Success and failure rates of tumor genotyping techniques in routine pathologic samples with non-small cell lung cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 8050-8050
Author(s):  
Paul A VanderLaan ◽  
Norihiro Yamaguchi ◽  
Erik Folch ◽  
Michael S Kent ◽  
Sidharta P Gangadharan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Success Rate ◽  
Failure Rate ◽  
Cancer Tissue ◽  
Failure Rates ◽  
Tissue Samples ◽  
Multiple Tumor ◽  
Tissue Acquisition ◽  
Never Smokers ◽  
Tumor Genotyping

8050 Background: Identification of somatic molecular alterrations in NSCLC has become evidence-based practice. The success and failure rate of using commercially-available tumor genotyping techniques in routine day-to-day NSCLC pathology samples is not well described. We sought to evaluate the success and failure rate of EGFR mutation, KRAS mutation and ALK FISH. Methods: Clinicopathologic data, tumor genotype success and failure rates were retrospectively compiled and analyzed from 381 patient-tumor samples sent for routine tumor genotype in clinical practice. Results: Mean age was 65, 61.2% women, 75.9% white, 27.8% never-smokers, 73.8% had advanced NSCLC and 86.1% adenocarcinoma histology. Tumor tissue was obtained from surgical biopsies in 48.8%, core biopsies in 17.9% and as cell blocks from aspirates/fluid in 33.3%. Anatomic sites for tissue collection included lung (49.3%), lymph nodes (22.3%), pleura (11.8%), bone (6.0%), brain (6.0%), among others. In the 207 tumors in which the three tests were ordered concurrently, the success rate for EGFR was 92.3%, for KRAS 91.8% and for ALK FISH 89.9%. The highest failure rates were observed when the tissue was obtained from core biopsies (30.8%, 20.5% and 30.8% for EGFR, KRAS and ALK tests, respectively) and bone specimens (23.1%, 15.4% and 23.1% for EGFR, KRAS and ALK tests, respectively). In specimens obtained from bone, the failure rate was significantly higher in non-surgical than surgical specimens (40% vs 0%, p=0.024 for EGFR) and decalcified than non-decalcified samples (60% vs 5.5%, p=0.021 for EGFR). Conclusions: The success rate of multiple tumor genomic analyses techniques for EGFR, KRAS and ALK gene abnormalities using routine lung cancer tissue samples was ~90%. The highest failure rates occurred in tumors obtained from core biopsies and in bone samples from core biopsies with decalcification; specimens that may need to be scrutinized before submission to molecular studies. Tumor genotype techniques are feasible in most other samples obtained with current tumor acquisition methods, and therefore expansion of routine tumor genotype into the care of patients with NSCLC may not require special tissue acquisition or manipulation.

scholarly journals RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

Biomedicines ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 114
Author(s):  
Maxim Sorokin ◽  
Kirill Ignatev ◽  
Elena Poddubskaya ◽  
Uliana Vladimirova ◽  
Nurshat Gaifullin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Correlation Study ◽  
Cancer Tissue ◽  
Transcriptional Level ◽  
Tissue Samples ◽  
Protein Levels ◽  
Formalin Fixed Paraffin Embedded

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman’s rho 0.65–0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

scholarly journals Non-small cell lung cancer microbiota characterization: Prevalence of enteric and potentially pathogenic bacteria in cancer tissues

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249832
Author(s):  
Nathan Dumont-Leblond ◽  
Marc Veillette ◽  
Christine Racine ◽  
Philippe Joubert ◽  
Caroline Duchaine
Keyword(s):  
Lung Cancer ◽  
Pathogenic Bacteria ◽  
Amplicon Sequencing ◽  
Healthy Tissue ◽  
Cancer Tissue ◽  
Rrna Gene ◽  
Variable Degree ◽  
Tissue Samples ◽  
Small Cancer ◽  
Cancer Tissues

