Digital histological markers based on routine H&E slides to predict benefit from maintenance immunotherapy in esophagogastric adenocarcinoma

Abstract

Background: Immune checkpoint inhibition (ICI) is an effective treatment for a subset of patients with inoperable esophagogastric (EG) adenocarcinoma. Robust predictive biomarkers are required to identify these patients and a variety of strategies including immunohistochemical staining of PD-L1 and tumor mutational burden (TMB) assessment have been employed. Here, we explore digital histological (dHis) markers based on routine hematoxylin and eosin (H&E) slides alone or in combination with molecular markers (PD-L1 and TMB) as predictive biomarkers of benefit from maintenance immunotherapy in patients with inoperable EG adenocarcinoma. Methods: We developed a deep learning based algorithm to construct novel digital histological (dHis) markers by summarizing the statistics of all different types of nuclei present in the tumor tissue sections, their morphological features and their colocalization across each of the whole slide image. The dHis markers were then input into a decision-tree based approach to test for prognostic and predictive power alone or in combination with molecular markers. We assessed two cohorts of patients randomized to surveillance (n=38) or maintenance durvalumab (n=35) after 18 weeks of first-line platinum-based chemotherapy in the PLATFORM trial (NCT02678182) according to the 12-week progression-free rate. We measured the accuracy as the area under the receiver operating characteristics curve (AUROC) to determine the prognostic and predictive power of each marker set. We conducted a stratified 3-fold cross-validation, repeated 5 times and report the overall AUROC results. Results: Molecular markers alone yielded an AUROC of 0.5581±0.0939 on the surveillance arm, 0.6671±0.1479 on the treatment arm, and 0.6376±0.0958 for both the arms. Digital histological markers alone yielded an AUROC of 0.8952±0.0638, 0.8995±0.0719 and 0.8488±0.0700 on surveillance, immunotherapy and both arms, respectively. When using these two sets of markers together for both arms, molecular markers offered a limited improvement (around 0.02). Patients with TMB in the highest tertile were associated with lower likelihood of having progressive disease 12 weeks after randomization. Interestingly, dHis markers from morphology of connective and inflammatory nuclei were highly predictive for treatment benefit. Conclusions: Preliminary results suggest digital histological markers offer significant improvement over PD-L1 and TMB markers alone for predicting benefit from immunotherapy in EG adenocarcinoma with the added advantages of scalable, rapid, low-cost and objective quantification on routine histology sections. We are further validating their effectiveness on a larger cohort.