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Molecular Pathway Analysis of Mutation Data for Biomarkers Discovery and Scoring of Target Cancer Drugs

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Nucleic Acid Detection and Structural Investigations

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2063))

Abstract

DNA mutations govern cancer development. Cancer mutation profiles vary dramatically among the individuals. In some cases, they may serve as the predictors of disease progression and response to therapies. However, the biomarker potential of cancer mutations can be dramatically (several orders of magnitude) enhanced by applying molecular pathway-based approach. We developed Oncobox system for calculation of pathway instability (PI) values for the molecular pathways that are aggregated mutation frequencies of the pathway members normalized on gene lengths and on number of genes in the pathway. PI scores can be effective biomarkers in different types of comparisons, for example, as the cancer type biomarkers and as the predictors of tumor response to target therapies. The latter option is implemented using mutation drug score (MDS) values, which algorithmically rank the drugs capacity of interfering with the mutated molecular pathways. Here, describe the mathematical basis and algorithms for PI and MDS values calculation, validation and implementation. The example analysis is provided encompassing 5956 human tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project, that totally make 2,316,670 mutations in 19,872 genes and 1748 molecular pathways, thus enabling ranking of 128 clinically approved target drugs. Our results evidence that the Oncobox PI and MDS approaches are highly useful for basic and applied aspects of molecular oncology and pharmacology research.

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Abbreviations

CDS length:

Coding DNA sequence length

COSMIC:

Catalogue of somatic mutations in cancer

FDA:

Food and Drug Administration

ICGC:

International Cancer Genome Consortium

MDS:

Mutational Drug Scores

MR:

Mutation rate

NIH:

The National Institutes of Health

nMR:

Normalized mutation rate

PI:

Pathway instability

ROC AUC:

Receiver operator characteristics area under the curve

TC:

Target conversion

TCGA:

The Cancer Genome Atlas

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Acknowledgment

Funding: This study was supported by the Oncobox research program in digital oncology, by the Russian Science Foundation grant no. 18-15-00061, by Amazon and Microsoft Azure grants for cloud-based computational facilities.

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Authors and Affiliations

  1. Omicsway Corp., Walnut, CA, USA

    Marianna Zolotovskaia, Maxim Sorokin, Andrew Garazha, Nikolay Borisov & Anton Buzdin

  2. Department of Oncology, Hematology and Radiotherapy of Pediatric Faculty, Pirogov Russian National Research Medical University, Moscow, Russia

    Marianna Zolotovskaia

  3. Laboratory of Clinical Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia

    Maxim Sorokin, Nikolay Borisov & Anton Buzdin

  4. Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia

    Maxim Sorokin & Anton Buzdin

Authors
  1. Marianna Zolotovskaia
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  2. Maxim Sorokin
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  3. Andrew Garazha
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  4. Nikolay Borisov
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  5. Anton Buzdin
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Correspondence to Anton Buzdin .

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Editors and Affiliations

  1. Department of Chemistry, Technical University of Denmark, Kongens Lyngby, Denmark

    Kira Astakhova

  2. Department of Chemistry, Technical University of Denmark, Kongens Lyngby, Denmark

    Syeda Atia Bukhari

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Zolotovskaia, M., Sorokin, M., Garazha, A., Borisov, N., Buzdin, A. (2020). Molecular Pathway Analysis of Mutation Data for Biomarkers Discovery and Scoring of Target Cancer Drugs. In: Astakhova, K., Bukhari, S. (eds) Nucleic Acid Detection and Structural Investigations. Methods in Molecular Biology, vol 2063. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0138-9_16

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  • DOI: https://doi.org/10.1007/978-1-0716-0138-9_16

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