A Deep Generative Mixture Model for Enhancing Circulating Tumor DNA Estimation

Mathilde Hartvig Diekema, Christian Marius Lillelund, Mads Heilskov Rasmussen, Claus Lindbjerg Andersen, Jakob Skou Pedersen

Keywords: Deep Learning, ctDNA Prediction, Cancer, Generative Models, Whole Genome Sequencing

TL;DR: We propose a deep generative mixture model that aggregates noisy WGS read-level signals for ctDNA estimation, showing proof-of-concept recovery in synthetic data and preliminary latent structure in real plasma samples.

Abstract:

Circulating tumor DNA (ctDNA) provides a minimally invasive measure of tumor
burden, but estimating ctDNA fraction from plasma is difficult when
tumor-derived molecules are rare relative to the cell-free DNA background. We
introduce a deep generative mixture model that estimates sample-level ctDNA
fraction from read-base events in whole-genome sequencing (WGS). Each event is modeled
as a noisy observation from healthy-derived or tumor-derived components, mixed by
the sample-specific ctDNA fraction. To capture batch effects and
patient-specific background noise, sample embeddings are learned jointly with a
decoder that uses read-level covariates, sequence context, and tumor-catalog
information to model allele probabilities. Self-normalized
inverse-probability weighting handles stratified genome-wide sampling, and one
trained model supports tumor-informed and tumor-agnostic inference. We apply the
framework to serial plasma WGS from a stage III colorectal
cancer cohort.

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Submission Number: 128