Expression profiling by high throughput sequencing
Summary
Chronic myeloid leukemia (CML) is initiated and initially maintained solely by the fusion gene BCR-ABL, encoding a mutant protein targeted in the clinic with tyrosine kinase inhibitors (TKIs) While TKI treatment is effective in inducing long-term remission, frequently is not curative. For these reasons, CML is an ideal disease to test our hypothesis that that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We hypothesized that transcriptome-based state-transition models accurately predict cancer evolution and treatment response.
Overall design
To test our hypothesis, we collected time-sequential peripheral blood samples from cohorts of tetracycline-off (Tet-Off) BCR-ABL-inducible transgenic mice. Using time-series bulk RNA-seq on the whole transcriptome to capture a system-wide view of all disease states, we applied state-transition theory to mathematically model CML development. We included four experimental cohorts of mice that were sampled weekly for 18 weeks or until mice became moribund with disease: Tet-on control mice where BCR-ABL expression was suppressed (n=3); Tet-off CML mice had BCR-ABL expression that induced disease that mimics human chronic phase (CP) CML (n=6); a Tet-off, Tet-on (TOTO) cohort where BCR-ABL expression allowed disease development and was then suppressed by turning Tet-on to simulate a hypothetical best-case treatment scenario (n=4); and TKI cohort to simulate a clinical setting where BCR-ABL expression was induced (Tet-off) and remained on during and after a four week nilotinib treatment window (n=7).
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