Augmenting TNM staging with machine learning-based immune profiling for improved prognosis prediction in muscle-invasive bladder cancer patients

Abstract

Background: Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease whose clinical reporting is based on TNMstaging. A more accurate and personalized prognosis could be achieved by profiling the immune contexture alongside clinical TNM staging. This study reports on features captured from the labelling of cells for CD3, CD8 and PD-L1 expression, within both the tumor and stroma of MIBC patients. This data is distilled to identify a novel immune-based prognostic signature which augments TNM staging. Methods: An image analysis solution was developed to quantify the density of cell populations across immunofluorescence (IF) labelled whole slide images from 105 MIBC patients with known survival data. A regression random forest was used for the detection of nuclei using the Hoechst channel [Brieu et al., ISBI2017] and a convolutional neural network employed for the segmentation of the tumor from the stroma using the pan-cytokeratin channel [Brieu et al., SPIE2018]. The CD3, CD8 and PD-L1 channels were used to classify the cells and calculate their proportion within either the tumor or stroma regions. A decision tree of depth two was trained on these proportions as well as cross-validated. More explicitly, the patients were recursively partitioned during training to maximize at each node their survival difference, leaves showing low survival difference (p-value>0.5) being finally merged. Results: The method yielded a decision tree which stratified patients into three groups utilizing only two parameters: the proportion of CD8(+) cells in the stroma and the proportion of PD-L1(+) cells across the whole tissue, which were negatively and positively correlated with cancer-specific death respectively. This method was used to replace TNM stages 1 to 3 while retaining the original stage 4 stratification. Testing for survival curve differences showed that this combined system yielded a higher prognostic value (Chisq=41.5, p-value=5.0x10-9) than the standalone TNMstaging system (Chisq=34.9, p-value=1.25x10-7). Conclusions: Our results suggest that immune profiling derived from image analysis provides additional prognostic value to TNM scoring for MIBC.