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Real-world outcomes in patients with metastatic renal cell carcinoma (mRCC) receiving dual immune checkpoint inhibitor (ICI-ICI) regimens or immune checkpoint inhibitor and tyrosine kinase inhibitor (ICI-TKI) combinations as first-line therapy: A British Columbia (BC) population-based analysis.

2023· article· en· W4324136707 on OpenAlexaffabout
Antoine Morin Coulombe, Faisal Abdullah Alsadoun, Xin Ye, Maryam Soleimani, Lucia Nappi, Bernhard J. Eigl, Kim N., Christian Kollmannsberger

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsMedicineSunitinibNivolumabIpilimumabRenal cell carcinomaInternal medicinePembrolizumabAxitinibOncologyPopulationTyrosine-kinase inhibitorProportional hazards modelCancerImmunotherapy

Abstract

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646 Background: Currently available first-line treatment regimens for mRCC include pembrolizumab-axitinib (ICI-TKI) and ipilimumab-nivolumab (ICI-ICI). Although both regimens are superior to sunitinib, direct comparisons between ICI-ICI and ICI-TKI are lacking and real-world data are sparse. Methods: Through BC Cancer pharmacy data and patient access programs queries, we identified 319 mRCC patients who were treated with first line ICI-TKI or ICI-ICI between May 2019 and March 2022. Patients’ charts were reviewed for baseline characteristics, including International mRCC Database Consortium (IMDC) risk criteria, pharmacy data, pathology and radiology reports, treatment toxicity, rationale for treatment choice when available, progression and survival outcomes. Treatment groups were compared using standard descriptive statistics; survival outcomes were compared using Kaplan Meier and Cox Regression analyses. Results: Among the first 214 patients, 64.9% and 35.1% received first line ICI-ICI and ICI-TKI, respectively. The most commonly cited reason for choosing ICI-ICI was unavailability of a publicly funded alternative at the time of prescription. IMDC risk group distribution was markedly different between regimens; in ICI-TKI patients, favorable, intermediate and poor-risk patients respectively represented 60.3%, 28.8% and 10.9%, versus 10.2%, 56.9% and 32.9% in ICI-ICI patients (p<0.001). Response rates in ICI-TKI compared favorably to ICI-ICI in the overall population with objective response rates (ORR) of 69% vs 50.4% (p=0.012) and primary progression rates of 12.7% vs 33.1% (p=0.001). However, in the intermediate-poor risk patients, survival outcomes were similar between ICI-TKI and ICI-ICI, with median progression-free survival (mPFS) of 7.5 versus 4.5 months respectively (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.41 to 1.09, p=0.107) and median overall survival (mOS) of not reached (NR) vs 22.6 months (HR 0.86, 95% CI 0.44 to 1.69, p=0.665). Of interest, in patients treated with ICI-ICI, more ipilimumab infusions in the induction phase was associated with improved mOS, rising from 3.9 to 13.6, 21.8 and 37.5 months for 1, 2, 3 or 4 infusions respectively (statistically significant by Log Rank [Mantel-Cox], p<0.001). Conclusions: In the real-world mRCC experience from BC, Canada, ICI-TKI was associated with statistically significantly improved ORR over ICI-ICI although the treated population was comparatively enriched for favorable risk patients by IMDC. PFS and OS did not differ between the two regimens in the intermediate-poor risk population. Updated results on all 319 patients will be presented.

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 score0.700
Threshold uncertainty score0.597

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.349
Teacher spread0.294 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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