Abstract IA06: Lifestyle induced susceptibility of immune related adverse events
Bibliographic record
Abstract
Abstract Immunotherapy has transformed cancer therapy, but emergence of immune-related adverse events (irAEs) remains a major challenge. Serious irAEs can lead to cessation of treatment & fatal toxicities. There remains limited mechanistic understanding of irAEs due to an absence of reliable preclinical models. To address this, we developed the L ifestyle I nduced S usceptibility to ir A Es (LISA) mouse model. LISA integrates dietary & microbiome modifications, utilizing a Western Diet (WD) rich in fats & simple sugars, which reshapes the microbiome and drives clinically relevant irAEs within the skin, gastrointestinal tract, & liver. irAE severity correlates with host factors such as sex, age, treatment modality (anti-PD1 vs. combination therapy), and duration of dietary exposure. scRNAseq and flow cytometry analyses revealed an upregulation of cycling CD8+ T cells & unstable CD4+ Tregs expressing inflammatory markers (T-bet, TNFα, IFNγ). Unstable Tregs were specific for microbial and dietary antigens, and both factors were required for Treg dysfunction and irAE development. Finally, we found that lifestyle intervention could prevent both Treg dysfunction and irAE symptoms. The LISA model offers a novel and effective tool for studying irAEs, mirroring key clinical features of adverse events. It provides valuable insights into the immune mechanisms underlying irAE severity and susceptibility, with potential to inform new therapeutic strategies for managing immunotherapy-related toxicities. Citation Format: Abby Overacre. Lifestyle induced susceptibility of immune related adverse events [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 IA06.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".