Comprehensive cross cancer analyses reveal mutational signature cancer specificity

Abstract

Mutational signatures refer to distinct patterns of DNA mutations that occur in a specific context or under certain conditions. It is a powerful tool to describe cancer etiology. We conducted a study to show cancer heterogeneity and cancer specificity from the aspect of mutational signatures through collinearity analysis and machine learning techniques. Through thorough training and independent validation, our results show that while the majority of the mutational signatures are distinct, similarities between certain mutational signature pairs can be observed through both mutation patterns and mutational signature abundance. The observation can potentially assist to determine the etiology of yet elusive mutational signatures. Further analysis using machine learning approaches demonstrated moderate mutational signature cancer specificity. Skin cancer among all cancer types demonstrated the strongest mutational signature specificity.

Keywords

cancer specificity / collinearity analysis / DNA mutational signatures / machine learning

References

[1] Martincorena I , Campbell PJ . Somatic mutation in cancer and normal cells. Science. 2015; 349 (6255): 1483- 9.

[2] Dey N , Williams C , Leyland-Jones B , De P . Mutation matters in precision medicine: a future to believe in. Cancer Treat Rev. 2017; 55: 136- 49.

[3] Alexandrov LB , Kim J , Haradhvala NJ , Huang MN , Tian Ng AW , Wu Y , et al. The repertoire of mutational signatures in human cancer. Nature. 2020; 578 (7793): 94- 101.

[4] Alexandrov LB , Nik-Zainal S , Wedge D , Campbell P , Stratton M . Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 2013; 3 (1): 246- 59.

[5] Islam SMA , Díaz-Gay M , Wu Y , Barnes M , Vangara R , Bergstrom EN , et al. Uncovering novel mutational signatures by de novo extraction with sig profiler extractor. Cell Genom. 2022; 2 (11): 100179.

[6] Burrell RA , McGranahan N , Bartek J , Swanton C . The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013; 501 (7467): 338- 45.

[7] Consortium ITP-CAOWG . Pan-cancer analysis of whole genomes. Nature. 2020; 578 (7793): 82- 93.

[8] Manders F , Brandsma AM , de Kanter J , Verheul M , Oka R , van Roosmalen MJ , et al. MutationalPatterns: the one stop shop for the analysis of mutational processes. BMC Genom. 2022; 23 (1): 134.

[9] Baez-Ortega A , Gori K . Computational approaches for discovery of mutational signatures in cancer. Briefings Bioinf. 2019; 20 (1): 77- 88.

[10] Blokzijl F , Janssen R , van Boxtel R , Cuppen E . MutationalPatterns comprehensive genome-wide analysis of mutational processes. Genome Med. 2018; 10 (1): 33.

[11] Nakamura M , Kajiwara Y , Otsuka A , Kimura H . LVQ-SMOTE - learning vector quantization based synthetic minority over-sampling technique for biomedical data. BioData Min. 2013; 6 (1): 16.

[12] Hooker G , Mentch L . Comments on: a random forest guided tour. Test. 2016; 25 (2): 254- 60.

[13] Chen TQ , Guestrin C . XGBoost: a scalable tree boosting system. In: Kdd'16: proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining; 2016. p. 785- 94.

[14] Pu J , Yu H , Guo Y . A novel strategy to identify prognosis‐relevant gene sets in cancers. Genes. 2022; 13 (5).

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2024 2024 The Author(s). Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.