EGFR-TKIs versus taxanes agents in therapy for nonsmall-cell lung cancer patients

An, Na; Zhang, Yingshi; Niu, Huibin; Li, Zuojing; Li, Qing

doi:10.1097/md.0000000000005601

Cited by 12 publications

(5 citation statements)

References 46 publications

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“…[ 2 ] The epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) gefitinib and erlotinib are part of a new treatment strategy for NSCLC patients with EGFR mutations and have improved the progression-free survival (PFS), overall survival (OS), and quality of life in patients compared to traditional platinum-based combination chemotherapy. [ 3 – 6 ] Epidermal growth factor receptor (EGFR) gene-activating mutations are strong predictive factors of response and survival to EGFR-TKIs. [ 7 , 8 ] It is generally accepted that 2 classical mutations (exon 19-del and exon 21-L858R) can enhance the sensitivity of tumor cells to EGFR-TKIs and are considered to be an effective predictor of the efficacy of EGFR-TKIs.…”

Section: Introductionmentioning

confidence: 99%

The peripheral blood neutrophil-to-lymphocyte ratio is a prognostic predictor for survival of EGFR-mutant nonsmall cell lung cancer patients treated with EGFR-TKIs

Zhang¹,

Feng

Zhu³

et al. 2018

Medicine

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Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the standard first-line treatment for EGFR-mutant nonsmall cell lung cancer (NSCLC) patients. However, studies have reported that not all NSCLC patients harboring kinase domain mutations in epidermal growth factor receptor (EGFR) show significant clinical benefits from EGFR-targeted tyrosine kinase inhibitors (TKIs). Therefore, it is necessary to establish feasible biomarkers to predict the prognosis of EGFR-mutant NSCLC patients treated with EGFR-TKIs. This study aimed to determine biomarkers using inflammatory parameters from complete blood counts to predict the prognosis of EGFR-mutant NSCLC patients treated with EGFR-TKIs.We retrospectively investigated 127 stage IIIB/IV NSCLC patients with activating EGFR mutations who were treated with EGFR-TKIs. We used receiver operating characteristic (ROC) curves to determine the optimal cut-off for the inflammatory markers as prognostic factors. Additionally, univariate and multivariate analyses were used to identify prognostic factors for progression-free survival (PFS) and overall survival (OS) of EGFR-mutant NSCLC patients treated with EGFR-TKIs.The receiver operating characteristic analysis indicated that the lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR) cut-off values were 3.37 and 2.90, respectively. The univariate analysis showed that a high LMR (>3.37) and low NLR (≤2.90) were significantly correlated with long-term PFS and OS (LMR, P = .007; NLR, P < .001). The multivariate Cox regression analysis revealed that only low NLR was an independent prognostic factor for long-term PFS and OS (PFS, HR = 0.573, 95% CI: 0.340–0.964, P = .036; OS, HR = 0.491, 95% CI: 0.262–0.920, P = .026).The data show that a low NLR was a good prognostic factor in EGFR-mutant NSCLC patients receiving EGFR-TKIs treatment. Moreover, the NLR measurement has better prognostic value than LMR.

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Section: Introductionmentioning

confidence: 99%

The peripheral blood neutrophil-to-lymphocyte ratio is a prognostic predictor for survival of EGFR-mutant nonsmall cell lung cancer patients treated with EGFR-TKIs

Zhang¹,

Feng

Zhu³

et al. 2018

Medicine

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“…Apart from histological studies, some researchers have focused on genetic mutations, whose results were controversial. The mutations of epidermal growth factor receptor (EGFR) are an indicator in targeted therapy of non-small cell lung cancer (NSCLC) [32]. Yip et al used MATLAB software and first reported 8 novel PET radiomic signatures, which helped to predict EGFR mutations of NSCLC in 348 patients successfully.…”

Section: Genetic Statusmentioning

confidence: 99%

Artiﬁcial intelligence applications for oncological positron emission tomography imaging

