Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results
Bibliographic record
Abstract
The introduction in 1998 of imatinib mesylate (IM) revolutionized management of patients with chronic myeloid leukemia (CML) and the second generation of tyrosine kinase inhibitors may prove superior to IM. Real-time quantitative polymerase chain reaction (RQ-PCR) provides an accurate measure of the total leukemiacell mass and the degree to which BCR-ABL transcripts are reduced by therapy correlates with progression-free survival. Because a rising level of BCR-ABL is an early indication of loss of response and thus the need to reassess therapeutic strategy, regular molecular monitoring of individual patients is clearly desirable. Here we summarize the results of a consensus meeting that took place at the National Institutes of Health (NIH) in Bethesda in October 2005. We make suggestions for (1) harmonizing the differing methodologies for measuring BCR-ABL transcripts in patients with CML undergoing treatment and using a conversion factor whereby individual laboratories can express BCR-ABL transcript levels on an internationally agreed scale; (2) using serial RQ-PCR results rather than bone marrow cytogenetics or fluorescence in situ hybridization (FISH) for the BCR-ABL gene to monitor individual patients responding to treatment; and (3) detecting and reporting Philadelphia (Ph) chromosome-positive subpopulations bearing BCR-ABL kinase domain mutations. We recognize that our recommendations are provisional and will require revision as new evidence emerges.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".