Following recent findings linking the human gut microbiota to gastrointestinal cancer and its treatment, the plausible relationship between lung microbiota and pulmonary cancer is explored. This study aims at characterizing the intratumoral and adjacent healthy tissue microbiota by applying a 16S rRNA gene amplicon sequencing protocol to tissue samples of 29 non-small cancer patients. Emphasis was put on contaminant management and a comprehensive comparison of bacterial composition between cancerous and healthy adjacent tissues of lung adenocarcinoma and squamous cell carcinoma is provided. A variable degree of similarity between the two tissues of a same patient was observed. Each patient seems to possess its own bacterial signature. The two types of cancer tissue do not have a distinct bacterial profile that is shared by every patient. In addition, enteric, potentially pathogenic and pro-inflammatory bacteria were more frequently found in cancer than healthy tissue. This work brings insights into the dynamic of bacterial communities in lung cancer and provides prospective data for more targeted studies.

scholarly journals Slug and Vimentin Downregulation at the Metastatic Site Is Associated With Skip-N2 Metastasis of Lung Adenocarcinoma

2022 ◽  
Author(s):  
Yasemin SAYGIDEGER ◽  
Alper AVCI ◽  
Emine BAGIR ◽  
Burcu SAYGIDEĞER DEMİR ◽  
Aycan SEZAN Ms ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Epithelial Mesenchymal Transition ◽  
Survival Rates ◽  
Cancer Tissue ◽  
Mrna Levels ◽  
Bioinformatics Analyses ◽  
Tissue Samples ◽  
Mesenchymal Transition ◽  
Slug Expression ◽  
E Cadherin

Abstract Objective: Lung cancer displays heterogeneity both in the tumor itself and in its metastatic regions. One interesting behavior of the tumor is known as Skip N2 metastasis, which N2 lymph nodes contain tumor cells while N1 are clean. In this study, mRNA levels of epithelial mesenchymal transition (EMT) related genes in skip N2 and normal N2 involvements of non-small cell lung cancer tissues were investigated to evaluate the possible molecular background that may contribute to the pathogenesis of Skip N2 metastasis. Materials and Methods: Eighty-three surgically resected and paraffin embedded lymph node samples of lung cancer patients were analyzed in this study, which 40 of them were Skip N2. N2 tissues were sampled from 50% tumor containing areas and total RNA was extracted. mRNA levels for 18S, E-cadherin, Vimentin, ZEB1 and SLUG were analyzed via qPCR and E-cadherin and vimentin protein levels via immunohistochemistry (IHC). Bioinformatic analysis were adopted using online datasets to evaluate significantly co-expressed genes with SLUG in lung cancer tissue samples.Results: Skip-N2 patients who had adenocarcinoma subtype had better survival rates. Comparative analysis of PCR results indicated that Skip N2 tumor tissues had increased E-Cadherin/Vimentin ratio and ZEB1 mRNA expression, and significantly decreased levels of SLUG. E-cadherin IHC staining were higher in Skip N2 and Vimentin were in Non-Skip N2. TP63 had a strong correlation with SLUG expression in the bioinformatics analyses.Conclusion: The results indicate that, at molecular level, Skip N2 pathogenesis has different molecular background and regulation of SLUG expression may orchestrate the process.

scholarly journals Radiomics and gene expression profile to characterize the disease and predict outcome in patients with lung cancer

2021 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Marinella Corbetta ◽  
Emanuele Voulaz ◽  
Noemi Gozzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Biology ◽  
Area Under The Curve ◽  
Standardized Uptake Value ◽  
Gene Expression Signature ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Cancer Tissue ◽  
Tissue Samples

Abstract Objectives The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F]-FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n=74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalized linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.Results Standardized Uptake Value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule in predicting the outcome resulted in an Area Under the Curve (AUC) of 0.87.Conclusions: Radiogenomic data provided clinically relevant information in NCSCL patients, regarding the histotype, aggressiveness, and progression. Gene expression may provide additional valuable information to guide patient management. The application of ML allows to increase the efficacy of radiogenomic analysis and provide novel insights into cancer biology.

