Abstract B10: A simple immunohistochemical algorithm to evaluate molecular subtypes and clinical associations in non-muscle invasive bladder cancer
Bibliographic record
Abstract
Abstract Introduction: Recent large-scale transcriptomic studies in muscle-invasive bladder cancer (MIBC) have identified promising predictive and prognostic properties of luminal, basal, and additional molecular subtypes. Transcriptomic profiling is laborious, expensive, and prone to errors due to infiltrating lymphocytes and variations in tumor cellularity. Immunohistochemistry (IHC) provides a faster, cheaper, and clinically feasible subtyping method that has been validated to subtype MIBC (1) and simplified into a three-antibody algorithm (2). Here we utilize this algorithm in non-muscle invasive bladder cancer (NMIBC), where, in contrast to MIBC, there are few data exploring IHC subtypes. Methods: We implemented the simplified three-antibody IHC-based algorithm to subtype a cohort of 523 non-muscle invasive (stages pTa low grade, pTa high grade, and pT1 high grade) transurethral resections of bladder tumor (TURBT) samples. Cases were sampled in duplicate on six tissue microarray blocks. Spatial localization, prevalence, and intensity of antibody staining was scored using digital image analysis. Results: Similar to previous transcriptome-based work (3), preliminary analysis identified the prevalence of Urothelial-Like (Uro-like), Genomically Unstable (GU), Basal-Squamous Cell Carcinoma-Like (BSq), and unclassified subtypes at 33% n=171, 48%, n=251, 9%, n=46, and 10%, n= 55, respectively. As expected, GU was enriched in pTa high grade and pT1 tumors, but surprisingly, BSq cancers were more similar to Uro-like in stage and grade distribution. Discussion: Molecular subtypes of bladder cancer can be identified easily using a three-antibody assay. Subtypes that signify high risk of metastasis in MIBC are surprisingly prevalent in NMIBC but may not yet have acquired the molecular lesions that drive metastatic progression. Further research into subtype and progression should be accelerated by the availability of rapid and inexpensive IHC-based assays. References 1. Sjödahl G, Eriksson P, Liedberg F, Höglund M. Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J Pathol 2017 May 1;242(1):113–25. 2. Sjödahl G, Jackson Chelsea L, Bartlett J, Robert SD, Berman David M. Molecular profiling in muscle invasive bladder cancer: More than the sum of its parts. J Pathol 2019 Jan 2. 3. Hedegaard J, Lamy P, Nordentoft I, Algaba F, Høyer S, Ulhøi BP, et al. Comprehensive transcriptional analysis of early-stage urothelial carcinoma. Cancer Cell 2016 Jul 11;30(1):27–42. Citation Format: Chelsea L. Jackson, Lina Chen, Kevin Y.M. Ren, Kash Visram, Robert Siemens, Gottfrid Sjödahl, David M. Berman. A simple immunohistochemical algorithm to evaluate molecular subtypes and clinical associations in non-muscle invasive bladder cancer [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 B10.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".