The role of gut microbiome in immune modulation in metastatic renal cell carcinoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Treatment of metastatic renal cell carcinomas (mRCC) has drastically improved since the advent of immunotherapy with immune checkpoint inhibitors (ICIs), with a significant proportion of patients achieving durable responses. While this has revolutionized treatment and improved outcomes for mRCC patients, a large subset of patients still does not respond to treatment with ICIs. Moreover, ICIs can induce various immune-related adverse events, limiting their use in many patients. Therefore, there is a need to identify the predictive biomarkers of both efficacy and toxicity associated with ICIs, which would allow for a more personalized approach and help with clinical decision-making. This review aims to explore the role of the gut microbiome in RCC to overcome primary resistance and predict response to treatment with ICIs. First, current therapeutic strategies and mechanisms of action of ICI therapies for RCC treatment will be reviewed. With the technological development of shotgun whole-genome sequencing, the gut microbiome has emerged as an exciting field of research within oncology. Thus, the role of the microbiome and its bidirectional interaction with ICIs and other drugs will be explored, with a particular focus on the microbiome profile in RCC. Lastly, the rationale for future clinical interventions to overcome resistance to ICIs using fecal microbiota transplantation in patients with RCC will be presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it