Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome
Bibliographic record
Abstract
The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".