Abstract PR008: Cell state transitions drive the evolution of disease progression in B-cell acute lymphoblastic leukemia
Authors: Gravenmier Curtis , Zhang Ling , Moscinski Lynn , West Jeffrey 
Published: February 2024

Abstract
A mathematical framework is constructed to predict the risk of B-lymphoblastic leukemia (B-ALL) relapse post-induction chemotherapy. The framework is a Markov chain model that quantifies spontaneous cell state transitions between distinct immunophenotypic subpopulations defined by CD34 and CD38 expression relative to neutrophils. Cell states are analyzed via flow cytometry pre- and post-treatment, providing insight into the evolution of cell state transition rates during disease progression. Cancer stem cells (CSCs) are hypothesized to promote tumor progression through innate chemoresistance and self-renewal. Ostensible CSCs were first identified in acute myeloid leukemia and were found to have a CD34+/CD38- immunophenotype similar to hematopoietic stem cells. However, the isolation of CSCs from B-ALL has proved more difficult. B-ALL cells with stem cell-like properties have been reported with variable immunophenotype, perhaps due to temporal variation of CD34 and CD38 expression in this setting. We hypothesized that transitions between stem cell-like, hematogone-like, and naive B-cell-like leukemia subpopulations play a significant role in B-ALL disease progression. To test this hypothesis, we trained a Markov chain mathematical model using flow cytometry characterization of four B-ALL cell states with their normal counterpart appearing in parentheses: CD34+/CD38- (hematopoietic stem cells), CD34+/CD38+ (stage 1 hematogones), CD34-/CD38+ (stage 2 and 3 hematogones), and CD34-/CD38- (naïve B-cells). An iterative numerical search procedure was used to derive patient-specific Markov matrices, describing the stochastic cell state transitions. This flow cytometry evaluation was performed on a cohort of patient samples of peripheral blood (N=46) and bone marrow (N=63) with matched clinical features such as BCR::ABL1 status, comprehensive genomic profiling, minimal residual disease (MRD) post-induction chemotherapy, and 3-year relapse. Critical to our goal of quantifying the evolution of state transition rates, we also obtained bone marrow measurements for a cohort of normal/healthy individuals. Patients were divided into post-induction flow MRD positive (N=16), MRD negative (N=30), healthy (N=6) cohorts, as well as relapsed and non-relapsed cohorts to compare features of the transition matrices. Importantly, pre-treatment flow cytometry derived cell state distribution alone is not predictive of relapse or MRD. In contrast, pre-treatment Markov chain transition parameters are found to be clinically predictive of relapse and MRD. MRD correlates to high reciprocity (the product of incoming and outgoing transitions) of the stem cell state. This approach provides supporting evidence that cell state transitions drive B-ALL disease progression. There is an additional strong correlation between Markov transition parameters and both BCR::ABL1 and BCR::ABL1-like B-ALL classification. Finally, comparison of Markov parameters pre- and post-treatment quantifies the evolutionary selection pressures acting on transition rates induced by chemotherapy treatment.
Citation Format: Curtis Gravenmier, Ling Zhang, Lynn Moscinski, Jeffrey West. Cell state transitions drive the evolution of disease progression in B-cell acute lymphoblastic leukemia [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR008.