FFixR: A Machine Learning Framework for Accurate Somatic Mutation Calling from FFPE RNA-Seq Data in Cancer
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Formalin fixed paraffin embedded (FFPE) tissues are widely used in clinical and research settings, yet their use for detecting somatic mutations from RNA sequencing (RNA seq) is hindered by artefactual mutations introduced by cytosine deamination and strand-specific damage. Existing FFPE noise filtering tools are tailored to DNA-seq and rely on strand bias, rendering them unsuitable for RNA seq. Here, we present FFixR, a machine learning based framework that filters FFPE induced artefacts from RNA-seq data without requiring matched normal samples. Trained on FFPE melanoma samples with matched DNA, FFixR leverages allele-specific read counts, variant features, and mutational signature probabilities. FFixR removed up to 98% of artefactual mutations while maintaining ~92% recall of true variants. SHAP analysis revealed key feature interactions guiding model decisions. When applied to an independent cohort, FFixR restored the correlation between RNA and DNA derived tumor mutational burden (R^2 = 0.881) and recovered biologically meaningful mutational signatures. FFixR enables accurate somatic variant calling from FFPE RNA-seq data, expanding the utility of archival samples for research and clinical applications