Screening of NSCLC samples from Chinese lung cancer patients for activating rearrangements of the ALK, RET, and ROS1 genes.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e22066-e22066
Author(s):  
Li-Mou Zheng ◽  
David B. Whyte ◽  
Li Ruan ◽  
Roman Song ◽  
Luo Fei ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Real Time ◽  
Real Time Pcr ◽  
Kinase Inhibitors ◽  
Ffpe Tissue ◽  
Cancer Tissue ◽  
Fusion Genes ◽  
Tissue Samples ◽  
Lung Cancers ◽  
Pcr Assays

e22066 Background: The ALK, RET, and ROS1 genes are involved in gene rearrangements in a fraction of non-small cell lung cancers. The resulting oncogenic fusion genes define molecular sub-types of NSCLC with distinct sensitivities to treatment with various kinase inhibitors. We developed real-time reverse transcriptase PCR assays to detect rearrangements of ALK, RET, and ROS1 in FFPE lung cancer tissue. Methods: mRNA from NSCLC FFPE tissue samples was reverse transcribed to cDNA. Multiplex quantitative PCR was performed to detect 9 variants of EML4-ALK fusions, 9 variants of RET fusions and 14 variants of ROS1 fusions. A total of 409 samples were analyzed: 267 were classified as adenocarcinoma, 104 as squamous cell carcinoma and 38 had undetermined histology. EGFR and KRAS mutation status is unknown. The junctions of fusion-positive samples were sequenced by Sanger sequencing. Results: Among the 409 NSCLC specimens tested the frequency was 5.4% (22/409) for EML4-ALK fusions, 1.5% (6/409) for RET fusions, and 2.2% (9/409) for ROS1 fusions. EML4-ALK fusions were more prevalent in patients that were less than 60 years old (9.1% versus 2.0%, p= 0.004). The TNM stage was not correlated with the presence of any of the fusions. The table below lists the frequencies for specific rearrangements as determined by sequencing the real-time PCR products. Conclusions: Real-time PCR assays based on cDNA from FFPE tissue can identify patients with ALK, RET and ROS1 fusion genes. The ALK, RET and ROS1 assays will allow selection of patients most likely to respond to therapies that specifically target these cancer drivers. Further clinical testing of NSCLC patients in the Chinese population will be performed to support SFDA registration of these assays in China. [Table: see text]

Amyloid-like fibrils are not detected in non-small cell lung cancer tissue samples

2011 ◽  
Vol 29 (2) ◽  
pp. 614-617
Author(s):  
Luiz Henrique de Lima Araújo ◽  
Luciana W. Pinto ◽  
Luciene F. Schluckebier ◽  
Joyce L. de Moraes ◽  
Paulo A. Faria ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Cancer Tissue ◽  
Lung Cancer Tissue ◽  
Small Cell Lung ◽  
Tissue Samples

Adequacy of Pleural Fluid Cytology and Pleural Biopsies for Multiple Tumor Genotyping Techniques in Non-small Cell Lung Cancer

CHEST Journal ◽  
2014 ◽  
Vol 146 (4) ◽  
pp. 609A
Author(s):  
Erik Folch ◽  
Adnan Majid ◽  
Paul VanderLaan ◽  
Norihiro Yamaguchi ◽  
Sidhu Gangadharan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Pleural Fluid ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Multiple Tumor ◽  
Tumor Genotyping ◽  
Fluid Cytology

scholarly journals Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer

2021 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Marinella Corbetta ◽  
Emanuele Voulaz ◽  
Noemi Gozzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Biology ◽  
Area Under The Curve ◽  
Gene Expression Signature ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Relevant Information ◽  
Cancer Tissue ◽  
Tissue Samples

Abstract Objective The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. Results Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. Conclusions Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

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