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Effectiveness of first-line therapy in patients with advanced non-clear renal cell carcinoma (nccRCC).

2022· article· en· W4212951999 on OpenAlexaffabout
Sophie Laramee, Sunita Ghosh, Christian Kollmannsberger, Aaron R. Hansen, Lori Wood, Denis Soulières, Christina M. Canil, Ramy Saleh, Vincent Castonguay, Georg A. Bjarnason, Naveen S. Basappa, Rodney H. Breau, Daniel Yick Chin Heng, Frédéric Pouliot, Anil Kapoor, Aly‐Khan A. Lalani

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsJuravinski Cancer CentreSunnybrook Health Science CentreUniversity of OttawaMcGill University Health CentreUniversité LavalCentre Hospitalier de l’Université de MontréalDalhousie UniversityHealth Sciences CentreOttawa HospitalUniversity Health NetworkUniversity of British ColumbiaQueen Elizabeth II Health Sciences CentreUniversity of AlbertaUniversity of CalgaryPrincess Margaret Cancer CentreHôtel-Dieu de QuébecBC Cancer AgencyMcMaster University
Fundersnot available
KeywordsMedicinePazopanibTemsirolimusSunitinibAxitinibNivolumabEverolimusInternal medicinePembrolizumabDiscontinuationRenal cell carcinomaOncologyIpilimumabSorafenibClinical endpointClinical trialCancerPI3K/AKT/mTOR pathwayImmunotherapyHepatocellular carcinomaDiscovery and development of mTOR inhibitors

Abstract

fetched live from OpenAlex

304 Background: Current treatment principles for advanced nccRCC have been largely extrapolated from guidelines for clear cell RCC. Given the emerging randomized data for select nccRCC subtypes, real-world outcomes for these patients are informative particularly in the contemporary checkpoint inhibitor era. Methods: We performed an analysis using the Canadian Kidney Cancer information system (CKCis), a prospective database involving 14 academic centers, on nccRCC patients undergoing first-line systemic therapy between January 2011 – December 2019. Treatment groups were defined as receipt of: vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGF-TKI), mammalian target of rapamycin inhibitors (mTORi), and PD-1/PD-L1 immune checkpoint inhibitors (ICI, mono- or combination therapy). Primary outcome was 1-yr overall survival (OS) rate. Secondary outcomes were median time to treatment failure ((TTF, months), defined as treatment discontinuation, change or death) and objective response rate (ORR, %). Results: We identified 265 nccRCC patients: 204 (77.0%) received VEGF-TKI, 19 (7.2%) received mTORi and 42 (15.8%) received ICI-based first-line therapy (Table). Overall, median age was 64 years, 75% were male, 84% were classified as IMDC intermediate/poor risk, and 16% underwent prior nephrectomy. Twenty-three percent of patients were enrolled in clinical trials. Patients received primarily sunitinib (81%) or pazopanib (15%) in the VEGF-TKI group (other: 4%), while mTORi-treated patients received temsirolimus (74%) or everolimus (26%). For the ICI-based treatment group, most patients received combination therapy as ipilimumab-nivolumab (71%) or pembrolizumab-axitinib (26%), with 3% receiving ICI monotherapy. 1-yr OS was 65.2% for VEGF-TKI, 57.9% for mTORi and 69.0% for ICI-treated patients. Median TTF was 3.3 for VEGF-TKI, 3.5 for mTORi and 7.1 mos for ICI-treated patients. ORR was 17%, 5%, and 37% respectively for the VEGF-TKI, mTORi and ICI-treated groups. Conclusions: We describe the effectiveness of first-line therapy for patients with nccRCC from a national database. This real-world data suggests an association between first-line ICI-based therapies and improved outcomes, albeit with cabozantinib not available for the indication during this time. Our data supports consensus recommendations for preferred use of ICI-based or VEGF-TKI over mTORi as first-line therapy in nccRCC.[Table: see text]

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)0.001
metaresearch head score (Gemma)0.003
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.042
GPT teacher head0.367
Teacher spread0.325 · 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 routes2
Has abstractyes

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