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Project 3: 2BLAST

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MAIN HYPOTHESIS:

A major knowledge gap has been how to integrate biostatistical and bioinformatic tools in defining/understanding biologically-based multi-symptom/multi-toxicity phenotypes, which are a set of characteristics or patterns of side effects of cancer treatments. These new toxicity phenotypes will improve quantification and precision of measuring symptoms and toxicities.

Research question: 

What tools can we use to better phenotype cancer symptoms and toxicities, and how do we implement them for research and clinical purposes?

Summary: 

As cancer survivorship increases, there is new emphasis on quality-of-life factors, particularly for symptom and toxicity management. The focus of this research is to develop a data science pipeline that integrates and analyzes cancer patient-reported and laboratory symptom/toxicity data. It combines the latest biostatistical and bioinformatic methodological approaches to generate appropriate phenotypes that accurately describe the complex nature of these variables.

Improved phenotyping of patient-reported symptom/toxicity measurement can improve methods for collecting such data routinely institution-wide, clinical patient management, and a wide range of research endeavours from genomic research to health services research. Symptom/toxicity data collection will only increase in clinical and research settings, with mounting evidence that symptoms/toxicities represent biologically-based outcomes. Therefore, proper phenotyping will be essential in clinical and research endeavours.

Scientific Goals:

(1) To create an automated symptom and toxicity data science integration pipeline that automatically pulls patient-reported symptom/toxicity accurately and completely

(2) To develop biostatistical and bioinformatic tools for identifying and predicting symptom/toxicity phenotypes

(3) To implement an analytical pipeline that demonstrates the utility of these phenotypes for clinical and research purposes

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UHN COMBIEL

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Geoffrey Liu Lab
101 College Avenue PMCRT 11-704
Toronto,  M4G0A3
Canada

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