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Record W4393204492 · doi:10.1016/j.jtocrr.2024.100669

Characteristics of Long-Term Survivors With EGFR-Mutant Metastatic NSCLC

2024· article· en· W4393204492 on OpenAlexafffund
William Tompkins, Connor B. Grady, Wei‐Ting Hwang, Krishna Chandrasekhara, Caroline E. McCoach, Fangdi Sun, Geoffrey Liu, Devalben Patel, Jorgé Nieva, Amanda Herrmann, Kristen A. Marrone, Vincent K. Lam, Vamsi Velcheti, Stephen V. Liu, Gabriela Liliana Bravo Montenegro, Tejas Patil, Jared Weiss, Kelsey Leigh Miller, William Schwartzman, Jonathan E. Dowell, Khvaramze Shaverdashvili, Liza C. Villaruz, Amanda Cass, Wade T. Iams, Dara L. Aisner, Charu Aggarwal, D. Ross Camidge, Melina E. Marmarelis, Lova Sun

Bibliographic record

VenueJTO Clinical and Research Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer Centre
FundersDaiichi Sankyo EuropeGilead SciencesNational Cancer InstituteCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchSanofi GenzymeAstraZeneca KoreaEMD SeronoGenentechDaiichi-SankyoG1 TherapeuticsBeiGeneMirati TherapeuticsAstraZenecaNovocureRegeneron PharmaceuticalsSanofiAmgenAmerican Association for Cancer ResearchEli Lilly and CompanyBristol-Myers SquibbSeagenLUNGevity FoundationPfizer
KeywordsOsimertinibOncologyMedicineRetrospective cohort studyCohortInternal medicineMutantTerm (time)CancerEpidermal growth factor receptorBiologyGeneticsGeneErlotinib

Abstract

fetched live from OpenAlex

Introduction Characteristics of long-term survivors in EGFR-mutant (EGFRm) NSCLC are not fully understood. This retrospective analysis evaluated a multi-institution cohort of patients with EGFRm NSCLC treated in the pre-osimertinib era and sought to describe characteristics of long-term survivors. Methods Clinical characteristics and outcomes were abstracted from the electronic medical records of patients with EGFRm metastatic NSCLC who started first-line therapy before 2015. Demographics and comutations were compared between greater than or equal to 5-year survivors and less than 5-year survivors. Multivariable Cox proportional hazard and logistic regression models were used to evaluate factors associated with survival and the odds of death within 5 years, respectively. Results Overall, 133 patients were greater than or equal to 5-year survivors; 127 were less than 5-year survivors. Burden of pathogenic comutations including TP53 and PIK3CA was similar between greater than or equal to 5-year survivors and less than 5-year survivors. Receipt of first-line chemotherapy rather than EGFR tyrosine kinase inhibitor was similar between the groups (22% of <5-y versus 31% of ≥5-y). Baseline brain metastasis and history of smoking were associated with higher odds of death within 5 years (odds ratio = 2.16, p = 0.029 and odds ratio = 1.90, p = 0.046, respectively). Among patients without baseline brain metastases, cumulative incidence of brain metastases at 5 years was 42.3%. Both baseline and post-baseline brain metastasis were associated with worse overall survival compared with no brain metastasis (hazard ratio = 3.26, p < 0.001 and hazard ratio = 4.99, p < 0.001, respectively). Conclusions Within patients treated for EGFRm metastatic NSCLC before 2015, absence of brain metastasis and nonsmoking status were predictive of 5-year survival. Our findings help to define a subset of patients with EGFRm NSCLC with excellent survival outcomes who may not require intensification of initial therapy.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.109
GPT teacher head0.503
Teacher spread0.394 · 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".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

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