Efficient and generalizable prediction of molecular alterations in multiple cancer cohorts using H&E whole slide images

Molecular testing of tumor samples for targetable biomarkers is restricted by a lack of standardization, turnaround-time, cost, and tissue availability across cancer types. Additionally, targetable alterations of low prevalence may not be tested in routine workflows. Algorithms that predict DNA alterations from routinely generated hematoxylin and eosin (H&E)-stained images could prioritize samples for confirmatory molecular testing. Costs and the necessity of a large number of samples containing mutations limit approaches that train individual algorithms for each alteration. In this work, models were trained for simultaneous prediction of multiple DNA alterations from H&E images using a multi-task approach. Compared to biomarker-specific models, this approach performed better on average, with pronounced gains for rare mutations. The models reasonably generalized to independent temporal-holdout, externally-stained, and multi-site TCGA test sets. Additionally, whole slide image embeddings derived using multi-task models demonstrated strong performance in downstream tasks that were not a part of training. Overall, this is a promising approach to develop clinically useful algorithms that provide multiple actionable predictions from a single slide.

Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images

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.

Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas from Hematoxylin and Eosin Images

MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC) and is the subject of active drug development. Challenges in identifying patients for these therapies include lack of access to validated testing, such as standardized immunohistochemistry (IHC) assessment, and consumption of valuable tissue for a single gene/protein assay. Development of pre-screening algorithms using routinely available digitized hematoxylin and eosin (H&E)-stained slides to predict MET overexpression could promote testing for those who will benefit most. While assessment of MET expression using IHC is currently not routinely performed in NSCLC, next-generation sequencing is common and in some cases includes RNA expression panel testing. In this work, we leveraged a large database of matched H&E slides and RNA expression data to train a weakly supervised model to predict MET RNA overexpression directly from H&E images. This model was evaluated on an independent holdout test set of 300 over-expressed and 289 normal patients, demonstrating an ROC-AUC of 0.70 (95th percentile interval: 0.66 - 0.74) with stable performance characteristics across different patient clinical variables and robust to synthetic noise on the test set. These results suggest that H&E-based predictive models could be useful to prioritize patients for confirmatory testing of MET protein or MET gene expression status.

Imaging-based histological features are predictive of MET alterations in Non-Small Cell Lung Cancer

MET is a proto-oncogene whose somatic activation in non-small cell lung cancer leads to increased cell growth and tumor progression. The two major classes of MET alterations are gene amplification and exon 14 deletion, both of which are therapeutic targets and detectable using existing molecular assays. However, existing tests are limited by their consumption of valuable tissue, cost and complexity that prevent widespread use. MET alterations could have an effect on cell morphology, and quantifying these associations could open new avenues for research and development of morphology-based screening tools. Using H&E-stained whole slide images (WSIs), we investigated the association of distinct cell-morphological features with MET amplifications and MET exon 14 deletions. We found that cell shape, color, grayscale intensity and texture-based features from both tumor infiltrating lymphocytes and tumor cells distinguished MET wild-type from MET amplified or MET exon 14 deletion cases. The association of individual cell features with MET alterations suggested a predictive model could distinguish MET wild-type from MET amplification or MET exon 14 deletion. We therefore developed an L1-penalized logistic regression model, achieving a mean Area Under the Receiver Operating Characteristic Curve (ROC-AUC) of 0.77 +/- 0.05sd in cross-validation and 0.77 on an independent holdout test set. A sparse set of 43 features differentiated these classes, which included features similar to what was found in the univariate analysis as well as the percent of tumor cells in the tissue. Our study demonstrates that MET alterations result in a detectable morphological signal in tumor cells and lymphocytes. These results suggest that development of low-cost predictive models based on H&E-stained WSIs may improve screening for MET altered tumors.