Abstract Background: Fusion detection with next-generation sequencing is challenging due to short read fragments, which can fail to fully resolve complex genomic rearrangements and map intronic breakpoints that lie outside targeted capture regions. Tumor methylation patterns, which reflect the functional state of cancer cells and do not rely on breakpoint coverage, are less impacted by sequencing fragment length and provide a robust orthogonal signal to augment genomic-based fusion calling. We developed a cell-free DNA (cfDNA) methylation-based fusion epigenotyping method to rescue fusions missed by genomic-based methods, focusing on ALK fusion detection in non-small lung cancer (NSCLC) to inform ALK inhibitor therapy selection. Methods: NSCLC samples were processed using Guardant360 Liquid test (Guardant Health, Palo Alto, CA). Genome-wide cfDNA methylation profiles across thousands of regulatory regions together with genomic molecule support, were used to train a binary classifier that discriminates EML4-ALK fusion-positive from fusion-negative NSCLC. A logistic regression model was trained on 175 EML4-ALK fusion-positive and 175 fusion-negative samples. Concordance with NSCLC tissue samples was assessed by comparing tissue EML4-ALK-associated differentially methylated regions (DMRs, p0.05) from TCGA data to fusion DMRs from Guardant360 Liquid cfDNA samples. To ensure clinical-grade specificity, a decision threshold targeting 99% specificity was calibrated on 11,000 genomic fusion-negative NSCLC samples. Model performance was evaluated on 102 independent positive cases (63 genomically detected, 39 genomically missed) with epigenomic tumor fraction 0.1% from samples with (a) ALK inhibitor resistance mutations, (b) prior ALK inhibitor treatment, or (c) longitudinal history of genomic fusion detection. Fusion rescue rate was defined as the fraction of genomically missed fusions rescued by the epigenotyping classifier. Results: Methylation concordance analysis between TCGA NSCLC tissues and Guardant360 Liquid cfDNA samples showed significant overlap, with 64% (1384/2168) of tissue EML4-ALK-associated differentially methylated regions (DMRs, p0.05) also significant in cfDNA. In the test cohort, the epigenotyping classifier achieved 74% sensitivity, detecting 100% of fusions identified by the genomic caller. Among genomically missed cases, the classifier rescued 31% (12/39) of fusions. Conclusion: A cfDNA methylation-based fusion epigenotyping approach provides a high-specificity orthogonal signal that augments genomic fusion detection, recovering a substantial fraction of EML4-ALK fusions missed by the genomic method. Clinically, patients with rescued ALK fusions could be considered for effective and low toxicity targeted ALK inhibitor therapies. Citation Format: Laura Tung, Anton Valouev, Justin Odegaard, Lauren Lawrence, Nicole Zhang, Martina Lefterova, Matthew Ellis, Tingting Jiang, Sheila Solomon, Darya Chudova, . Fusion epigenotyping using cell-free DNA methylation improves detection of actionable ALK fusions in non-small cell lung cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1409.
Tung et al. (Fri,) studied this question.