ALK inhibitors for non-small cell lung cancer: A systematic review and network meta-analysis

Elliott, Jesse; Bai, Zemin; Hsieh, Shu‐Ching; Kelly, Shannon; Chen, Li; Skidmore, Becky; Yousef, Saeed; Zheng, Carine; Stewart, David J.; Wells, George

doi:10.1371/journal.pone.0229179

Cited by 60 publications

(53 citation statements)

References 45 publications

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“…Target therapy with ALK‐rearranged ADC was more effective than SCC. The duration time of treatment (our study: 6.4 ± 4.4 months) ALK‐rearranged SCC was shorter than those in previous clinical trials of ALK‐rearranged ADC studies (PROFILE1007:7.7 months, PROFILE1014:10.9 months; ALESIANCT02838420:14.6 months), 34,35 suggesting that outcomes are worse for ACC as compared to the ADC patient when ALK inhibitors are used. Therefore, ALK‐rearranged SCC patients obtained clinical benefit from ALK‐inhibitor drug treatment, but it was not as beneficial to those patients who had ALK‐rearranged ADC.…”

Section: Discussionmentioning

confidence: 54%

ALK‐rearranged squamous cell carcinoma of the lung

et al. 2021

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Background ALK rearrangement is a very rare subset of squamous cell carcinoma (SCC) and one of the clinical features in patients is lack of data. Here, we report eight patients diagnosed with SCC of the lung harboring ALK rearrangement. Methods We collected primary NSCLC samples at the Beijing Chest Hospital between January 2012 and December 2018 for Ventana (D5F3) immunohistochemical detection. Among the 148 patients was diagnosed ALK‐rearranged non small cell lung cancer (NSCLC), only eight cases was SCC. We collected patients information from electronic patent records (EPRs). Results The eight cases of SCC were diagnosed by immunohistochemistry (IHC). Two were given crizotinib as second‐line therapy. One patient had stable disease (SD) and progression‐free survival (PFS) of six months. The other patient had progressive disease (PD) but PFS was only one month. The side effects were tolerable. This report identified 31 cases of ALK rearrangement in SCC patients from a literature search (including the eight patients in this study). These fusion genes are often seen in a younger age group (mean age: 55.6 years) and non‐smokers (18/31, 58.1%). A total of 20 cases received an ALK inhibitor as first‐ or second‐line treatment which included 11 with a partial response (PR), four with SD, and five with PD. The DCR and ORR was 75.0% (15/20) and 55.0% (11/20), respectively. The median duration time of therapy was 6.4 ± 4.4 months. Conclusions Patients with ALK‐rearranged SCC obtained clinical benefit from ALK‐inhibitor therapy, especially those who were non‐smokers and whose tumors had been identified by IHC+/FISH+.

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Section: Discussionmentioning

confidence: 54%

ALK‐rearranged squamous cell carcinoma of the lung

et al. 2021

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“…In order to understand the efficacy and AEs of these new-generation ALKIs, several meta-analyses have been performed to identify the most beneficial ALKIs for ALK-positive lung cancer patients. For example, a recent meta-analysis showed that all ALKIs (crizotinib, ceritinib, alectinib, and brigatinib) were superior to chemotherapy in PFS, and that both alectinib and brigatinib demonstrated significantly longer PFS compared to crizotinib and ceritinib [ 26 ]. However, this network meta-analysis enrolled patients who had been treated with chemotherapy and ALKIs, which complicated the heterogeneity of the study population and hindered the application into real-world practice.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review and Network Meta-Analysis of Anaplastic Lymphoma Kinase (ALK) Inhibitors for Treatment-Naïve ALK-Positive Lung Cancer

Chuang

Chen

Chang

et al. 2021

Cancers

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Several anaplastic lymphoma kinase inhibitors (ALKIs) have demonstrated excellent efficacy on overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and also better adverse effect (AE) profiles compared to cytotoxic chemotherapy in advanced stage anaplastic lymphoma kinase (ALK) rearrangement-positive non-small cell lung cancer (NSCLC) in phase III randomized clinical trials (RCTs). We conducted this systematic review and network meta-analysis to provide a ranking of ALKIs for treatment-naïve ALK-positive patients in terms of PFS, ORR, and AEs. In addition, a sub-group analysis of treatment benefits in patients with baseline brain metastasis was also conducted. Contrast-based analysis was performed for multiple treatment comparisons with the restricted maximum likelihood approach. Treatment rank was estimated using the surface under the cumulative ranking curve (SUCRA), as well as the probability of being the best (Prbest) reference. All next-generation ALKIs were superior to crizotinib in PFS but lorlatinib and brigatinib had increased AEs. The probability of lorlatinib being ranked first among all treatment arms was highest (SUCRA = 93.3%, Prbest = 71.8%), although there were no significant differences in pairwise comparisons with high- (600 mg twice daily) and low- (300 mg twice daily) dose alectinib. In subgroup analysis of patients with baseline brain metastasis, low-dose alectinib had the best PFS (SUCRA = 87.3%, Prbest = 74.9%). Lorlatinib was associated with the best ranking for ORR (SUCRA = 90.3%, Prbest = 71.3%), although there were no significant differences in pairwise comparisons with the other ALKIs. In addition, low-dose alectinib had the best safety performance (SUCRA = 99.4%, Prbest = 97.9%). Lorlatinib and low-dose alectinib had the best PFS and ORR in the overall population and baseline brain metastasis subgroup, respectively. Low-dose alectinib had the lowest AE risk among the available ALKIs. Further head-to-head large-scale phase III RCTs are needed to verify our conclusions.

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“…The application of tyrosine kinase inhibitors against specific gene targets (EGFR, ALK and ROS1) has revolutionized the treatment for lung adenocarcinoma ( 31 ). ALK inhibitors, such as Crizotinib and Ceritinib, have been widely used to treat cancers with mutations of ALK, especially for non-small cell lung cancers ( 32 , 33 ). For example, a small subset of lung cancer patients with rearrangements of ALK or ROS1 genes are sensitive to ALK inhibitors ( 34 , 35 ).…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

et al. 2021

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ObjectivesAnaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics.MethodsFive hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test.ResultsA total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model.ConclusionsThis study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.

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ALK inhibitors for non-small cell lung cancer: A systematic review and network meta-analysis

Cited by 60 publications

References 45 publications

ALK‐rearranged squamous cell carcinoma of the lung

ALK‐rearranged squamous cell carcinoma of the lung

Systematic Review and Network Meta-Analysis of Anaplastic Lymphoma Kinase (ALK) Inhibitors for Treatment-Naïve ALK-Positive Lung Cancer

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

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