Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI‐based approach
| Autor: | Sutanu Nandi, Noopur Sinha, Ram Rup Sarkar, Mudita Shukla, Rochi Saurabh |
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| Rok vydání: | 2020 |
| Předmět: |
Drug
media_common.quotation_subject Antineoplastic Agents Gene mutation Bioinformatics Polymorphism Single Nucleotide Biochemistry Cohort Studies Machine Learning Germline mutation Neoplasms Glioma Drug Discovery medicine Humans Gene Survival rate media_common Pharmacology business.industry Organic Chemistry Cancer medicine.disease Survival Rate Tumor progression Mutation Molecular Medicine Transcriptome business |
| Zdroj: | Chemical Biology & Drug Design. 96:1005-1019 |
| ISSN: | 1747-0285 1747-0277 |
| DOI: | 10.1111/cbdd.13668 |
| Popis: | The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction of tumor grades and patients' overall survival are important for prognosis, risk factors identification and betterment of the treatment strategy, especially for highly lethal tumors, like gliomas. Here, with the help of more accurate and widely used machine learning-based approaches, we propose an integrative computational pipeline that incorporates somatic mutations and gene expression profile for survival and grade prediction of glioma patients and simultaneously relates it to the drugs to be administered. This study gives us a clear understanding that the same drug is not effective for the treatment of same grade of cancer if the gene mutations are different. The alteration in a specific gene plays a very important role in tumor progression and should also be considered for the selection of appropriate drugs. This proposed framework includes all the necessary factors required for enhancement of therapeutic designs and could be useful for clinicians in determining an accurate and personalized treatment strategy for individual patients suffering from different life threatening diseases. |
| Databáze: | OpenAIRE |
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