MétaCan
Menu
← Back to cohort

Failure of initial VEGF-targeted therapy in metastatic renal cell carcinoma (mRCC): What next?

2009· article· en· W2282340687 on OpenAlexaffabout
Michael M. Vickers, Toni K. Choueiri, Ivan Zama, Teresa Cheng, Scott North, Jennifer J. Knox, Christian Kollmannsberger, David F. McDermott, Brian I. Rini, D.Y.C. Heng

Bibliographic record

VenueJournal of Clinical Oncology · 2009
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsPrincess Margaret Cancer CentreBC Cancer Agency
Fundersnot available
KeywordsMedicineSunitinibSorafenibTemsirolimusEverolimusBevacizumabInternal medicineTargeted therapyRenal cell carcinomaOncologyAxitinibPopulationCancerHepatocellular carcinomaPI3K/AKT/mTOR pathwayChemotherapyDiscovery and development of mTOR inhibitors

Abstract

fetched live from OpenAlex

5098 Background: The characterization and efficacy of second-line targeted therapy in patients with metastatic RCC who failed first-line VEGF-targeted therapy in a population-based setting is of clinical relevance but remains to be assessed. Methods: Provincial registries and clinical databases from seven cancer centers (3 in US and 4 in Canada) identified patients with mRCC who received first-line anti-VEGF targeted therapy between 2005–2007. Patient characteristics, data on second-line therapy and outcomes were analyzed. Results: 645 patients with mRCC who received initial VEGF-targeted therapy were identified (sunitinib, sorafenib or bevacizumab) and had a median follow-up of 25 mos. Of these, 218 patients (34%) received second-line targeted therapy: the median age was 62 yrs (range, 41–87), median KPS was 90%, 90% had prior nephrectomy, 3.8% had non-clear cell histology, 5.8% had brain metastases and 79% had > 1 metastatic site. Second-line therapy included anti-VEGF agents (sunitinib n = 93, sorafenib n = 80, bevacizumab n = 11, axitinib n = 8) and mTOR-inhibiting agents (temsirolimus n = 21, everolimus n = 3). Patient characteristics were similar aside from more non-clear cell histology in patients receiving second-line mTOR-inhibiting agents (14% vs 3% p = 0.045). On multivariable analysis, only a higher baseline KPS score prior to first-line therapy predicted which patients were more likely to receive second-line therapy (p < 0.0001). The median time to treatment failure (TTF) of second-line therapy was 4.9 mos for anti-VEGF therapy and 2.5 mos for mTOR inhibitors (p = 0.014). After adjusting for MSKCC prognostic profile (favorable, intermediate, poor), the hazard ratio for TTF was 0.52 (95%CI:0.29–0.91) in pts receiving anti-VEGF therapy. Overall survival from start of second-line therapy was not different between anti-VEGF or anti-mTOR drugs (14.2 vs 10.6 respectively; p = 0.38). 70 patients (10%) received third-line therapy. Conclusions: Baseline KPS is an independent predictor of receiving second-line targeted therapy. Patients who receive a second-line anti-VEGF drug appear to have a longer TTF than those who receive a second-line anti-mTOR drug. However, patient selection may account for this finding and overall survival was not significantly different. Results of ongoing randomized trials are awaited. [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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.144
GPT teacher head0.449
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2009
Admission routes2
Has abstractyes

Explore more

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