Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes

2020 ◽  
Author(s):  
Alvaro Andres Sandino ◽  
Charlems Alvarez-Jimenez ◽  
Andres Mosquera-Zamudio ◽  
Satish E. Viswanath ◽  
Eduardo Romero
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Density ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cancer Subtypes ◽  
Histopathological Images

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.

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

scholarly journals Assessment of Bronchial and Pulmonary Blood Supply in Non-Small Cell Lung Cancer Subtypes Using Computed Tomography Perfusion

2015 ◽  
Vol 50 (3) ◽  
pp. 179-186 ◽  
Author(s):  
Thi Dan Linh Nguyen-Kim ◽  
Thomas Frauenfelder ◽  
Klaus Strobel ◽  
Patrick Veit-Haibach ◽  
Martin W. Huellner
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Blood Supply ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Computed Tomography Perfusion ◽  
Cancer Subtypes

scholarly journals Molecular profiles of small cell lung cancer subtypes: therapeutic implications

2021 ◽  
Vol 20 ◽  
pp. 470-483
Author(s):  
Anna Schwendenwein ◽  
Zsolt Megyesfalvi ◽  
Nandor Barany ◽  
Zsuzsanna Valko ◽  
Edina Bugyik ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cancer Subtypes ◽  
Therapeutic Implications ◽  
Molecular Profiles

scholarly journals Association of PARP1-specific polymorphisms and haplotypes with non-small cell lung cancer subtypes

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243509
Author(s):  
Jing Jin ◽  
Heather Robeson ◽  
Pebbles Fagan ◽  
Mohammed S. Orloff
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
P Value ◽  
Small Cell Lung ◽  
Tissue Samples ◽  
Cancer Subtypes ◽  
Normal Tissues ◽  
Dna And Rna

Objective The carcinogenesis role of PARP1 in lung cancer is still not clear. Analysis at allelic levels cannot fully explain the function of PARP1 on lung cancer. Our study aims to further explore the relation between PARP1 haplotypes and lung cancer. Materials and methods DNA and RNA were extracted from non-small cell lung cancer (NSCLC) tumor and adjacent normal fresh frozen tissue. Five PARP1-SNPs were genotyped and PARP1-specific SNPs were imputed using IMPUTE and SHAPEIT software. The SNPs were subjected to allelic, haplotype and SNP-SNP interaction analyses. Correlation between SNPs and mRNA/protein expressions were performed. Results SNP imputation inferred the ungenotyped SNPs and increased the power for association analysis. Tumor tissue samples are more likely to carry rs1805414 (OR = 1.85; 95% CI: 1.12–3.06; P-value: 0.017) and rs1805404 (OR = 2.74; 95%CI 1.19–6.32; P-value: 0.015) compared to normal tissues. Our study is the first study to show that haplotypes comprising of 5 SNPs on PARP1 (rs1136410, rs3219073, rs1805414, rs1805404, rs1805415) is able to differentiate the NSCLC tumor from normal tissues. Interaction between rs3219073, rs1805415, and rs1805414 were significantly associated with the NSCLC tumor with OR ranging from 3.61–6.75; 95%CI from 1.82 to 19.9; P-value<0.001. Conclusion PARP1 haplotypes may serve as a better predictor in lung cancer development and prognosis compared to single alleles.

scholarly journals Small Cell Lung Cancer Neuroendocrine Subtypes are Associated with Different Immune Microenvironment and Checkpoint Molecule Distribution

2020 ◽  
Author(s):  
David Dora ◽  
Christopher Rivard ◽  
Hui Yu ◽  
Paul Bunn ◽  
Kenichi Suda ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Density ◽  
Cell Lung Cancer ◽  
Immune Cell ◽  
Small Cell ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Small Cell Lung ◽  
Primary Tumors

ABSTRACTSmall cell lung cancer (SCLC) has recently been sub-categorized into neuroendocrine (NE)- high and NE-low subtypes showing ‘immune desert’ and ‘immune oasis’ phenotypes, respectively. We aimed to characterize the immune cell localization and the microenvironment according to immune checkpoints and NE subtypes in human SCLC tissue samples at the protein level. In this cross-sectional study, we included 32 primary tumors and matched lymph node (LN) metastases of resected early-stage, histologically confirmed SCLC patients, which were previously clustered into NE subtypes using NE-associated key RNA genes. Immunohistochemistry (IHC) was performed on FFPE TMAs with antibodies against CD45, CD3, CD8 and immune checkpoints including poliovirus receptor (PVR) and Indoleamine 2,3-dioxygenase (IDO).According to our results, the stroma was significantly more infiltrated by immune cells both in primary tumors and LN metastases (vs tumor cell nests). Immune (CD45+) cell density was significantly higher in tumor nests (110.6 ± 24.95 vs 42.74 ± 10.30, cell/mm2, p= 0.0048), with increased CD8+ effector T cell infiltration (21.81 ± 5.458 vs 3.16 ± 1.36 cell/mm2, p < 0.001) in NE-low vs NE-high tumors. Furthermore, the expression of IDO was confirmed on stromal and endothelial cells, and it positively correlated (r= 0.755, p<0.01) with higher immune cell density both in primary tumors and LN metastases, regardless of the NE pattern. Expression of IDO in tumor nests was significantly higher in NE-low (vs NE-high) primary tumors. PVR expression was significantly higher in NE-low (vs NE-high) patients both in primary tumors) and LN metastases.To our knowledge, this is the first human study that demonstrates in situ that NE-low tumors are associated with increased immune cell infiltration compared to NE-high tumors. PVR and IDO are potential new targets in SCLC, with increased expression in the NE-low subtype, providing key insight for further prospective studies on potential biomarkers and targets for SCLC immunotherapies.

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