223P - Artificial intelligence-based pathomics biomarker predict primary resistance to first-line treatment in metastatic colorectal cancers

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 ∼25% of patients, while ∼15% are primary refractory. Our study aims at developing an accurate response predictor via a pathomics model based on Hematoxylin & Eosin (H&E)-stained digital slides (WSI).

Methods

We trained an unsupervised bag-of-words artificial intelligence (AI)-based model on 62 patients classified as response outliers as follow: i) super-sensitive if they achieved CR or LLR >10 months to any SOC (N=20); ii) refractory if progression occurred at first disease reassessment (N=42). WSIs of the resected primary tumors were tiled into patches of 224x224 pixel (0.5μm/pixel). First-order and texture features were subsequently extracted from all tumoral tiles, and grouped into homogenous clusters through a 3x3 self-organized map. For each patient, the percentage of tiles belonging to each tiles’ cluster was computed and used by a dendrogram to create clusters of similar patients. Patients’ survival was calculated by landmark analysis logrank test. Main clinicopathological features were matched to treatment response by Fisher’s exact test.

Results

Primary resistant patients had significantly worse survival [logrank HR 4.2 95% CI (2.7-7.3)]. While outliers showed similar clinicopathological features (including stage, RAS/BRAF status, histotype and sidedness), pathomics signatures were significantly associated to primary resistance (p=.005). According to the unsupervised clustering, we classified as resistant patients belonging to four out of eight clusters obtaining an 88% (23/26) negative predictive value. We observed that patients classified as belonging to one of the four resistant clusters were predominantly characterized by a specific pattern of patches.

Conclusions

We demonstrated that a pathomics signature has the potential to predict resistance to SOC in MSS mCRC. Validation of these preliminary findings on a further cohort of 125 patients is ongoing.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy.

Funding

Fondazione AIRC under 5 per Mille 2018 - ID. 21091 program. Fondazione Oncologia Niguarda ONLUS.

Disclosure

G. Mauri: Financial Interests, Other, Honoraria: COR2ED. A. Sartore Bianchi: Financial Interests, Advisory Board: Amgen, Bayer, Novartis, Sanofi, Servier. M. Nieva Munoz: Financial Interests, Other, Travel support: Merck; Financial Interests, Invited Speaker, Speaker honoraria: Merck. N. Saoudi Gonzalez: Financial Interests, Other, Travel expenses: Amgen, Merck; Financial Interests, Speaker, Consultant, Advisor: Amgen. C. Marchiò: Financial Interests, Advisory Board: Bayer, Roche, AstraZeneca, Daiichi Sankyo. S. Siena: Financial Interests, Personal, Advisory Board, Advisory Board Member: Agenus, AstraZeneca, BMS, Checkmab, Daiichi Sankyo, GSK, Novartis, Seagen, T-One-Therapeutics. A. Bardelli: Financial Interests, Speaker, Consultant, Advisor: Illumina, Inivata, Guardant Health; Financial Interests, Stocks/Shares: NeoPhore; Financial Interests, Research Grant: NeoPhore, AstraZeneca, Inivata; Financial Interests, Advisory Board: Neophore, Inivata, Roche Genentech. All other authors have declared no conflicts of interest.