Rayleen Bowman

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.

Systematic review of multidisciplinary teams in the management of lung cancer.

BACKGROUND In several countries, clinical practice guidelines for lung cancer recommend that multidisciplinary (MD) teams should be used to plan the management of all lung cancer patients. We conducted a systematic review to evaluate and critically appraise the effectiveness of multidisciplinary teams for lung cancer. MATERIALS AND METHODS Medline searches were carried out for the period 1984 to July 2007. We included any study that mentioned team working among specialists with diagnostic and curative therapeutic intent, where members of the team met at a specified time, either in person or by video or teleconferencing, to discuss the diagnosis and management of patients with suspected lung cancer. All study designs were included. We were particularly interested in whether multidisciplinary working improved survival but also considered other outcomes such as practice patterns and waiting times. RESULTS Sixteen studies met the criteria for inclusion. Statistical pooling was not possible due to clinical heterogeneity. Only two of the primary studies reported an improvement in survival. Both were before-and-after designs, providing weak evidence of a causal association. Evidence of the effect of MD teams was stronger for changing patient management than for affecting survival. Six of the studies reported an increase in the percentage of patients undergoing surgical resection or an increase in the percentage of patients undergoing chemotherapy or radiotherapy with curative intent. CONCLUSION This systematic review shows limited evidence linking MD teams with improved lung cancer survival. This does not mean that MD teams do not improve survival, merely that currently available evidence of this is limited. It seems intuitively obvious that MD teams should improve outcomes for lung cancer patients, but there are difficulties in conducting randomised trials to show this. The best way forward would be prospective evaluation of the effectiveness of MD teams as they are implemented, paying particular attention to collecting data on potential confounders.

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.

Epigenetics of lung cancer

Abstract:  Epigenetics is the study of heritable changes in gene expression that occur without changes in DNA sequence. It has a role in determining when and where a gene is expressed during development. Perhaps the most well known epigenetic mechanism is DNA methylation whereby cytosines at position 5 in CpG dinucleotides are methylated. Histone modification is another form of epigenetic control, which is quite complex and diverse. Histones and DNA make up the nucleosome which is the structural unit of chromatin which are involved in packaging DNA. Apart from the crucial role epigenetics plays in embryonic development, transcription, chromatin structure, X chromosome inactivation and genomic imprinting, its role in an increasing number of human diseases is more and more recognized. These diseases include cancer, and lung cancer in particular has been increasingly studied for the potential biological role of epigenetic changes with the promise of better and novel diagnostic and therapeutic tools.

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.

Biomarkers of progression of chronic obstructive pulmonary disease (COPD)

Disease progression of chronic obstructive pulmonary disease (COPD) is variable, with some patients having a relatively stable course, while others suffer relentless progression leading to severe breathlessness, frequent acute exacerbations of COPD (AECOPD), respiratory failure and death. Radiological markers such as CT emphysema index, bronchiectasis and coronary artery calcification (CAC) have been linked with increased mortality in COPD patients. Molecular changes in lung tissue reflect alterations in lung pathology that occur with disease progression; however, lung tissue is not routinely accessible. Cell counts (including neutrophils) and mediators in induced sputum have been associated with lung function and risk of exacerbations. Examples of peripheral blood biological markers (biomarkers) include those associated with lung function (reduced CC-16), emphysema severity (increased adiponectin, reduced sRAGE), exacerbations and mortality [increased CRP, fibrinogen, leukocyte count, IL-6, IL-8, and tumor necrosis factor α (TNF-α)] including increased YKL-40 with mortality. Emerging approaches to discovering markers of gene-environment interaction include exhaled breath analysis [volatile organic compounds (VOCs), exhaled breath condensate], cellular and systemic responses to exposure to air pollution, alterations in the lung microbiome, and biomarkers of lung ageing such as telomere length shortening and reduced levels of sirtuins. Overcoming methodological challenges in sampling and quality control will enable more robust yet easily accessible biomarkers to be developed and qualified, in order to optimise personalised medicine in patients with COPD.

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.

Exploratory study of the 'weekend effect' for acute medical admissions to public hospitals in Queensland, Australia.

Aims:  To determine whether in‐hospital deaths of patients admitted through emergency departments with acute exacerbations of chronic obstructive pulmonary disease (COPD), acute myocardial infarction, intracerebral haemorrhage and acute hip fracture are increased by weekend versus weekday admission (the ‘weekend effect’).

Genetic association study of CYP1A1 polymorphisms identifies risk haplotypes in nonsmall cell lung cancer.

Lung cancer remains a leading cause of disease globally, with smoking being the largest single cause. Phase I enzymes, including cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1), are involved in the activation of carcinogens, such as polycyclic aromatic hydrocarbons, to reactive intermediates that are capable of binding covalently to DNA to form DNA adducts, potentially initiating the carcinogenic process. The aim of the present study was to investigate the association of CYP1A1 gene polymorphisms and haplotypes with lung cancer risk. A case–control study was carried out on 1,040 nonsmall cell lung cancer (NSCLC) cases and 784 controls to investigate three CYP1A1 variants, CYP1A1*2A (rs4646903; thymidine to cytosine substitution at nucleotide 3801 (3801T>C)), CYP1A1*2C (rs1048943; 2455A>G; substitution of isoleucine 462 with valine (exon 7)) and CYP1A1*4 (rs1799814; 2453C>A; substitution of threonine 461 with asparagine (exon 7)) using PCR restriction fragment length polymorphism methods. The CYP1A1*2A and CYP1A1*2C variants were significantly over-represented in NSCLC cases compared with controls, whereas the CYP1A1*4 variant was under-represented. CYP1A1 haplotypes (in allele order CYP1A1*4, CYP1A1*2C, CYP1A1*2A) CGC and CGT were associated with an increased risk of lung cancer, whereas AAT was associated with decreased lung cancer risk in this population. The present study has identified risk haplotypes for CYP1A1 in NSCLC and confirmed that CYP1A1 polymorphisms are a minor risk factor for NSCLC.