MétaCan
Menu
Back to cohort
Record W4391693415 · doi:10.1016/j.isci.2024.109179

Multiomics profiling of urothelial carcinoma in situ reveals CIS-specific gene signature and immune characteristics

2024· article· en· W4391693415 on OpenAlexaff
Meenakshi Anurag, Trine Strandgaard, Sung Han Kim, Yongchao Dou, Éva Compérat, Hikmat Al‐Ahmadie, Brant A. Inman, Ann Taber, Iver Nordentoft, Jørgen Bjerggaard Jensen, Lars Dyrskjøt, Seth P. Lerner

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsWestern University
FundersNational Cancer Institute
KeywordsUrotheliumBiologyPhenotypeCancer researchARID1AGenePathologyGene expressionGene expression profilingMolecular biologyMutationMedicineGeneticsUrinary systemEndocrinology

Abstract

fetched live from OpenAlex

Urothelial carcinoma in situ (CIS) is an aggressive phenotype of non-muscle-invasive bladder cancer. Molecular features unique to CIS compared to high-grade papillary tumors are underexplored. RNA sequencing of CIS, papillary tumors, and normal urothelium showed lower immune marker expression in CIS compared to papillary tumors. We identified a 46-gene expression signature in CIS samples including selectively upregulated known druggable targets MTOR , TYK2 , AXIN1 , CPT1B , GAK , and PIEZO1 and selectively downregulated BRD2 and NDUFB2 . High expression of selected genes was significantly associated with CIS in an independent dataset. Mutation analysis of matched CIS and papillary tumors revealed shared mutations between samples across time points and mutational heterogeneity. CCDC138 was the most frequently mutated gene in CIS. The immunological landscape showed higher levels of PD-1-positive cells in CIS lesions compared to papillary tumors. We identified CIS lesions to have distinct characteristics compared to papillary tumors potentially contributing to the aggressive phenotype.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueiScienceSame topicBladder and Urothelial Cancer TreatmentsFrench-language works237,207