A computational framework for detecting low abundance clonal hematopoiesis mutations in large-scale tumor sequence datasets
Creators: name: Mahaganapathy, Vaidhyanathan
Publisher: name: No Publisher Supplied
Year: 2023

description: All blood cells in our body originate from hematopoietic stem cells. Clonal hematopoiesis of indeterminate potential (CHIP) is the expansion of hematopoietic stem cells that harbor mutations in genes recurrently altered in leukemia. CHIP can be found by advanced DNA sequencing methods in ~5% of individuals older than 60 and 15–25% of those older than 80. These mutations are also associated with a high risk of heart disease or development of leukemia and have also been detected in normal peripheral blood of cancer patients presenting with solid tumors. The mutational landscape of CHIP is dominated by alterations in DNMT3A, followed by TET2, ASXL1, and spliceosome genes. The documentation of CHIP prior to chemotherapeutic exposure suggests a permissive role in therapy-related secondary myeloid disease development in patients with solid tumors. Our prior analysis of clinical sequencing data from solid tumors detected CHIP clones in the solid tumor microenvironment, due to the presence of admixed mutated hematopoietic elements. We showed that CHIP-associated variants had significantly higher detection rates in solid tumor specimens from older patients across 21 cancer types. However, in contrast to results from sequencing peripheral blood, TET2 variants were detected at a higher rate compared to DNMT3A variants present within the solid tumor microenvironment. Despite the knowledge of presence of CHIP in solid tumor microenvironments, its detection in tumor specimens has been hindered by large cancer cohorts’ low depth of sequence, and its contribution to conditions in the tumor have remained under-investigated. In this project, we hypothesized that CHIP is present in the solid tumor microenvironment and it can be detected across large tumor datasets. To test this hypothesis, we first developed a scalable, and robust bioinformatics pipeline that is capable of analyzing large numbers of samples, and models locus-specific sequencing background error to discern low-abundance mutations in low-depth whole exome sequencing data. We have tested and optimized this improved agnostic variant caller for deployment on local high-performance computing platforms, containerized with Docker for easy application to any dataset, and deployed it on Cancer Genomics Cloud to facilitate broader access to the cancer research community. Next, we performed a pan-cancer analysis of CHIP and applied our framework to thousands of solid tumor and matched normal sample pairs from The Cancer Genome Atlas (TCGA). Our study successfully detected low abundance mutations in over 16,000 exome sequences and recovered hotspot CHIP mutations in 113 CHIP associated genes from 4 different TCGA Investigations of 4,233 samples without arbitrary thresholding of allele frequencies. Lastly, we demonstrated the applicability of our approach to high-depth sequencing datasets by analyzing CHIP in blood samples collected before and after treatment from breast cancer patients. It is still not possible to treat CHIP, despite its known negative effects and possible evolution to leukemia. However, our results from large groups of cancer patients will help with the important task of identifying CHIP and hence, those with a high risk of developing therapy-related hematological complications for continuous monitoring, and a rapid and efficient response.