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Record W4387116241 · doi:10.1001/jamaoncol.2023.3809

Radiotherapy Plus Cisplatin With or Without Lapatinib for Non–Human Papillomavirus Head and Neck Carcinoma

2023· article· en· W4387116241 on OpenAlexaff
Stuart J. Wong, Pedro A. Torres‐Saavedra, Nabil F. Saba, George Shenouda, Jeffrey M. Bumpous, Robert E. Wallace, Christine H. Chung, Adel K. El‐Naggar, Clement K. Gwede, Barbara Burtness, Paul Tennant, Neal Dunlap, Rebecca Redman, William Stokes, Soumon Rudra, Loren K. Mell, Assuntina G. Sacco, Sharon A. Spencer, Lisle M. Nabell, Min Yao, Fabio Cury, Darrion Mitchell, Christopher U. Jones, S. Firat, Joseph N. Contessa, Thomas J. Galloway, Adam Currey, Jonathan Harris, Walter J. Curran, Quynh‐Thu Le

Bibliographic record

VenueJAMA Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Center for Advancing Translational SciencesNational Cancer Institute
KeywordsMedicineLapatinibInternal medicineOncologyCetuximabChemoradiotherapyProgression-free survivalRadiation therapyClinical endpointPlaceboHead and neck cancerCancerRandomized controlled trialBreast cancerChemotherapyTrastuzumabPathology

Abstract

fetched live from OpenAlex

Importance: Patients with locally advanced non-human papillomavirus (HPV) head and neck cancer (HNC) carry an unfavorable prognosis. Chemoradiotherapy (CRT) with cisplatin or anti-epidermal growth factor receptor (EGFR) antibody improves overall survival (OS) of patients with stage III to IV HNC, and preclinical data suggest that a small-molecule tyrosine kinase inhibitor dual EGFR and ERBB2 (formerly HER2 or HER2/neu) inhibitor may be more effective than anti-EGFR antibody therapy in HNC. Objective: To examine whether adding lapatinib, a dual EGFR and HER2 inhibitor, to radiation plus cisplatin for frontline therapy of stage III to IV non-HPV HNC improves progression-free survival (PFS). Design, Setting, and Participants: This multicenter, phase 2, double-blind, placebo-controlled randomized clinical trial enrolled 142 patients with stage III to IV carcinoma of the oropharynx (p16 negative), larynx, and hypopharynx with a Zubrod performance status of 0 to 1 who met predefined blood chemistry criteria from October 18, 2012, to April 18, 2017 (median follow-up, 4.1 years). Data analysis was performed from December 1, 2020, to December 4, 2020. Intervention: Patients were randomized (1:1) to 70 Gy (6 weeks) plus 2 cycles of cisplatin (every 3 weeks) plus either 1500 mg per day of lapatinib (CRT plus lapatinib) or placebo (CRT plus placebo). Main Outcomes and Measures: The primary end point was PFS, with 69 events required. Progression-free survival rates between arms for all randomized patients were compared by 1-sided log-rank test. Secondary end points included OS. Results: Of the 142 patients enrolled, 127 (median [IQR] age, 58 [53-63] years; 98 [77.2%] male) were randomized; 63 to CRT plus lapatinib and 64 to CRT plus placebo. Final analysis did not suggest improvement in PFS (hazard ratio, 0.91; 95% CI, 0.56-1.46; P = .34) or OS (hazard ratio, 1.06; 95% CI, 0.61-1.86; P = .58) with the addition of lapatinib. There were no significant differences in grade 3 to 4 acute adverse event rates (83.3% [95% CI, 73.9%-92.8%] with CRT plus lapatinib vs 79.7% [95% CI, 69.4%-89.9%] with CRT plus placebo; P = .64) or late adverse event rates (44.4% [95% CI, 30.2%-57.8%] with CRT plus lapatinib vs 40.8% [95% CI, 27.1%-54.6%] with CRT plus placebo; P = .84). Conclusion and Relevance: In this randomized clinical trial, dual EGFR-ERBB2 inhibition with lapatinib did not appear to enhance the benefit of CRT. Although the results of this trial indicate that accrual to a non-HPV HNC-specific trial is feasible, new strategies must be investigated to improve the outcome for this population with a poor prognosis. Trial Registration: ClinicalTrials.gov Identifier: NCT01711658.

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 score0.250
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.058
GPT teacher head0.394
Teacher spread0.336 · 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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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