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Clinical and Molecular Features of Epidermal Growth Factor Receptor (EGFR) Mutation Positive Non-Small-Cell Lung Cancer (NSCLC) Patients Treated with Tyrosine Kinase Inhibitors (TKIs): Predictive and Prognostic Role of Co-Mutations.

ABSTRACT: Tyrosine kinase inhibitors (TKIs) show variable efficacy in epidermal growth factor receptor mutation-positive (EGFR+) NSCLC patients, even in patients harbouring the same mutation. Co-alterations may predict different outcomes to TKIs. We retrospectively analysed all consecutive EGFR+ advanced NSCLC treated with first-line TKIs at our Institutions. NGS with a 22 genes clinical panel was performed on diagnostic specimens. PD-L1 expression was also evaluated. Of the 106 analysed specimens, 59 showed concomitant pathogenic mutations. No differences in OS (mOS 22.8 vs. 29.5 months; p = 0.088), PFS (mPFS 10.9 vs. 11.2 months; p = 0.415) and ORR (55.9% vs. 68.1%; p = 0.202) were observed comparing patients without and with co-alterations. Subgroup analysis by EGFR mutation type and TKIs generation (1st/2nd vs. 3rd) did not show any difference too. No correlations of PD-L1 expression levels by co-mutational status were found. Significant associations with presence of co-alterations and younger age (p = 0.018) and baseline lymph nodes metastases (p = 0.032) were observed. Patients without concomitant alterations had a significant higher risk of bone progression (26.5% vs. 3.3%, p = 0.011). Pathogenic co-alterations does not seem to predict survival nor efficacy of EGFR TKIs in previously untreated advanced NSCLC.

SUBMITTER: Bironzo P

PROVIDER: S-EPMC8156829 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Clinical and Molecular Features of Epidermal Growth Factor Receptor (EGFR) Mutation Positive Non-Small-Cell Lung Cancer (NSCLC) Patients Treated with Tyrosine Kinase Inhibitors (TKIs): Predictive and Prognostic Role of Co-Mutations.

Bironzo Paolo P Reale Maria Lucia ML Sperone Tessa T Tabbò Fabrizio F Caglio Andrea A Listì Angela A Passiglia Francesco F Di Maio Massimo M Righi Luisella L Bussolino Federico F Scagliotti Giorgio V GV Novello Silvia S

Cancers 20210517 10

<h4>Background</h4>Tyrosine kinase inhibitors (TKIs) show variable efficacy in epidermal growth factor receptor mutation-positive (EGFR+) NSCLC patients, even in patients harbouring the same mutation. Co-alterations may predict different outcomes to TKIs.<h4>Methods</h4>We retrospectively analysed all consecutive EGFR+ advanced NSCLC treated with first-line TKIs at our Institutions. NGS with a 22 genes clinical panel was performed on diagnostic specimens. PD-L1 expression was also evaluated.<h4> ...[more]

PMID: 34067823

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Project description:BackgroundsEpidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non-small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline 18F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.MethodsA sum of 313 NSCLC patients with pre-treatment 18F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.ResultsThree hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.ConclusionsThe PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients' survival outcome of targeted therapy in clinical practice.

Project description:BackgroundEpidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the established first-line treatment for non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, but survival advantages in advanced-stage cases remain modest and show interpatient variability. This study seeks to delineate real-world survival outcomes in stage IV EGFR-mutant NSCLC patients undergoing treatment with EGFR-TKIs and to identify independent prognostic factors impacting overall survival (OS). The findings are expected to be a reference for clinicians in managing advanced lung cancer patients.MethodsClinical characteristics from advanced EGFR-mutant NSCLC patients who received EGFR-TKIs were retrospectively analyzed to investigate the association between clinical factors and overall survival.ResultsThe median OS was 40.60 months (95% confidence interval: 34.72-46.48 months), with expected 1-, 3-, and 5-year OS of 87.2%, 57.8%, and 34.0%, respectively. Patients exhibiting specific characteristics, such as being under 60 years old, having stage IVA disease, adenocarcinoma histology, no liver or brain metastases, negative for neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), cytokeratin fragment 19 (CYFRA 21 - 1), progastrin-releasing peptide (ProGRP), and those who received chemotherapy and radiotherapy exhibited significantly longer survival times compared to patients with the opposite characteristics (being 60 years or older, having stage IVB or IVC disease, non-adenocarcinoma histology, presence of liver or brain metastases, positive for CEA, NSE, ProGRP, CYFRA 21 - 1, and not receiving chemotherapy or radiotherapy) (P < 0.05). Cox univariate and multivariate analyses identified pathological type, liver metastasis, brain metastasis, NSE, CYFRA 21 - 1, EGFR-TKIs type, radiotherapy, and chemotherapy as independent prognostic factors for patients with stage IV NSCLC treated with EGFR-TKIs.ConclusionsPathological type, liver metastasis, brain metastasis, NSE, and CYFRA 21 - 1 were identified as independent risk factors for stage IV NSCLC patients treated with EGFR-TKIs, while chemotherapy and radiotherapy were determined to be independent protective factors. Taking icotinib or gefitinib, as opposed to osimertinib, was an independent risk factor for advanced NSCLC patients.

Dataset Information

Clinical and Molecular Features of Epidermal Growth Factor Receptor (EGFR) Mutation Positive Non-Small-Cell Lung Cancer (NSCLC) Patients Treated with Tyrosine Kinase Inhibitors (TKIs): Predictive and Prognostic Role of Co-Mutations.

Publications

Clinical and Molecular Features of Epidermal Growth Factor Receptor (EGFR) Mutation Positive Non-Small-Cell Lung Cancer (NSCLC) Patients Treated with Tyrosine Kinase Inhibitors (TKIs): Predictive and Prognostic Role of Co-Mutations.

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