Edwina Duhig

MicroRNA-218 is deleted and downregulated in lung squamous cell carcinoma.

MicroRNAs (miRNAs) are a family of small, non-coding RNA species functioning as negative regulators of multiple target genes including tumour suppressor genes and oncogenes. Many miRNA gene loci are located within cancer-associated genomic regions. To identify potential new amplified oncogenic and/or deleted tumour suppressing miRNAs in lung cancer, we inferred miRNA gene dosage from high dimensional arrayCGH data. From miRBase v9.0 (http://microrna.sanger.ac.uk), 474 human miRNA genes were physically mapped to regions of chromosomal loss or gain identified from a high-resolution genome-wide arrayCGH study of 132 primary non-small cell lung cancers (NSCLCs) (a training set of 60 squamous cell carcinomas and 72 adenocarcinomas). MiRNAs were selected as candidates if their immediately flanking probes or host gene were deleted or amplified in at least 25% of primary tumours using both Analysis of Copy Errors algorithm and fold change (≥±1.2) analyses. Using these criteria, 97 miRNAs mapped to regions of aberrant copy number. Analysis of three independent published lung cancer arrayCGH datasets confirmed that 22 of these miRNA loci showed directionally concordant copy number variation. MiR-218, encoded on 4p15.31 and 5q35.1 within two host genes (SLIT2 and SLIT3), in a region of copy number loss, was selected as a priority candidate for follow-up as it is reported as underexpressed in lung cancer. We confirmed decreased expression of mature miR-218 and its host genes by qRT-PCR in 39 NSCLCs relative to normal lung tissue. This downregulation of miR-218 was found to be associated with a history of cigarette smoking, but not human papilloma virus. Thus, we show for the first time that putative lung cancer-associated miRNAs can be identified from genome-wide arrayCGH datasets using a bioinformatics mapping approach, and report that miR-218 is a strong candidate tumour suppressing miRNA potentially involved in lung cancer.

Symbolic rule-based classification of lung cancer stages from free-text pathology reports

OBJECTIVE To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. DESIGN By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. MEASUREMENTS The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. RESULTS Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The systems performance was also comparable to support vector machine classification approaches. CONCLUSION A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

Common pathogenic mechanisms and pathways in the development of COPD and lung cancer

Introduction: Lung cancer and COPD commonly coexist in smokers, and the presence of COPD increases the risk of developing lung cancer. In addition to smoking cessation and preventing smoking initiation, understanding the shared mechanisms of these smoking-related lung diseases is critical, in order to develop new methods of prevention, diagnosis and treatment of lung cancer and COPD. Areas covered: This review discusses the common mechanisms for susceptibility to lung cancer and COPD, which in addition to cigarette smoke, may involve inflammation, epithelial–mesenchymal transition, abnormal repair, oxidative stress, and cell proliferation. Furthermore, we discuss the underlying genomic and epigenomic changes (single nucleotide polymorphisms (SNPs), copy number variation, promoter hypermethylation and microRNAs) that are likely to alter biological pathways, leading to susceptibility to lung cancer and COPD (e.g., altered nicotine receptor biology). Expert opinion: Strategies to study genomics, epigenomics and gene-environment interaction will yield greater insight into the shared pathogenesis of lung cancer and COPD, leading to new diagnostic and therapeutic modalities.

Collection of Cancer Stage Data by Classifying Free-text Medical Reports

Cancer staging provides a basis for planning clinical management, but also allows for meaningful analysis of cancer outcomes and evaluation of cancer care services. Despite this, stage data in cancer registries is often incomplete, inaccurate, or simply not collected. This article describes a prototype software system (Cancer Stage Interpretation System, CSIS) that automatically extracts cancer staging information from medical reports. The system uses text classification techniques to train support vector machines (SVMs) to extract elements of stage listed in cancer staging guidelines. When processing new reports, CSIS identifies sentences relevant to the staging decision, and subsequently assigns the most likely stage. The system was developed using a database of staging data and pathology reports for 710 lung cancer patients, then validated in an independent set of 179 patients against pathologic stage assigned by two independent pathologists. CSIS achieved overall accuracy of 74% for tumor (T) staging and 87% for node (N) staging, and errors were observed to mirror disagreements between human experts.

