Abstract
Abstract Clonal hematopoiesis (CH), such as clonal hematopoiesis of indeterminant potential (CHIP), is diagnosed based on somatic genomic alterations in the absence of hematologic malignancy. At present, CHIP is diagnosed using peripheral blood, where putative driver point mutations and small insertions/deletions whose variant allele frequency is greater or equal to two percent. Generally, the prevalence of CH increases as an individual ages and conveys a risk for progression to a malignancy. CH is thought to be driven by the underlying hematopoietic stem cells of an unknown quantity, with estimates in the literature for stem cell numbers differing by orders of magnitude. Previously, we developed a method using fluctuating CpG (fCpG) sites to serve as a fluctuating methylation clock to uncover stem cell dynamics in glandular tissues and orthogonally validated our method using publicly available datasets of human blood from normal cohorts and malignant cohorts. Here we expand on this work by presenting 38 new patients with distinct VAF groups from 1-2% VAF up to greater than 10% VAF for putative drivers with corresponding DNA methylation profiles using the Illumina EPIC array platform. We identify fCpG from our normal and CHIP cohorts to train and validate a machine learning approach that allows us to diagnose CHIP without DNA sequencing. Importantly, our approach allows for the identification of patients who may have CH driven by structural variants such as copy number alterations. We use this method to evaluate two publicly available methylation datasets of reportedly normal patients (n=656 and n=732) showing that evidence of CHIP can be found in 19% and 29% of these datasets, respectively. We then evaluate copy number differences in burden within our CHIP cohort and these newly identified CHIP cohorts. Using a mechanistic model of hematopoietic stem cells containing fCpGs we examine the temporal dynamics of competing founder CHIP drivers and the number of stem cells in the hematopoietic stem cell compartment. Citation Format: Ryan O. Schenck, Niels Asger Jakobsen, Virginia Turati, Darryl Shibata, Paresh Vyas, Simon Leedham, Alexander R.A. Anderson. Mutation agnostic diagnosis of clonal hematopoiesis of indeterminate potential (CHIP) using fluctuating methylation clocks [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A019.
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