Dataset Information

Three-dimensional nanostructured substrates enable dynamic detection of ALK-rearrangement in circulating tumor cells from treatment-naive patients with stage III/IV lung adenocarcinoma.

ABSTRACT:

Background

Circulating tumor cells (CTC) shows great prospect to realize precision medicine in cancer patients.

Methods

We developed the NanoVelcro Chip integrating three functional mechanisms. NanoVelcro CTC capture efficiency was tested in stage III or IV lung adenocarcinoma. Further, ALK-rearrangement status was examined through fluorescent in situ hybridization in CTCs enriched by NanoVelcro.

Results

NanoVelcro system showed higher CTC-capture efficiency than CellSearch in stage III or IV lung adenocarcinoma. CTC counts obtained by both methods were positively correlated (r?=?0.45, p?ConclusionNanoVelcro provides a sensitive method for CTC counts and characterization in advanced NSCLC. ALK-rearrangement can be detected in CTCs collected from advanced NSCLC patients by NanoVelcro, facilitating diagnostic test and prognosis analysis, most importantly offering one noninvasive method for real-time monitoring of treatment reaction.

SUBMITTER: He W

PROVIDER: S-EPMC6339314 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Three-dimensional nanostructured substrates enable dynamic detection of ALK-rearrangement in circulating tumor cells from treatment-naive patients with stage III/IV lung adenocarcinoma.

He Weiling W Xu Di D Wang Zhuo Z Wu Hui H Xiang Xianhong X Tang Bing B Jiang Wenting W Cui Yongmei Y Wang Han H Jiang Neng N Sun Yu Y Chen Yangshan Y Li Shuhua S Hou Minzhi M Zhang Yang Y Wang Liantang L Ke Zun-Fu ZF

Journal of translational medicine 20190118 1

<h4>Background</h4>Circulating tumor cells (CTC) shows great prospect to realize precision medicine in cancer patients.<h4>Methods</h4>We developed the NanoVelcro Chip integrating three functional mechanisms. NanoVelcro CTC capture efficiency was tested in stage III or IV lung adenocarcinoma. Further, ALK-rearrangement status was examined through fluorescent in situ hybridization in CTCs enriched by NanoVelcro.<h4>Results</h4>NanoVelcro system showed higher CTC-capture efficiency than CellSearch ...[more]

PMID: 30658713

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Project description:IntroductionAnaplastic lymphoma kinase (ALK) fusion mutation is more common in younger and never-smoking lung cancer patients. The association of smoking and ALK-tyrosine kinase inhibitors (TKIs) on overall survival (OS) of treatment-naïve ALK-positive advanced lung adenocarcinoma remains unclear in real-world.MethodsThis retrospective study evaluated all 33170 lung adenocarcinoma patients registered in the National Taiwan Cancer Registry from 2017 to 2019, of whom 9575 advanced stage patients had ALK mutation data.ResultsAmong the 9575 patients, 650 (6.8%) patients had ALK mutation with the median follow-up survival time 30.97 months (median age, 62 years; 125 [19.2%] were aged ≥75 years; 357 (54.9%) females; 179 (27.5) smokers, 461 (70.9%) never-smokers, 10 (1.5%) with unknown smoking status; and 544 (83.7%) with first-line ALK-TKI treatment). Overall, of 535 patients with known smoking status who received first-line ALK-TKI treatment, never-smokers and smokers had a median OS of 40.7 months (95% confidence interval (CI), 33.1-47.2 months) and 23.5 months (95% CI, 11.5-35.5 months) (P=0.015), respectively. Among never-smokers, those who received first-line ALK-TKI treatment had a median OS of 40.7 months (95% CI, 22.7-57.8 months), while those ALK-TKI not as first-line treatment had a median OS of 31.7 months (95% CI, 15.2-42.8 months) (P=0.23). In smokers, the median OS for these patients was 23.5 months (95% CI, 11.5-35.5 months) and 15.6 months (95% CI, 10.2-21.1 months) (P=0.026), respectively.Conclusions and relevanceFor patients with treatment-naïve advanced lung adenocarcinoma, the ALK test should be performed irrespective of smoking status and age. Smokers had shorter median OS than never-smokers among treatment-naïve-ALK-positive patients with first-line ALK-TKI treatment. Furthermore, smokers not receiving first-line ALK-TKI treatment had inferior OS. Further investigations for the first-line treatment of ALK-positive smoking advanced lung adenocarcinoma patients are needed.

Project description:Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features. Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly divided into a primary cohort (268 patients; 90 ALK-rearranged; and 178 ALK wild-type) and a test cohort (67 patients; 22 ALK-rearranged; and 45 ALK wild-type). One thousand two hundred and eighteen quantitative radiomic features were extracted from the semi-automatically delineated volume of interest (VOI) of the entire tumor using both the original and the pre-processed non-enhanced CT images. Twelve conventional CT features and seven clinical features were also collected. Normalized features were selected using a sequential of the F-test-based method, the density-based spatial clustering of applications with noise (DBSCAN) method, and the recursive feature elimination (RFE) method. Selected features were then used to build three predictive models (radiomic, radiological, and integrated models) for the ALK-rearranged phenotype by a soft voting classifier. Models were evaluated in the test cohort using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and the performances of three models were compared using the DeLong test. Results: Our results showed that the addition of clinical information and conventional CT features significantly enhanced the validation performance of the radiomic model in the primary cohort (AUC = 0.83-0.88, P = 0.01), but not in the test cohort (AUC = 0.80-0.88, P = 0.29). The majority of radiomic features associated with ALK mutations reflected information around and within the high-intensity voxels of lesions. The presence of the cavity and left lower lobe location were new imaging phenotypic patterns in association with ALK-rearranged tumors. Current smoking was strongly correlated with non-ALK-mutated lung adenocarcinoma. Conclusions: Our study demonstrates that radiomics-derived machine learning models can potentially serve as a non-invasive tool to identify ALK mutation of lung adenocarcinoma.

Project description: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.

Dataset Information

Three-dimensional nanostructured substrates enable dynamic detection of ALK-rearrangement in circulating tumor cells from treatment-naive patients with stage III/IV lung adenocarcinoma.

Background

Methods

Results

Publications

Three-dimensional nanostructured substrates enable dynamic detection of ALK-rearrangement in circulating tumor cells from treatment-naive patients with stage III/IV lung adenocarcinoma.

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