Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer

Abstract

The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer-specific death from digital images of immunohistochemically (IHC) stained tissue microarrays (TMAs). Specifically, we evaluated a cohort of 248 gastric cancer patients using convolutional neural networks (CNNs) in an end-to-end weakly supervised scheme independent of subjective pathologist input. To account for the time-to-event characteristic of the outcome data, we developed new survival models to guide the network training. In addition to the standard H&E staining, we investigated the prognostic value of a panel of immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki67). Our CNN-derived risk scores provided additional prognostic value when compared to the gold standard prognostic tool TNM stage. The CNN-derived risk scores were also shown to be superior when systematically compared to cell density measurements or a CNN score derived from binary 5-year survival classification, which ignores time-to-event. To better understand the underlying biological mechanisms, we qualitatively investigated risk heat maps for each marker which visualised the network output. We identified patterns of biological interest that were related to low risk of cancer-specific death such as the presence of B-cell predominated clusters and Ki67 positive sub-regions and showed that the corresponding risk scores had prognostic value in multivariate Cox regression analyses (Ki67&CD20 risks: hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.15-1.89, p = 0.002; CD20&CD68 risks: HR = 1.33, 95% CI = 1.07-1.67, p = 0.009). Our study demonstrates the potential additional value that deep learning in combination with a panel of IHC markers can bring to the field of precision oncology.

Single-Cell Atlas of Lineage States, Tumor Microenvironment, and Subtype-Specific Expression Programs in Gastric Cancer

“…In our study, scRNA-seq enabled lineage-based comparisons of the TME between these histological subtypes, leading to the discovery of increased plasma cells in diffusetype GCs. Compared to T-cells, there are fewer studies on B-cell and plasma cell populations in GC (82,83). Derks et al, reported a higher proportion of tertiary lymphoid structures in GS tumors with enrichment of B-cells and CD4 T-cells (84).…”

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review

“…There was only one study dealing with the prediction of cancer-specific death in GC. Meier et al [41] analysed HE and immunohistochemically stained tissue to identify survival-related features and predict the risk of cancer-specific death. As indicators for immunogenicity and aggressive growth, they looked at immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki-67).…”

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

“…This model could effectively identify CRC patients and predict the survival benefits from curative CRC surgery[ 140 , 141 ], which was a quantum leap in non-invasive screening. Several immune markers, including CD8, CD20, and CD68, combined with the proliferation marker Ki-67 by CNN showed great prognostic value in GC[ 135 ]. DNA aneuploidy, tumor-stroma ratio, and RNA sequencing were also proven to have great value in clinical prediction that were evacuated by ANN[ 142 - 144 ].…”