An Integrated Framework for Identifying Context-Specific Gene-Drug Interactions

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Title: An integrated framework for identifying context specific gene-drug interactions among cancer patients: An advanced step towards personalized medicine.

Description: Cancer is one of the leading cause of death worldwide and according to the National Cancer Institute cancer deaths will be projected from 8 million to 13 million in the next two decades. Each cancer patient is unique and traditional clinical therapy "one-dose-fits-all" has proven having adverse effect in patient's health and survival. A study on adverse drug reaction in US published that 67% of hospitalized patients including cancer patients are affected by the adverse drug reactions and for this reason the number of deaths exceeds 100,000 cases annually. Thus, it is essential to make cancer treatment "personalized" to provide the proper treatment to the proper person at the proper time. Recent studies analyzed tissue specific genetic datasets to model drug response in cancer. These studies suffer from the high dimensionality of datasets and low sample size and they are limited by the toxic effect of drug in patient specific context.

The goal of this study is to develop a computational tool that integrates genomic, genetic and metabolomics datasets with clinical datasets such as drug toxicology to predict context specific drug response in cancer. This tool will be helpful in the application of personalized medication of cancer patients. This computational tool will be further evaluated by the new findings in personalized DNA biomarkers the role of which can be further studied for the prediction of the overall survival outcome of a patient. Incorporation of metabolomics and toxicology studies in cell line with cancer drug will be able to deal with toxicity effects which is the biggest challenge in personalized cancer treatment.

Students will work with a team of PhD students and the faculty mentor and contribute to various parts of this project.

Students are expected to be proficient in programming. Experience in molecular biology, basic Linux commands and high performance computing is preferred, but not required.

Student learning objectives: After this project, students will

  • Have a basic understanding of molecular biology, high-dimensional biological datasets and drug treatment data.
  • Be familiar with R or Python programming language and some bioinformatics libraries in those languages.
  • Learn gather biological data from public repositories
  • Build a computational pipeline that pre-processes and integrates high-dimensional biological datasets
  • Be familiar with data visualization tools to analyze and visualize gene networks
  • Learn methods to evaluate predictive models by computing true positive rate, false positive rate, precision, recall, ROC curves, etc.