Project description:BACKGROUND Autofluorescence bronchoscopy (AFB) is a valid strategy for detecting premalignant endobronchial lesions. However, no biomarker can reliably predict lung cancer risk of subjects with AFB-visualized premalignant lesions. Our present study was set out to identify AFB-visualized squamous metaplastic lesions with malignant potential by DNA copy number profiling. METHODS Regular AFB-examinations in 474 subjects at risk of lung cancer identified 6 subjects with SqM lesions at baseline and carcinoma (in situ) at the initial SqM site at follow-up bronchoscopy. These progressive SqM lesions were compared for immunostaining pattern and arrayCGH-based chromosomal profiles to 23 SqM of subjects who remained cancer-free. Specific copy number alterations (CNAs) linked to cancer risk were identified and accuracy of CNAs to predict endobronchial cancer in this series was determined. RESULTS At baseline, p53, p63 and Ki-67 immunostaining were not predictive for a differential clinical outcome of SqM lesions. The mean number of CNAs in baseline SqM of cases was significantly higher compared to controls (p<0.01). Chromosomal regions significantly more frequently altered in SqM of cases were 3p26.3-p11.1, 3q26.2-q29, 9p13.3-p13.2, and 17p13.3-p11.2 (FWER<0.10). CNAs were specifically detected at the site of future cancer. In cases, baseline-detected CNAs persisted in subsequent biopsies taken from the initial site, and levels increased towards cancer progression. CNAs at 3p26.3-p11.1, 3q26.2-29, and 6p25.3-24.3, predicted cancer risk for AFB-visualized SqM with 97% accuracy. CONCLUSION Our data strongly suggest that the presence of specific DNA copy number alterations in endobronchial SqM lesions predict endobronchial cancer.
Project description:Individuals who present with premalignant endobronchial lesions are considered at high risk of lung cancer. Nonetheless, premalignant lesions behave erratically and only a minority progresses towards lung cancer. Therefore, biomarkers need to be discovered that can aid in assessing an individual’s risk for subsequent cancer to better tailor treatment choices and avoid unnecessary follow-up procedures. We recently proposed a classifier of DNA copy number alterations (CNAs) at 3p26.3-p11.1, 3q26.2-29, and 6p25.3-24.3 as risk predictor for endobronchial cancer. The current study was set out to validate the classifier among an independent series of premalignant endobronchial lesions with various histological grades. A series of 36 endobronchial premalignant lesions (8 squamous metaplasia, and 28 various grades of dysplasia) identified during autofluorescence bronchoscopy of 12 case subjects who had carcinoma in situ or carcinoma (≥CIS) during follow-up bronchoscopy at the initial site and 24 control subjects who remained cancer-free, was subjected to array Comparative Genomic Hybridization (arrayCGH). DNA copy number profiles were related to lesion outcome. Prediction accuracy of the previously defined molecular classifier to predict endobronchial cancer in this series was determined. Unsupervised hierarchical clustering analysis revealed a significant association between cluster assignment and lesion outcome (p< 0.001), independent of histological grade, with quiescent profiles in controls (24/24) and aberrant profiles in the majority of cases (9/12). Our pre-defined classifier demonstrated 92% accuracy for predicting cancer outcome in the current sample series. Our validated classifier holds great promise for stratification of patients with premalignant endobronchial lesions for risk of subsequent cancer. Fresh frozen specimens of 36 premalignant endobronchial biopsies. Test samples were compared to an external pool of normal male/female reference DNA.
