State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemia.
Leukemia
; 38(4): 769-780, 2024 Apr.
Article
in En
| MEDLINE
| ID: mdl-38307941
ABSTRACT
Chronic myeloid leukemia (CML) is initiated and maintained by BCRABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also 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) BCRABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML state-space and a three-well leukemogenic potential landscape. The potential's stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCRABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention, before phenotypic changes become detectable.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Leukemia, Myelogenous, Chronic, BCR-ABL Positive
/
Transcriptome
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Animals
Language:
En
Journal:
Leukemia
Journal subject:
HEMATOLOGIA
/
NEOPLASIAS
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: