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Record W4387322507 · doi:10.48083/blpv3411

Genetics and Tumor Microenvironment of Renal Cell Carcinoma

2022· article· en· W4387322507 on OpenAlexvenueno aff
Sari Khaleel, Christopher J. Ricketts, W. Marston Linehan, Mark W. Ball, Brandon J. Manley, Samra Turajilic, James Brugarolas, A. Ari Hakimi

Bibliographic record

VenueSociété Internationale d’Urologie Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsRenal cell carcinomaClear cell renal cell carcinomaBiologyDiseaseTumor microenvironmentKidney cancerCancerCellCancer researchPathologyMedicineGenetics

Abstract

fetched live from OpenAlex

Renal cell carcinoma is a diverse group of diseases that can be distinguished by distinct histopathologic and genomic features. In this comprehensive review, we highlight recent advancements in our understanding of the genetic and microenvironmental hallmarks of kidney cancer. We begin with clear cell renal cell carcinoma (ccRCC), the most common subtype of this disease. We review the chromosomal and genetic alterations that drive initiation and progression of ccRCC, which has recently been shown to follow multiple highly conserved evolutionary trajectories that in turn impact disease progression and prognosis. We also review the diverse genetic events that define the many recently recognized rare subtypes within non-clear cell RCC. Finally, we discuss our evolving understanding of the ccRCC microenvironment, which has been revolutionized by recent bulk and single-cell transcriptomic analyses, suggesting potential biomarkers for guiding systemic therapy in the management of advanced ccRCC.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.027
GPT teacher head0.264
Teacher spread0.236 · 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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueSociété Internationale d’Urologie Journal→Same topicRenal cell carcinoma treatment→French-language works237,207→