Project description:Individuals who present with premalignant endobronchial lesions are considered at high risk of lung cancer. Nonetheless, premalignant lesions behave erratically and only a minority progresses towards lung cancer. Therefore, biomarkers need to be discovered that can aid in assessing an individual’s risk for subsequent cancer to better tailor treatment choices and avoid unnecessary follow-up procedures. We recently proposed a classifier of DNA copy number alterations (CNAs) at 3p26.3-p11.1, 3q26.2-29, and 6p25.3-24.3 as risk predictor for endobronchial cancer. The current study was set out to validate the classifier among an independent series of premalignant endobronchial lesions with various histological grades. A series of 36 endobronchial premalignant lesions (8 squamous metaplasia, and 28 various grades of dysplasia) identified during autofluorescence bronchoscopy of 12 case subjects who had carcinoma in situ or carcinoma (≥CIS) during follow-up bronchoscopy at the initial site and 24 control subjects who remained cancer-free, was subjected to array Comparative Genomic Hybridization (arrayCGH). DNA copy number profiles were related to lesion outcome. Prediction accuracy of the previously defined molecular classifier to predict endobronchial cancer in this series was determined. Unsupervised hierarchical clustering analysis revealed a significant association between cluster assignment and lesion outcome (p< 0.001), independent of histological grade, with quiescent profiles in controls (24/24) and aberrant profiles in the majority of cases (9/12). Our pre-defined classifier demonstrated 92% accuracy for predicting cancer outcome in the current sample series. Our validated classifier holds great promise for stratification of patients with premalignant endobronchial lesions for risk of subsequent cancer.
Project description:Primary tumor recurrence occurs commonly after surgical resection of lung squamous cell carcinoma (SCC). The aim of this study was to identify genes involved in recurrence in lung squamous cell carcinoma patients. Array comparative genomic hybridization (aCGH) was performed on DNA extracted from tumour tissue from 62 patients with primary lung squamous cell carcinomas. aCGH data was analysed to identify genes affected by copy number alterations that may be involved in SCC recurrence. Candidate genes were then selected for technical validation based on differential copy number between recurrence and non-recurrence SCC tumour samples. Genes technically validated advanced to tests of biological replication by qPCR using an independent test set of 72 primary lung SCC tumour samples. 18q22.3 loss was identified by aCGH as significantly associated with recurrence (p=0.038). Although aCGH copy number loss associated with recurrence was found for seven genes within 18q22.3, only SOCS6 copy number loss was both technically replicated by qPCR and biologically validated in the test set. DNA copy number profiling using 44K element array comparative genomic hybridization microarrays of 62 primary lung squamous cell carcinomas.
Project description:Primary tumor recurrence occurs commonly after surgical resection of lung squamous cell carcinoma (SCC). The aim of this study was to identify genes involved in recurrence in lung squamous cell carcinoma patients. Array comparative genomic hybridization (aCGH) was performed on DNA extracted from tumour tissue from 62 patients with primary lung squamous cell carcinomas. aCGH data was analysed to identify genes affected by copy number alterations that may be involved in SCC recurrence. Candidate genes were then selected for technical validation based on differential copy number between recurrence and non-recurrence SCC tumour samples. Genes technically validated advanced to tests of biological replication by qPCR using an independent test set of 72 primary lung SCC tumour samples. 18q22.3 loss was identified by aCGH as significantly associated with recurrence (p=0.038). Although aCGH copy number loss associated with recurrence was found for seven genes within 18q22.3, only SOCS6 copy number loss was both technically replicated by qPCR and biologically validated in the test set.
Project description:Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, insertions and deletions. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.
Project description:84 NSCLC cell lines were collected from various sources (Supplemental Table 1) and formed the basis for all subsequent experiments. Cell lines were derived from tumors representing all major subtypes of NSCLC tumors, including adenocarcinoma, squamous-cell carcinoma and large-cell carcinoma. The genomic landscape of these cell lines was characterized by analyzing gene copy number alterations using high-resolution single-nucleotide polymorphism (SNP) arrays (250K Sty1). We used the statistical algorithm Genomic Identification of Significant Targets in Cancer (GISTIC) to distinguish biologically relevant lesions from background noise. The application of GISTIC revealed 16 regions of recurrent, high-level copy number gain (inferred copy number > 2.14) and 20 regions of recurrent copy number loss (inferred copy number < 1.86)
Project description:We analyzed 155 squamous- cell lung cancer and 77 adenocarcinoma of the lung samples using 6.0 SNP-arrays all normal tissues that were used to infer copy number are provided DNA was extracted from fresh frozen tissues and copy number data was generated for all samples