Automation of quantifying and mapping of stromal and malignant cells in pancreatic adenocarcinoma using deep learning.

Abstract

266 Background: The characteristic histological feature of pancreatic adenocarcinoma (PAD) is extensive desmoplasia alongside leukocytes and cancer-associated fibroblasts. Desmoplasia is a known barrier to the absorption and penetration of therapeutic drugs. Stromal cells are key elements for a clinical response to chemotherapy and immunotherapy, but few models exist to analyze the spatial and architectural elements that compose the complex tumor microenvironment in PAD. Methods: We created a deep learning algorithm to analyze images and quantify cells and fibrotic tissue. Histopathology slides of PAD patients (pts) were then used to automate the recognition and mapping of adenocarcinoma cells, leukocytes, fibroblasts, and degree of desmoplasia, defined as the ratio of the area of fibrosis to that of the tumor gland. This information was correlated with mutational burden, defined as mutations (mts) per megabase (mb) of each pt. Results: The histopathology slides (H&E stain) of 126 pts were obtained from The Cancer Genome Atlas (TCGA) and analyzed with the deep learning model. Pt with the largest mutational burden (733 mts/mb, n = 1 pt) showed the largest number of leukocytes (585/mm2). Those with the smallest mutational burden (0 mts/mb, n = 16 pts) showed the fewest leukocytes (median, 14/mm2). Mutational burden was linearly proportional to the number of leukocytes (R2 of 0.7772). The pt with a mutational burden of 733 was excluded as an outlier. No statistically significant difference in the number of fibroblasts, degree of desmoplasia, or thickness of the first fibrotic layer (the smooth muscle actin-rich layer outside of the tumor gland), was found among pts of varying mutational burden. The median distance from a tumor gland to a leukocyte was inversely proportional to the number of leukocytes in a box of 1 mm2 with a tumor gland at the center. Conclusions: A deep learning model enabled automated quantification and mapping of desmoplasia, stromal and malignant cells, revealing the spatial and architectural relationship of these cells in PAD pts with varying mutational burdens. Further biomarker driven studies in the context of immunotherapy and anti-fibrosis are warranted.