Heterogeneity in Lung Cancer

Sousa, Vítor; Carvalho, Lina

doi:10.1159/000487440

Cited by 208 publications

(137 citation statements)

References 149 publications

(190 reference statements)

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“…Firstly, the required biopsies or surgical resection may not be attainable for vulnerable and advanced cancer patients. In addition, recent studies have reported a 30-87.5% intra-tumoural genetic heterogeneity rate for ALK fusions in NSCLCs, which challenges the accuracy of traditional ALK fusion tests based on tissues from a routine biopsy procedure (8)(9)(10). Moreover, given the low occurrence of ALK mutations among NSCLCs, the purchasing of the devices and antibodies required for such molecular tests were cost-inefficient for both hospitals and patients.…”

Section: Introductionmentioning

confidence: 99%

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Song

Zhu

Mao³

et al. 2020

Front. Oncol.

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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 Song et al. Radiomic-Based Model for ALK Mutations 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.

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Section: Introductionmentioning

confidence: 99%

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Song

Zhu

Mao³

et al. 2020

Front. Oncol.

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“…Mounting evidence shows that tumors are far more heterogenous than expected in due to genetic diversity of the tumor cells as well as their phenotypic plasticity [22,23]. This phenotypic plasticity is in fact phenotype switching, which is understood as a phenomenon whereby cancer cells transition between different phenotypes in response to environmental cues, without acquiring new mutations [24].…”

Section: Discussionmentioning

confidence: 99%

Genetically Engineered Lung Cancer Cells for Analyzing Epithelial–Mesenchymal Transition

Kiełbus

Czapiński

Kałafut

et al. 2019

Cells

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Cell plasticity, defined as the ability to undergo phenotypical transformation in a reversible manner, is a physiological process that also exerts important roles in disease progression. Two forms of cellular plasticity are epithelial–mesenchymal transition (EMT) and its inverse process, mesenchymal–epithelial transition (MET). These processes have been correlated to the poor outcome of different types of neoplasias as well as drug resistance development. Since EMT/MET are transitional processes, we generated and validated a reporter cell line. Specifically, a far-red fluorescent protein was knocked-in in-frame with the mesenchymal gene marker VIMENTIN (VIM) in H2170 lung cancer cells. The vimentin reporter cells (VRCs) are a reliable model for studying EMT and MET showing cellular plasticity upon a series of stimulations. These cells are a robust platform to dissect the molecular mechanisms of these processes, and for drug discovery in vitro and in vivo in the future.

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“…Mounting evidence shows that tumors are far more heterogenous than expected in due to genetic diversity of the tumor cells as well as their phenotypic plasticity [22,23]. This phenotypic plasticity is in fact a phenotype switching, what is understood as a phenomenon whereby cancer cells transit between different phenotypes in response to environmental cues, without acquiring new mutations [24].…”

Section: Discussionmentioning

confidence: 99%

Genetical engineered lung cancer cell for analyzing Epithelial-Mesenchymal transition

Kiełbus

Czapiński

Kałafut

et al. 2019

Preprint

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Cell plasticity, defined as the ability to undergo phenotypical transformation in a reversible 12 manner, is a physiological processes that also exert important roles in disease progression Two forms 13 of cellular plasticity are epithelial-mesenchymal transition (EMT) and its inverse process, 14 mesenchymal-epithelial transition (MET). These processes have been correlated to the poor outcome 15 of different types of neoplasias as well as drug resistance development. Since EMT/MET are 16 transitional processes, we have generated and validated a reporter cell line. Specifically, a far-red 17 fluorescent protein was knocked-in in-frame with the mesenchymal gene marker VIMENTIN (VIM) 18 in H2170 lung cancer cells. The vimentin reporter cells (VRCs) are a reliable model for studying EMT 19 and MET showing cellular plasticity upon a series of stimulations. These cells are a robust platform 20 to dissect the molecular mechanisms of these processes, and for drug discovery in vitro and in the 21 future in vivo.22 Keywords: epithelial-mesenchymal transition (EMT); mesenchymal-epithelial transition (MET); 23 cancer cell line; vimentin; reporter; 24 25 29 transition (EMT) and mesenchymal-epithelial transition (MET), respectively. EMT has been 30 correlated to the metastasis and invasion potential of many types of malignancies [2]. The fact that 31 the poor outcome of many types of neoplasias correlates with EMT/MET, makes these molecular 32 phenomena important focus for research and drug targeting [3-6]. Despite the clinical association,33 the role of EMT/MET in metastasis is inconclusive, for example mesenchymal-like prostate cancer 34 cells survive in circulation but, unlike epithelial or cells undergoing EMT, they are unable to form 35 macrometastases [7]. In fact, breast cancer cell metastasis to lung tissue in mice was not affected by36 decreasing EMT -by targeting EMT-triggering transcription factors such as SNAI1 (Snail) and SNAI2 37 (Slug) by overexpressing miR-200 [8]. An additional role of EMT in tumorigenesis might be the 38 development of apoptotic tolerance and increased resistance to chemotherapy as has been found in 39animal models and patients [8,9]. Recently, a hybrid stage between epithelial and mesenchymal 40 2 of 19 phenotypes (hybE/M) has been recognized, and such hybE/M cells migrate outside the primary 41 tumors displaying some mesenchymal features such as spindle-like morphology, increased nuclear 42 levels of ZEB1 transcription factor, and epithelial ones such as cell-cell adhesion potential [10,11]. 43In order to study the transitory stages of EMT, MET and hybE/M, in particular at in vivo settings, 44 there is need to generate reporter cells to visualize phenotypical changes. There have been different 45 approaches to this, for example the use of heterologous fluorescent proteins driven under stage-46 specific promoters such as mesenchymal (ZEB1) and epithelial (CDH1) [12], mesenchymal snai1 and 47 epithelial sox10 in zebrafish [13], or mesenchymal Vimentin (Vim) in mice [14]. Nevertheless, 48 hete...

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Heterogeneity in Lung Cancer

Cited by 208 publications

References 149 publications

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Genetically Engineered Lung Cancer Cells for Analyzing Epithelial–Mesenchymal Transition

Genetical engineered lung cancer cell for analyzing Epithelial-Mesenchymal transition

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