scholarly journals Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 22
Author(s):  
Tcharé Adnaane Bawa ◽  
Yalçın Özkan ◽  
Çiğdem Selçukcan Erol
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Microarray Gene Expression Data ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung ◽  
Microarray Gene Expression ◽  
Tree Models ◽  
Public Datasets

Cancer is one of the leading causes of death in many countries, and this continues to be the case because of the lack of sufficient treatment. One of the most common types is non-small-cell lung cancer (NSCLC). The increasingly large and diverse public datasets about NSCLC constitute a rich source of data on which several analyses can be performed so as to find candidate oncogenic drivers or therapeutic targets. The aim of this study is to reanalyze an existing NSCLC NCBI GEO Dataset (accession = GSE19804) in order to see if novel involved genes can be found. For this, we used microarray technology for preprocessing and, based on random forest, support vector machine and C5.0 decision tree models, made a comparison of the 10 most important genes recorded. This study was realized with R-Studio 4.0.2 and Bioconductor 3.11. In conclusion, the EFNA4 gene and other genes, namely KANK3, GRK5, CLIC5, SH3GL3, ACACB, LIN7A, JCAD, and NEDD1, are thought to be potential genes that may play a role in NSCLC and it is recommended that researchers working in the wet laboratory should focus on these genes.

A systems pathology model for predicting overall survival in patients with refractory, advanced non-small cell lung cancer (NSCLC) treated with gefitinib

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 18065-18065
Author(s):  
M. J. Donovan ◽  
A. Kotsianti ◽  
V. Bayer-Zubek ◽  
D. Verbel ◽  
M. Clayton ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Tyrosine Kinase ◽  
Small Cell Lung Cancer ◽  
Cyclin D1 ◽  
Cell Lung Cancer ◽  
Caspase 3 ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung

18065 Background: The abundant expression of the epidermal growth factor receptor (EGFR) in a variety of solid tumors including non-small cell lung cancer (NSCLC), head and neck, breast, colon and brain has made it an attractive target for various selective molecular therapeutics, including the tyrosine kinase inhibitor gefitinib. The recent evidence of activating mutations in EGFR combined with clinical - demographic features has suggested that subgroups of patients with NSCLC are most likely to respond to selective therapies. We sought to determine whether the integration of clinical variables, tumor morphometry and quantitative protein profiles using support vector machines could identify a set of features which predicts overall survival in patients with NSCLC treated with gefitinib. Methods: We analyzed tumor samples from 109 patients with advanced refractory NSCLC treated with gefitinib. Formalin fixed, paraffin embedded tissue samples were evaluated with the following assays: Hematoxylin and Eosin image morphometry, EGFR DNA mutation analysis, EGFR protein immunohistochemistry and quantitative immunofluorescence with the following antibodies: CK18, Ki67, Caspase 3 activated, cd34, EGFR, phosphorylated-EGFR, phosphorylated-ERK, phosphorylated-AKT, PTEN, Cyclin D1, phosphorylated-m-TOR, PI3-K, VEGF, KDR (VEGFR2) and phosphorylated KDR. A predictive model was developed using support vector regression for censored data. Results: 4 of 87 patients had tyrosine kinase domain mutations in exons 19, 20 or 21. Utilizing 51 patients with complete data profiles (i.e. clinical, image morphometry and immunofluorescence), a model predicting overall survival was developed with a concordance index of 0.74. Poor performance status, poorly differentiated histology by morphometry and increased levels of activated caspase 3, phosphorylated KDR (VEGFR2) and cyclin D1 were associated with reduced survival. Conclusion: The integration of clinical, imaging and biomarker data identified a set of features which were associated with a more aggressive disease phenotype and resulted in overall poor survival. [Table: see text]

scholarly journals Identifying transcription patterns of histology and radiomics features in NSCLC with neural networks

2020 ◽  
Author(s):  
Nova F. Smedley ◽  
Denise R. Aberle ◽  
William Hsu
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung ◽  
Gene Expressions ◽  
Gene Sets ◽  
Tumor Gene

