BCR-ABL1 RT-qPCR for Monitoring the Molecular Response to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia
Bibliographic record
Abstract
The pathognomonic genetic alteration in chronic myeloid leukemia is the formation of the BCR-ABL1 fusion gene, which produces a constitutively active tyrosine kinase that drives leukemic transformation. Targeted tyrosine kinase inhibitor treatment with imatinib, nilotinib, dasatinib, bosutinib, and ponatinib is the cornerstone of modern therapy for this hematologic malignancy. Real-time quantitative RT-PCR (RT-qPCR, also RQ-PCR) of BCR-ABL1 RNA is a necessary laboratory technique for monitoring the efficacy of tyrosine kinase inhibitor therapy and quantitatively assessing minimal residual disease. The molecular response measured by BCR-ABL1 RT-qPCR assists in identifying suboptimal responses and can help inform the decision to switch to alternative therapies that may be more efficacious (or to pursue more stringent monitoring). Furthermore, the tyrosine kinase inhibitor-mediated molecular response provides valuable risk stratification and prognostic information on long-term outcomes. Despite these attributes, informed, universal, practical utilization of this well-established monitoring test will require heightened efforts by the molecular diagnostics laboratory community to adopt the standardized reporting units of the International Scale. Without widespread adoption of the International Scale, the consensus major molecular response and early molecular response treatment thresholds will not be definable, and optimal clinical outcomes for patients with chronic myeloid leukemia may not be achieved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".