Abstract A56: Effect of chemotherapy on gut microbiota and microbiota-derived metabolites in children with cancer
Bibliographic record
Abstract
Abstract Chemotherapy used to treat pediatric cancers often causes significant side effects including vomiting, diarrhea, and severe infections. Bacteria composing the intestinal microbiota play a major role in the health of individuals. The modification of the gut microbiota profile (by nutrition, antibiotics, or other chemical agents) can lead to beneficial or adverse effects on health status. It has been suggested that the gut microbiota may modulate response to treatment, including side effects from chemotherapy. However, studies in the field are lacking to prove this concept. We aim to examine whether the modifications of gut microbiota profiles are associated with negative sides effects of pediatric cancer treatment and lead to higher peripheral inflammation and oxidative stress. To explore this new line of research, we are studying the longitudinal changes in gut microbiota, bacterial-derived metabolites, and state of inflammation and oxidative stress during cancer treatment. A total of 38 children with acute lymphoblastic leukemia and non-Hodgkin lymphoma will be recruited at Sainte-Justine UHC in Montreal. We are collecting clinical data, blood, and fecal samples at different time points during treatment. Nutritional data are gathered with 24-hour recalls assessing diet prior to sample collection. Our methodology includes multiplexed massively parallel sequencing, ELISA, high-performance liquid chromatography, gas chromatography-mass spectrometry, and enzymatic techniques. Our findings will serve as a foundation for mechanistic studies and the development of biomarkers for therapeutic use, and contribute to a better understanding of individual responses to childhood cancer therapy. Citation Format: Abderrahim Benmoussa, Véronique Bélanger, Emile Levy, Caroline Laverdière, Alain Stintzi, Daniel Sinnett, Valérie Marcil. Effect of chemotherapy on gut microbiota and microbiota-derived metabolites in children with cancer [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A56.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".