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Safety and efficacy of osimertinib in patients with NSCLC and uncommon tumoral <i>EGFR</i> mutations: A systematic review and meta-analysis.

2024· review· en· W4400038187 on OpenAlexaff
Jonathan N. Priantti, Maysa Vilbert, Francisco Cézar Aquino de Moraes, Isabella Michelon, Caio Castro, Natasha B. Leighl, Ludimila Cavalcante, Yu Fujiwara, Alfredo Addeo, Jair Bar, Alessio Cortellini, Amin Nassar, Abdul Rafeh Naqash

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineOsimertinibOncologyInternal medicineMeta-analysisEpidermal growth factor receptorCancerErlotinib

Abstract

fetched live from OpenAlex

8642 Background: Osimertinib is broadly used for advanced EGFR-mutant NSCLC patients. However, the activity of osimertinib is not fully characterized in tumors harboring uncommon EGFR mutations, which represents about 10% of EGFR-mutated NSCLC cases. Hence, we conducted a systematic review and meta-analysis to assess the efficacy and safety of osimertinib in patients with NSCLC and uncommon tumoral EGFR mutations. Methods: PubMed, Embase, and the Cochrane Library were searched for eligible studies. Uncommon EGFR mutations were defined as any mutation other than the exon 19 deletion, L858R and T790M mutations, and exon 20 insertion, except when in compound. Efficacy was assessed by objective response rate (ORR), disease control rate (DCR), duration of response (DOR), progression-free survival (PFS), and overall survival (OS). Heterogeneity was examined with I2 statistics and random-effect model was used for a meta-analysis. Results: Nine studies comprising 331 patients were included. Median follow-up ranged from 12.6 to 22.0 months (mos). Median age in each study ranged from 51 to 72 years old (table). Overall, 62% (205/331) of patients were female and 77% (256/331) of patients had an ECOG PS ≤1. About 78% (258/331) of patients received Osimertinib in a 1st line setting. Uncommon tumoral EGFR mutations included G719X in 40% (131/331) of patients, L861X in 27% (91/331), and S768I in 15% (49/331). Pooled analysis showed an overall ORR of 49.5% (95% CI, 42.2 – 56.9), a DCR of 90.2% (95% CI, 86.2 – 94.3), a median OS of 24.5 mos (95% CI, 11.9 – 24.5), a median PFS of 9.5 mos (95% CI, 8.2 – 11.0), and a median DOR of 17.4 mos (95% CI, 7.9 – 22.7). Intracranial (i) efficacy had an iORR of 50.3% (95% CI, 30.3 – 70.3), an iDCR of 92.8% (95% CI, 76.2 – 100), and a median iPFS of 6.1 mos (95% CI, 4.5 – 6.6). In a subgroup analysis according to EGFR mutation, overall ORR was significantly different in patients with tumoral G719X, S768I and L861 mutations (ORRs of 28.8%, 33.3%, and 67.7%, respectively, p for subgroup differences < 0.01). Overall, osimertinib was well tolerated with a frequency of all-grade AEs of 86.2% (95% CI, 61.2 – 100) and ≥ G3 of 18.0% (95% CI, 1.2 – 34.7). Conclusions: This systematic review and meta-analysis suggests that patients with NSCLC with uncommon tumoral EGFR mutations may benefit from osimertinib treatment. Patients harboring tumoral L861 mutation achieved a significantly higher ORR compared to the modest ORR in G719X, S768I cases. [Table: see text]

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.029
Bibliometrics0.0050.007
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.134
GPT teacher head0.516
Teacher spread0.383 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractyes

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