MétaCan
Menu
← Back to cohort
Record W4411934388 · doi:10.1200/jco-25-00234

Tumor-Intrinsic and Microenvironmental Determinants of Impaired Antitumor Immunity in Chromophobe Renal Cell Carcinoma

2025· article· en· W4411934388 on OpenAlexaff
Chris Labaki, Eddy Saad, Katrine N. Madsen, Charbel Hobeika, Kevin Bi, Michel Alchoueiry, Sabrina Y. Camp, Yue Hou, Ziad Bakouny, Sayed Matar, Nourhan El Ahmar, Jackson Nyman, Long Zhang, Carmen Priolo, Melissa Daou, Damir Khabibullin, S. Salem, Nicholas R. Schindler, Renée Maria Saliby, Kevin Meli, J. Connor Wells, Erica Pimenta, Kosuke Takemura, Jihye Park, Marc Eid, Karl Semaan, Jingxin Fu, Thomas Denize, Razane El Hajj Chehade, Marc Machaalani, Rashad Nawfal, Wassim Daoud Khatoun, Mustafa Saleh, Jad El Masri, Nina Rossa Haddad, Wenxin Xu, Bradley A. McGregor, Michelle S. Hirsch, Wanling Xie, Daniel Yick Chin Heng, David F. McDermott, Sabina Signoretti, Eliezer M. Van Allen, Sachet A. Shukla, Toni K. Choueiri, Elizabeth P. Henske, David A. Braun

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of CalgaryBC Cancer Agency
FundersNational Cancer Institute
KeywordsImmune systemClear cellRenal cell carcinomaMedicineChromophobe cellImmune checkpointCD8Cancer researchClear cell renal cell carcinomaPathologyImmunotherapyImmunology

Abstract

fetched live from OpenAlex

PURPOSE While immune checkpoint inhibition (ICI) has transformed the management of many advanced renal cell carcinomas (RCCs), the determinants of effective antitumor immunity for chromophobe RCC (ChRCC) and renal oncocytic tumors remain an unmet clinical and scientific need. METHODS Single-cell transcriptomic and T-cell receptor profiling was performed on tumor and adjacent normal tissue of patients with ChRCC and renal oncocytic neoplasms. Using machine learning, the cellular origin of renal oncocytic neoplasms was evaluated, with analysis of associated oncogenic pathways. Using immunohistochemistry, immune infiltration was analyzed in renal oncocytic neoplasms in comparison with clear cell RCC (ccRCC). Immune checkpoint expression, clonal expansion, and tumor specificity were compared between ChRCC and ccRCC. Using the International Metastatic RCC Database Consortium data set, clinical outcomes of patients with metastatic ChRCC (mChRCC) treated with first-line systemic regimens were compared with those of patients with ccRCC. RESULTS We validated α-intercalated cells as the cellular origin of renal oncocytic neoplasms. We identified a downregulation of HLA class I molecules with enrichment of potentially targetable pathways including mammalian target of rapamycin and ferroptosis in ChRCC. The tumor microenvironment of ChRCC showed markedly decreased immune infiltration, with a pronounced depletion in tumor-infiltrating CD8 + T cells. ChRCC-infiltrating CD8 + T cells demonstrated lower immune checkpoint expression, diminished clonal expansion, and decreased tumor specificity. Clinical analysis identified poor survival outcomes selectively among patients with mChRCC treated with immune-based therapies. CONCLUSION Immunogenomic analysis of ChRCC revealed profound depletion of T cells, with an immune phenotype marked by a lack of expression of immune checkpoints and poor tumor specificity, suggesting that the few T cells in these tumor types are likely nonspecific bystanders. This immune-cold environment hinders an effective response to immunotherapy and underscores the need for ChRCC-tailored treatments designed to improve tumor-specific T-cell infiltration into the microenvironment.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
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
Bibliometrics0.0000.000
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 head0.050
GPT teacher head0.379
Teacher spread0.329 · 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

Citations11
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicRenal cell carcinoma treatment→French-language works237,207→