AbstractPurposeTo investigate the use of deep neural networks to learn associations between gene expression and radiomics or histology in non-small cell lung cancer (NSCLC).Materials and MethodsDeep feedforward neural networks were used for radio-genomic mapping, where 21,766 gene expressions were inputs to individually predict histology and 101 CT radiomic features. Models were compared against logistic regression, support vector machines, random forests, and gradient boosted trees on 262 training and 89 testing patients. Neural networks were interpreted using gene masking to derive the learned associations between subsets of gene expressions to a radiomic feature or histology type.ResultsNeural networks outperformed other classifiers except in five radiomic features, where training differences were <0.026 AUC. In testing, neural networks classified histology with AUCs of 0.86 (adenocarcinoma), 0.91 (squamous), and 0.71 (other); and 14 radiomics features with >= 0.70 AUC. Gene masking of the models showed new and previously reported histology-gene or radiogenomic associations. For example, hypoxia genes could predict histology with >0.90 test AUC and published gene signatures for histology prediction were also predictive in our models (>0.80 test AUC). Gene sets related to the immune or cardiac systems and cell development processes were predictive (>0.70 test AUC) of several different radiomic features while AKT signaling, TNF, and Rho gene sets were each predictive of tumor textures.ConclusionWe demonstrate the ability of neural networks to map gene expressions to radiomic features and histology in NSCLC and interpret the models to identify predictive genes associated with each feature or type.Author SummaryNon-small-cell lung cancer (NSCLC) patients can have different presentations as seen in the CT scans, tumor gene expressions, or histology types. To improve the understanding of these complementary data types, this study attempts to map tumor gene expressions associated with a patient’s CT radiomic features or a histology type. We explore a deep neural network approach to learn gene-radiomic associations (i.e., the subsets of co-expressed genes that are predictive of a value of an individual radiomic feature) and gene-histology associations in two separate public cohorts. Our modeling approach is capable of learning relevant information by showing the model can predict histology and that the learned relationships are consistent with prior works. The study provides evidence for coherent patterns between gene expressions and radiomic features and suggests such integrated associations could improve patient stratification.

Computer extracted measurements of vessel tortuosity on baseline CT scans to predict response to nivolumab immunotherapy for non-small cell lung cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11566-11566
Author(s):  
Monica Khunger ◽  
Mehdi Alilou ◽  
Rajat Thawani ◽  
Anant Madabhushi ◽  
Vamsidhar Velcheti
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Lung Nodule ◽  
Small Cell ◽  
Ct Scans ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Cell Lung ◽  
Vessel Tortuosity

11566 Background: Immune-checkpoint blockade treatments, particularly drugs targeting the programmed death-1 (PD-1) receptor, demonstrate promising clinical efficacy in patients with non-small cell lung cancer (NSCLC). We sought to evaluate whether computer extracted measurements of tortuosity of vessels in lung nodules on baseline CT scans in NSCLC patients(pts) treated with a PD-1 inhibitor, nivolumab could distinguish responders and non-responders. Methods: A total of 61 NSCLC pts who underwent treatment with nivolumab were included in this study. Pts who did not receive nivolumab after 2 cycles due to lack of response or progression per RECIST were classified as ‘non-responders’, patients who had radiological response per RECIST or had clinical benefit (defined as stable disease >10 cycles) were classified as ‘responders’. A total of 35 quantitative tortuosity features of the vessels associated with lung nodule were investigated. In the training cohort (N=33), the features were ranked in their ability to identify responders to nivolumab using a support vector machine (SVM) classifier. The three most informative features were then used for training the SVM, which was then validated on a cohort of N=28 pts. Results: The maximum curvature ( f1), standard deviation of the torsion ( f2) and mean curvature ( f3) were identified as the most discriminating features. The area under Receiver operating characteristic (ROC) curve (AUC) of the SVM was 0.84 for the training and 0.72 for the validation cohort. Conclusions: Vessel tortuosity features were able to distinguish responders from non-responders for patients with NSCLC treated with nivolumab. Large scale multi-site validation will need to be done to establish vessel tortuosity as a predictive biomarker for immunotherapy. [Table: see text]

scholarly journals Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2704
Author(s):  
Mark Kriegsmann ◽  
Christiane Zgorzelski ◽  
Rita Casadonte ◽  
Kristina Schwamborn ◽  
Thomas Muley ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Mass Spectrometry Imaging ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung ◽  
Linear Discriminant

Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.

scholarly journals A CT-Based Radiomics Approach to Predict Nivolumab Response in Advanced Non-Small-Cell Lung Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Chang Liu ◽  
Jing Gong ◽  
Hui Yu ◽  
Quan Liu ◽  
Shengping Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Clinical Outcomes ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Support Vector ◽  
Small Cell Lung ◽  
Primary Tumors ◽  
Significant Difference ◽  
Nsclc Patients

PurposeThis study aims to develop a CT-based radiomics model to predict clinical outcomes of advanced non-small-cell lung cancer (NSCLC) patients treated with nivolumab.MethodsForty-six stage IIIB/IV NSCLC patients without EGFR mutation or ALK rearrangement who received nivolumab were enrolled. After segmenting primary tumors depicting on the pre-anti-PD1 treatment CT images, 1,106 radiomics features were computed and extracted to decode the imaging phenotypes of these tumors. A L1-based feature selection method was applied to remove the redundant features and build an optimal feature pool. To predict the risk of progression-free survival (PFS) and overall survival (OS), the selected image features were used to train and test three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier, and Gaussian Naïve Bayes classifier. Finally, the overall patients were stratified into high and low risk subgroups by using prediction scores obtained from three classifiers, and Kaplan–Meier survival analysis was conduct to evaluate the prognostic values of these patients.ResultsTo predict the risk of PFS and OS, the average area under a receiver operating characteristic curve (AUC) value of three classifiers were 0.73 ± 0.07 and 0.61 ± 0.08, respectively; the corresponding average Harrell’s concordance indexes for three classifiers were 0.92 and 0.79. The average hazard ratios (HR) of three models for predicting PFS and OS were 6.22 and 3.54, which suggested the significant difference of the two subgroup’s PFS and OS (p&lt;0.05).ConclusionThe pre-treatment CT-based radiomics model provided a promising way to predict clinical outcomes for advanced NSCLC patients treated with nivolumab.

scholarly journals Machine Learning for Histologic Subtype Classification of Non-Small Cell Lung Cancer: A Retrospective Multicenter Radiomics Study

2021 ◽  
Vol 10 ◽  
Author(s):  
Fengchang Yang ◽  
Wei Chen ◽  
Haifeng Wei ◽  
Xianru Zhang ◽  
Shuanghu Yuan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Training Dataset ◽  
Support Vector ◽  
Features Selection ◽  
Small Cell Lung ◽  
Testing Set

BackgroundHistologic phenotype identification of Non-Small Cell Lung Cancer (NSCLC) is essential for treatment planning and prognostic prediction. The prediction model based on radiomics analysis has the potential to quantify tumor phenotypic characteristics non-invasively. However, most existing studies focus on relatively small datasets, which limits the performance and potential clinical applicability of their constructed models.MethodsTo fully explore the impact of different datasets on radiomics studies related to the classification of histological subtypes of NSCLC, we retrospectively collected three datasets from multi-centers and then performed extensive analysis. Each of the three datasets was used as the training dataset separately to build a model and was validated on the remaining two datasets. A model was then developed by merging all the datasets into a large dataset, which was randomly split into a training dataset and a testing dataset. For each model, a total of 788 radiomic features were extracted from the segmented tumor volumes. Then three widely used features selection methods, including minimum Redundancy Maximum Relevance Feature Selection (mRMR), Sequential Forward Selection (SFS), and Least Absolute Shrinkage and Selection Operator (LASSO) were used to select the most important features. Finally, three classification methods, including Logistics Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) were independently evaluated on the selected features to investigate the prediction ability of the radiomics models.ResultsWhen using a single dataset for modeling, the results on the testing set were poor, with AUC values ranging from 0.54 to 0.64. When the merged dataset was used for modeling, the average AUC value in the testing set was 0.78, showing relatively good predictive performance.ConclusionsModels based on radiomics analysis have the potential to classify NSCLC subtypes, but their generalization capabilities should be carefully considered.

