ECLIPSE: A Composable Pipeline for Predicting ecDNA Formation, Evolution, and Therapeutic Vulnerabilities in Cancer

TL;DR

ECLIPSE introduces a comprehensive, rigorous framework for ecDNA analysis in cancer, improving prediction accuracy, modeling dynamics, and identifying therapeutic vulnerabilities without relying on specialized sequencing.

Contribution

The paper presents the first methodologically sound, modular pipeline for ecDNA prediction, modeling, and targeting, emphasizing rigorous feature curation and domain physics integration.

Findings

ecDNA status is predictable without specialized sequencing (AUROC 0.812)

Physics-constrained neural SDEs achieve r > 0.997 in modeling ecDNA dynamics

Causal inference identifies therapeutic vulnerabilities with 80x enrichment over chance

Abstract

Extrachromosomal DNA (ecDNA) represents one of the most pressing challenges in cancer biology: circular DNA structures that amplify oncogenes, evade targeted therapies, and drive tumor evolution in ~30% of aggressive cancers. Despite its clinical importance, computational ecDNA research has been built on broken foundations. We discover that existing benchmarks suffer from circular reasoning -- models trained on features that already require knowing ecDNA status -- artificially inflating performance from AUROC 0.724 to 0.967. We introduce ECLIPSE, the first methodologically sound framework for ecDNA analysis, comprising three modules that transform how we predict, model, and target these structures. ecDNA-Former achieves AUROC 0.812 using only standard genomic features, demonstrating for the first time that ecDNA status is predictable without specialized sequencing, and that careful feature curation matters more than complex architectures. CircularODE captures ecDNA's unique stochastic dynamics through physics-constrained neural SDEs, achieving r > 0.997 on experimental data via zero-shot transfer. VulnCausal applies causal inference to identify therapeutic vulnerabilities, achieving 80x enrichment over chance and 3.7x higher validation than standard approaches by filtering spurious correlations. Together, these modules establish rigorous baselines for an emerging application area and reveal a broader lesson: in high-stakes biomedical ML, methodological rigor -- eliminating leakage, encoding domain physics, addressing confounding -- outweighs architectural innovation. ECLIPSE provides both the tools and the template for principled computational oncology.