Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference
| Autor: | Xia Jiang, Xiaojun Ma, Kevin N. Lu, Songjian Lu, Chunhui Cai, Harry Hochheiser, Gregory F. Cooper, Shuping Xu, Zhenlong Zhao, Adrian V. Lee, Lujia Chen, Liyue Yu, Vicky Ping Chen, Nathan L. Clark, Yifan Xue, Q. Jane Wang, Xinghua Lu, Xueer Chen |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Somatic cell Carcinogenesis Epidemiology Gene Expression Functional impact medicine.disease_cause Genome Transcriptome Bayes' theorem 0302 clinical medicine Mathematical and Statistical Techniques Neoplasms Gene duplication Medicine and Health Sciences Cancer biology Biology (General) Precision Medicine 0303 health sciences Ecology Cancer Risk Factors Statistics Phenotype 3. Good health Computational Theory and Mathematics Oncology 030220 oncology & carcinogenesis Modeling and Simulation Physical Sciences Algorithms Research Article QH301-705.5 Genetic Causes of Cancer Computational biology Biology Research and Analysis Methods 03 medical and health sciences Cellular and Molecular Neuroscience Metabolomics Gene Types medicine Genetics Humans Gene Regulation Statistical Methods Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology Models Genetic Genome Human Gene Amplification Computational Biology Biology and Life Sciences Bayes Theorem Oncogenes Precision medicine 030104 developmental biology Causal inference Medical Risk Factors Mutation Regulator Genes 030217 neurology & neurosurgery Mathematics Forecasting |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 15, Iss 7, p e1007088 (2019) |
| ISSN: | 1553-7358 |
| Popis: | Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors. |
| Databáze: | OpenAIRE |
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