35P Detection of methylated circulating tumor DNA by digital PCR in patients with metastatic non-small cell lung cancer
Bibliographic record
Abstract
Background: Non-muscle-invasive bladder cancer (NMIBC) presents challenges due to the varied responses to treatments.The most effective treatment approach is intravesical immunotherapy with BCG (Bacillus Calmette-Gurin), which induces inflammation and an anti-tumor response in patients who respond.However, predicting which patients will benefit is difficult, with around 50% showing inflammation without clinical improvement.Liquid biopsies, especially urine-based methods, offer a promising, non-invasive alternative for personalizing NMIBC treatment. Methods:The nCounter Human v3 miRNA panel was used to identify differentially expressed (DE) miRNAs from the primary tumour of responder (R) and nonresponder (NR) patients.To validate these results, miRNA-specific RT-qPCR was performed in urine samples collected before transurethral resection (pre-TURBT) or before starting BCG treatment (pre-treatment).Identification and validation of predictive cytokines in urine samples was performed by LEGENDplex and ELISA analysis, respectively.Results: With the aim of solving patient welfare problems and reducing the high costs that bladder cancer (BC) entails, we have developed BlaDimiR: a new and efficient system for the early detection of BC.BlaDimiR is based on the detection of two miRNA molecules in the urine of patients, which has shown an accuracy greater than 95%.In addition, we have developed and evaluated BlaDimiRplus, a system for predicting response to intravesical instillation of BCG alone or in combination with anti-PDL1 by combining 6 miRNA determinations with 2 specific cytokines.We validated individual ratios that distinguished between R and NR patients in pretreatment samples (ROC AUC=0.82-0.91)and validated in a second cohort of 74 samples (ROC AUC = 0.74).A multifactorial analysis yielded a ROC AUC=1 for a combination of 6 miRNAs and 2 cytokines.Conclusions: BlaDimiR and BlaDimiRplus are an innovative, fast, and accurate diagnostic tools that utilizes urinary biomarkers for the early, non-invasive detection and therapy response prediction of bladder cancer.Both BlaDimiR components aims to improve patient outcomes and reduce healthcare burdens.
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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".