Abstract B015: 3D Organoid-Based Therapeutics with Translational Potential in Cancer Immunity and Autoimmune Risk Prediction
Bibliographic record
Abstract
Abstract Organoids represent a transformative advancement in cancer research: 3D models that mimic in vivo tumor behavior. In this study, we present a nano-sized in vitro-vivo organoid system designed to improve therapeutic monitoring and predictive accuracy in cancer treatment. We observed significant increases in organoid survival in treated groups (p < 0.01). Organoids' true potential lies in expansion by integrating immune components such as CD8+ T cells or checkpoint blockade agents; this system could model immune surveillance, cytotoxicity, and even autoimmune toxicity associated with immunotherapies. As personalized medicine advances, anticipate immune-related adverse events and study tumor-immune escape mechanisms in a controlled, patient-specific environment. This study provides the foundation for a next-generation organoid platform that bridges the gap between preclinical drug screening and mechanistic studies in cancer immunity and autoimmunity, paving the way for safer, more effective immunotherapeutic strategies. Citation Format: Md Noushad Javed, SDanish Kadir. 3D Organoid-Based Therapeutics with Translational Potential in Cancer Immunity and Autoimmune Risk Prediction [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr B015.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".