Therapy-Related Clonal Hematopoiesis in Patients with Non-hematologic Cancers Is Common and Associated with Adverse Clinical Outcomes

Abstract

Clonal hematopoiesis (CH), as evidenced by recurrent somatic mutations in leukemia-associated genes, commonly occurs among aging human hematopoietic stem cells. We analyzed deep-coverage, targeted, next-generation sequencing (NGS) data of paired tumor and blood samples from 8,810 individuals to assess the frequency and clinical relevance of CH in patients with non-hematologic malignancies. We identified CH in 25% of cancer patients, with 4.5% harboring presumptive leukemia driver mutations (CH-PD). CH was associated with increased age, prior radiation therapy, and tobacco use. PPM1D and TP53 mutations were associated with prior exposure to chemotherapy. CH and CH-PD led to an increased incidence of subsequent hematologic cancers, and CH-PD was associated with shorter patient survival. These data suggest that CH occurs in an age-dependent manner and that specific perturbations can enhance fitness of clonal hematopoietic stem cells, which can impact outcome through progression to hematologic malignancies and through cell-non-autonomous effects on solid tumor biology.

Keywords: biological sciences; cancer; cancer genomics; diseases; genetics; health sciences; hematological cancer; hematological diseases; mutation.

Figure 1

Characteristics of clonal hematopoiesis (CH) mutations identified in the cohort. A. Number of mutations harbored per patient. B. Density of mutations by variant allele fraction in blood and tumor tissue C. Patterns of mutation co-occurrence and number of patients with mutations in ten most recurrently mutated genes. The first 1912 patients in the cohort underwent sequencing with a 341-gene assay, which did not include PPM1D but included all other genes listed. The remaining patients (N=6,898) underwent sequencing with a 410-gene assay, which included all genes shown. D. Distribution of DNMT3A mutations within cohort. Gene models are based on uniprot database. E. Distribution of PPM1D mutations within cohort. F. CH association with age and age distribution of patients in cohort.

Figure 2

A. Detailed tumor types and percent of patients with clonal hematopoiesis and corresponding age distributions. This includes the 16 most frequently sequenced tumors, and patients with less common tumors are not included on this figure. Note that for detailed tumor types depicted, that Non-Small Cell Lung Cancer does not include Small Cell Lung Cancers or Mesotheliomas. B. Effect of smoking on observed clonal hematopoiesis (CH) mutations. Rates of different substitution types that lead to silent and non-silent CH mutations are compared between four groups: Treatment naïve never smokers, treatment naïve smokers, previously treated (chemotherapy, RT, or both) never smokers and previously treated (chemotherapy, RT, or both) smokers. C. Rates of different substitutions with their nucleotide context are compared amongst the same four groups of patients.

Figure 3

Clonal hematopoiesis (CH) and clonal hematopoiesis in presumptive drivers (CH-PD) and associations with subsequent hematologic cancers and overall survival. A. Incidence of new hematologic cancer for patients with and without CH. B. Incidence of new hematologic cancer for patients with and without CH-PD. C. OS in non-CH vs. CH, all ages. Time-point estimates for non-CH vs. CH are 12-month OS: 0.70 (0.68–0.73) vs. 0.74 (0.73–0.76); 18-month OS: 0.57 (0.54–0.60) vs. 0.64 (0.62–0.66); and 24-month OS: 0.50 (0.46–0.53) vs. 0.55 (0.53–0.57). D. OS in non-CH-PD vs. CH-PD, all ages. Time-point estimates for non-CH-PD vs. CH-PD are 12-month OS: 0.67 (0.62 vs. 0.74) vs. 0.74 (0.72–0.75); 18-month OS: 0.55 (0.49–0.63) vs. 0.63 (0.61–0.64); and 24-month OS: 0.48 (0.40–0.57) vs. 0.54 (0.52–0.56). Differences in the incidence of new hematologic cancer were compared using Gray’s test, while survival differences were assessed using the Peto & Peto modification of the Gehan-Wilcoxon test. Survival was compared both overall and stratified based on categories of age, gender, and smoking status.