scholarly journals A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer

Biomolecules ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 477
Author(s):  
Hong-Yi Zhi ◽  
Lu Zhao ◽  
Cheng-Chun Lee ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Dynamics Simulation ◽  
Support Vector ◽  
Dihydroorotate Dehydrogenase ◽  
Small Cell Lung ◽  
Novel Concept

Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydrogenase (DHODH) has been demonstrated to be a therapeutic target for SCLC. Network pharmacology analysis and virtual screening were utilized to find out related proteins and investigate candidates with high docking capacity to multiple targets. Graph neural networks (GNNs) and machine learning were used to build reliable predicted models. We proposed a novel concept of multi-GNNs, and then built three multi-GNN models called GIAN, GIAT, and SGCA, which achieved satisfactory results in our dataset containing 532 molecules with all R^2 values greater than 0.92 on the training set and higher than 0.8 on the test set. Compared with machine learning algorithms, random forest (RF), and support vector regression (SVR), multi-GNNs had a better modeling effect and higher precision. Furthermore, the long-time 300 ns molecular dynamics simulation verified the stability of the protein–ligand complexes. The result showed that ZINC8577218, ZINC95618747, and ZINC4261765 might be the potentially potent inhibitors for DHODH. Multi-GNNs show great performance in practice, making them a promising field for future research. We therefore suggest that this novel concept of multi-GNNs is a promising protocol for drug discovery.

scholarly journals Prediction of histological grade using preoperative multi-sequence MRI-based radiomics signature in patients with non-small-cell lung cancer

2020 ◽  
Author(s):  
Xing Tang ◽  
Guoyan Bai ◽  
Yuanqiang Zhu ◽  
Hong Wang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Clinical Features ◽  
Cell Lung Cancer ◽  
Discrimination Performance ◽  
Predictive Performance ◽  
Small Cell ◽  
Support Vector ◽  
Svm Classifier ◽  
Small Cell Lung

Abstract Background: Non-small cell lung cancer (NSCLC) is treatable when caught early, yet limited non-invasive methods exist for grading NSCLC patients. In the present study, we aimed to examine the diagnostic utility of multi-sequence magnetic resonance imaging (MRI) radiomics and clinical features for grading NSCLC. Methods: In this retrospective study, 148 patients with postoperative pathologically-confirmed NSCLC were recruited. Both preoperative T2-weighted imaging (T2WI) and multi-b-value diffusion-weighted imaging (DWI) were performed on a 1.5 T MRI scanner. A total of 2775 radiomics features were extracted from the T2WI, DWI, and the corresponding apparent diffusion coefficient (ADC) maps of patients. The least absolute shrinkage and selection operator (LASSO) and stepwise regression method were used for feature selection using the training cohort (n=110). Next, these features were further evaluated assessed in the two cohorts using a non-linear support vector machine (SVM) classifier. Lastly, a Radscore model was used to develop the radiomics-clinical nomogram.Results: Favorable discrimination performance was obtained for five of the optimal features using both cohorts, as demonstrated by the area under the curves (AUC) of 0.761 and 0.753. In addition, the radiomics-clinical nomogram, which integrated the Radscore with four independent clinical predictors, showed higher discriminative power, with AUCs of 0.814 and 0.767 for the X.T cohort and H.Y cohort, respectively. The nomogram showed excellent predictive performance and potential clinical utility for grading NSCLC.Conclusions: Multi-sequence MRI radiomics features can stratify NSCLC tumor grades noninvasively. The radiomics features can be integrated with the clinical features to improve its predictive performance.

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