||1R01CA283179-01 Interpret this number
||University Of California, San Francisco
||Analyzing Patient-Level Data in a Breast Cancer Clinical Trial
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.