Biology subjects

Michiels, S.

Publications and source records attributed to Michiels, S..

2 recordsLinked to original sources

bioRxiv2026

SHERLOC: An interpretable deep learning model for longitudinal circulating tumor DNA data in survival analysis

Longitudinal circulating tumor DNA (ctDNA) measurements offer a noninvasive means to monitor treatment response, but clinical trial data present substantial methodological challenges due to high-dimensional short longitudinal ctDNA sequences and limited sample sizes. We introduce SHERLOC, a deep learning framework specifically designed for survival analysis using longitudinal on-treatment ctDNA data, which integrates shared temporal representations of gene-level variant allele frequencies, feature-specific temporal trajectories of panel-level ctDNA biomarkers, and survival-aware genomic representations pre-trained on a large pan-cancer tissue-biopsy dataset (MSK-CHORD), within an interpretable Cox proportional hazards framework. Benchmarked against diverse statistical, ensemble, and deep learning approaches in a non-small-cell lung cancer cohort from the phase III IMpower150 trial, SHERLOC consistently achieved superior survival discrimination and calibration, while remaining interpretable and robust to reductions in the number of available longitudinal liquid biopsy time points per patient. The resulting ctDNA-based risk score provided prognostic information both independent of and complementary to standard radiographic response assessments, and enabled patient stratification within homogeneous RECIST response groups--highlighting its potential as an early, non-invasive decision-support tool to guide treatment adaptation and patient management.

cancer biology↗

bioRxiv2024

Multimodal CustOmics: A Unified and Interpretable Multi-Task Deep Learning Framework for Multimodal Integrative Data Analysis in Oncology

Characterizing cancer poses a delicate challenge as it involves deciphering complex biological interactions within the tumors microenvironment. Histology images and molecular profiling of tumors are often available in clinical trials and can be leveraged to understand these interactions. However, despite recent advances in representing multimodal data for weakly supervised tasks in the medical domain, numerous challenges persist in achieving a coherent and interpretable fusion of whole slide images and multi-omics data. Each modality operates at distinct biological levels, introducing substantial correlations both between and within data sources. In response to these challenges, we propose a deep-learning-based approach designed to represent multimodal data for precision medicine in a readily interpretable manner. While demonstrating superior performance compared to state-of-the-art methods across multiple test cases, our approach also provides robust results and extracts various scores characterizing the activity of each modality and their interactions at the pathway and gene levels. The strength of our method lies in its capacity to unravel pathway activation through multimodal relationships and extend enrichment analysis to spatial data for supervised tasks. We showcase the efficiency and robustness of its predictive capacity and interpretation scores through an extensive exploration of multiple TCGA datasets and validation cohorts, underscoring its value in advancing our understanding of cancer. The method is publicly available in Github.

cancer biology↗