A Biologically Grounded Structural Causal Model Enables cfRNA Specific In-Context Learning

Abstract

Cell-free RNA (cfRNA) in human plasma provides a minimally invasive readout of tissue physiology, yet its extreme sparsity, heavy-tailed abundance distributions, and weak but structured correlation patterns create major challenges for machine learning. Conventional tabular foundation models are typically trained on synthetic datasets that assume generic statistical properties, and as a result, they fail to capture the distinctive characteristics of cfRNA. These limitations become even more pronounced in settings where labeled data are scarce. We introduce cfRNA-ICL, a cfRNA-specific in-context learning model trained entirely on tasks generated from a biologically grounded structural causal model (SCM). The SCM produces realistic cfRNA-like scenarios by incorporating empirical measurements of gene-level dropout, overdispersion, tissue-mixture-driven latent factors, compositional variability, and sequencing noise. This synthetic task universe enables cfRNA-ICL to acquire inductive biases that closely reflect the geometry of real cfRNA data. Across multiple cancer classification benchmarks, cfRNA-ICL demonstrates consistently higher performance than tabular ICL models trained on generic synthetic data. The gains are most sub-stantial in few-shot settings, where the model benefits from its exposure to cfRNA-specific statistical regimes during meta-training. Representation-level analyses further show that cfRNA-ICL organizes samples into biologically coherent manifolds, preserving cancer-type identity without the use of supervised constraints. This finding indicates strong alignment between the synthetic prior and real cfRNA structure. Taken together, these results show that domain-aware generative priors can meaningfully enhance in-context learning for biological tabular data. cfRNA-ICL provides a generalizable framework for cfRNA modeling and establishes a practical path toward foundation-scale models that are intrinsically adapted to the statistical landscape of plasma cfRNA.