Abstract A043: Validation of an artificial intelligence derived histological biomarker for gemcitabine response in resected pancreatic ductal adenocarcinoma (PDAC)

Abstract

Abstract Background: The prognosis for patients diagnosed with pancreatic ductal adenocarcinoma remains poor, even after successful resection. While multiple regimens have proven to improve outcomes following resection, no biomarkers routinely used in clinical practice can predict which regimen is optimal for an individual patient to facilitate a precision medicine approach. Artificial intelligence (AI) approaches can enable the identification of subvisual morphologic features in digital scans of routine histologic slides that are associated with specific treatment responses. Previous work had developed an AI-derived morphologic signature correlated with response to gemcitabine-based chemotherapy in resected PDAC specimens from The Cancer Genome Atlas (TCGA) (c-index:0.69). We validate this previously developed signature on an external cohort of postoperatively treated PDAC cases from a single institution. Methods: Digitized histological H&E TMA sections corresponding to 45 post-operatively treated resected PDAC patients from 2011-2015 were used in this study. Of the 45 patients, 22 were neoadjuvantly treated with either gemcitabine or 5-FU backbone cytotoxic chemotherapy. Using the histologic images, we extracted nuclei images from tissue regions using segmentation models and computed geometric features of these nuclei. Patients were stratified by the signature previously associated with gemcitabine response in a dataset from TCGA into low and high risk groups, and Disease Specific Survival (DSS) and Recurrence Free Survival (RFS) was compared between the stratified groups via Kaplan Meier estimators and log-rank test. Results: The morphologic signature previously found to be associated with gemcitabine treatment response stratified both DSS and RFS in the external cohort (log-rank test, DSS: p=0.03, RFS: p=0.01). A set of features describing variations in nuclear geometry were most correlated with the prediction, with increased variance being associated with higher risk. Kaplan-Meier analysis demonstrated the signature was able to separate the cohort robustly with a statistically significant hazard ratio of 0.45 [95% CI 0.22, 0.93] for DSS and 0.39 [95% CI 0.19, 0.77] for RFS. The median DSS was 16 months (95% CI: 10.9, 50.1) in the high risk group and 43 months (95% CI: 26.8, 63.8) in the low risk group, a difference of 27 months. Similarly, the median RFS was 9.1 months (95% CI: 6.1, 14.7) in the high risk group and 22.6 months (95% CI: 14.1, 44.8) in the low risk group, a difference of 13.5 months. Conclusion: The AI derived Valar morphological signature previously found to be associated with gemcitabine treatment response effectively stratifies patients into low and high risk groups in an external resected PDAC cohort (hazard ratio: 0.45 for DSS, 0.39 for RFS). Citation Format: Vrishab Krishna, Viswesh Krishna, Ekin Tiu, Vivek Nimgaonkar, Damir Vrabac, Katelyn Smith, Anirudh Joshi, Aatur Singhi, Eric Collisson. Validation of an artificial intelligence derived histological biomarker for gemcitabine response in resected pancreatic ductal adenocarcinoma (PDAC) [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A043.