Integrating Clinical, Pathologic, and Immune Features to Predict Breast Cancer Recurrence and Chemotherapy Benefit

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Abstract Breast cancer is the leading cause of cancer death for women globally, with over 2.3 million cases diagnosed each year. Most cases are hormone receptor positive and effectively treated with anti-estrogen therapy, but some patients have aggressive disease and are at risk for recurrence and death without chemotherapy. Gene expression based recurrence assays, such as OncotypeDX, were designed to predict recurrence on hormonal therapy and are used to select patients for chemotherapy. However, these assays are expensive (> $3,000 per test), take considerable time to perform leading to treatment delays, and testing is underutilized or frankly unavailable in low resource settings in the US and globally. Conversely, every patient with breast cancer has a biopsy to confirm the diagnosis, which is routinely analyzed by pathologist to determine subtype of breast cancer and grade. Deep learning is an emerging technique for quantitative image analysis, and can identify non-intuitive features from pathology, including gene expression patterns. In preliminary work, I have demonstrated that deep learning on pathology samples can provide rapid and cost-effective prediction of OncotypeDX score using readily available data, and can identify patients at low risk of recurrence on hormonal therapy. However, OncotypeDX remains an imperfect predictor of chemotherapy benefit, as it was developed to predict recurrence on hormonal therapy. By refining my deep learning biomarker to incorporate clinical and immune features of breast cancer, I can improve accuracy in prediction of chemotherapy benefit and thus the ability to personalize treatment. First, I will capitalize on the recent expansion of clinical data in the National Cancer Data Base to develop a more accurate clinical models of prognosis and chemotherapy benefit. Next, I will use multiplex immunofluorescence to better characterize spatial and cell density features associated with chemotherapy benefit, and use deep learning models to infer these features from standard hematoxylin and eosin stained digital pathology. Finally, I will integrate these clinical and immune models with my existing deep learning pathologic model and validate the integrated model in a multi-institutional cohort. The result of this work will result in a prognostic and predictive deep learning biomarker that makes accurate predictions from readily available clinical, pathologic, and inferred immune features. This approach has the potential to reduce chemotherapy delays due to rapid turnaround time, combat healthcare disparities through improved availability of testing, and improve personalization of treatment by tailoring a biomarker for prediction of chemotherapy benefit.