Immune-Related Colitis Is Associated with Fecal Microbial Dysbiosis and Can Be Mitigated by Fecal Microbiota Transplantation
Bibliographic record
Abstract
Colitis induced by treatment with immune-checkpoint inhibitors (ICI), termed irColitis, is a substantial cause of morbidity complicating cancer treatment. We hypothesized that abnormal fecal microbiome features would be present at the time of irColitis onset and that restoring the microbiome with fecal transplant from a healthy donor would mitigate disease severity. Herein, we present fecal microbiota profiles from 18 patients with irColitis from a single center, 5 of whom were treated with healthy-donor fecal microbial transplantation (FMT). Although fecal samples collected at onset of irColitis had comparable α-diversity to that of comparator groups with gastrointestinal symptoms, irColitis was characterized by fecal microbial dysbiosis. Abundances of Proteobacteria were associated with irColitis in multivariable analyses. Five patients with irColitis refractory to steroids and biologic anti-inflammatory agents received healthy-donor FMT, with initial clinical improvement in irColitis symptoms observed in four of five patients. Two subsequently exhibited recurrence of irColitis symptoms following courses of antibiotics. Both received a second "salvage" FMT that was, again, followed by clinical improvement of irColitis. In summary, we observed distinct microbial community changes that were present at the time of irColitis onset. FMT was followed by clinical improvements in several cases of steroid- and biologic-agent-refractory irColitis. Strategies to restore or prevent microbiome dysbiosis in the context of immunotherapy toxicities should be further explored in prospective clinical trials.
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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 | 0.000 |
| 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) | 0.001 | 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".