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Series GSE244990 Query DataSets for GSE244990
Status Public on Nov 02, 2023
Title State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia (CML)
Organism Mus musculus
Experiment type 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).
Contributor(s) Frankhouser DE, Rockne R, Uechi L, Zhao D, Branciamore S, O'Meally D, Izarriy J, Ghoda L, Ali H, Trent JM, Forman S, Fu YH, Kuo YH, Zhang B, Marcucci G
Citation(s) 37873185, 38307941
Submission date Oct 10, 2023
Last update date Apr 24, 2024
Contact name Denis OMeally
Phone 6262188434
Organization name City of Hope
Department Arthur Riggs Diabetes & Metabolism Research Institute
Lab Diabetes & Cancer Discovery Science
Street address 1500 E Duarte Rd
City Duarte
State/province California
ZIP/Postal code 91024
Country USA
Platforms (1)
GPL24247 Illumina NovaSeq 6000 (Mus musculus)
Samples (298)
GSM7833410 COHP_44940_480_TET_OFF_B_0wks
GSM7833411 COHP_44941_480_TET_OFF_B_1wks
GSM7833412 COHP_44942_480_TET_OFF_B_2wks
BioProject PRJNA1026584

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Supplementary file Size Download File type/resource
GSE244990_cml_mrna_processed_1tpm_in_5_samples.tsv.gz 16.5 Mb (ftp)(http) TSV
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