Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects

Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) increases rapidly in prevalence beyond age 60 and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in hematopoietic stem and progenitor cells (HSPCs). Because mutations in HSPCs often drive leukemia, we hypothesized that HSPC fitness substantially contributes to transformation from CHIP to leukemia. HSPC fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. If mutations in different genes lead to distinct fitness advantages, this could enable patient stratification. We quantified the fitness effects of mutations over 12 years in older age using longitudinal sequencing and developed a filtering method that considers individual mutational context alongside mutation co-occurrence to quantify the growth potential of variants within individuals. We found that gene-specific fitness differences can outweigh inter-individual variation and, therefore, could form the basis for personalized clinical management.

Defining clonal hematopoiesis of indeterminate potential: evolutionary dynamics and detection under aging and inflammation

“…They have proposed a novel algorithmic approach, LiFT, for CHIP detection that is better able to detect relevant variants compared to VAF thresholding at >=2%. Using LiFT, Robertson et al (2022) were able to identify high-fitness CHIP mutations existing at very low variant allele frequencies. Abelson et al (2018) proposed two models for predicting transition to acute myeloid leukemia (AML), one based on the somatic mutations in pre-AML and benign ARCH cases, and the other on clinical data commonly stored in electronic health records (e.g., complete blood count data).…”

“…Reliably distinguishing high-risk CHIP diagnoses (i.e., in those who are likely to develop leukemia or are predisposed to cardiovascular [CVD] and/or pulmonary disease) from low-risk cases remains an important step in effective preventative efforts and management of CHIP as a condition. While no single definitive biomarker exists to differentiate the trajectory of benign, canonically age-related clonal hematopoiesis (ARCH) from that which will eventually progress to hematological malignancy, a number of risk factors have been associated with the likelihood of malignancy and can be used as indicators for closer or more frequent monitoring, or the possibility of therapeutic intervention, such as clone size, the identity, type, and count of driver mutations, and mutations in splicing factor genes, among other factors (Miller and Steensma 2020;Robertson et al 2022) (Figure 1).…”

Diagnostic Approach, Clinical Implications and Management of Clonal Cytopenia of Undetermined Significance

“…43 Moreover, CHIP clones have shown a higher repopulation capacity in the context of autologous HSCT 44 and the use of CHIP donors seems safe in allo-HSCT. 45 Moreover, different mutations may have gene-specific fitness effects in clonal dominance, 46 however, the differential impact of CHIP mutations and their co-occurrence needs further investigation.…”

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures

“…And these developments might even be further revalorized by their application in concert with AF2. Having access to accurate structures, as well as multiple configurational states could result in increased application of structure based DD projects, a proof of that is AlphaFold-Database [18,19], which has been widely used in many projects [20,21]. Thus, it is important to assess the performance of AF2 structures when combined with current molecular modeling methods, an aspect that has already gained significant attention elsewhere [22].…”