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Patient Subgroup Analysis of Quality-of-Life Outcomes in Checkmate 205, a Phase 2 Study of Nivolumab in Patients with Classical Hodgkin Lymphoma

2016· article· en· W2739450639 on OpenAlexaboutno aff
Andreas Engert, Fiona Taylor, Bryan Bennett, Ishan Hirji, Kim Cocks, Jeffrey G. McDonald, Erin Mann, Kazunobu Kato, David Cella

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNivolumabInternal medicineQuality of life (healthcare)CohortBrentuximab vedotinOncologyVisual analogue scaleCancerLymphomaSurgeryHodgkin lymphomaImmunotherapy

Abstract

fetched live from OpenAlex

Abstract Introduction: Nivolumab is a fully human IgG4 immune checkpoint inhibitor targeting programmed death receptor-1 (PD-1). It has recently been approved by the FDA in the US for patients with classical Hodgkin lymphoma (cHL) who have failed autologous hematopoietic stem cell transplantation (auto-HSCT) and post-transplant brentuximab vedotin (cohort B of CheckMate 205). The objective response rates in cohort B of CheckMate 205 (NCT02181738) per independent radiologic review committee (IRRC) assessment was 66%, with median time to response of 2.1 months. Furthermore, mean EuroQol Five Dimensions (EQ-5D) visual analog scale (VAS) score increased over time during treatment with nivolumab, and European Organisation for Research and Treatment of Cancer Core Quality-of-Life questionnaire (EORTC QLQ-C30) data suggested an improvement from baseline (BL) across functional, symptom, and global health scores [Younes et al. Lancet Oncol 2016; Jul 20 (Epub ahead of print)]. The aim of the current analysis is to understand if quality-of-life (QoL) changes observed in the cohort of patients with cHL vary across patient subgroups. Methods: CheckMate 205 is a multi-cohort study evaluating the efficacy and safety of nivolumab 3 mg/kg by IV infusion every 2 wks in 3 cohorts of patients with cHL post-auto-HSCT. Patient-reportedgeneral health status was assessed using the 3-level version of the EQ-5D questionnaire, and cancer-specific QoL using the EORTC QLQ-C30. Questionnaires were administered to patients every 4 cycles (every 8 wks). All treated patients with BL and at least 1 post-BL assessment in cohort B were included in this analysis population. Scores were examined across prespecified patient subgroups, including age, sex, Eastern Cooperative Oncology Group (ECOG) performance status (PS) at BL, smoking status, B symptoms at BL, region (USA/Canada or Europe), and best overall response (BOR) per investigator assessment or IRRC at wks 9, 17, 25, and 33. Least squares (LS) means were used to describe post-BL score changes over time for the overall analysis population and subgroups. Minimally important differences were prespecified. Results: 72 patients (90%) from CheckMate 205, cohort B, completed BL and at least 1 post-BL EORTC QLQ-C30 or EQ-5D assessment. By wk 33, 58% of patients were included. The mean (SD) age was 38.9 (13.3) years. 34.7% of patients were female, 45.8% had BL ECOG PS 1, and 68.1% had stage IV disease at study entry. LS mean (standard error) score change from BL at wk 33 was 19.1 (3.1) for EQ-5D VAS and 7.6 (2.3) for the EORTC QLQ-C30 global health/QoL status scale. For the global health status subscale, all subgroup estimates were consistent with the overall changes from BL, except patients with absence of B symptoms who experienced significantly smaller changes at wk 17 only. For other EORTC subscales where statistically significant improvements in LS mean from BL were observed at each time point (fatigue, dyspnea, appetite loss, physical functioning, role functioning), although all subgroup estimates were in line with the overall change, there were some trends for non-smokers (vs smokers), ECOG PS 0 (vs 1), USA/Canada (vs Europe), and B symptoms at BL (vs none) toward better symptom improvement. Changes from BL across responders and non-responders were consistent with overall changes from BL. Conclusions: In this small cohort of patients with cHLwho had failed auto-HSCT and subsequent brentuximab vedotin, the improvement in QoL observed while on-treatment with nivolumab was mostly consistent across the subgroups investigated. Funding: Bristol-Myers Squibb (BMS). Medical writing: S Addison, Caudex, funded by BMS. Disclosures Engert: Takeda, BMS: Consultancy, Honoraria, Research Funding. Taylor:Bristol-Myers Squibb: Consultancy. Bennett:Bristol-Myers Squibb: Consultancy. Hirji:Bristol-Myers Squibb: Employment. Cocks:Bristol-Myers Squibb: Consultancy. McDonald:Bristol-Myers Squibb: Consultancy. Mann:Bristol-Myers Squibb: Consultancy. Kato:Bristol-Myers Squibb: Employment. Cella:Alexion, Inc., Astellas, Biogen Idec, Celgene, Clovis Oncology, Inc., Daiichi Sankyo, Eli Lilly, Evidera, Inc., Exelixis, Fiborgen, Genetech, Helsinn Therapeutics, Inc., Immunogen, Ipsen Pharma, Janssen, Lexicon Pharmaceuticals, Inc., Merck, Novartis, Onc: Consultancy, Research Funding; Bristol-Meyers Squibb: Consultancy, Research Funding; Abbvie, Inc.: Consultancy, Research Funding; GlaxoSmithKline: Consultancy, Research Funding; Bayer Pharmaceuticals, Inc.: Consultancy, Research Funding; Facit.org: Other: President.

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)0.004
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.019
GPT teacher head0.299
Teacher spread0.280 · 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 designNon-randomized 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".

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Citations5
Published2016
Admission routes1
Has abstractyes

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