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Abstract Chronic myeloid leukemia (CML) is initiated and initially maintained by BCR-ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). Although TKIs can induce long-term remission, they are frequently not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR-ABL-inducible transgenic mice that recapitulates human chronic phase CML and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape that describes CML state-transition from health to disease. The potential’s stable critical points were used to define three distinct disease states. The Early state was characterized by anti-CML genes (n=35) that opposed leukemia progression, whereas an expanding number of pro-CML genes characterized the Transition state (n=357) and Late states (n=1, 858). Gene modules that were co-regulated at the unstable points in the potential landscape were identified as the drivers of transition between stable disease states. To investigate CML therapies we used two treatments: silencing of BCR/ABL by readministering Tet (Tet-on, Tet-off TOTO) which represented a best-case scenario of disease being cured, and TKI (nilotinib) therapy which represents current clinical practice. TOTO returned the diseased mice transcriptomes to a near health state, without reaching it, suggesting partly irreversible transformation. TKI, however, only reverted the transcriptome to an intermediate disease state, without approaching health, and disease relapse occurred soon after treatment. Finally, using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response to both therapies for each individual mouse. In conclusion, these results show state-transition analysis is a valuable approach to gain real time insights into CML development, progression and ongoing treatment response that can also be applied to other type of leukemia and cancers in general. We predicted disease evolution and treatment response in a murine model that recapitulates human disease, supporting this as a potentially valuable approach to time clinical intervention even before phenotypic changes become detectable. Citation Format: David E. Frankhouser, Russell C. Rockne, Dandan Zhao, Sergio Branciamore, Lisa Uechi, Denis O'Meally, Yu-Hsuan Fu, Ya-Huei Kuo, Bin Zhang, Guido Marcucci. State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia (CML) abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 2307.
Frankhouser et al. (Fri,) studied this question.