Fecal, duodenal, and tumor microbiota composition of esophageal carcinoma patients, a longitudinal prospective cohort
Bibliographic record
Abstract
BACKGROUND: The microbiome has been associated with chemotherapy and immune checkpoint inhibitor efficacy. How this pertains to resectable esophageal carcinoma is unknown. Our aim was to identify microbial signatures in resectable esophageal carcinoma associated with response to neoadjuvant chemoradiotherapy with or without an immune checkpoint inhibitor. METHODS: From 2 prospectively collected esophageal carcinoma cohorts (n = 172 in total) treated with neoadjuvant chemoradiotherapy alone (n = 132) or a combination of neoadjuvant chemoradiotherapy and an immune checkpoint inhibitor (n = 40), fecal samples were available at baseline, during treatment, and presurgery. Additionally, in the immune checkpoint inhibitor-treated patients, tumor and duodenal snap frozen biopsies were collected over time. Fecal, tumor, and duodenal DNA were extracted for 16S ribosomal RNA sequencing. Associations were investigated between microbiome composition pathological complete response and progression-free survival (PFS). RESULTS: There was a statistically significant shift in the microbiota profile of the fecal, tumor, and duodenal microbiota over time. In the total cohort, patients with a pathological complete response had a stable fecal alpha diversity, while the diversity of poor responders decreased during treatment (P = .036). Presurgery, lower alpha diversity (<4.12) was related to worse PFS (log-rank P = .025). Baseline tumor biopsies of patients with short PFS had more Fusobacterium. A low baseline duodenal alpha diversity (<3.96) was associated with worse PFS (log-rank P = .012). CONCLUSIONS: Lower intestinal alpha diversity was associated with worse response and survival of esophageal carcinoma patients. In tumor biopsies, Fusobacterium was more abundant in patients with poor PFS. After further mechanistic validation, these findings may aid in response prediction and the design of novel microbiome modulating treatments for esophageal carcinoma patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".