scholarly journals Nivolumab-Induced Impressive Response of Refractory Pulmonary Sarcomatoid Carcinoma with Brain Metastasis

2018 ◽  
Vol 11 (3) ◽  
pp. 615-621 ◽  
Author(s):  
Massimiliano Salati ◽  
Cinzia Baldessari ◽  
Fiorella Calabrese ◽  
Giulio Rossi ◽  
Elisa Pettorelli ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Brain Lesion ◽  
Sarcomatoid Carcinoma ◽  
Small Cell ◽  
Dramatic Improvement ◽  
Small Cell Lung ◽  
Cancer Subtypes ◽  
Pulmonary Sarcomatoid Carcinoma

Background: Pulmonary sarcomatoid carcinoma is a rare, poorly differentiated, and highly aggressive type of non-small cell lung cancer. High tumor mutational burden and PD-L1 overexpression make it an excellent candidate for immunotherapy. Objectives and Method: We presented the case of a patient who underwent left inferior lobectomy with concurrent right paravertebral muscular metastasectomy for an infiltrative neoplastic mass, whose final diagnosis was consistent with stage IV pulmonary sarcomatoid carcinoma. He received first- and second-line chemotherapy without any benefit. Since March 2016, he has been treated with the anti-PD1 agent nivolumab with a dramatic improvement of symptoms, disappearance of a brain lesion, and partial response on other metastatic sites. He tolerated the treatment well and is still responding after 22 months from the beginning. Results and Conclusions: In very lethal non-small cell lung cancer subtypes such as the sarcomatoid variants, high tumor burden and deteriorated general conditions should not preclude, at least in some cases, the use of immunotherapy. Anti-PD1 may also have a reliable role in disease control in the brain. Lastly, the strong rationale behind sarcomatoid histology should further prompt trials exploring immunotherapeutic approaches in this subset of non-small cell lung cancer.

scholarly journals Response to Gefitinib/Crizotinib Combination in a Pulmonary Sarcomatoid Carcinoma Patient Harboring Concurrent EGFR Mutation and MET Amplification

2021 ◽  
Author(s):  
Xiaomeng Wang ◽  
Jie Cao ◽  
Shui Cao ◽  
Weijiao Du ◽  
Weihong Zhang
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Egfr Mutation ◽  
Sarcomatoid Carcinoma ◽  
Small Cell ◽  
Small Cell Lung ◽  
Carcinoma Patient ◽  
Met Amplification ◽  
Pulmonary Sarcomatoid Carcinoma

Pulmonary sarcomatoid carcinoma; non-small cell lung cancer; EGFR mutation; MET amplification; crizotinib/gefitinib

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
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