Talactoferrin Immunotherapy in Metastatic Renal Cell Carcinoma: A Case Series of Four Long-Term Survivors
Bibliographic record
Abstract
UNLABELLED: Talactoferrin alfa (also known as recombinant human lactoferrin, rhLF) is a novel immunomodulatory protein that has previously demonstrated anti-tumor properties in animal models. Following a successful phase I trial, it was administered orally to patients with metastatic renal cell carcinoma (RCC) in a phase II trial conducted at the Michael E. DeBakey Veterans Affairs Medical Center in Houston, Texas, among other sites. We report a case series of 4 patients treated at our institution with very encouraging progression-free survivals, all exceeding 30 months, in order to suggest that this agent merits further study. These four patients with radiographically progressive metastatic RCC received single-agent oral talactoferrin in daily doses of 9 grams, given in cycles of 2 weeks on/2 weeks off, until evidence of toxicity or disease progression. Given the small sample size and the heterogenous tumor biology of RCC, tumor growth rate was used as a primary endpoint so that each patient could serve as their own control. The agent's effectiveness was then determined through radiographic tracking of the tumors before, during, and after treatment, with use of the Response Evaluation Criteria in Solid Tumors (RECIST) protocol to follow target lesions. The results showed that the drug was well tolerated, with no occurrence of talactoferrin-related grade 3 or 4 adverse events or laboratory anomalies by NCI-CTEP criteria. The four patients described in the case series demonstrated very encouraging progression-free survivals, all exceeding 30 months. We conclude that decreased tumor growth rate may correlate with increased progression-free survival. Talactoferrin is a promising, well-tolerated agent whose clinical benefits should be evaluated in a randomized phase III study with a placebo control arm. KEYWORDS: Talactoferrin; Immunotherapy; Renal cell carcinoma; Metastatic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".