RNA-sequencing to identify three different molecular grades and immune checkpoint cascades with distinct clinical behaviour in NMIBC.
Bibliographic record
Abstract
412 Background: NMIBC has a highly variable clinical behavior not adequately predicted by histological grade or clinical parameters. Some are indolent; others quickly progress to MIBC. Discrepancies between phenotype and genotype is compounded further by interobserver variability in pathological grading. There is an unmet need to improve the prediction of NMIBC. Methods: Whole transcriptomic analysis of 178 bladder tumors (158 NMIBC, 20 MIBC/metastatic) was performed from FFPE tissue incorporating messenger RNA expression, splice variants, gene fusion, mutation detection and immune checkpoint inhibitor cascades. CTLA, PD-1, LAG3, TIM3, TIGIT and B7 were compiled as an index including all major cascade genes. Data were integrated and tested for correlations with pathological grading and clinical outcomes. Conventional pathological grading for WHO 1973 (Grade 1, 2, 3) and 2004 (LG vs HG) classifications was reviewed by 3 expert uro-pathologists. Kappa statistic for interobserver variability was calculated. For validation we used an independent RNA-seq dataset (n = 209, Hedegaard et al. 2016 Cancer Cell). Results: Unsupervised clustering of RNA-Seq data distinguished 3 molecular subtypes of NMIBC; Molecular Grade Related Index (MGRI) 1, MGRI2, MGRI3. MGRI1 comprised of almost exclusively LG tumors. MGRI3 clustered with HG MIBC. Kappa for interobserver variability of expert pathologists was 0.40 and 0.78 in 1973 and 2004 WHO classification, respectively. FGFR3 mutations, FGFR3::TACC3 fusion events and MGRI1 genes were associated with components of xenobiotic metabolism (p = 2.51x10-09) signalling systems, in particular, GTPase regulation (p = 0.002), respiratory cycle genes (p = 0.004), HOX cluster (p = 0.005). MGRI independently predicted progression to MIBC (n = 138, HR = 2.96, 95%CI = 1.70-5.13, p = 1.20x10-04). 5-year PFS in a combined data set (n = 347) differed significantly for MGRI1 (100%) vs MGRI2 (92.2%) vs MGRI3 (73.5%, p = 1.99x10-05, Gray’s test). PD-1 ICC independently predicted progression (OR = 2.85, p < 0.05). Conclusions: RNA-seq delineates 3 molecular classes of NMIBC with different risks of progression to MIBC compared to conventional histologic grading.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".