IFN-γ (interferon-gamma) Genotype Predict Cytogenetic and Molecular Response to Imatinib Therapy in Chronic Myeloid Leukemia.
Bibliographic record
Abstract
Abstract Abstract 2178 Poster Board II-155 Purpose: Interferon therapy had served as a standard therapy before introduction of imatinib into the treatment of chronic myeloid leukemia (CML) although its action mechanism is yet fully elucidated. After introduction of imatinib therapy, it provides significant therapeutic benefit to CML patients, however its response varies person-by-person. Some patient responds quickly and maintain long-term response without development of resistance, while others do not respond well or lose their response quickly thus developing resistance to imatinib. One of interesting observation is that the patients previously treated with interferon could maintain long-term complete molecular response (CMR) even after withdrawal of imatinib therapy, suggesting interferon signaling pathway seemed to be associated with favorable response to imatinib therapy. Variable response to imatinib therapy in CML can be explained by inter-individual variation of candidate genes involved in the biologic activity of CML cells such as apoptosis or angiogenesis as well as drug transport/metabolism of imatinib in addition to Interferon gamma signaling pathway. Methods: In the current study, we investigated 80 single nucleotide polymorphism (SNP) markers involved in the pathways of apoptosis (n=31; BCL2, BAX, BCL2L2, BCL6, BCL2L11, BIRC5, CASP1, CASP3, CASP7,CASP8, CASP9, CASP10, FAS, FASL, APAF1, TNFR2, PDCD1, GZMB), angiogenesis (n=7; VEGFA, VEGFR2), myeloid cell growth (n=13; FLT2, CSF3, CSF2, JAK3, IL1A, IL1B, IL1R), xenobiotic metabolism (n=13; ABCB1, ABCG2, CYP3A5, HOCT1), WT1 signaling (n=7), interferon signaling (n=4; IFNG, IFNGR1, IFNGR2) and others (n=5; GNB3, ULK3, ORM, PTK2). Discovery cohort includes 244 patients treated at the Princess Margaret Hospital, Toronto, ON, Canada. The DNAs from peripheral blood samples were genotyped with MALDI-TOF based technique (Sequenom). The results were validated internally using a Bootstrap procedure, and externally in an independent validation cohort of 187 Korean CML patients treated at the Samsung Medical Center, Seoul, Korea or Chonnam National University Hwasun Hospital, Hwasun, Korea. Results: In a single marker analysis, several genotypes were found to be correlated with complete cytogenetic response (CCR; IFNG “p-value, 0.01”, FAS “0.03”, FASL “0.006”, CASP8 “0.04”, CASP10 “0.04”), major molecular response (MMR; IFNG “0.04”, FAS “0.05”, JAK3 “0.03”), loss of response (IFNG “0.02”, BIRC5 “0.02”), treatment failure (IFNG “0.07”), or dose escalation of imatinib (IFNG “0.03”, ABCG2 “0.02”, APAF1 “0.04”, CASP2, “0.03”). Bootstrap methods showed a good correlation of each genotype with clinical outcomes. External validation was performed in an independent cohort with 187 Korean CML patients, the IFNG genotype (rs2069705) was validated that is able to predict CCR (HR, 0.46; p=3×10-5) or MMR (HR, 0.51; p=7×10-5) in CML patients. Conclusions: The current study suggested that the interferon gamma genotype seemed to predict the response to imatinib therapy, proposing potential involvement of interferon-gamma signaling pathway in the action mechanism of imatinib therapy in CML. Further detailed study on IFNG genotype and functional study of interferon gamma phenotype will help us to reach a clear conclusion on the role of IFNG gene in the action mechanism of imatinib therapy in CML. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".