SpliceHetero: An information theoretic approach for measuring spliceomic intratumor heterogeneity from bulk tumor RNA-seq
Autoři:
Minsu Kim aff002; Sangseon Lee aff002; Sangsoo Lim aff001; Sun Kim aff001
Působiště autorů:
Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Korea
aff001; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea
aff002; Bioinformatics Institute, Seoul National University, Seoul, 08826, Korea
aff003
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0223520
Souhrn
Motivation
Intratumor heterogeneity (ITH) represents the diversity of cell populations that make up cancer tissue. The level of ITH in a tumor is usually measured by a genomic variation profile, such as copy number variation and somatic mutation. However, a recent study has identified ITH at the transcriptome level and suggested that ITH at gene expression levels is useful for predicting prognosis. Measuring ITH levels at the spliceome level is a natural extension. There are serious technical challenges in measuring spliceomic ITH (sITH) from bulk tumor RNA sequencing (RNA-seq) due to the complex splicing patterns.
Results
We propose an information-theoretic method to measure the sITH of bulk tumors to overcome the above challenges. This method has been extensively tested in experiments using synthetic data, xenograft tumor data, and TCGA pan-cancer data. As a result, we showed that sITH is closely related to cancer progression and clonal heterogeneity, along with clinically significant features such as cancer stage, survival outcome and PAM50 subtype. As far as we know, it is the first study to define ITH at the spliceome level. This method can greatly improve the understanding of cancer spliceome and has great potential as a diagnostic and prognostic tool.
Klíčová slova:
Evolutionary genetics – Transcriptome analysis – Cancer genomics – Introns – RNA sequencing – Breast cancer – Somatic mutation – RNA alignment
Zdroje
1. Boland CR, Goel A. Somatic evolution of cancer cells. In: Seminars in cancer biology. vol. 15. Elsevier; 2005. p. 436–450.
2. Nowell PC. The clonal evolution of tumor cell populations. Science. 1976;194(4260):23–28.
3. Marusyk A, Polyak K. Tumor heterogeneity: causes and consequences. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer. 2010;1805(1):105–117. doi: 10.1016/j.bbcan.2009.11.002
4. Greaves M, Maley CC. Clonal evolution in cancer. Nature. 2012;481(7381):306. doi: 10.1038/nature10762 22258609
5. Sun Xx, Yu Q. Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment. Acta Pharmacologica Sinica. 2015;36(10):1219. doi: 10.1038/aps.2015.92 26388155
6. 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
7. Venet D, Pecasse F, Maenhaut C, Bersini H. Separation of samples into their constituents using gene expression data. Bioinformatics. 2001;17(suppl_1):S279–S287. doi: 10.1093/bioinformatics/17.suppl_1.s279 11473019
8. Park SY, Gönen M, Kim HJ, Michor F, Polyak K. Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype. The Journal of clinical investigation. 2010;120(2):636–644. doi: 10.1172/JCI40724 20101094
9. Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, et al. Increased methylation variation in epigenetic domains across cancer types. Nature genetics. 2011;43(8):768. doi: 10.1038/ng.865 21706001
10. Morris LG, 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):10051. doi: 10.18632/oncotarget.7067 26840267
11. Yang F, Wang Y, Li Q, Cao L, Sun Z, Jin J, et al. Intratumor heterogeneity predicts metastasis of triple-negative breast cancer. Carcinogenesis. 2017;38(9):900–909. doi: 10.1093/carcin/bgx071 28911002
12. Oh BY, Shin HT, Yun JW, Kim KT, Kim J, Bae JS, et al. Intratumor heterogeneity inferred from targeted deep sequencing as a prognostic indicator. Scientific reports. 2019;9(1):4542. doi: 10.1038/s41598-019-41098-0 30872730
13. 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. doi: 10.1038/nbt.2203 22544022
14. 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. doi: 10.1038/nmeth.2883 24633410
15. Park Y, Lim S, Nam JW, Kim S. Measuring intratumor heterogeneity by network entropy using RNA-seq data. Scientific reports. 2016;6:37767. doi: 10.1038/srep37767 27883053
16. Mazor T, Pankov A, Song JS, Costello JF. Intratumoral heterogeneity of the epigenome. Cancer cell. 2016;29(4):440–451. doi: 10.1016/j.ccell.2016.03.009 27070699
17. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic acids research. 2016;45(D1):D353–D361. doi: 10.1093/nar/gkw1092 27899662
18. David CJ, Manley JL. Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged. Genes & development. 2010;24(21):2343–2364. doi: 10.1101/gad.1973010
19. Surget S, Khoury MP, Bourdon JC. Uncovering the role of p53 splice variants in human malignancy: a clinical perspective. OncoTargets and therapy. 2014;7:57.
