MétaCan
Menu
Back to cohort

Overall survival in patients who received checkpoint inhibitors after completing tebentafusp in a phase 3 randomized trial of first-line metastatic uveal melanoma.

2021· article· en· W3169526934 on OpenAlexaff
Marlana Orloff, Richard D. Carvajal, Alexander N. Shoushtari, Joseph J. Sacco, Max Schlaak, Claire Watkins, Shaad E. Abdullah, Howard Goodall, Marcus O. Butler

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineIpilimumabInterim analysisOncologyNivolumabHazard ratioPembrolizumabProgression-free survivalCombination therapyRefractory (planetary science)Clinical trialCancerOverall survivalImmunotherapyConfidence interval

Abstract

fetched live from OpenAlex

9526 Background: Tebentafusp (tebe) is a bispecific consisting of an affinity-enhanced T cell receptor fused to an anti-CD3 effector that can redirect T cells to target gp100+ cells. Tebe significantly improved OS compared to investigator’s choice (IC) in first line (1L) mUM [NCT03070392]. In a phase (ph) 2 study of tebe in 2L+ mUM (NCT02570308), several checkpoint inhibitor (CPI) refractory pts who were retreated with CPI after tebe achieved durable clinical benefit [1]. We therefore evaluated clinical outcomes of post-tebe CPI in patients treated on the ph3 trial of tebe versus investigator’s choice (IC) [NCT03070392]. Methods: In the ph3 trial, 378 HLA-A*02:01+ 1L mUM pts were randomized 2:1 to tebe (n=252) or IC (n=126) [pembrolizumab (82%), ipilimumab (12%) or dacarbazine (6%)]. No crossover to tebe was permitted, investigators were free to choose subsequent therapy, and there was no re-randomization at time of subsequent therapy. This analysis was conducted on the first interim analysis (data extracted Nov-2020). When pts received more than one subsequent therapy, the first was used in these analyses. Medians and 1-yr OS from the start of post-study therapy are obtained from standard Kaplan-Meier analyses; hazard ratios (HR) are from Cox regression models adjusted for age and gender. Results: 106/252 (42%) tebe pts received ≥ 1 subsequent therapy: 35% CPI, 9% chemo, 6% liver directed therapy (LDT), 6% other. 55/126 (44%) of IC pts received ≥ 1 subsequent therapy: 21% CPI, 10% chemo, 12% LDT, 10% other. Median time to first subsequent therapy was longer for tebe pts at 5.2 mo vs. IC pts at 3.8 mo. The median duration from start of first subsequent CPI to end date was longer in the prior tebe pts at 4 mo vs prior IC pts at 2.8 mo. From the start of any first subsequent therapy, prior tebe pts had longer OS compared to prior IC pts, HR 0.67 (95% CI 0.42, 1.07). Most of the subsequent therapy was CPI, and the OS benefit was also seen in this subset, HR 0.62 (95% CI 0.34, 1.14). For prior tebe pts, the median and 1-yr OS rates from start of any first subsequent therapy were 13 mo and 53% and from start of first subsequent CPI were 16 mo and 63%. Both were higher than the sequence of IC followed by any therapy (11 mo and 44%), IC followed by CPI (9 mo and 47%) and a recent meta-analysis of 2L+ mUM (7 mo and ̃35% 1-yr OS rate). Conclusions: Pts who progressed on tebe and then received CPI had better OS compared to pts who progressed on IC and then received CPI. Further analysis will explore whether confounding factors are influencing this effect. These exploratory data suggest that tebe, relative to IC, may improve outcomes to subsequent CPI. (1)Yang J. et al. ASCO 2019, J . Cli n Oncol 37:15_suppl, 9592. Clinical trial information: NCT03070392.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.081
GPT teacher head0.435
Teacher spread0.355 · 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 designRandomized trial
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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicOcular Oncology and TreatmentsFrench-language works237,207