545P - HIBRID: Histology and ct-DNA based risk-stratification with deep learning

Background

Although surgical resection is the standard therapy for patients with Stage II/III colorectal cancer (CRC) and those with resectable oligometastasis, recurrence rates exceed 30% and 60%, respectively. Circulating tumor DNA (ctDNA) has emerged as a promising predictor of recurrence through the detection of molecular residual disease (MRD). However, morphological tissue information remains unseen by ctDNA. Deep Learning (DL) has been shown to be able to predict prognosis directly from routine histomorphology. By integrating MRD status derived from ctDNA with DL–models trained on histomorphology, we aim to improve patient stratification and recurrence detection.

Methods

We developed a DL pipeline utilizing vision transformers to predict disease free survival (DFS) based on histological hematoxylin&eosin (H&E) stained whole slide images (WSIs) from n=3321 patients with resectable stage II-IV CRC. This model was trained on the DACHS cohort (n=1766) and subsequently validated on the GALAXY cohort (n=1555). Patients were categorized into high-risk or low-risk groups based on the DL-prediction scores. In the GALAXY cohort, we combined the DL-scores with the MRD status from the four weeks post-surgery to perform survival analysis. The prognostic efficacy of these biomarkers was assessed using Kaplan-Meier methods and hazard ratios derived from Cox models.

Results

In the GALAXY cohort the DL-models categorized 307 patients as high-risk and 1248 patients as low-risk (p<0.001; HR of 2.60, CI 95% 2.11-3.21). ctDNA analysis alone stratified 241 patients as MRD-positive and 1312 patients as MRD-negative (p<0.001; HR of 11.4, CI 95% 9.28-14). Combining these biomarkers, the DFS was significantly stratified in both the MRD-positive group into high-risk (n=81) and low-risk (n=160) with a HR of 1.58 (CI 95% 1.17-2.11; p=0.002) as well as in the MRD negative group into high-risk (n=226) and low-risk (n=1088) with a HR of 2.37 (CI 95% 1.73-3.23; p<0.001).

Conclusions

Our results show that even within highly prognostic subsets as yielded by MRD-based stratification, DL is able to further identify patients at high risk of relapse even without measurable MRD. This can be used to improve follow-up and enable early intervention strategies in this vulnerable subset of CRC patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

JNK is supported by the German Cancer Aid (DECADE, 70115166), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A; TANGERINE, 01KT2302 through ERA-NET Transcan), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (TransplantKI, 01VSF21048) the European Union’s Horizon Europe and innovation programme (ODELIA, 101057091; GENIAL, 101096312), the European Research Council (ERC; NADIR, 101114631) and the National Institute for Health and Care Research (NIHR, NIHR203331) Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was funded by the European Union. Views and opinions expressed are however those of thÏe author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. German Cancer Aid, German Federal Ministry of Education and Research, German Academic Exchange Service, German Federal Joint Committee, European Union’s Horizon Europe and innovation programme, European Research Council, National Institute for Health and Care Research.

Disclosure

C.M.L. Loeffler: Financial Interests, Personal, Speaker, Consultant, Advisor: AstraZeneca. Y. Nakamura: Financial Interests, Personal, Speaker, Consultant, Advisor: Chugai, Guardant health, Genomedia, Daiichi Sankyo, Seagan, Roche Diagnsotics. M. Liu: Financial Interests, Institutional, Funding: Eisai, Exact Sciences, Genentech, Genomic Health, GRAIL, Menarini Silicon Biosystems, Merck, Novartis, Seattle genetics, Tesaro; Financial Interests, Personal, Sponsor/Funding: AstraZeneca, Gemomic Health, Ionis; Financial Interests, Institutional, Advisory Board: Celgene, Roche/Genentech, Genomic Health, GRAIL, Merck, Pfizer, Seattle Genetics, Syndax; Financial Interests, Personal, Advisory Board: Syndax. A. Jurdi: Financial Interests, Personal, Stocks or ownership: Natera. H. Bando: Financial Interests, Personal, Speaker, Consultant, Advisor: Ono, Taiho, Eli Lilly. J.N. Kather: Financial Interests, Personal, Speaker, Consultant, Advisor: Owkin, DoMore, Panakeia, Scailyte, Mindpeak, MultiplexDx, AstraZeneca, Bayer, Eisai, Janssen, MSD, BMS, Roche, Pfizer, Freseius; Financial Interests, Personal, Stocks/Shares: StraifAI; Non-Financial Interests, Personal and Institutional, Research Grant: GSK. T. Yoshino: Financial Interests, Funding: Taiho, Chugai, Eli Lilly, Merck, Bayer Yakuhin, Ono, MSD, Ono, Sanofi, Daiichi Sankyo, Parexel, Pfizer, Taiho, MSD, Amgen, Genomedia, Sysmex, Chugai, Nippon Boerhinger Ingelheim S.S.. All other authors have declared no conflicts of interest.