Long-Term Studies Assessing Outcomes of Ibrutinib Therapy in Patients With Del(11q) Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
BACKGROUND: Certain genomic features, such as del(11q), expression of unmutated immunoglobulin heavy-chain variable region (IGHV) gene, or complex karyotype, predict poorer outcomes to chemotherapy in patients with chronic lymphocytic leukemia (CLL). PATIENTS AND METHODS: We examined the pooled long-term follow-up data from PCYC-1115 (RESONATE-2), PCYC-1112 (RESONATE), and CLL3001 (HELIOS), comprising a total of 1238 subjects, to determine the prognostic significance of these markers in patients treated with ibrutinib. RESULTS: With a median follow-up of 47 months, ibrutinib-treated patients had longer progression-free survival (PFS) than patients treated in the comparator arm, regardless of genomic risk factors. Among patients treated with ibrutinib, we found that high-risk genomic features were not associated with shorter PFS (63-75% across all subgroups at 42 months) or overall survival (79-83% across all subgroups at 42 months). Surprisingly, we observed that ibrutinib-treated patients with del(11q) actually had a significantly longer PFS than ibrutinib-treated patients without del(11q) (42-month PFS rate 70% vs. 65%, P = .02). CONCLUSION: These analyses not only demonstrate that genomic risk factors previously associated with poor outcomes lose their adverse prognostic significance but also that del(11q) can be associated with a superior PFS with ibrutinib therapy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".