Abstract

DNA mutations play a crucial role in cancer development and progression. Mutation profiles vary dramatically in different cancer types and between individual tumors. Mutations of several individual genes are known as reliable cancer biomarkers, although the number of such genes is tiny and does not enable differential diagnostics for most of the cancers. We report here a technique enabling dramatically increased efficiency of cancer biomarkers development using DNA mutations data. It includes a quantitative metric termed Pathway instability (PI) based on mutations enrichment of intracellular molecular pathways. This method was tested on 5,956 tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project. Totally, we screened 2,316,670 mutations in 19,872 genes and 1,748 molecular pathways. Our results demonstrated considerable advantage of pathway-based mutation biomarkers over individual gene mutation profiles, as reflected by more than two orders of magnitude greater numbers by high-quality [ROC area-under-curve (AUC)>0.75] biomarkers. For example, the number of such high-quality mutational biomarkers distinguishing between different cancer types was only six for the individual gene mutations, and already 660 for the pathway-based biomarkers. These results evidence that PI value can be used as a new generation of complex cancer biomarkers significantly outperforming the existing gene mutation biomarkers.

Highlights

  • Cancer is a multifactorial disease which is conditioned by alterations arising from biological, chemical, radiological impacts, as well as inherited

  • The DNA mutation data was extracted from the Catalog Of Somatic Mutations In Cancer (COSMIC) database [32]

  • To avoid the bias linked with the coding DNA sequences (CDS) lengths, we introduced a Normalized mutation rate value expressed by the formula: nMRn=

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Summary

Introduction

Cancer is a multifactorial disease which is conditioned by alterations arising from biological, chemical, radiological impacts, as well as inherited. The large scale projects like Wellcome Trust Sanger Institute’s Cancer Genome Project, the International Cancer Genome Consortium (ICGC), The Cancer Genome Atlas (TCGA) showed very high molecular heterogeneity of cancer, between different cancer types, and among the individual tumors of the same type [5,6,7,8]. This allowed to considerably advance current understanding of carcinogenetic molecular mechanisms by documenting complete or near-complete landscapes of pathological somatic mutations including base substitutions and gene fusions. Many of the alterations revealed appeared promising for molecular cancer diagnostics in order to improve and personalize the treatment regimens [9, 10]

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