NEW YORK – A team led by investigators at the Princess Margaret Cancer Centre and the University of Toronto has used single-cell sequencing and computational methods to tease out distinct differentiation states for healthy hematopoietic cells. Using this reference atlas, they then highlighted a dozen recurrent aberrant differentiation patterns linked to acute myeloid leukemia (AML).
"These findings reveal that the phenotypic heterogeneity in AML arises from the interplay between genetic drivers and the specific cellular context, helping us begin to decipher the 'rules' governing the factors that shape the disease," Andy Zeng, a graduate student affiliated with the University of Toronto and the Princess Margaret Cancer Centre and the lead author on a paper describing the study, said in a statement.
For their study, published in the journal Blood Cancer Discovery on Monday, Zeng and colleagues generated single-cell RNA sequencing profiles for 263,159 individual hematopoietic cells representing 55 distinct cellular states in 45 healthy bone marrow donors. Using these profiles, they put together a human hematopoietic cell atlas using a computational tool known as BoneMarrowMap.
"We present a single-cell reference atlas of human hematopoiesis and a computational tool for rapid mapping and classification of healthy and leukemic cells," the authors reported.
They then used this atlas to analyze single-cell RNA-seq profiles for 13 individuals with AML, focusing in on a dozen altered differentiation patterns present in these AML samples — findings that were backed up by a follow-up meta-analysis on more than 1.2 million individual cells from patients with AML, pediatric AML, or mixed-phenotype acute leukemia, along with bulk RNA-seq data on samples from another 1,224 AML cases from five research cohorts.
"By establishing a high-resolution single-cell reference atlas of hematopoiesis, we've not only advanced our understanding of how differentiation goes awry in AML, but we've also provided an accessible toolkit for other researchers to rapidly map and classify their own blood samples profiled by single-cell RNA sequencing," explained Zeng, who was scheduled to present the work during a hematology session at the American Association for Cancer Research annual meeting in Chicago on Monday.
Through follow-up genotype-to-phenotype analyses, the team went on to explore ties between altered differentiation patterns detected in AML samples and the presence of driver mutations affecting dozens of genes including FLT3, KRAS, BRAF, and NF1.
"[T]hese genotype-to-phenotype associations enabled us to capture phenotypic convergence between diverse genetic drivers and to identify characteristic differentiation landscapes for common driver alterations in AML," the authors explained.
In a related commentary in the same journal, Charité-University Medicine Berlin researchers Livius Penter and Jonas Berger, who were not involved in the study, noted that the BoneMarrowMap computational tool used to establish the new bone marrow reference "provides a framework to study interactions between drug sensitivity, differentiation states, and AML genetics in a way that may ultimately enable personalized drug treatments in the clinic."
More broadly, the duo noted that "Zeng and colleagues have performed a tour de force study of differentiation landscapes in AML that could serve as a blueprint for future investigations."