Abstract 900: Development and validation of an artificial-intelligence-based pathomics biomarker to predict resistance to first-line treatment in metastatic colorectal cancer
Authors: Luca Lazzari, Gianluca Mauri, Valentina Giannini, Debora Cafaro, Giulia Nicoletti, Caterina Marchiò, Andrea Sartore-Bianchi, Federica Marmorino, Maria Nieva Munoz, Nadia Saoudi Gonzalez, Alberto Puccini, Chiara Cremolini, Clara Montagut, Elena Elez, Stefania Sciallero, Enrico Berrino, Martina Carullo, Pietro Paolo Vitiello, Maria Costanza Aquilano, Martina Di Como, Emanuela Bonoldi, Salvatore Siena, Alberto Bardelli, Daniele Regge, Silvia Marsoni
Journal: Cancer Research (2024)

Abstract Background: The backbone chemotherapy of first-line standard of care (SOC) for microsatellite stable (MSS) metastatic colorectal cancer (mCRC) combines 5-Fluorouracil to oxaliplatin and/or irinotecan. There are no biomarkers to predict response, which is complete or long-lasting (CR/LLR) in 20-25% of patients, while 10-15% are primary refractory. The aim of this work is to develop a predictive biomarker, based on digital pathology images, that can help stratify patients according to their risk of resistance. Methods: We trained a supervised bag-of-words artificial intelligence (AI)-based model on a cohort of response outliers mCRC patients classified as “really sensitive” (RS) if they achieved CR or LLR &amp;gt;10 months to any SOC, or “really resistant” (RR) if progression occurred at first disease reassessment. Whole-slide Imaging (WSI) of the resected primary tumors were tiled into patches of 224 × 224 pixel (0.5μm/pixel). First-order and texture features were subsequently extracted from all tumoral tiles and grouped into homogenous clusters through a k-means algorithm (k=6). For each patient, the percentage of tiles belonging to each tiles’ cluster was computed to represent new features (called bag of words) with whom different machine learning classifiers were trained. Main clinicopathological features were matched to treatment response by Fisher’s exact test. Results: To date, we analyzed 82 response outlier patients, of whom 35 were classified as RS and 46 RR. Of them, patients identified at Italian centers were used as construction cohort (N=47; 27 RR and 20 RS) and those identified at Spanish centers were used a validation cohort (N=35; 19 RR and 16 RS). The best result was obtained using a stepwise logistic regression, reaching a negative predictive value (NPV) of 90% (18/20; 95% CI=70-97%) and 71% (10/14; 95% CI=49-87%), in the construction and validation sets. No standard clinicopathological features (including stage, RAS/BRAF status, histology and sidedness) was associated with the chance of being RR. Conclusions: We demonstrated that a pathomics signature has the potential to predict resistance to SOC in MSS mCRC. Further validation of these preliminary findings on a larger cohort of response outlier patients is ongoing. Citation Format: Luca Lazzari, Gianluca Mauri, Valentina Giannini, Debora Cafaro, Giulia Nicoletti, Caterina Marchiò, Andrea Sartore-Bianchi, Federica Marmorino, Maria Nieva Munoz, Nadia Saoudi Gonzalez, Alberto Puccini, Chiara Cremolini, Clara Montagut, Elena Elez, Stefania Sciallero, Enrico Berrino, Martina Carullo, Pietro Paolo Vitiello, Maria Costanza Aquilano, Martina Di Como, Emanuela Bonoldi, Salvatore Siena, Alberto Bardelli, Daniele Regge, Silvia Marsoni. Development and validation of an artificial-intelligence-based pathomics biomarker to predict resistance to first-line treatment in metastatic colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 900.