Authors

  1. Kumar Das, Dibash PhD

Article Content

Hematologic malignancies progress through a broad differentiation hierarchy rooted in a population of hematopoietic stem cells (HSCs). However, genetic diagnostic data have been scarce in the field of hematology due to the low rate of proliferation of lymphoid and myeloid diseases.

 

As individuals age, somatic mutations are observed with increasing frequency in HSCs or their pluripotent descendants in a process known as age-related clonal hematopoiesis (ARCH). While ARCH is a known risk factor for blood malignancies such as acute myeloid leukemia (AML) and various cardiovascular (CVD) conditions, "only a small proportion of individuals who harbor ARCH driver mutations progress to disease, and mechanisms driving the transformation to malignancy remain unclear. As such, to fully exploit the predictive potential of ARCH to guide clinical decisions for cancer, it is critical to determine the evolutionary processes governing the impact of age-associated mutations on hematopoietic fitness" (Nat Commun 2021; https://doi.org/10.1038/s41467-021-25172-8).

 

This new research has the potential to significantly advance the early detection and treatment of AML by providing insights into why some with ARCH go on to develop AML and others don't.

 

The researchers "generated somatic variant calls from 92 individuals who subsequently progressed to AML (preleukemic cases), and 385 age-matched and sex-matched healthy controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) study."

 

From these samples, the team computationally generated more than 5 million blood populations and performed advanced statistical and deep genomic interrogation of blood profiles to study the underlying evolutionary mechanisms driving cellular dynamics in pre-cancerous and normal hematopoietic populations. The study is one of the first to use a single system of tools to capture the interaction of the multiple evolutionary forces at play in ARCH.

 

Research results demonstrated that an interplay of positive, neutral, and negative evolutionary selection acting on mutations in aging blood stem cells can lead to AML in some individuals with ARCH. The team did so by illustrating how negative selection, or "purifying selection," acting on benign or mildly damaging passenger mutations, was present in people who did not develop a malignancy, and thus prevented disease-related cells from dominating the cell population. The authors concluded that these discoveries highlight the critical role that mildly damaging mutations play in preventing cancer prevention and can potentially enable clinicians to better identify individuals with ARCH who are at increased risk of developing AML with high accuracy.

 

Oncology Times connected with study co-lead and senior principal investigator, Philip Awadalla, PhD, Director of Computational Biology at the Ontario Institute for Cancer Research, to further discuss their study and the role of genetics in diagnosis and treatment in hematological malignancies.

 

Oncology Times: Why has genetic diagnostic data been limited in the field of hematology?

 

Awadalla: "I would say that it's been limited in some contexts, but not all. For example, sickle cell anemia is an important blood disease and phenotype, and genetics plays an important role in diagnosis and treatment. Hematology is an area which has benefited from genetic diagnostic data as the discovery of ARCH has really shown how disease-predisposing mutations can be used to predict who might be at a greater risk for hematological malignancies.

 

"Typically, in the context of ARCH, much attention is paid to driver mutations which increase a cell's ability to survive and proliferate. However, 99 percent of the mutations we accumulate in our blood cells as we age are either neutral or mildly damaging, termed passenger mutations, and their contribution to ARCH has not been well-elucidated. Indeed, a major limitation previously has been that passenger mutations, which are often under negative selection, are found at extremely low frequencies in our blood and, as such, require deep coverage sequencing to even observe them let alone study their contribution to ARCH."

 

Oncology Times: What was the significance of deep coverage sequencing in this study?

 

Awadalla: "Deep coverage sequencing is critical to capture and quantify the frequency of rare and mildly damaging mutations which segregate at low frequencies in our blood, in addition to common mutations which have been well-characterized previously. The deeper the coverage, the more accurate the snapshot of mutation frequencies and our ability to make predictions about their impact on health."

 

Oncology Times: How do you envision this novel application of deep learning tools and population genetic models be potentially used to improve patient outcomes and support clinical decision making?

 

Awadalla: "A fundamental goal of population genetics is to understand how mutations evolve and segregate in a population. The trajectories of mutations in a population offer important insights into the impact of mutations on health. Not all mutations will lead to cancer for example, and in fact some mutations may be detrimental to a cancer cell, or rather, confer protection to an individual.

 

"Our work allows us to study the population genetics of hematopoietic stem cells within an individual. In particular, our deep coverage sequencing, coupled with machine learning approaches, allows us to capture a range of selective pressures which shape the trajectory of mutations in healthy and premalignant blood samples. In doing so, we can start to understand which mutations are more (or less) likely to lead to diseases as individuals age."

 

Oncology Times: Are there any limitations of the current study that will be addressed in future studies?

 

Awadalla: "Next steps are to follow participants over time and collect longitudinal and prospective samples. So far, we have only been able to understand the evolutionary dynamics of ARCH retrospectively using a single time point blood sample. Looking forward allows us to determine how predictive these tools are."

 

Dibash Kumar Das is a contributing writer.