Liu

Cheng

et al. 2021

European Journal of Radiology

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Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Radiomics allows a high-throughput extraction of multiple features from images with artificial intelligence (AI) approaches and develops rapidly worldwide. Quantitative and objective features of medical images have been explored to recognize reliable biomarkers, with the development of PET radiomics. This paper will review the current clinical exploration of PET-based classical machine learning and deep learning methods, including disease diagnosis, the prediction of histological subtype, gene mutation status, tumor metastasis, tumor relapse, therapeutic side effects, therapeutic intervention and evaluation of prognosis. The applications of AI in oncology will be mainly discussed. The image-guided biopsy or surgery assisted by PETbased AI will be introduced as well. This paper aims to present the applications and methods of AI for PET imaging, which may offer important details for further clinical studies. Relevant precautions are put forward and future research directions are suggested.

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“…New targets continue to emerge, such as BRAF mutations (dabrafenib and trametinib) or the ROS1-gene targeted by crizotinib. Numerous meta-analyses have investigated the expected benefits for patients managed with these treatments showing significant gains in progressionfree survival [21][22][23][24][25][26][27] and impact on OS being currently investigated. For our study, the 4-month gain in median survival delivered by the 10 new therapies over four decades (and 28 more used off-label) seems low when compared with the threshold of at least a 3.25-to 4-month gain as a measure of meaningful improvement of a new therapy over standard of care recommended by the American Society of Clinical Oncology (ASCO) [28].…”

Section: Plos Onementioning

confidence: 99%

Retrospective observational cohort study on innovation in oncology and progress in survival: How far have we gotten in the two decades of treating patients with advanced non-small cell lung cancer as a single population?

Justo

Nilsson²,

Korytowsky

et al. 2020

PLoS ONE

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We assessed the impact of new antineoplastic agents on the overall survival (OS) of advanced non-small cell lung cancer (aNSCLC) patients followed up until 2012. Multivariate regression models were run for OS (outcome) and four proxies for innovation (exposure): Index (InnovInd, for SEER-Research data 1973-2012) and three levels of aggregation of Mean Medication Vintage, i.e. Overall (MMV Overall), using data aggregated at the State Level (MMV State), and using patient-level data (MMV Patient) using data from the US captured in SEER-Medicare 1991-2012. We derived Hazard ratios (HR) from Royston-Parmar models and odds ratios (OR) from a logistic regression on 1-year OS. Including 164,704 patients (median age 72 years, 56.8% stage IV, 61.8% with no comorbidities, 37.8% with adenocarcinoma, 22.9% with squamous-cell, 6.1% were censored). One-year OS improved from 0.22 in 1973 to 0.39 in 2012, in correlation with InnovInd (r = 0.97). Ten new NSCLC drugs were approved and 28 more used off-label. Regression-models results indicate that therapeutic innovation only marginally reduced the risk of dying (HROverall = 0.98 [0.98-0.98], HR MMV-Patient = 0.98 [0.97-0.98], and HR MMV-State = 0.98 [0.98-0.98], and slightly improved 1-year survival (OR MMV-Overall = 1.05 95%CI [1.04-1.05]). These results were validated with data from the Swedish National Health Data registers. Until 2013, aNSCLC patients were treated undifferentiated and the introduction of innovative therapies had statistically significant, albeit modest, effects on survival. Most treatments used off-guidelines highlight the high unmet need; however new advancements in treatment may further improve survival.

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EGFR-TKIs versus taxanes agents in therapy for nonsmall-cell lung cancer patients

Abstract: Supplemental Digital Content is available in the text

Cited by 12 publications

References 46 publications

The peripheral blood neutrophil-to-lymphocyte ratio is a prognostic predictor for survival of EGFR-mutant nonsmall cell lung cancer patients treated with EGFR-TKIs

The peripheral blood neutrophil-to-lymphocyte ratio is a prognostic predictor for survival of EGFR-mutant nonsmall cell lung cancer patients treated with EGFR-TKIs

Artiﬁcial intelligence applications for oncological positron emission tomography imaging

Retrospective observational cohort study on innovation in oncology and progress in survival: How far have we gotten in the two decades of treating patients with advanced non-small cell lung cancer as a single population?

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