Supplementary Figure S6 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

Model performance in the training cohort by number of mutant reads supporting the mutant DNA molecules. Receiver operator curve and AUC values for subsets of training cohort as determined by the number of mutant reads. As the number of mutant reads decreases, the margin of error in the estimated value of the summary statistics comparing distributions between wild-type and mutant reads increases. To evaluate the impact of this on the overall model performance, we determined the model performance in subsets of variants based on number of mutant reads: 3-5 reads inclusive, 6-10 reads, 11-20 reads, 21-100 reads, and >100 reads. For variants with only 3-5 mutant reads, the model performed slightly less well, with AUC = 0.841. For all subsets with >5 mutant reads, the model performed similarly well with AUC 0.89-0.939.

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