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Sequencing of systemic therapies in advanced NSCLC with <i>MET</i> exon 14 skipping mutation: A multicenter experience.

2021· article· en· W3171453021 on OpenAlexaffabout
Sally C. M. Lau, Kirstin Perdrizet, Danilo Giffoni de Mello Morais Mata, Andrea S. Fung, Geoffrey Liu, Penelope Ann Bradbury, Frances A. Shepherd, Adrian G. Sacher, Brandon S. Sheffield, David Hwang, Ming‐Sound Tsao, Susanna Y. Cheng, Parneet Cheema, Natasha B. Leighl

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsWilliam Osler Health SystemSunnybrook Health Science CentreUniversity Health NetworkUniversity of TorontoPrincess Margaret Cancer CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineInternal medicineOncologyLung cancerPopulationnon-small cell lung cancer (NSCLC)CancerAdenocarcinoma

Abstract

fetched live from OpenAlex

e21123 Background: The treatment landscape for patients with metastatic non-small cell lung cancer (mNSCLC) with a MET exon 14 skipping mutation ( MET ex14) is rapidly changing, with recent approvals of MET selective tyrosine kinase inhibitors (TKIs) and reports of durable response to immune checkpoint inhibitors (ICI), particularly among those with sarcomatoid histology. Currently there are no published data that inform the sequencing of TKIs and ICI regimens. We sought to characterize treatment patterns and outcomes in this population at 3 Ontario cancer centres. Methods: We reviewed all mNSCLC patients with MET ex14 identified by tissue or plasma NGS in the last 4 years. Patients with EGFR co-mutation or MET amplification alone were excluded. All systemic therapies and outcomes of overall response (ORR), progression free survival (PFS), overall survival (OS), and adverse events (AEs) were captured. Results: We identified 43 patients with MET alterations, of whom 29 had MET ex14: median age 73 years (54-92), 66% female, 79% non-smokers. Tumor histology was adenocarcinoma in 76%, pleomorphic/sarcomatoid in 21% and adenosquamous in 3% of patients. 69% of patients had PD-L1 ≥50%. At presentation, 20% of patients had high disease burden and ECOG ≥2. Among 15 patients who received ICI, ORR with ICI monotherapy was 45% (10/11 had PD-L1 ≥50%) and ORR with ICI plus chemotherapy was 75% (4/4 had PD-L1 0-49%). Responses were seen in 50% of non-smokers (7/12 had PD-L1 ≥50%). The median PFS with ICI was 10.6 months (1.7-NR). MET TKIs were received by 18 patients (16 crizotinib, 1 capmatinib, 1 cabozantinib), with an ORR of 28% (30% amongst those who received crizotinib first line). The median PFS with TKIs was 2.6 months (1.2-8.9). Median OS for the entire cohort was 24.4 months (10.1-48.3). Patients who received initial ICI (n = 13) compared to those who received initial TKI (n = 11) had significantly longer OS (48.3 vs 13.6 months; p = 0.005), not controlled for prognostic factors. All patients who progressed after ICI (9/13) received further treatment while only 50% of patients who progressed after TKI (8/11) received subsequent therapy. 7 patients received TKI therapy after ICI with a median time to TKI of 35 days (24-181). 6 patients (85.7%) experienced an early grade ≥3 AE (4 transaminitis, 2 pneumonitis) resulting in permanent discontinuation of TKI in half of patients. There were no treatment-related deaths. Conclusions: Patients with MET ex14 NSCLC benefit from ICI irrespective of PD-L1 expression and smoking history. ORR and PFS with earlier generation TKIs (crizotinib) were poor. Increased toxicity is seen when a TKI is used after ICI and careful monitoring is necessary. Future studies focusing on the optimal sequencing of TKIs and ICI-containing therapy should be prioritized, as well as broader access to newer generation MET TKIs with greater activity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
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)0.0010.000

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 head0.076
GPT teacher head0.478
Teacher spread0.402 · 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 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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Citations3
Published2021
Admission routes2
Has abstractyes

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