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