ID:2048 Computational Methods for Personalized Medicine in Cancer Research
Personalized medicine is a medical procedure that stratify patients into different groups with specific treatments relied on their predicted response or risk of disease. To support researches in personalized medicine and manufacture viral vaccines and other products of biotechnology (e.g., drug), both tumors and cell lines, which are grown from the tumors, are used. With rapid growth of biomedical and clinical data, many computational methods have been proposed to identify candidate disease-associated cellular components, to predict novel targets of drugs, to predict response of drugs, or to repurpose the use of drug for other diseases, etc.… Specially for personalized medicine, computational methods have been proposed to classify patients into different subtypes according to genomic, epigenomic profiles and/or drug responses on cell lines or patient tumors [1-5], and therefore able to predict response of untested drugs , drug synergy  as well as to identify predictive genomic features and treatment for each group of patients. Among diseases, cancer is a complex genetic disease and has been received most focus in personalized medicine research . In addition, many data resources for are publicly available for the research such as CCLE , COSMIC , TCGA  and GDSC .
2. Le Van T, van Leeuwen M, Carolina Fierro A, De Maeyer D, Van den Eynden J, Verbeke L, De Raedt L, Marchal K, Nijssen S: Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinformatics 2016, 32(17):i445-i454.
3. Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, Haibe-Kains B, Goldenberg A: Similarity network fusion for aggregating data types on a genomic scale. Nat Meth 2014, 11(3):333-337.
4. Planey CR, Gevaert O: CoINcIDE: A framework for discovery of patient subtypes across multiple datasets. Genome Medicine 2016, 8(1):1-17.
5. Wang L, Li F, Sheng J, Wong ST: A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles. BMC Genomics 2015, 16(7):1-8.
6. Costello JC, Heiser LM, Georgii E, Gonen M, Menden MP, Wang NJ, Bansal M, Ammad-ud-din M, Hintsanen P, Khan SA et al: A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotech 2014, 32(12):1202-1212.
7. Bansal M, Yang J, Karan C, Menden MP, Costello JC, Tang H, Xiao G, Li Y, Allen J, Zhong R et al: A community computational challenge to predict the activity of pairs of compounds. Nat Biotech 2014, 32(12):1213-1222.
8. Azuaje F: Computational models for predicting drug responses in cancer research. Briefings in bioinformatics 2016:1–10.
9. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012, 483.
10. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S et al: COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Research 2015, 43(D1):D805-D811.
11. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM: The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013, 45.
12. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR et al: Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research 2013, 41(D1):D955-D961.
Volume & Issue : Vol 4 No S (2017): Abstract Proceeding: International conference INNOVATIONS IN CANCER RESEARCH AND REGENERATIVE MEDICINE 2017
Page No.: S77-S78
Published on: 2017-09-05
Copyrights & License
This work is licensed under a Creative Commons Attribution 4.0 International License.
- HTML viewed - 1769 times
- Download PDF downloaded - 616 times