Biological Efficacy of a Dendritic Cell-Based Vaccine in a Patient with Metastatic Colorectal Cancer
Bibliographic record
Abstract
Colorectal cancer is a serious health problem affecting de novo more than one million people every year in the developed world. Despite recent advances in the development of novel therapeutic agents, metastatic colorectal cancer remains mostly incurable and its survival rates ominous even when patients respond to the most advanced treatments. Here, we describe a case in which a patient with metastatic colorectal cancer and high risk of relapse remains disease-free while being treated solely with twelve doses of autologous dendritic cells vaccines pulsed with autologous tumor lysate. A sustained, specific immune response elicited by vaccination has also been documented. Prior to receiving this experimental treatment, the patient had undergone both tumor resections and chemotherapy treatments six times, invariably relapsing/progressing within a year from each resection. We believe that the use of autologous vaccines consisting in dendritic cells pulsed with tumor lysate should be further investigate in human clinical trials, particularly in patients with minimal tumor burden and high risk of relapse. We also believe that this type of immunotherapy is more likely to be successful when used as an early rather than merely compassionate treatment option, given the fact that the more toxicity the immune system has received from previous approaches, the less it will be able to respond to tumor vaccination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 teacher head, 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".