Using MRI to evaluate and predict therapeutic success from depot-based cancer vaccines
Bibliographic record
Abstract
In the preclinical development of immunotherapy candidates, understanding the mechanism of action and determining biomarkers that accurately characterize the induced host immune responses is critical to improving their clinical interpretation. Magnetic resonance imaging (MRI) was used to evaluate in vivo changes in lymph node size in response to a peptide-based cancer vaccine therapy, formulated using DepoVax (DPX). DPX is a novel adjuvant lipid-in-oil-based formulation that facilitates enhanced immune responses by retaining antigens at the injection site for extended latencies, promoting increased potentiation of immune cells. C57BL/6 mice were implanted with C3 (HPV) tumor cells and received either DPX or control treatments, 5 days post-implantation. Complete tumor eradication occurred in DPX-vaccinated animals and large volumetric increases were observed in the vaccine-draining right inguinal lymph node (VRILN) in DPX mice, likely corresponding to increased localized immune response to the vaccine. Upon evaluating the relative measure of vaccine-potentiated immune activation to tumor-induced immune response (VRILN/VLILN), receiver-operating characteristic (ROC) curves revealed an area under the curve (AUC) of 0.90 (±0.07), indicating high specificity and sensitivity as a predictive biomarker of vaccine efficacy. We have determined that for this tumor model, early MRI lymph node volumetric changes are predictive of depot immunotherapeutic success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".