scholarly journals Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers

2019 ◽  
Vol 15 (10) ◽  
pp. e1007343 ◽  
Author(s):  
David J. Wooten ◽  
Sarah M. Groves ◽  
Darren R. Tyson ◽  
Qi Liu ◽  
Jing S. Lim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Network Modeling ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cancer Subtypes ◽  
Master Regulators

scholarly journals Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers

2018 ◽  
Author(s):  
DJ Wooten ◽  
SF Maddox ◽  
DR Tyson ◽  
Q Liu ◽  
JS Lim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Systems Approach ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cancer Subtypes ◽  
Master Regulators

AbstractAdopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes, while the fourth is a previously undescribed neuroendocrine variant (NEv2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

scholarly journals Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3663
Author(s):  
Charlems Alvarez-Jimenez ◽  
Alvaro A. Sandino ◽  
Prateek Prasanna ◽  
Amit Gupta ◽  
Satish E. Viswanath ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Density ◽  
Squamous Cell ◽  
Cell Lung Cancer ◽  
Ct Images ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cancer Subtypes ◽  
Initial Results

(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.

scholarly journals Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Fei Long ◽  
Jia-Hang Su ◽  
Bin Liang ◽  
Li-Li Su ◽  
Shu-Juan Jiang
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Expression Patterns ◽  
Small Cell ◽  
Normal Lung ◽  
Specific Gene ◽  
Small Cell Lung ◽  
Cancer Subtypes ◽  
Gene Modules

Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.

Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3

2017 ◽  
Vol 0 (4) ◽  
pp. 1
Author(s):  
U A Boyarskikh ◽  
S S Pintus ◽  
N V Mandrik ◽  
D E Stelmashenko ◽  
I N Kiselev ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cancer Cells ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Lung Cancer Cells ◽  
Small Cell Lung ◽  
Master Regulators

Abstract LB-27: Dissecting signaling transduction network to infer master regulators of non small cell lung cancer

2011 ◽  
Author(s):  
Mukesh Bansal ◽  
Mukesh Bansal ◽  
Manjunath Kustagi ◽  
Celine Lefebvre ◽  
Gabrielle Rieckhof ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Signaling Transduction ◽  
Master Regulators

Abstract PR11: Dissecting signaling transduction network to infer master regulators of non small cell lung cancer

2012 ◽  
Author(s):  
Mukesh Bansal ◽  
Michael Peyton ◽  
Manjunath Kustagi ◽  
Luc Girard ◽  
Klarisa Rikova ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Signaling Transduction ◽  
Master Regulators

Glutathione-S-Transferase M1, M3, T1 and P1 polymorphisms and susceptibility to non-small-cell lung cancer subtypes and hamartomas

2001 ◽  
Vol 11 (9) ◽  
pp. 757-764 ◽  
Author(s):  
Angela Risch ◽  
Harriet Wikman ◽  
Stephen Thiel ◽  
Peter Schmezer ◽  
Lutz Edler ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Glutathione S Transferase ◽  
Cancer Subtypes
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