Ofatumumab and Complement Replacement in Relapsed/Refractory Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
BACKGROUND: While many humanized monoclonal antibodies utilize complement-dependent cytotoxicity, the complement depleting effects of these antibodies and the impact of complement replacement on treatment response are not well-described. METHODS: We conducted a phase 2 trial involving patients with relapsed/refractory chronic lymphocytic leukemia (CLL). Patients were treated with ofatumumab with fresh frozen plasma (FFP) used as a source of complement replacement. The primary endpoint was objective response rate. Correlative endpoints included complement levels (C3 and C4) and complement activity (CH50) which was drawn at baseline and after ofatumumab with FFP administration. RESULTS: Among 12 enrolled patients, overall response rate was 83% with two patients (17%) achieving a complete response. While only two (17%) patients had low complement activity at baseline, eight (67%) developed low levels of complement activity after ofatumumab treatment with FFP replacement. The magnitude of complement depletion did not correlate with response. Adverse events were minimal. The combination of ofatumumab and FFP demonstrated tolerability and surprising activity in high-risk CLL patients. CONCLUSIONS: The combination of ofatumumab and FFP demonstrated tolerability and surprising activity in high-risk CLL patients. Complement replacement should be studied further as a minimally toxic approach to improve efficacy of monoclonal antibody-based regimens.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".