Abstract 3524: Role of the gut microbiota in the development of immune-mediated diarrhea and colitis induced by immune checkpoint inhibitors
Bibliographic record
Abstract
Abstract The accelerating use of combined immune checkpoint inhibitors (ICI; anti-PD-1 plus anti-CTLA-4 antibodies) enhanced therapeutic responses in various cancers while significantly increasing immune-related adverse events (irAE). The mechanism involved in the occurrence of immune-mediated diarrhea and colitis (IMDC) and its potential implication on ICI efficacy remain unclear. The gut microbiome is progressively recognized as a key biomarker of ICI efficacy and preliminary results suggest its potential association with irAE. However, the link between microbiota and IMDC still needs to be elucidated. In this study, we investigated IMDC-associated intestinal bacteria.We performed metagenomics sequencing of baseline pre-ICI fecal samples from 26 advanced melanoma patients and recorded IMDC. Culturomics using MALDI-TOF was used to isolate bacteria from 19 patient samples: 11 IMDC-free (fIMDC), 5 baseline pre-IMDC (pIMDC) and 8 during IMDC (dIMDC). Serum multiplex cytokine panel was measured for 36 patients. Then, we induced colitis with Dextran Sulfate Sodium (DSS) in MCA-205 murine tumor model treated with oral supplementation of human bacteria and ICI.Metagenomics profiling demonstrated lower α-diversity at baseline in patients that developed irAE. Moreover, pIMDC samples present a lower Simpson α-diversity than fIMDC samples (p=0.08). ß-diversity was also different between both groups (p=0.10) suggesting a different baseline microbiome composition. Lefse analysis revealed an overrepresentation of Enterococcus faecium and Bilophila wadsworthia in pIMDC samples compared to fIMDC samples. Culturomics showed that dIMDC samples exhibited a shift in gut microbiota with a significant decrease in bacterial diversity mostly anaerobic species and an enrichment of Clostridium paraputricum, Clostridium perfringens, Paraclostridium bifermentans and Paeniclostridium sordelii compared to fIMDC and pIMDC samples. Immunological analysis of serum revealed that IMDC patients had a higher concentration of pro-inflammatory cytokines including GMCSF (p=0.04), IL12-23p40 (p=0.04) and IL-16 (p=0.03) compared to fIMDC patients. In our DSS-induced colitis mouse model, we showed that P. bifermentans oral supplementation related with diarrhea and a shorter colon length, indicating more severe colitis. Moreover, we showed that P. bifermentans increased anti-CLTA-4 and anti-PD-1 anti-tumor activity.Results showed that gut microbiota composition of dIMDC patients differed from that of pIMDC and fIMDC patients with a decrease of diversity and colonization of P.bifermentans and P.sordelii. In IMDC patients, we isolated bacteria that aggravated colitis in murine models and are known to be associated with inflammatory bowel disease. These results provide more insight on potential microbiome-modifying strategies to damper IMDC and increase anti-cancer efficacy. Citation Format: Khoudia Diop, Benlaïfaoui Myriam, Wiam Belkaïd, Alexis Nolin-Lapalme, Julie Malo, Marion Tonneau, Ponce Mayra, Afnan Al-Saleh, Catalin Mihalcioiu, Ian Watson, Arielle Elkrief, Karla Lee, Meriem Messaoudene, Bertrand Routy, Corentin Richard. Role of the gut microbiota in the development of immune-mediated diarrhea and colitis induced by immune checkpoint inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3524.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".