Cancer relevance of human genes

Background It is unclear how many of genes contribute to the biology of cancer. We hypothesize that genes that interact with core cancer gene (CCG) in a protein-protein interaction network (PPI) may have functional importance. Methods We categorized genes into 1- (n=6791), 2- (n=7724), 3- (n=1587), and >3-steps (n=362) removed from the nearest CCG in the STRING PPI and demonstrate that the cancer-biology related functional contribution of the genes in these different neighborhood categories decreases as their distance from the CCGs increases. Results Genes closer to cancer genes manifest greater connectedness in the network, show greater importance in maintaining cell viability in a broad range of cancer cells in vitro, are also under greater negative germline selection pressure in the healthy populations, and have higher somatic mutation frequency and cancer effect. Conclusions Approximately 70% of human genes are 1 or 2 steps removed from cancer genes in protein network and show functional importance in cancer-biology. These results suggest that the universe of cancer-relevant genes extends to thousands of genes that can contribute functional effects when dysregulated.

[1]  M. Gerstein,et al.  Germline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden , 2020, Nature Communications.

[2]  Anne-Claude Gingras,et al.  Rare driver mutations in head and neck squamous cell carcinomas converge on NOTCH signaling , 2020, Science.

[3]  Stephen J. Elledge,et al.  Tissue-specificity in cancer: The rule, not the exception , 2019, Science.

[4]  Gonçalo Abecasis,et al.  Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank , 2019, bioRxiv.

[5]  Peter J. Campbell,et al.  Somatic mutant clones colonize the human esophagus with age , 2018, Science.

[6]  Damian Szklarczyk,et al.  STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets , 2018, Nucleic Acids Res..

[7]  C. Cole,et al.  The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers , 2018, Nature Reviews Cancer.

[8]  Steven J. M. Jones,et al.  CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer , 2018, bioRxiv.

[9]  E. Koonin,et al.  Cancer-mutation network and the number and specificity of driver mutations , 2018, Proceedings of the National Academy of Sciences.

[10]  J. Townsend,et al.  Neutral Theory and the Somatic Evolution of Cancer. , 2018, Molecular biology and evolution.

[11]  Ville Mustonen,et al.  The repertoire of mutational signatures in human cancer , 2018, Nature.

[12]  Steven J. M. Jones,et al.  Comprehensive Characterization of Cancer Driver Genes and Mutations , 2018, Cell.

[13]  Li Ding,et al.  Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. , 2018, Cell systems.

[14]  J. Townsend,et al.  Effect Sizes of Somatic Mutations in Cancer , 2017, bioRxiv.

[15]  Nancy R. Zhang,et al.  Functional germline variants as potential co-oncogenes , 2017, npj Breast Cancer.

[16]  M. Stratton,et al.  Universal Patterns of Selection in Cancer and Somatic Tissues , 2017, Cell.

[17]  David P. Nusinow,et al.  Estimating the Selective Effects of Heterozygous Protein Truncating Variants from Human Exome Data , 2017, Nature Genetics.

[18]  Roland Rad,et al.  Tissue-specific tumorigenesis: context matters , 2017, Nature Reviews Cancer.

[19]  K. Kinzler,et al.  Evaluating the evaluation of cancer driver genes , 2016, Proceedings of the National Academy of Sciences.

[20]  Donavan T. Cheng,et al.  Precision medicine at Memorial Sloan Kettering Cancer Center: clinical next-generation sequencing enabling next-generation targeted therapy trials. , 2015, Drug discovery today.

[21]  James Y. Zou.  Analysis of protein-coding genetic variation in 60,706 humans , 2015, Nature.

[22]  Donavan T. Cheng,et al.  Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. , 2015, The Journal of molecular diagnostics : JMD.

[23]  Doris Berger.  The hunting of the src , 2014, Im Focus Onkologie.

[24]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[25]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[26]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[27]  A. R. Jonckheere,et al.  A DISTRIBUTION-FREE k-SAMPLE TEST AGAINST ORDERED ALTERNATIVES , 1954 .

[28]  W. Ewens.  The neutral theory of molecular evolution , 1985 .

[29]  S. Holm.  A Simple Sequentially Rejective Multiple Test Procedure , 1979 .