.immuni.2013.10.003 24138885

39. Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Research. 2018;46(D1):D754–D761. doi: 10.1093/nar/gkx1098 29155950

40. Salcedo A, Tarabichi M, Espiritu SMG, Deshwar AG, Buchanan A, Lalansingh CM, et al. Creating Standards for Evaluating Tumour Subclonal Reconstruction. 2018.

41. Rosenberg A, Hirschberg J. V-measure: A conditional entropy-based external cluster evaluation measure. Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language (EMNLP-CoNLL’07). 2007;1(June):410–420.

42. Malikic S, Jahn K, Kuipers J, Sahinalp SC, Beerenwinkel N. Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. Nature Communications. 2019;10(1):1–12. doi: 10.1038/s41467-019-10737-5

43. Gawad C, Koh W, Quake SR. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(50):17947–17952. doi: 10.1073/pnas.1420822111 25425670

44. Wang Y, Waters J, Leung ML, Unruh A, Roh W, Shi X, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature. 2014;512(7513):155–160. doi: 10.1038/nature13600 25079324

45. Leung ML, Davis A, Gao R, Casasent A, Wang Y, Sei E, et al. Single-cell DNA sequencing reveals a latedissemination model in metastatic colorectal cancer. Genome Research. 2017;27(8):1287–1299. doi: 10.1101/gr.209973.116 28546418

46. Li H, Durbin R. Fast and accurate short read alignment with Burrows—Wheeler transform. 2009;25(14):1754–1760.

47. Institute B. Picard Tools;. http://broadinstitute.github.io/picard/.

48. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. 2010;26(6):841–842.

49. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment / Map format and SAMtools. 2009;25(16):2078–2079.

50. Mckenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. 2010; p. 1297–1303.

51. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotechnology. 2013;31(3):213–219. doi: 10.1038/nbt.2514 23396013

52. Shen R, Seshan VE. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. 2016;(8):1–9.

53. Pereira B, Chin SF, Rueda OM, Vollan HKM, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nature Communications. 2016;7(May):11479. doi: 10.1038/ncomms11479 27161491

54. Karn T, Jiang T, Hatzis C, Sänger N, El-Balat A, Rody A, et al. Association Between Genomic Metrics and Immune Infiltration in Triple-Negative Breast Cancer. JAMA Oncology. 2017;3(12):1707–1711. doi: 10.1001/jamaoncol.2017.2140 28750120

55. Venkatesan S, Swanton C. Tumor Evolutionary Principles: How Intratumor Heterogeneity Influences Cancer Treatment and Outcome. American Society of Clinical Oncology Educational Book. 2016;36:e141–e149. doi: 10.1200/EDBK_158930

56. Eaton J, Wang J, Schwartz R. Deconvolution and phylogeny inference of structural variations in tumor genomic samples. Bioinformatics. 2018;34(13):i357–i365. doi: 10.1093/bioinformatics/bty270 29950001

57. Bhandari V, Liu LY, Salcedo A, Espiritu SMG, Morris QD, Boutros PC. The Inter and Intra-Tumoural Heterogeneity of Subclonal Reconstruction. bioRxiv. 2018.

58. Turajlic S, Swanton C. TRACERx Renal: tracking renal cancer evolution through therapy. Nature Reviews Urology. 2017;14(10):575–576. doi: 10.1038/nrurol.2017.112

59. Kim C, Gao R, Sei E, Brandt R, Hartman J, Hatschek T, et al. Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing. Cell. 2018;173(4):879–893.e13. doi: 10.1016/j.cell.2018.03.041 29681456

60. Shi W, Ng CKY, Lim RS, Jiang T, Kumar S, Li X, et al. Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity; 2018. Available from: https://doi.org/10.1016/j.celrep.2018.10.046.

61. Zhou H, Neelakantan D, Ford HL. Clonal cooperativity in heterogenous cancers. Seminars in Cell & Developmental Biology. 2017;64:79–89. doi: 10.1016/j.semcdb.2016.08.028

62. McGranahan N, Favero F, De Bruin EC, Birkbak NJ, Szallasi Z, Swanton C. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Science Translational Medicine. 2015;7(283):283ra54–283ra54. doi: 10.1126/scitranslmed.aaa1408 25877892

63. Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E, et al. Clonal competition with alternating dominance in multiple myeloma. Blood. 2012;120(5):1067–1076. doi: 10.1182/blood-2012-01-405985 22498740

64. Scott J, Marusyk A. Somatic clonal evolution: A selection-centric perspective. Biochimica et Biophysica Acta—Reviews on Cancer. 2017;1867(2):139–150. doi: 10.1016/j.bbcan.2017.01.006 28161395

65. Cross WCH, Graham TA, Wright NA. New paradigms in clonal evolution: punctuated equilibrium in cancer. Journal of Pathology. 2016;240(2):126–136. doi: 10.1002/path.4757 27282810

66. Sottoriva A, Barnes CP, Graham TA. Catch my drift? Making sense of genomic intra-tumour heterogeneity. Biochimica et Biophysica Acta (BBA)—Reviews on Cancer. 2017;1867(2):95–100. doi: 10.1016/j.bbcan.2016.12.003

67. Maley CC, Aktipis A, Graham TA, Sottoriva A, Boddy AM, Janiszewska M, et al. Classifying the evolutionary and ecological features of neoplasms. Nature Reviews Cancer. 2017;17(10):605–619. doi: 10.1038/nrc.2017.69 28912577

68. Safonov A, Jiang T, Bianchini G, Győrffy B, Karn T, Hatzis C, et al. Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer. Cancer Research. 2017;77(12):3317–3324. doi: 10.1158/0008-5472.CAN-16-3478 28428277

69. Caravagna G, Heide T, Williams M, Zapata L, Nichol D, Chkhaidze K, et al. Model-based tumor subclonal reconstruction. 2019; p. 1–31.


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