Distinct gene expression patterns identify patients who relapse after neoadjuvant pembrolizumab and radical cystectomy in the PURE-01 study.
Bibliographic record
Abstract
549 Background: The PURE-01 clinical trial reported the use of neoadjuvant treatment with pembrolizumab prior to radical cystectomy (RC) in patients with muscle-invasive bladder cancer (MIBC; cT2–3N0M0; PMID 30343614). We have previously reported that specific molecular subtypes (i.e. basal or claudin-low) and immune signatures are associated with more favorable survival. However, reports on the detailed tumor biology of patients (pts) who relapsed after neoadjuvant pembrolizumab are lacking. Herein, we provide a detailed characterization of relapsing pts and identify distinct gene expression patterns with potential utility as novel biomarkers. Methods: Microarray data from transurethral resection of the bladder tumor (TURBT; N=102) and matched RC (N=25) tissue from the PURE-01 trial were analyzed. Gene expression signatures and molecular subtypes were assigned as previously described (PMID 32165065). The Kaplan-Meier method was used to estimate differences in patient outcomes. Immune-signatures were split by median for survival analysis. All significance testing used a two-sided t-test at a threshold of 0.05. Results: Differential gene expression analysis identified KRT20 and H19 to be enriched in relapsing patients (both p<0.001). Expression below the median was predictive of favourable 3-year overall (OS) and recurrence-free (RFS) survival (OS, 37.3% vs. 21.6%, p=0.118; RFS, 35.3% vs. 21.6%, p<0.01 and OS, 41.2% vs. 17.6%, p=0.03; RFS, 39.2% vs. 17.6%, p=0.012, respectively). Distribution of molecular subtypes showed tumors with neuronal character (NE-like) were enriched in pts who relapsed. Low immune-signatures, including Interferon-gamma and -alpha, were predictive of relapse (3-year RFS, 31.4% vs. 25.5%, p<0.001 and 31.4% vs. 25.5%, p<0.01, respectively), whereas Immunophenoscore for CD4 was not. None of these markers were predictive for relapse in matched RC-samples. Conclusions: Relapsing patients demonstrated distinct biological patterns in pre-therapy tumor samples that show prognostic value, potentially making an earlier identification and therefore adapted treatment possible. Low expression of immune-related signatures was associated with relapse.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".