19MO Desaminotyrosine is a key driver of the success of immunotherapy with fecal microbial transplantation in cancer
Bibliographic record
Abstract
Background: LB has become a practical, non-invasive tool for genomic profiling.However, its interpretation often requires a MTB.We present results from the PRECISO program evaluating the integration of blood-based NGS into a real-world MTB for TM. Methods:We conducted a prospective analysis of patients (pts) with TM enrolled in the PRECISO program in Hospital 12 de Octubre (Feb 2024-Apr 2025).All underwent LB using FoundationOne Liquid CDx (Roche).We collected clinical variables, genomic findings, and MTB recommendations.Results: A total of 346 pts were included (45.1% female; 85.9% metastatic disease, median age 65).Most had ECOG 0-1 (86.7%) and NSCLC (98.3%: 20.5% squamous (sq), 77.8% non-sq); 1.6% mesothelioma and 0.1% thymic tumors.Samples were obtained at diagnosis (31.3%), progression (PD) (59.6%), or other settings (9.1%).Median turnaround time for NGS results was 15 days.ESCAT I/II alterations were detected in 33.2%/21.7% of pts.Excluding diagnostic samples, actionable ESCAT I alterations were identified in 10.1% of sq-NSCLC and 25.8% of non-sq NSCLC without prior molecular findings.At diagnosis, 78.8% of LBs were informative.Non-informative results were more likely in pts with non-progressive disease (OR 2.25; 2 p = 0.10), radiological response (OR 10.8; 2 p = 0.008) and non-metastatic status (OR 9.1; 2 p < 0.001).The MTB recommended targeted therapy in 26% of pts (13.9% trials, 7.8% standard, 4.3% compassionate use); 7.2% had suspected germline variants.
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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".