20. Paronetto MP, Passacantilli I, Sette C. Alternative splicing and cell survival: from tissue homeostasis to disease. Cell death and differentiation. 2016;23(12):1919. doi: 10.1038/cdd.2016.91 27689872
21. Read A, Natrajan R. Splicing dysregulation as a driver of breast cancer. Endocrine-related cancer. 2018;25(9):R467–R478. doi: 10.1530/ERC-18-0068 29848666
22. Kahles A, Lehmann KV, Toussaint NC, Hüser M, Stark SG, Sachsenberg T, et al. Comprehensive analysis of alternative splicing across tumors from 8,705 patients. Cancer cell. 2018;34(2):211–224. doi: 10.1016/j.ccell.2018.07.001 30078747
23. Sveen A, Kilpinen S, Ruusulehto A, Lothe R, Skotheim RI. Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes. Oncogene. 2016;35(19):2413. doi: 10.1038/onc.2015.318 26300000
24. Rajan P, Elliott DJ, Robson CN, Leung HY. Alternative splicing and biological heterogeneity in prostate cancer. Nature Reviews Urology. 2009;6(8):454. doi: 10.1038/nrurol.2009.125 19657379
25. Wan Y, Larson DR. Splicing heterogeneity: separating signal from noise. Genome biology. 2018;19(1):86. doi: 10.1186/s13059-018-1467-4 29986741
26. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013;498(7453):236. doi: 10.1038/nature12172 23685454
27. Dvinge H, Bradley RK. Widespread intron retention diversifies most cancer transcriptomes. Genome medicine. 2015;7(1):45. doi: 10.1186/s13073-015-0168-9 26113877
28. Eswaran J, Horvath A, Godbole S, Reddy SD, Mudvari P, Ohshiro K, et al. RNA sequencing of cancer reveals novel splicing alterations. Scientific reports. 2013;3:1689. doi: 10.1038/srep01689 23604310
29. Jayasinghe RG, Cao S, Gao Q, Wendl MC, Vo NS, Reynolds SM, et al. Systematic analysis of splice-site-creating mutations in cancer. Cell reports. 2018;23(1):270–281. doi: 10.1016/j.celrep.2018.03.052 29617666
30. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344(6190):1396–1401. doi: 10.1126/science.1254257 24925914
31. Ardui S, Ameur A, Vermeesch JR, Hestand MS. Single molecule real-time (SMRT) sequencing comes of age: applications and utilities for medical diagnostics. Nucleic acids research. 2018;46(5):2159–2168. doi: 10.1093/nar/gky066 29401301
32. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886
33. Lin J. Divergence measures based on the Shannon entropy. IEEE Transactions on Information theory. 1991;37(1):145–151. doi: 10.1109/18.61115
34. Joyce JM. Kullback-leibler divergence. In: International Encyclopedia of Statistical Science. Springer; 2011. p. 720–722.
35. Capra JA, Singh M. Predicting functionally important residues from sequence conservation. Bioinformatics. 2007;23(15):1875–1882. doi: 10.1093/bioinformatics/btm270 17519246
36. Azad RK, Li J. Interpreting genomic data via entropic dissection. Nucleic acids research. 2012;41(1):e23–e23. doi: 10.1093/nar/gks917 23036836
37. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic acids research. 2015;44(D1):D733–D745. doi: 10.1093/nar/gkv1189 26553804
38. MIT. WgSim; 2011. Available from: https://github.com/lh3/wgsim.
39. Graf JF, Zavodszky MI. Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures. PloS one. 2017;12(11):e0188878. doi: 10.1371/journal.pone.0188878 29190747
40. Network CGA, et al. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61. doi: 10.1038/nature11412
41. Zhu D, Zhaozu X, Cui G, Chang S, See YX, Lim MGL, et al. Single-cell transcriptome analysis reveals estrogen signaling augments the mitochondrial folate pathway to coordinately fuel purine and polyamine synthesis in breast cancer cells. bioRxiv. 2018; p. 246363.
42. Chen H, Lin F, Xing K, He X. The reverse evolution from multicellularity to unicellularity during carcinogenesis. Nature communications. 2015;6:6367. doi: 10.1038/ncomms7367 25751731
43. Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, Ellrott K, et al. The cancer genome atlas pan-cancer analysis project. Nature genetics. 2013;45(10):1113. doi: 10.1038/ng.2764 24071849
44. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of clinical oncology. 2009;27(8):1160. doi: 10.1200/JCO.2008.18.1370 19204204
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