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Record W4401363835 · doi:10.1093/oncolo/oyae181.036

35 Efficacy of treatments post-lenvatinib in patients with advanced renal cell carcinoma (aRCC)

2024· article· en· W4401363835 on OpenAlexaff
Justine Panian, Caiwei Zhong, Sharon Choi, Roxanne Quinn, Evan Ferrier, Eddy Saad, Renée Maria Saliby, Carmel Malvar, Sumanta Pal, Hedyeh Ebrahimi, Ben Tran, Evon Jude, Aly‐Khan A. Lalani, Cristina Suárez, Guillermo de Velasco, Ravindran Kanesvaran, Martín Zarbá, Razane El Hajj Chehade, Toni K Choueiri, Daniel Yick Chin Heng, Rana R. McKay

Bibliographic record

VenueThe Oncologist · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsMcMaster University Medical CentreBaker Hughes (Canada)University of Calgary
Fundersnot available
KeywordsLenvatinibMedicineCabozantinibInternal medicineRenal cell carcinomaOncologyCohortPembrolizumabDiscontinuationSorafenibCancerHepatocellular carcinomaImmunotherapy

Abstract

fetched live from OpenAlex

Abstract Background Lenvatinib is a tyrosine kinase inhibitor (TKI) that targets both vascular endothelial growth factor (VEGF) receptor and fibroblast growth factor receptor. It has demonstrated efficacy both in the upfront and refractory disease settings. However, there is a lack of data surrounding the efficacy of TKIs post-lenvatinib exposure. In this study, we investigate the activity of therapies post-lenvatinib in patients with aRCC. Methods We conducted a retrospective analysis utilizing the International Metastatic Database Consortium (IMDC). Patients having received treatment post-lenvatinib exposure were eligible and divided into two cohort: patients post-1st line lenvatinib (2nd line cohort) and patients post-2nd line lenvatinib (3rd line cohort). The primary objective was objective response rate (ORR) and time to treatment failure (TTF). ORR was summarized with 95% two-sided exact binomial confidence interval. TTF was defined as time from treatment initiation to drug cessation for any reason censored at the date of last follow-up. Results Overall, 84 patients received 1st line lenvatinib of whom 43 (51%) remain on therapy, 20 (24%) received 2nd line treatment, and 21 (25%) received no subsequent treatment. The median duration of prior lenvatinib was 9.7 months. All patients received 1st line pembrolizumab + lenvatinib (ORR 50%, median TTF 19.7 months). Reason for lenvatinib discontinuation was progression (50%), progression + toxicity (20%), toxicity (15%), or other (15%). For the 2nd line cohort, median age was 61 years, most patients were male (85%), had prior nephrectomy (75%), clear cell histology (85%), and were IMDC intermediate/poor risk (55%). 2nd line therapy regimens included TKI monotherapy (80%), TKI-IO (5%), and other (15%). The median follow up from 2nd-line treatment initiation was 4.9 months. The ORR to 2nd line treatment was 5% (95% CI 0.2-25) and median TTF was 5.8 months (95% CI 1.9-14.9). Of 2nd line lenvatinib-exposed patients (n=84), 24 (29%) remain on treatment, 34 (40%) received 3rd line treatment, and 26 (31%) did not receive additional therapy. The median duration of prior lenvatinib was 5.9 months. Most patients received 2nd line everolimus + lenvatinib (97%) (ORR 31%, median TTF 9.2 months). Reason for lenvatinib discontinuation was progression (59%), progression + toxicity (9%), toxicity (12%), or other (21%). For the 3rd line cohort, median age was 67 years, most patients were male (68%), had prior nephrectomy (88%), clear cell histology (68%), and were IMDC intermediate/poor risk (77%). 3rd line treatments included TKI alone (50%), IO-TKI (38%), and other (12%). The median follow up from 3rd-line treatment initiation was 14.9 months. The ORR to 3rd line treatment was 12% (95% CI 3.3-27) and median TTF was 2.8 months (95% CI 1.9-7.4). Conclusions In this analysis, we demonstrate modest activity of TKI-based therapy post-lenvatinib exposure. Our study highlights the need for improved treatment options for patients progressing on lenvatinib-based therapies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.504

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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