  Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for
immune checkpoint inhibitor therapy. However, MSI status is not routinely
tested in prostate cancer, in part due to low prevalence and assay cost. As
such, prediction of MSI status from hematoxylin and eosin (H&E) stained
whole-slide images (WSIs) could identify prostate cancer patients most likely
to benefit from confirmatory testing and becoming eligible for immunotherapy.
Prostate biopsies and surgical resections from de-identified records of
consecutive prostate cancer patients referred to our institution were analyzed.
Their MSI status was determined by next generation sequencing. Patients before
a cutoff date were split into an algorithm development set (n=4015, MSI-H 1.8%)
and a paired validation set (n=173, MSI-H 19.7%) that consisted of two serial
sections from each sample, one stained and scanned internally and the other at
an external site. Patients after the cutoff date formed the temporal validation
set (n=1350, MSI-H 2.3%). Attention-based multiple instance learning models
were trained to predict MSI-H from H&E WSIs. The MSI-H predictor achieved area
under the receiver operating characteristic curve values of 0.78 (95% CI
[0.69-0.86]), 0.72 (95% CI [0.63-0.81]), and 0.72 (95% CI [0.62-0.82]) on the
internally prepared, externally prepared, and temporal validation sets,
respectively. While MSI-H status is significantly correlated with Gleason
score, the model remained predictive within each Gleason score subgroup. In
summary, we developed and validated an AI-based MSI-H diagnostic model on a
large real-world cohort of routine H&E slides, which effectively generalized to
externally stained and scanned samples and a temporally independent validation
cohort. This algorithm has the potential to direct prostate cancer patients
toward immunotherapy and to identify MSI-H cases secondary to Lynch syndrome.
