Clinical outcomes in patients relapsed/refractory after ≥2 prior lines of therapy for follicular lymphoma: a systematic literature review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Patients with follicular lymphoma (FL) can have high response rates to early lines of treatment. However, among FL patients relapsed/refractory (r/r) after ≥2 prior lines of therapy (LOT), remission tends to be shorter and there is limited treatment guidance. This study sought to evaluate the clinical outcomes for r/r FL after ≥2 prior LOT identified through systematic literature review. METHODS: Eligible studies included comparative or non-comparative interventional or observational studies of systemic therapies among adults with FL r/r after ≥2 prior LOT published prior to 31st May 2021. Prior LOT must have included an anti-CD20 monoclonal antibody and an alkylating agent, in combination or separately. Overall response rate (ORR) and complete response (CR) were estimated using inverse-variance weighting with Freeman-Tukey double-arcsine transformations. Kaplan-Meier (KM) curves for progression-free survival (PFS) and overall survival (OS) estimated by reconstructing digitized curves using the Guyot algorithm, and survival analyses were conducted, stratified by ≥2 prior LOT and ≥ 3 prior LOT groups (as defined in the source material). Restricting the analyses to the observational cohorts was investigated as a sensitivity analysis. RESULTS: The analysis-set included 20 studies published between 2014 and 2021. Studies were primarily US and/or European based, with the few exceptions using treatments approved in US/Europe. The estimated ORR was 58.47% (95% confidence interval [CI]: 51.13-65.62) and proportion of patients with CR was 19.63% (95% CI: 15.02-24.68). The median OS among those ≥2 prior LOT was 56.57 months (95% CI: 47.8-68.78) and median PFS was 9.78 months (95% CI: 9.01-10.63). The 24-month OS decreased from 66.50% in the ≥2 prior LOT group to 59.51% in the ≥3 prior LOT group, with a similar trend in PFS at 24-month (28.42% vs 24.13%). CONCLUSIONS: This study found that few r/r FL patients with ≥2 prior LOT achieve CR, and despite some benefit, approximately 1/3 of treated patients die within 24 months. The shorter median PFS with increasing prior LOT suggest treatment durability is suboptimal in later LOT. These findings indicate that patients are underserved by treatments currently available in the US and Europe.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".