Supplementary Figure S2 from plasmaCHORD: A Machine Learning Approach to Distinguish Clonal Hematopoiesis–Derived Variants in Liquid Biopsies from Patients with Solid Tumors

Le résumé fourni par la source

Assessment of fragmentomic features. (A-B) Illustrative examples of fragmentome distribution differences seen between mutant and wild-type reads from CH-origin versus tumor origin variants. (A) Tumor-derived plasma variant TP53 p.S215R with fragment length density left-shifted (shorter) for mutant compared to wild-type fragments (middle), and with the fragment endpoints relative to mutation location closer together and left-shifted in mutant versus wild-type fragments (bottom). (B) CH-origin variant TP53 p.M237I with overlapping fragment length densities (middle) and fragment endpoint locations (bottom) between mutant and wild-type reads. (C) The serial cohort used to assess fragmentomic features over time. Each column represents a variant, with points on the y-axis depicting VAF at baseline (black) and at follow-up (gray) time points. This cohort included patients with colorectal, esophageal, and NSCLC, stage I-III and included both tumor-origin and WBC-origin plasma variants. (D) Pearson’s product moment correlation of fragmentomic feature values at baseline and follow-up timepoints. Correlation of features between baseline and follow-up timepoint was calculated regardless of variant origin (black bars). Features that were correlated over time with p-value <0.05 were subsequently used for development of the machine learning model. Correlation of features between baseline and follow-up timepoint was also calculated for the subset of tumor-derived variants (red bars) and CH-derived variants (blue bars), demonstrating similar correlation of features over time regardless of variant origin.

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