Clinical Outcomes in a Large Canadian Centralized CLL Clinic Based on Treatment and Molecular Factors over a Decade
Bibliographic record
Abstract
FISH cytogenetics, TP53 sequencing, and IGHV mutational status are increasingly used as prognostic and predictive markers in chronic lymphocytic leukemia (CLL), particularly as components of the CLL International Prognostic Index (CLL-IPI) and in directing therapy with novel agents. However, testing outside of clinical trials is not routinely available in Canada. As a centralized CLL clinic at CancerCare Manitoba, we are the first Canadian province to evaluate clinical outcomes and survivorship over a long period of time, incorporating the impact of molecular testing and the CLL-IPI score. We performed a retrospective analysis on 1315 patients diagnosed between 1960 and 2018, followed over a 12-year period, where 411 patients had molecular testing and 233 patients had a known CLL-IPI score at the time of treatment. Overall, 40.3% (n = 530) of patients received treatment, and 47.5% (n = 252) of patients received multiple lines of therapy. High-risk FISH and CLL-IPI (4-10) were associated with higher mortality (HR 2.03, p = 0.001; HR 2.64, p = 0.002), consistent with other studies. Over time, there was an increase in the use of targeted agents in treated patients. The use of Bruton’s tyrosine kinase inhibitors improved survival in patients with unmutated IGHV and/or TP53 aberrations (HR 2.20, p = 0.001). The major cause of death in patients who received treatment was treatment/disease-related (32%, n = 42) and secondary malignancies (57%, n = 53) in those who were treatment-naïve. Our data demonstrate the importance of molecular testing in determining survivorship in CLL and underpinning the likely immune differences in outcomes for those treated for CLL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".