Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU View Full Text


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Article Info

DATE

2020-02-06

AUTHORS

Qais Al Hajri, Sajal Dash, Wu-chun Feng, Harold R. Garner, Ramu Anandakrishnan

ABSTRACT

Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy. More... »

PAGES

2022

References to SciGraph publications

  • 2019-01-30. Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations in SCIENTIFIC REPORTS
  • 2009-11-02. A common gain of function of p53 cancer mutants in inducing genetic instability in ONCOGENE
  • 2017-03-03. Tissue-specific tumorigenesis: context matters in NATURE REVIEWS CANCER
  • 2017-02-24. Evaluating Variant Calling Tools for Non-Matched Next-Generation Sequencing Data in SCIENTIFIC REPORTS
  • 2012-07-10. Combinatorial drug therapy for cancer in the post-genomic era in NATURE BIOTECHNOLOGY
  • 2018-06-05. Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network in BMC BIOINFORMATICS
  • 2016-12-08. Novel KCNB1 mutation associated with non-syndromic intellectual disability in JOURNAL OF HUMAN GENETICS
  • 1954-03. The Age Distribution of Cancer and a Multi-stage Theory of Carcinogenesis in BRITISH JOURNAL OF CANCER
  • 1969-06. The two "hit" and multiple "hit" theories of carcinogenesis. in BRITISH JOURNAL OF CANCER
  • 2016-10. Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing data in BMC BIOINFORMATICS
  • 2019-03-27. Titin mutations and muscle disease in PFLÜGERS ARCHIV - EUROPEAN JOURNAL OF PHYSIOLOGY
  • 2014-09-19. Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis in BMC BIOINFORMATICS
  • 1982-12. Promoter mapping and DNA sequencing of the F plasmid transfer genes traM and traJ in MOLECULAR GENETICS AND GENOMICS
  • 2005-05. Estimating the number of rate limiting genomic changes for human breast cancer in BREAST CANCER RESEARCH AND TREATMENT
  • 2019-10-29. Homozygous missense variant in the TTN gene causing autosomal recessive limb-girdle muscular dystrophy type 10 in BMC MEDICAL GENETICS
  • 2015-10-19. De novo KCNB1 mutations in infantile epilepsy inhibit repetitive neuronal firing in SCIENTIFIC REPORTS
  • 1988-08-01. Schwerste chronische organbefunde im sanitätspolizeilichen obduktionsgut — ein medizin-soziologischer beitrag in INTERNATIONAL JOURNAL OF LEGAL MEDICINE
  • 2016-09-12. Unsupervised detection of cancer driver mutations with parsimony-guided learning in NATURE GENETICS
  • 2014-01-05. Discovery and saturation analysis of cancer genes across 21 tumor types in NATURE
  • 1953-03. A New Theory on the Cancer-inducing Mechanism in BRITISH JOURNAL OF CANCER
  • 2016-04-13. Cocktails for cancer with a measure of immunotherapy in NATURE
  • 1994. Relationship between p53 and c-erbB-2 Overexpression in Tissue Sections and Cyst Fluid Cells of Patients with Ovarian Cancer in TUMOR BIOLOGY
  • 2007-04-08. p53 gain-of-function cancer mutants induce genetic instability by inactivating ATM in NATURE CELL BIOLOGY
  • 2013-06-16. Mutational heterogeneity in cancer and the search for new cancer genes in NATURE
  • 2013-08-10. RCircos: an R package for Circos 2D track plots in BMC BIOINFORMATICS
  • 2017-02-08. Role of KCNB1 in the prognosis of gliomas and autophagy modulation in SCIENTIFIC REPORTS
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    http://scigraph.springernature.com/pub.10.1038/s41598-020-58785-y

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