Analyzing Patient-Level Data in a Breast Cancer Clinical Trial

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ABSTRACT Most women treated for breast cancer will experience some form of drug-related toxicity and subsequent impairments in Health-related Quality of Life (HRQOL), yet toxicity is assessed inconsistently in oncology trials. Although the potential for side effects of treatments is of great importance to patients in making informed choices about their treatment, the toxicities are often under-reported. When assessing symptoms of trial participants, patients and providers do not always attribute symptoms to the study drug, which can result in misclassification of the maximum tolerated dose. Furthermore, many drug toxicities such as neuropathy, fatigue and diarrhea are often underreported by providers in trials, and thus a patient-centered assessment may lead to earlier recognition of reversable side effects. A major gap in knowledge is how to analyze and utilize patient level toxicity data in real time, and how to present the data to providers in a format that can result in early toxicity mitigation. While the number of lower- grade toxicities may increase given the reporting of patient outcomes, acting on these lower grade toxicities can mitigate serious adverse events (SAEs). We have recently instantiated an electronic patient reported outcomes (ePRO) platform across 26 sites in I- SPY2 where we collect adverse events and quality of lie information. I-SPY2 is an adaptive platform trial for high risk, early-stage breast cancer that continuously evaluates the efficacy of new neoadjuvant breast cancer therapies. The overall objective of this proposal is to refine and implement new methodology using interpretable machine learning that can be used to underpin a framework to redirect treatment and avoid more serious illnesses. Such methodology does not exist in clinical trials today and can hugely benefit patients, their providers and the clinical care team by tracking the inflection points of patient distress that could otherwise be missed but may require more immediate intervention. The methods will be developed through a computational framework in discussion with providers, at different stages of treatment, such as when the severity of a single symptom really impacts physical functioning (primary outcome), or when constellation of symptoms herald a significant deterioration in overall health. The central hypothesis of this proposal is that the methodology that we are developing on who will develop chronic conditions and symptoms that may affect quality of life will mitigate the event of a serious adverse reaction and improve overall quality of life, particularly physical functioning. We will test our methodology in a group of I-SPY patients and Breast Care Center early-stage participants at UCSF.