Abstract B023: An integrated approach to understanding the evolutionary dynamics of childhood acute lymphoblastic leukemia from diagnosis to relapse

Abstract Up to 20% of children with acute lymphoblastic leukemia (cALL) will relapse after initially responding to treatment. Dissecting the evolutionary population dynamics leading to relapse would help explain treatment failure from a mechanistic standpoint, aiding the design of more effective therapies. Comparisons of genetic heterogeneity at diagnosis and relapse have shown that relapse is often dominated by either a specific diagnostic subclone or its evolutionary progeny, leading to the idea that selection during treatment of cALL primarily operates at the genotype level. However, due to the technical difficulties associated with analyzing the rare cells that survive treatment in patients - definitive support for this idea is thus far missing. To overcome this challenge, we have previously developed a xenograft model of cALL induction chemotherapy treatment. Combining this with single-cell resolution analysis, we showed that, despite a massive reduction in leukemic burden, the first 28 days of chemotherapy have little impact on the genetic heterogeneity of cALL. This finding was inconsistent with the idea of selection acting at the level of genotypes. Instead, treatment induced a bottleneck at the level of cell state, determining the survival of a transcriptionally homogeneous population broadly characterized by reduced biosynthetic activity and cell dormancy. However, cALL treatment lasts several years and cannot be entirely modelled in vivo. Hence to assess whether genetic selection could act on a larger timescale or whether the clonal dominance frequently observed at relapse results from stochastic sweeps, we have implemented a data-driven mathematical model using the Hybrid Automata Library (HAL) to simulate longer treatment courses. The model allows for explicit spatial and temporal tracking of the evolutionary trajectories of individual cALL cells from diagnosis to relapse. Surprisingly we found that preserved genetic heterogeneity post-induction treatment and clonal dominance at relapse are features of virtually all relapsed leukemias; regardless of whether subclones with equal or varied fitness populate the diagnostic disease. This finding highlights the misinterpretation risks associated with limited disease snapshot analysis. Crucially, although genetically driven leukemias and leukemias in which all subclones have a similar probability of entering dormancy had similar endpoints, their temporal evolutionary dynamics largely differed. In the latter, reproducibly fewer cells survived induction chemotherapy, and relapse occurred on longer timelines, predominantly post-treatment. This observation provides the first empirical evidence of the notion that early and late relapse in cALLs may result from distinct selection mechanisms. Our preliminary data further suggest that even when high-fitness subclones are present, specifically targeting them is, in many cases, unlikely to improve overall outcome. Alternative dose fractionation protocols leveraging the epigenetically homogenous nature of residual cells may hold a better promise. Citation Format: Virginia A. Turati, Javier Herrero Sanchez, Jeffrey West, Mark Robertson-Tessi, Tariq Enver, Andriy Marusyk, Alexander R. A. Anderson. An integrated approach to understanding the evolutionary dynamics of childhood acute lymphoblastic leukemia from diagnosis to relapse [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B023.