Diagnostic molecular biomarkers for malignant pleural effusions

Malignant pleural effusions (MPEs) are a common and important cause of cancer-related mortality and morbidity. Prompt diagnosis using minimally invasive tests is important because the median survival after diagnosis is only 4-9 months. Pleural fluid cytology is pivotal to current MPE diagnostic algorithms but has limited sensitivity (30-60%). Consequently, many patients need to undergo invasive diagnostic tests such as thoracoscopic pleural biopsy. Recent genomic, transcriptomic, methylation and proteomic studies on cells within pleural effusions have identified novel molecular diagnostic biomarkers that demonstrate potential in complementing cytology in the diagnosis of MPEs. Several challenges will need to be addressed prior to the incorporation of these molecular tests into routine clinical diagnosis, including validation of molecular diagnostic markers in well-designed prospective, comparative and cost-effectiveness studies. Ultimately, minimally invasive diagnostic tests that can be performed quickly will enable clinicians to provide the most effective therapies for patients with MPEs in a timely fashion.

High specificity of combined narrow band imaging and autofluorescence mucosal assessment of patients with head and neck cancer

The purpose of this study was to evaluate combined autofluorescence (AF) and narrow band imaging (NBI) for detection of mucosal lesions additional to known primary head and neck cancers and to determine impact on management.

Whole genome sequencing for lung cancer

Lung cancer is a leading cause of cancer related morbidity and mortality globally, and carries a dismal prognosis. Improved understanding of the biology of cancer is required to improve patient outcomes. Next-generation sequencing (NGS) is a powerful tool for whole genome characterisation, enabling comprehensive examination of somatic mutations that drive oncogenesis. Most NGS methods are based on polymerase chain reaction (PCR) amplification of platform-specific DNA fragment libraries, which are then sequenced. These techniques are well suited to high-throughput sequencing and are able to detect the full spectrum of genomic changes present in cancer. However, they require considerable investments in time, laboratory infrastructure, computational analysis and bioinformatic support. Next-generation sequencing has been applied to studies of the whole genome, exome, transcriptome and epigenome, and is changing the paradigm of lung cancer research and patient care. The results of this new technology will transform current knowledge of oncogenic pathways and provide molecular targets of use in the diagnosis and treatment of cancer. Somatic mutations in lung cancer have already been identified by NGS, and large scale genomic studies are underway. Personalised treatment strategies will improve care for those likely to benefit from available therapies, while sparing others the expense and morbidity of futile intervention. Organisational, computational and bioinformatic challenges of NGS are driving technological advances as well as raising ethical issues relating to informed consent and data release. Differentiation between driver and passenger mutations requires careful interpretation of sequencing data. Challenges in the interpretation of results arise from the types of specimens used for DNA extraction, sample processing techniques and tumour content. Tumour heterogeneity can reduce power to detect mutations implicated in oncogenesis. Next-generation sequencing will facilitate investigation of the biological and clinical implications of such variation. These techniques can now be applied to single cells and free circulating DNA, and possibly in the future to DNA obtained from body fluids and from subpopulations of tumour. As costs reduce, and speed and processing accuracy increase, NGS technology will become increasingly accessible to researchers and clinicians, with the ultimate goal of improving the care of patients with lung cancer.

ADAM28: a potential oncogene involved in asbestos-related lung adenocarcinomas.

Asbestos‐related lung cancer accounts for 4–12% of all lung cancers worldwide. Since putative mechanisms of carcinogenesis differ between asbestos and tobacco induced lung cancers, tumors induced by the two agents may be genetically distinct. To identify gene expression biomarkers associated with asbestos‐related lung tumorigenicity we performed gene expression array analysis on tumors of 36 patients with primary lung adenocarcinoma, comparing 12 patients with lung asbestos body counts above levels associated with urban dwelling (ARLC‐AC: asbestos‐related lung cancer‐adenocarcinoma) with 24 patients with no asbestos bodies (NARLC‐AC: non‐asbestos related lung cancer‐adenocarcinoma). Genes differentially expressed between ARLC‐AC and NARLC‐AC were identified on fold change and P value, and then prioritized using gene ontology. Candidates included ZNRF3, ADAM28, PPP1CA, IRF6, RAB3D, and PRDX1. Expression of these six genes was technically and biologically replicated by qRT‐PCR in the training set and biologically validated in three independent test sets. ADAM28, encoding a disintegrin and metalloproteinase domain protein that interacts with integrins, was consistently upregulated in ARLC across all four datasets. Further studies are being designed to investigate the possible role of this gene in asbestos lung tumorigenicity, its potential utility as a marker of asbestos related lung cancer for purposes of causal attribution, and its potential as a treatment target for lung cancers arising in asbestos exposed persons.

