The role of gut microbiome in immune modulation in metastatic renal cell carcinoma
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".