Abstract B07: A long noncoding RNA-based genomic classifier identifies a subset of luminal muscle-invasive bladder cancer patients with favorable prognosis
Bibliographic record
Abstract
Abstract Background: Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease and gene expression profiling has identified different molecular subtypes, each having distinct biologic and clinicopathologic characteristics. Subtyping MIBC has primarily been messenger RNA (mRNA)-based, although noncoding RNAs, including long noncoding RNAs (lncRNAs), have potential utility in providing additional resolution to current molecular subtyping models. Materials and Methods: The expression profiles of over 6,000 lncRNAs were quantified from whole-transcriptome microarray data of a MIBC patient cohort treated by neoadjuvant chemotherapy (NAC) and radical cystectomy (N=223). Unsupervised consensus clustering of the most highly variant lncRNAs identified a four-cluster solution, which was further characterized using a panel of MIBC biomarkers, gene signatures, and survival analysis. The four-cluster consensus was validated using the publicly available The Cancer Genome Atlas (TCGA) radical cystectomy cohort (N=405). A single-sample genomic classifier (GC) was trained using ridge-penalized logistic regression and then validated in two independent patient cohorts (N=255 and N=94). Results: In the NAC and TCGA cohorts, survival analysis of a lncRNA-based consensus cluster solution revealed a lncRNA-cluster (LC3) with strikingly good prognosis that was enriched for tumors of the luminal-papillary mRNA subtype. In both cohorts, the luminal-papillary tumors from this cluster (LPL-C3) were clinically less aggressive than other luminal-papillary tumors. Patients having LPL-C3 tumors were younger and had more frequent organ-confined, node-negative disease than other luminal-papillary tumors. LPL-C3 tumors were characterized by enhanced FGFR3 pathway activity, wild-type P53 expression, and robust SHH signaling. In the TCGA cohort, LPL-C3 tumors were also enriched for FGFR3 mutations and depleted for TP53 and RB1 mutations. A GC trained to identify these LPL-C3 patients showed robust performance in two validation cohorts. Conclusions: Using lncRNA expression profiling, we identified a biologically distinct subgroup of luminal-papillary MIBC with less-aggressive molecular characteristics and favorable prognosis. These data suggest that lncRNAs can provide additional information in resolving higher-resolution subtypes for more precise patient management strategies. Citation Format: Joep J. de Jong, Yang Liu, Roland Seiler, A. Gordon Robertson, Michiel S. van der Heijden, Jonathan L. Wright, James Douglas, Marc Dall'Era, Simon J. Crabb, Bas W.G. van Rhijn, Kim E.M. van Kessel, Elai Davicioni, Yair Lotan, Ellen C. Zwarthoff, Peter C. Black, Joost L. Boormans, Ewan A. Gibb. A long noncoding RNA-based genomic classifier identifies a subset of luminal muscle-invasive bladder cancer patients with favorable prognosis [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr B07.
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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 | 0.001 | 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) | 0.002 | 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".