Abstract B005: Investigating the effects of cancer treatment on gut microbiota in colorectal cancer patients: Study protocol
Bibliographic record
Abstract
Abstract Background: Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer mortality. Acute adverse effects (AEs) from CRC treatments (surgery, chemotherapy, radiation therapy) may cause dose limitations and/or treatment discontinuation. Chronic AEs may include bowel symptoms, fatigue, anxiety, depression, and sarcopenic obesity. These acute and chronic AEs significantly impact quality of life (QoL). A comprehensive understanding of the pathophysiological mechanism(s) driving these AEs is lacking. Evidence supports the hypothesis that the gut microbiota may be an integrative point in the pathogenesis of several AEs. Dysbiosis alters the normal function of the gut and gut-brain-axis. CRC treatments can lead to dysbiosis and in turn may drive acute and chronic AEs. Our aims are to explore how CRC treatment affects the microbiota and the further path to recovery. Methods: A prospective feasibility study of n=35 participants in Calgary, Alberta of stage I-III CRC patients to evaluate: 1) The feasibility of collecting microbiota samples at diagnosis to one-year post diagnosis; 2) Longitudinal changes to microbiota over a 1-year period; and 3) Preliminary associations between changes in the microbiota and treatment completion, treatment AEs, clinical and tumor characteristics, and changes to patient reported outcomes (PROs). Inclusion: Newly diagnosed stage I-III CRC, aged ≥18, English speaking, and willing to provide 4 fecal samples. Exclusion: Inflammatory bowel disease, hereditary CRC syndromes, or stage IV. Convenience sampling will be used. Feasibility will include recruitment and retention rates, adherence to specimen collection protocols, specimen quality, and patient satisfaction. Microbiota will be evaluated using longitudinal fecal sampling for metabolomics, culture, and mechanistic studies to examine intra-individual differences in microbiota (α and b diversity). Shotgun sequencing libraries will be prepared to generate approximately 4M 150 bp read pairs/sample. Clinical data on tumor characteristics, treatments, and treatment AEs will be abstracted from medical records. Demographic data and a battery of PROs (diet, physical activity, depression, anxiety, QoL, CRC symptoms, cognitive function, and fatigue using validated questionnaires) will be collected. Results: This study will determine the feasibility of longitudinal prospective collection of biospecimen, clinical, and PROs in newly diagnosed stage I-III CRC patients. This study will also provide preliminary data on changes to the gut microbiota as a result of treatments and how these changes may in turn impact clinical and PROs. Conclusions: This novel investigation into dysbiosis as an integrative point driving CRC treatment AEs is timely and warranted given the persistence of debilitating problems post CRC treatment. Building on data from this project we plan to conduct a population-based cohort study. Our goal is to ultimately inform interventions to manage treatment AEs, improve clinical outcomes, and improve QoL for CRC survivors. Citation Format: Colleen Ann Cuthbert, Kathy McCoy, Anthony MacLean, Lin Yang, May Lynn Quan, Donald Buie. Investigating the effects of cancer treatment on gut microbiota in colorectal cancer patients: Study protocol [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr B005.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.019 |
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".