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Squamous-cell carcinoma variant histology (SCC-VH) in muscle-invasive bladder cancer (MIBC): A comprehensive clinical, genomic, and therapeutic assessment from multiple datasets.

2019· article· en· W2947988059 on OpenAlexaff
Marco Bandini, Filippo Pederzoli, Russell W. Madison, Alberto Briganti, Elizabeth R. Plimack, Jeffrey S. Ross, Günter Niegisch, Evan Y. Yu, Aristotelis Bamias, Neeraj Agarwal, Srikala S. Sridhar, Jonathan E. Rosenberg, Joaquim Bellmunt, Matt D. Galsky, Andrea Gallina, Andrea Salonia, Francesco Montorsi, Siraj M. Ali, Jon Chung, Andrea Necchi

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineBladder cancerInternal medicineOncologyProportional hazards modelCohortLogistic regressionCancerGastroenterology

Abstract

fetched live from OpenAlex

4535 Background: Pure or predominant SCC-VH is not uncommon in MIBC. Nevertheless, very few data are available about the efficacy of neoadjuvant chemotherapy (NAC). Here, we examined the outcomes after NAC, explored novel therapeutic targets, and propose new results in these patients (pts) by integrating multiple datasets. Methods: Within RISC and San Raffaele databases (1990-2018), we identified 2858 MIBC pts with urothelial cancer (UC, N = 2229) or VH (N = 629) who received RC +/- NAC. Kaplan-Meier and Cox regression analyses compared cancer-specific survival (CSS) between SCC and UC with NAC stratification. Logistic regression models tested the odds of clinical-to-pathological downstaging (cT > pT). Foundation Medicine (FMI) dataset was queried for SCC-VH. 97 pts were assayed with hybrid-capture based comprehensive genomic profiling (CGP). Finally, we looked at the results from the PURE-01 study, that is now amended and enrolling pts with VH (NCT02736266). Results: Overall, 127 (4.4%) had predominant SCC-VH, 157 (5.5%) UC+SCC. Among the NAC-treated pts, SCC was the only VH (N = 44) significantly associated with worse CSS, (p < 0.001) and higher mortality (HR 2.10, p = 0.003) vs. UC. After NAC adjustment, SCC-VH showed lower rate of downstaging (3.7 vs 9.3%, OR 0.4, p = 0.028) vs. UC. Similar negative trends were confirmed in pN0 pts, where SCC exhibited worse CSS (p = 0.006) and higher mortality (HR 5.15, p = 0.002). In the FMI cohort, the median tumor mutational burden (TMB) of SCC was 6.25 mut/mb (vs 6.9 mut/mb of 1984 UC), 27% of pts having > 10 mut/mb and 14% > 20 mut/mb. Clinically relevant alterations occurred in PIK3CA (42%), CCND1 (15%), PTEN (9.3%), FGFR3 (9.3%), and ERBB2 (6.2%). In the PURE-01 study, 13/84 (15.5%) SCC-VH pts received pembrolizumab before RC. PD-L1 combined positive score was ≥10 in 11/13 pts; results yielded 4 pT0 (30.8%), 10 pT≤1 (76.9%), and no deaths (median FUP: 10.4 mo). Conclusions: We present a comprehensive assessment of SCC-VH in MIBC. SCC represents the VH with the lowest activity of NAC. While CGP revealed multiple opportunities for targeted therapy, the efficacy of neoadjuvant pembrolizumab in SCC is encouraging.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.113
GPT teacher head0.443
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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