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Dermatologic prophylaxis and impact on patient-reported outcomes in first-line <i>EGFR</i> -mutant advanced NSCLC treated with amivantamab plus lazertinib: Results from the phase 2 COCOON trial.

2025· article· en· W4410802745 on OpenAlexaff
Byoung Chul Cho, Weimin Li, Nicolas Girard, Milena Perez Mak, Maxwell Sauder, Farastuk Bozorgmehr, Jiunn-Liang Tan, Jin‐Yuan Shih, Danny Nguyen, Enriqueta Felip, Julia Schuchard, Tonatiuh Romero, Karen Xia, Joshua Bauml, Parthiv J. Mahadevia, Mark Wildgust, Alexander I. Spira

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineInternal medicineOncologyDermatology

Abstract

fetched live from OpenAlex

8641 Background: The phase 2 COCOONtrial (NCT06120140) is evaluating the impact of enhanced dermatologic management (DM) in combination with amivantamab (ami) + lazertinib (laz) on reduction of skin and nail adverse events (AEs). At the interim analysis, enhanced DM (COCOON DM) significantly reduced the incidence of grade ≥2 dermatologic AEs by Wk 12 vs standard of care dermatologic management (SoC DM). We assessed patient-reported outcomes (PROs) from COCOON to determine if reducing dermatologic AEs impacts the quality of life (QoL) of patients with EGFR -mutant advanced NSCLC. Methods: Participants (pts) with previously untreated EGFR -mutant (Ex19del/L858R) advanced NSCLC were randomized 1:1 to receive COCOON DM or SoC DM per site practice. Pts received the approved doses of IV ami + oral laz. COCOON DM included oral doxycycline/minocycline (100 mg BID Wks 1–12), clindamycin 1% lotion on scalp (QD Wks 13–52), chlorhexidine 4% to wash hands and feet QD, and non-comedogenic ceramide-based moisturizer to body and face QD. The COCOON DM arm received a digital health tool with training on dermatologic AEs and reminders to increase adherence to the DM regimen. Dermatologic symptoms and impact on pts’ health-related QoL were measured with PRO instruments every 2 weeks. The Skindex-16 questionnaire assesses the impact of skin conditions on QoL using 3 subscales (functioning, emotional, symptoms) and an average score (0 – no effect to 100 – effect experienced all the time). Patient’s Global Impression of Severity (PGI-S) is a self-reported 4-point rating scale (no symptoms, mild, moderate, severe) assessing severity of nail infection, skin condition, and rash over time. All P values reported are nominal. Results: As of 13 Nov 2024, 138 pts received COCOON DM (n=70) or SoC DM (n=68) and had ≥12 wks of follow-up (median, 4.2 mo). This analysis focuses on PROs through 12 wks of follow-up (three 28-day ami+laz treatment cycles). Substantial and consistent separation favoring COCOON DM was observed in all post-baseline Skindex subscales indicating lower severity of dermatologic AEs and reduced impact of those AEs on QoL. More specifically, at Cycle 3 Day 15 (~10 wks), a lower average Skindex total score was observed with COCOON DM vs SoC DM ( P =0.02). More pts in the COCOON DM arm vs SoC DM reported mild or no PGI-S rash, skin condition, or nail infection across the first 3 cycles. At Cycle 3 Day 15, there was a meaningful 3-fold difference for COCOON DM vs SoC DM in pts reporting no symptoms for PGI-S rash (21% vs 7%; P =0.04) and skin condition (23% vs 7%; P =0.02). There was also a numeric improvement in pts reporting no symptoms for nail infections (27% vs 16%; P =0.13). Conclusions: Among pts with EGFR -mutant advanced NSCLC, COCOON DM reduced the severity of dermatologic AEs and reduced the impact of those AEs on QoL compared to SoC DM. Clinical trial information: NCT06120140 .

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.474
Teacher spread0.418 · 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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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