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Record W3128607514 · doi:10.1038/s41467-021-21068-9

Integrative molecular characterization of sarcomatoid and rhabdoid renal cell carcinoma

2021· article· en· W3128607514 on OpenAlexaff
Ziad Bakouny, David A. Braun, Sachet A. Shukla, Wenting Pan, Xīn Gào, Yue Hou, Abdallah Flaifel, Stephen Tang, Alice Bosma-Moody, Meng Xiao He, Natalie I. Vokes, Jackson Nyman, Wanling Xie, Amin H. Nassar, Sarah Abou Alaiwi, Ronan Flippot, Gabrielle Bouchard, John A. Steinharter, Pier Vitale Nuzzo, Miriam Ficial, Miriam Sant’Angelo, Juliet Forman, Jacob E. Berchuck, Shaan Dudani, Kevin Bi, Jihye Park, Sabrina Y. Camp, Maura Sticco-Ivins, Laure Hirsch, Sylvan C. Baca, Megan Wind‐Rotolo, Petra Ross‐Macdonald, Maxine Sun, Gwo‐Shu Mary Lee, Steven L. Chang, Xiao X. Wei, Bradley A. McGregor, Lauren C. Harshman, Giannicola Genovese, Leigh Ellis, Mark M. Pomerantz, Michelle S. Hirsch, Matthew L. Freedman, Michael B. Atkins, Catherine J. Wu, Thai H. Ho, W. Marston Linehan, David F. McDermott, Daniel Y.C. Heng, Srinivas R. Viswanathan, Sabina Signoretti, Eliezer M. Van Allen, Toni K. Choueiri

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of Calgary
FundersCongressionally Directed Medical Research ProgramsFondation ARC pour la Recherche sur le CancerKidney Cancer AssociationDana-Farber/Harvard Cancer CenterDana-Farber Cancer InstituteBristol-Myers SquibbG. Harold and Leila Y. Mathers FoundationLeukemia and Lymphoma SocietyNational Cancer InstituteNational Institutes of HealthU.S. Department of Health and Human ServicesU.S. Department of Defense
KeywordsCDKN2ABAP1Renal cell carcinomaCancer researchImmune systemPhenotypeBiologyImmune checkpointDownregulation and upregulationGeneImmunotherapyMedicineImmunologyPathologyMelanomaGenetics

Abstract

fetched live from OpenAlex

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings build on prior work and shed light on the molecular drivers of aggressivity and responsiveness to ICI of S/R RCC.

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 score0.199
Threshold uncertainty score0.437

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 head0.013
GPT teacher head0.269
Teacher spread0.256 · 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

Citations168
Published2021
Admission routes1
Has abstractyes

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