Can the microbiota predict response to systemic cancer therapy, surgical outcomes, and survival? The answer is in the gut
Bibliographic record
Abstract
Khalid El Bairiab* , Rachid Jabibc, Dario Trapanid , Hanae Boutallakae, Bouchra Ouled Amar Bencheikhf , Mohammed Bouzianebc, Mariam Amranig, Said Afqirabh & Adil Malebbi a Cancer Biomarkers Working Group, Mohamed Ist University, Oujda, Moroccob Faculty of Medicine and Pharmacy, Mohamed Ist University, Oujda, Moroccoc Department of Visceral Surgery, Mohamed VI University Hospital, Oujda, Moroccod Department of Haematology and Oncology, European Institute of Oncology, IEO, IRCCS, University of Milano, Milan, Italye Department of Gastroenterology and Digestive Endoscopy, Mohamed V Military Teaching Hospital of Rabat, Mohamed V University, Rabat, Moroccof Montreal Neurological Institute and Hospital, McGill University, Montreal, Canadag Department of Pathology, National Institute of Oncology, Faculty of Medicine and Pharmacy, Mohamed V University, Rabat, Moroccoh Department of Medical Oncology, Mohamed VI University Hospital, Oujda, Moroccoi Department of Microbiology, Mohamed VI University Hospital, Oujda, MoroccoCONTACT Khalid El Bairi k.elbairi@ump.ac.ma Faculty of Medicine and Pharmacy, Mohamed Ist University, Oujda, MoroccoABSTRACTIntroduction The gut microbiota seems to play a key role in tumorigenesis, across various hallmarks of cancer. Recent evidence suggests its potential use as a biomarker predicting drug response and adding prognostic information, generally in the context of immuno-oncology.Areas covered In this review, we focus on the modulating effects of gut microbiota dysbiosis on various anticancer molecules used in practice, including cytotoxic and immune-modulating agents, primarily immune-checkpoint inhibitors (ICI). Pubmed/Medline-based literature search was conducted to find potential original studies that discuss gut microbiota as a prognostic and predictive biomarker for cancer therapy. We also looked at the US ClinicalTrials.gov website to find additional studies particularly ongoing human clinical trials.Expert commentary Sequencing of stool-derived materials and tissue samples from cancer patients and animal models has shown a significant enrichment of various bacteria such as Fusobacterium nucleatum and Bacteroides fragilis were associated with resistant disease and poorer outcomes. Gut microbiota was also found to be associated with surgical outcomes and seems to play a significant role in anastomotic leak (ATL) after surgery mainly by collagen breakdown. However, this research field is just at the beginning and the current findings are not yet ready to change clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".