Array-Comparative Genomic Hybridization Reveals Loss of SOCS6 Is Associated with Poor Prognosis in Primary Lung Squamous Cell Carcinoma

Background Primary tumor recurrence commonly occurs after surgical resection of lung squamous cell carcinoma (SCC). Little is known about the genes driving SCC recurrence. Methods We used array comparative genomic hybridization (aCGH) to identify genes affected by copy number alterations that may be involved in SCC recurrence. Training and test sets of resected primary lung SCC were assembled. aCGH was used to determine genomic copy number in a training set of 62 primary lung SCCs (28 with recurrence and 34 with no evidence of recurrence) and the altered copy number of candidate genes was confirmed by quantitative PCR (qPCR). An independent test set of 72 primary lung SCCs (20 with recurrence and 52 with no evidence of recurrence) was used for biological validation. mRNA expression of candidate genes was studied using qRT-PCR. Candidate gene promoter methylation was evaluated using methylation microarrays and Sequenom EpiTYPER analysis. Results 18q22.3 loss was identified by aCGH as being significantly associated with recurrence (p = 0.038). Seven genes within 18q22.3 had aCGH copy number loss associated with recurrence but only SOCS6 copy number was both technically replicated by qPCR and biologically validated in the test set. SOCS6 copy number loss correlated with reduced mRNA expression in the study samples and in the samples with copy number loss, there was a trend for increased methylation, albeit non-significant. Overall survival was significantly poorer in patients with SOCS6 loss compared to patients without SOCS6 loss in both the training (30 vs. 43 months, p = 0.023) and test set (27 vs. 43 months, p = 0.010). Conclusion Reduced copy number and mRNA expression of SOCS6 are associated with disease recurrence in primary lung SCC and may be useful prognostic biomarkers.

Pleural fluid cell-free DNA integrity index to identify cytologically negative malignant pleural effusions including mesotheliomas

BackgroundThe diagnosis of malignant pleural effusions (MPE) is often clinically challenging, especially if the cytology is negative for malignancy. DNA integrity index has been reported to be a marker of malignancy. The aim of this study was to evaluate the utility of pleural fluid DNA integrity index in the diagnosis of MPE.MethodsWe studied 75 pleural fluid and matched serum samples from consecutive subjects. Pleural fluid and serum ALU DNA repeats [115bp, 247bp and 247bp/115bp ratio (DNA integrity index)] were assessed by real-time quantitative PCR. Pleural fluid and serum mesothelin levels were quantified using ELISA.ResultsBased on clinico-pathological evaluation, 52 subjects had MPE (including 16 mesotheliomas) and 23 had benign effusions. Pleural fluid DNA integrity index was higher in MPE compared with benign effusions (1.2 vs. 0.8; p<0.001). Cytology had a sensitivity of 55% in diagnosing MPE. If cytology and pleural fluid DNA integrity index were considered together, they exhibited 81% sensitivity and 87% specificity in distinguishing benign and malignant effusions. In cytology-negative pleural effusions (35 MPE and 28 benign effusions), elevated pleural fluid DNA integrity index had an 81% positive predictive value in detecting MPEs. In the detection of mesothelioma, at a specificity of 90%, pleural fluid DNA integrity index had similar sensitivity to pleural fluid and serum mesothelin (75% each respectively).ConclusionPleural fluid DNA integrity index is a promising diagnostic biomarker for identification of MPEs, including mesothelioma. This biomarker may be particularly useful in cases of MPE where pleural aspirate cytology is negative, and could guide the decision to undertake more invasive definitive testing. A prospective validation study is being undertaken to validate our findings and test the clinical utility of this biomarker for altering clinical practice.