  Failures in Phase 3 clinical trials contribute to expensive cost of drug
development in oncology. To drastically reduce such cost, responders to an
oncology treatment need to be identified early on in the drug development
process with limited amount of patient data before the planning of Phase 3
clinical trials. Despite the challenge of small sample size, we pioneered the
use of deep-learning derived digital pathology scores to identify responders
based on the immunohistochemistry images of the target antigen expressed in
tumor biopsy samples from a Phase 1 Non-small Cell Lung Cancer clinical trial.
Based on repeated 10-fold cross validations, the deep-learning derived score on
average achieved 4% higher AUC of ROC curve and 6% higher AUC of
Precision-Recall curve comparing to the tumor proportion score (TPS) based
clinical benchmark. In a small independent testing set of patients, we also
demonstrated that the deep-learning derived score achieved numerically at least
25% higher responder rate in the enriched population than the TPS clinical
benchmark.
