A collective non-mutational transition governs the development of chronic myeloid leukemia Free

Introduction: The field of carcinogenesis has been dominated by the concept that cancer is caused by the accumulation of genetic mutations, both germline and somatic, in normal tissues. However, this paradigm has been challenged recently by the observation that normal tissues acquire multiple mutations with age, including pathogenic variants in validated cancer driver genes, yet rarely progress to malignancy.Increasingly, it is appreciated that non-mutational events, such as epigenetic alterations and environmental factors including the tissue microenvironment, contribute to the development of cancer. Chronic myeloid leukemia (CML) is perhaps the most genetically simple human cancer, where a single mutation (the BCR::ABL1 gene fusion) arising in a hematopoietic stem cell (HSC) is thought to be sufficient for leukemogenesis. Yet some CML patients in treatment-free remission (TFR) exhibit low or fluctuating levels of BCR::ABL1 mRNA transcripts in peripheral blood leukocytes without ever progressing to clinical relapse. To address this and other outstanding questions regarding the biology and therapy of CML, we recently developed mathematical models of normal and CML myelopoiesis that incorporate feedback and feed-forward interactions among cells (Rodriguez et al., eLife 2023;12:e84149). Here, we utilized a novel conditional BCR::ABL1 transgenic mouse model that accurately reflects early events in CML pathogenesis to further validate and extend this model.

Methods: A physiological mouse model of early CML was produced by transplantation of bone marrow (BM) from conditional double transgenic BCR::ABL1 donors (B6 CD45.2⁺ background; Koschmieder et al., Blood 2005;105:324) into unirradiated congenic CD45.1⁺CD45.2⁺ B6 recipients to create BM chimeras bearing a clone of BCR::ABL1⁺ HSC in an unperturbed BM environment, where the size of the leukemic stem cell (HSC^L) clone depends on the size of the initial donor BM graft. Following stable engraftment at two months post-transplantation, doxycycline is removed from drinking water to induce BCR::ABL1 expression.

Results: BM chimeras with initial HSC^L compartments of ~10% uniformly developed CML-like leukemia but only after a latent period of 2-3 months and with substantial contributions of normal cells to the expansion of myelopoiesis. Chimeras with ~90% HSC^L showed no latent period, suggesting that HSC^L expansion is required for disease progression to leukemia and providing direct evidence of a selective advantage of BCR::ABL1⁺ HSC in vivo. Surprisingly, a threshold of ~4% HSC^L chimerism was required for leukemia development. Below the threshold, mice behave like patients in treatment-free remission, maintaining detectable circulating BCR::ABL1⁺ granulocytes without developing disease. Above the threshold, they develop polyclonal CML-like leukemia that cannot be attributed to the acquisition of de novo mutations or clonal selection, implying a collective event in leukemia initiation. A refinement of the previous mathematical model shows that the observed threshold, latency and stochasticity of CML development can be accounted for by a combination of negative feedback—due at least in part to p19^Arf-dependent, cell-intrinsic oncogene-induced replicative stress—and collective (i.e., intercellular) positive feedback. In addition, a prediction of the mathematical model that intrinsic resistance to TKI therapy is influenced by the stem cell self-renewal probability was validated in mice, using a Tet2 mutation to increase self-renewal within BCR::ABL1-expressing stem cells.

Conclusions: Together, these results challenge the paradigm that BCR::ABL1 is necessary and sufficient for CML leukemogenesis, and demonstrate that random, non-mutational steps in cancer development can occur through critical transitions that arise out of collective cell-cell interactions. These studies inform clinical strategies for improving TKI therapy and treatment-free remission, and also illustrate the value of mathematical modeling in predicting and explaining the dynamics of cancer initiation and therapeutic response.