A deep learning-based computational prediction model for characterizing cellular biomarker distribution in tumor microenvironment

Evaluation of cancer cell and immune cell distribution in tumor microenvironment (TME) is one of the most important factors for guiding cancer immunotherapy and assessing therapeutic response. Multiplexed immunohistochemistry (mIHC) is often used to obtain the different types of cellular biomarker expression and distribution information in TME, but mIHC is limited by time-consuming and cost-intensive, and pathologists' objectives etc. In this work, we proposed a deep learning-based modified U-Net ( m-Unet), by replacing the original convolution sub-module with a modified block to predict the distribution of several typical cellular biomarkers' expression and distribution information in TME. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners. The model can extract segmentation information from Hematoxylin and Eosin (H&E) images, and predict the cellular biomarker distributions including panCK for colon cancer cells, CD3 and CD20 for tumor infiltrating lymphocytes (TILs) and DAPI for nucleus. We have demonstrated that our model can be trained in both fully supervised and semi-supervised manners and. the performance of the m-Unet is better than the U-Net in this work. The optimal prediction accuracy of m-Unet is 88.3% on the test dataset. In general, this model possesses the potential to assist the clinical TME analysis.