Anaplastic Lymphoma Kinase Mutation-Positive Non-Small Cell Lung Cancer

The prognostic relationship of 18F-FDG PET/CT metabolic and volumetric parameters in metastatic ALK + NSCLC

OBJECTIVE

The aim of this study is to determine the role of metabolic and volumetric parameters obtained from 18Fluorine-Fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) imaging on progression-free survival (PFS) and overall survival (OS) in patients with advanced nonsquamous cell lung carcinoma (NSCLC) with anaplastic lymphoma kinase (ALK) rearrangement.

METHODS

Pre and post-treatment PET/CT images of the ALK + NSCLC patients between January 2015 and July 2020 were evaluated. The highest standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values were obtained from pre-tyrosine kinase inhibitor (TKI) basal PET/CT (PETpre) and post-TKI PET/CT (PETpost) images. Total MTV (tMTV) and total TLG (tTLG) values were calculated by summing MTV and TLG values in all tumor foci. The change (Δ) in pSUVmax, pMTV, pTLG, tMTV and tTLG before and after treatment was calculated.The relationship of these parameters with OS and PFS was analyzed.

RESULTS

tTLGpre, tMTVpre, pTLGpre, pMTVpre, ∆SUVmax, ∆tMTV and ∆tTLG values were found to be associated with OS; ∆tMTV, ∆tTLG, tTLGpre, tMTVpre, pTLGpre and pMTVpre were associated with PFS. The cutoff values in both predicting OS and PFS were calculated as -31.6 and 391.1 for ∆tMTV and tTLGpre, respectively. In Cox regression analysis, ∆tMTV and stage for OS and ∆tMTV and tTLGpre for PFS were obtained as prognostic factors.

CONCLUSIONS

Metabolic and volumetric parameters, especially TLG values in the whole body before treatment and change in whole body MTV value, obtained from PET/CT may be useful in predicting prognosis and determining treatment strategies for patients with advanced ALK + NSCLC.

Alectinib rescue therapy in advanced ALK rearranged lung adenocarcinoma: a case report

Alectinib is a highly selective tyrosine kinase inhibitor of anaplastic lymphoma kinase (ALK) that is approved as first-line treatment in adult patients with ALK-positive non-small cell lung cancer (NSCLC) and as second-line in patients previously treated with crizotinib, and has been shown in the literature to significantly prolong progression-free survival compared to chemotherapy in patients with advanced non-small cell lung cancer. The authors describe a clinical case of a 24-year-old woman with malignant massive pleural effusion caused by ALK rearranged pulmonary adenocarcinoma with pleural and pericardial metastasis, in which, despite a dramatic clinical debut, the correct and timely management of the diagnostic and therapeutic path allowed for extraordinary therapeutic success.

The Change in Paradigm for NSCLC Patients with EML4–ALK Translocation

The severe prognosis linked with a lung cancer diagnosis has changed with the discovery of oncogenic molecularly driven subgroups and the use of tailored treatment. ALK-translocated advanced lung cancer is the most interesting model, having achieved the longest overall survival. Here, we report the most important paradigmatic shifts in the prognosis and treatment for this subgroup population occurred among lung cancer.

A Comprehensive Analysis of Metabolomics and Transcriptomics Reveals Novel Biomarkers and Mechanistic Insights on Lorlatinib Crosses the Blood-Brain Barrier

Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.

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

Objectives

Anaplastic 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.

Methods

Five 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.

Results

A 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.

Conclusions

This 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.

Efficacy and safety of bevacizumab in advanced lung adenocarcinoma patients with stable disease after two cycles of first-line chemotherapy: A multicenter prospective cohort study

Bevacizumab is the first antiangiogenetic monoclonal antibody, combined with platinum‐based double agent chemotherapy, which has been reported to improve the objective response rate (ORR) and progression‐free survival (PFS) in patients with advanced nonsquamous non‐small cell lung cancer (NSCLC), and to improve overall survival (OS) in patients when combined with carboplatin and paclitaxel. However, serious adverse effects have been reported to be associated with bevacizumab therapy. In this multicenter prospective cohort study of advanced lung adenocarcinoma patients with stable disease after two cycles of platinum‐based double agent chemotherapy, we will compare the ORR between the group who continued with their original chemotherapy regimen and the group in which bevacizumab was added to the original regimen. It is expected that there will be an ORR improvement of 20% in patients in the bevacizumab group plus chemotherapy, compared with those in the original chemotherapy group. This study has been registered as Clinical Trial NCT03240549.

Bevacizumab combined with platinum‐based double agent chemotherapy has been reported to improve the objective response rate (ORR) and progression‐free survival (PFS) in patients with advanced nonsquamous non‐small cell lung cancer (NSCLC), but also cause more adverse effects. We hypothesize that the improvement in the response rate in the bevacizumab group comes from the patients with stable disease in the chemotherapy group. A multicenter prospective cohort study will be conducted in advanced lung adenocarcinoma patients with stable disease after two cycles of platinum‐based double agent chemotherapy, in which we will compare the ORR between the group who continued with their original chemotherapy regimen and those where bevacizumab was added to the original regimen.