Abstract
e19073 Background: Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous tumor that originates from normal B-cells. Despite the use of combination chemotherapy, around 40% of DLBCL patients die (de Jonge, et al. European Journal of Cancer, 2016). Limited studies have investigated the role of collagen in the acellular tumor microenvironment. In this study, we present a novel digital signature of the proximity of tumor cells and collagen-VI (COL6) that can predict overall survival (OS) in DLBCL patients. To the best of our knowledge, this is the first study of its kind to employ automated image analysis. Methods: The proposed digital proximity signature (DPS) aggregates summary-level statistics from the entire whole slide image (WSI) and serves as a marker of regions, categorizing weak, moderate, significant, and strong tumor-collagen proximity and can be described as a surrogate for signaling. To accomplish this, we developed a novel artificial intelligence (AI) based multi-task model for simultaneous detection and classification of tumor cells and another bespoke method for automatically identifying COL6 fiber. The tumor-collagen proximity analysis was then performed by aggregating tumor cell statistics within the vicinity of COL6 fibres. Finally, the prognostic significance of DPS for OS in DLBCL was investigated with Kaplan-Meier analysis, stratifying patients into two groups based on the median of the DPS values. Results: We took WSIs of DLBCL tissue slides for 32 cases immunohistochemically stained with COL6 and Hematoxylin counterstain. The AI model for tumor cell identification achieved a high F1-score of 0.84, outperforming recent single-task learning models. Our results show that strong proximity of COL6 and tumor cells is linked to better OS in DLBCL patients ( p = 0.03). Conclusions: Our novel digitally computed COL6-tumor proximity signature shows prognostic significance for overall survival on a pilot dataset of 32 DLBCL patients. We are further validating the utility of this novel signature as a prognostic biomarker in larger cohorts of DLBCL patients.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call