The Impact Of Prior Exposure To Rituximab On Autologous Stem Cell Transplantation In Patients With Follicular And Transformed Lymphoma
Bibliographic record
Abstract
Introduction: Addition of rituximab to chemotherapy (CT) for follicular lymphoma (FL) has been shown to improve many outcome parameters. Often, high-dose therapy is followed by autologous stem-cell transplantation (ASCT) after 1, 2 or more relapses. Kang et al. (BMT (2007) 40, 973) investigated whether prior exposure to rituximab had any influence on a subsequent ASCT and found no differences in the outcomes analyzed. However, there has been evidence suggesting that that such prior exposure may alter the phenotype of these tumour cells so that they no longer express CD20, and thus potentially altering their behaviour. Methods: We performed a retrospective review on all patients having received an ASCT at the Ottawa Hospital with an initial diagnosis of FL. They were grouped into four categories according to their prior exposure to or lack of prior exposure to rituximab and according to their pre-ASCT diagnosis, non-transformed FL (FL-NT) vs. transformed (FL-T). Results: 259 patients who underwent an autoHSCT for FL were divided into 4 groups: 184 FL non-transformed (FL-NT) (31 patients received rituximab and 153 did not), and 75 FL-transformed (FL-T) (24 patients received rituximab and 51 did not). The 5-year progression-free survivals (PFS) were 61.2% and 27.6%, respectively (p<0.0001) and the overall survivals (OS) were 72.5% and 39.3%, respectively, (p<0.0001). In the FL-NT group, no differences existed in PFS or OS between FL-NT rituximab-naïve and rituximab-treated patients (5-year PFS 61% vs. 64%, p=0.69; 5-year OS 73% vs. 68%, p=0.80). Within the FL-T group, the subsequent 5-year PFS of the rituximab-naive vs. pre-treated groups were 22% and 55% (p =0.20), respectively, and the 5-year OS were 36% and 51% (p=0.39), respectively. Prior exposure to R had a positive effect (Hazard Ratio (HR): 0.44, 95% CI 0.20-0.97, p=0.04) on PFS and OS (HR: 0.5, 95% CI 0.21-1.18, p=0.11). Pre-treatment with rituximab in FL-NT prior to ASCT does not adversely impact ASCT outcomes. There is a suggestion of an increase in the number of patients with FL-T being transplanted in the post-rituximab era and as expected, the FL-T had a poorer outcome than the FL-NT patients. However, prior exposure to rituximab appeared to demonstrate a trend toward an improved OS within the FL-T group. In summary, previous rituximab exposure may be associated with an increased rate of transformation, leading to the poorer outcome of patients originally diagnosed with FL-NT.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".