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Record W2969712092 · doi:10.1016/j.eururo.2019.07.048

First-line Immuno-Oncology Combination Therapies in Metastatic Renal-cell Carcinoma: Results from the International Metastatic Renal-cell Carcinoma Database Consortium

2019· article· en· W2969712092 on OpenAlexaff
Shaan Dudani, Jeffrey Graham, J. Connor Wells, Ziad Bakouny, Sumanta K. Pal, Nazlı Dizman, Frede Donskov, Camillo Porta, Guillermo de Velasco, Aaron R. Hansen, Marco Iafolla, Benoit Beuselinck, Ulka N. Vaishampayan, Lori Wood, Elizabeth Liow, Flora Yan, Takeshi Yuasa, Georg A. Bjarnason, Toni K. Choueiri, Daniel Y.C. Heng

Bibliographic record

VenueEuropean Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsPrincess Margaret Cancer CentreSunnybrook HospitalQueen Elizabeth II Health Sciences CentreCancerCare ManitobaUniversity of Calgary
FundersNational Cancer InstituteFoundation MedicinePfizer JapanNovartis PharmaEMD SeronoGenentechIpsenAlexion PharmaceuticalsEisaiNational Comprehensive Cancer NetworkSanofiExelixisGlaxoSmithKlineAstellas PharmaCelldex TherapeuticsPfizerAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineRenal cell carcinomaOncologyInternal medicineMetastasisCarcinomaCancer

Abstract

fetched live from OpenAlex

BACKGROUND: In metastatic renal-cell carcinoma (mRCC), recent data have shown efficacy of first-line ipilimumab and nivolumab (ipi-nivo) as well as immuno-oncology (IO)/vascular endothelial growth factor (VEGF) inhibitor combinations. Comparative data between these strategies are limited. OBJECTIVE: To compare the efficacy of ipi-nivo versus IO-VEGF (IOVE) combinations in mRCC, and describe practice patterns and effectiveness of second-line therapies. DESIGN, SETTING, AND PARTICIPANTS: Using the International Metastatic Renal-cell Carcinoma Database Consortium (IMDC) dataset, patients treated with any first-line IOVE combination were compared with those treated with ipi-nivo. INTERVENTION: All patients received first-line IO combination therapies. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: First- and second-line response rates, time to treatment failure (TTF), time to next treatment (TNT), and overall survival (OS) were analysed. Hazard ratios were adjusted for IMDC risk factors. RESULTS AND LIMITATIONS: In total, 113 patients received IOVE combinations and 75 received ipi-nivo. For IOVE combinations versus ipi-nivo, first-line response rates were 33% versus 40% (between-group difference 7%, 95% confidence interval [CI] -8% to 22%, p = 0.4), TTF was 14.3 versus 10.2 mo (p = 0.2), TNT was 19.7 versus 17.9 mo (p = 0.4), and median OS was immature but not statistically different (p = 0.17). Adjusted hazard ratios for TTF, TNT, and OS were 0.71 (95% CI 0.46-1.12, p = 0.14), 0.65 (95% CI 0.38-1.11, p = 0.11), and 1.74 (95% CI 0.82-3.68, p = 0.14), respectively. Sixty-four (34%) patients received second-line treatment. In patients receiving subsequent VEGF-based therapy, second-line response rates were lower in the IOVE cohort than in the ipi-nivo cohort (15% vs 45%; between-group difference 30%, 95% CI 3-57%, p = 0.04; n = 40), though second-line TTF was not significantly different (3.7 vs 5.4 mo; p = 0.4; n = 55). Limitations include the study's retrospective design and sample size. CONCLUSIONS: There were no significant differences in first-line outcomes between IOVE combinations and ipi-nivo. Most patients received VEGF-based therapy in the second line. In this group, second-line response rate was greater in patients who received ipi-nivo initially. PATIENT SUMMARY: There were no significant differences in key first-line outcomes for patients with metastatic renal-cell carcinoma receiving immuno-oncology/vascular endothelial growth factor inhibitor combinations versus ipilimumab and nivolumab.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.263
Teacher spread0.230 · 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.

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

Citations90
Published2019
Admission routes1
Has abstractyes

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