351. INVESTIGATING THE IMPACT OF INDUCTION CHEMOTHERAPY FOR ESOPHAGEAL ADENOCARCINOMA ON THE GUT MICROBIOME
Bibliographic record
Abstract
Abstract Background Esophageal adenocarcinoma (EAC) is a highly heterogeneous cancer with a current five-year survival rate of ~18%. To improve outcomes, understanding factors impacting treatment response is imperative. Gut microbiome signatures are one such factor and have been associated with response to immunotherapy and survival in several solid cancers. Thus, we aim to identify variations in gut microbiome signatures at different stages of treatment and elucidate the link of these signatures to treatment responses and patient outcomes. Methods Fecal samples were collected from 50 patients prior to induction and 18 during or post induction and stored at −20 ̊C before extracting DNA using the QIAamp DNA Mini Kit. Extracted DNA was quantified and 16S gene expression was confirmed by qPCR using primers targeting the V3-V4 region of the 16S gene, following which 16S rRNA sequencing was performed. FastQC, MultiQC and Cut Adapt were used for quality control of sequence data before assembly with vsearch. The resulting data was processed following the deblur pipeline. Taxonomy was assigned using Qiime2 classift-hybrid-vsearch-sklean function. Statistical comparison of diversity was conducted in R. Results Species diversity within individual samples, the alpha diversity, showed no significant difference between baseline and post-induction samples. Likewise, the comparison of diversity between samples, the beta diversity, showed no significant clustering on baseline compared to post-induction. On the genus level, there was no significant statistical difference between the normalized counts of individual genera shared between baseline and post-induction samples. Conclusion These preliminary results suggest that induction therapy has minimal impact upon species richness of the gut microbiome when compared to baseline as measured by fecal microbiome. Future directions of this project include examining the gut microbiome at further stages of treatment and investigating the microbiome of the tumour itself.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".