Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood

• Published in: Nature communications 2021-08, Vol.12 (1), p.4921-4921, Article 4921
• Main Authors: Skead, Kimberly, Ang Houle, Armande, Abelson, Sagi, Agbessi, Mawusse, Bruat, Vanessa, Lin, Boxi, Soave, David, Shlush, Liran, Wright, Stephen, Dick, John, Morris, Quaid, Awadalla, Philip
• Format: Article
• Language: English
• Subjects: 45/23; 631/114/1305; 631/181/2474; 631/208/457; 631/67/1990/283; Acute Disease; Acute myeloid leukemia; Adult; Age; Aged; Aging; Arches; Blood; Clonal Evolution; Clonal Hematopoiesis - genetics; Deep Learning; Evolution; Genetics; Genetics, Population - methods; Genetics, Population - statistics & numerical data; Health risks; Hematopoiesis; Hematopoietic stem cells; Hematopoietic Stem Cells - cytology; Hematopoietic Stem Cells - metabolism; Humanities and Social Sciences; Humans; Kaplan-Meier Estimate; Leukemia; Leukemia, Myeloid - genetics; Leukemia, Myeloid - pathology; Malignancy; Middle Aged; Models, Genetic; multidisciplinary; Mutation; Myeloid leukemia; Negative selection; Outcome Assessment, Health Care - methods; Outcome Assessment, Health Care - statistics & numerical data; Pluripotency; Population genetics; Risk analysis; Risk factors; Science; Science (multidisciplinary); Stem cells
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-25172-8

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy. Age-related clonal hematopoiesis is associated with risk for diseases like acute myeloid leukemia (AML), yet it is unclear why some individuals do not progress despite having AML driver mutations. Here, the authors use deep learning and population genetics models to investigate how the interplay of positive and negative selection influences AML progression.