Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer

Immunotherapy is revolutionizing the management of multiple solid tumors, and early data have revealed the clinical activity of PD-1/PD-L1 antagonists in small numbers of metastatic breast cancer patients. Clinical activity appears more likely if the tumor is triple negative, PD-L1+, and/or harbors higher levels of TILs. Responses to atezolizumab and pembrolizumab appear to be durable in metastatic triple negative breast cancer (TNBC), suggesting these agents may transform the lives of responding patients. Current clinical efforts are focused on developing immunotherapy combinations that convert non-responders to responders, deepen those responses that do occur, and surmount acquired resistance to immunotherapy. Identifying biomarkers that can predict the potential for response to single agent immunotherapy, identify the best immunotherapy combinations for a particular patient, and guide salvage immunotherapy in patients with progressive disease are high priorities for clinical development. Smart clinical trials testing rational immunotherapy combinations that include robust biomarker evaluations will accelerate clinical progress, moving us closer to effective immunotherapy for almost all breast cancer patients.

The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.