Charles A. Powell

International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society: international multidisciplinary classification of lung adenocarcinoma: executive summary.

INTRODUCTION The American Thoracic Society is a cosponsor of a newly published lung adenocarcinoma classification. METHODS An international multidisciplinary panel of experts was formed. A systematic review was performed and recommendations were graded by strength and quality of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. RESULTS The classification addresses both resection specimens and small biopsies/cytology. The terms bronchioloalveolar carcinoma and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ and minimally invasive adenocarcinoma for small solitary adenocarcinomas with pure lepidic growth and predominant lepidic growth with ≤ 5 mm invasion, respectively. Invasive adenocarcinomas are classified by predominant pattern after using comprehensive histologic subtyping with lepidic, acinar, papillary, and solid patterns; micropapillary is added. In the new aspect of this classification that provides guidance for small biopsies and cytology specimens, non-small cell lung carcinomas (NSCLC), in patients with advanced stage disease, are to be classified into more specific types, such as adenocarcinoma or squamous cell carcinoma, whenever possible, for several reasons: (1) adenocarcinoma or NSCLC not otherwise specified should be tested for EGFR mutations, because the presence of these mutations is predictive of responsiveness to EGFR tyrosine kinase inhibitors, (2) adenocarcinoma histology is a strong predictor for improved outcome with pemetrexed therapy, and (3) squamous histology is a risk factor for life-threatening hemorrhage with bevacizumab therapy. NSCLC- not otherwise specified by light microscopy alone should be studied with immunohistochemistry and/or mucin stains. CONCLUSIONS This classification is intended to support clinical practice as well as research investigation and clinical trials.

Non-Small-Cell Lung Cancer Molecular Signatures Recapitulate Lung Developmental Pathways

Current paradigms hold that lung carcinomas arise from pleuripotent stem cells capable of differentiation into one or several histological types. These paradigms suggest lung tumor cell ontogeny is determined by consequences of gene expression that recapitulate events important in embryonic lung development. Using oligonucleotide microarrays, we acquired gene profiles from 32 microdissected non-small-cell lung tumors. We determined the 100 top-ranked marker genes for adenocarcinoma, squamous cell, large cell, and carcinoid using nearest neighbor analysis. Results were validated by immunostaining for 11 selected proteins using a tissue microarray representing 80 tumors. Gene expression data of lung development were accessed from a publicly available dataset generated with the murine Mu11k genome microarray. Self-organized mapping identified two temporally distinct clusters of murine orthologues. Supervised clustering of lung development data showed large-cell carcinoma gene orthologues were in a cluster expressed in pseudoglandular and canalicular stages whereas adenocarcinoma homologues were predominantly in a cluster expressed later in the terminal sac and alveolar stages of murine lung development. Representative large-cell genes (E2F3, MYBL2, HDAC2, CDK4, PCNA) are expressed in the nucleus and are associated with cell cycle and proliferation. In contrast, adenocarcinoma genes are associated with lung-specific transcription pathways (SFTPB, TTF-1), cell adhesion, and signal transduction. In sum, non-small-cell lung tumors histology gene profiles suggest mechanisms relevant to ontogeny and clinical course. Adenocarcinoma genes are associated with differentiation and glandular formation whereas large-cell genes are associated with proliferation and differentiation arrest. The identification of developmentally regulated pathways active in tumorigenesis provides insights into lung carcinogenesis and suggests early steps may differ according to the eventual tumor morphology.

Pathologic Diagnosis of Advanced Lung Cancer Based on Small Biopsies and Cytology: A Paradigm Shift

With some exceptions, the field of lung cancer disease has been relatively static during the past several decades with few major practice-changing advances. In this issue of the journal 2, articles address the diagnosis of non-small cell lung cancer (NSCLC) based on small biopsies and/or cytology, an area of lung cancer diagnosis in which a paradigm shift has occurred for both pathologists and clinicians. 1,2 This topic is important because the majority patients with lung cancer present with unresectable disease, and the diagnosis is established based on such small specimens. Moreover, with increasing use of minimally invasive biopsy methods, pathologists are being asked to do more with less tissue. HISTORY OF LUNG CANCER DIAGNOSIS IN SMALL BIOPSIES AND CYTOLOGY The World Health Organization classifications of lung tumors through the 1999 edition 3‐5 did not address lung cancer diagnosis based on small biopsies and cytology, because these were recommendations for the histologic classifications of resection specimens. In the 2004 World Health Organization classification, cytology was addressed for the first time, but classification in small biopsies was not addressed. 6 Currently, no internationally recognized standard of criteria or terminology for the diagnosis of lung cancer in small biopsies is available.

Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

OBJECTIVE Accurate classification methods are critical in computer-aided diagnosis (CADx) and other clinical decision support systems. Previous research has reported on methods for combining genetic algorithm (GA) feature selection with ensemble classifier systems in an effort to increase classification accuracy. In this study, we describe a CADx system for pulmonary nodules using a two-step supervised learning system combining a GA with the random subspace method (RSM), with the aim of exploring algorithm design parameters and demonstrating improved classification performance over either the GA or RSM-based ensembles alone. METHODS AND MATERIALS We used a retrospective database of 125 pulmonary nodules (63 benign; 62 malignant) with CT volumes and clinical history. A total of 216 features were derived from the segmented image data and clinical history. Ensemble classifiers using RSM or GA-based feature selection were constructed and tested via leave-one-out validation with feature selection and classifier training executed within each iteration. We further tested a two-step approach using a GA ensemble to first assess the relevance of the features, and then using this information to control feature selection during a subsequent RSM step. The base classification was performed using linear discriminant analysis (LDA). RESULTS The RSM classifier alone achieved a maximum leave-one-out Az of 0.866 (95% confidence interval: 0.794-0.919) at a subset size of s=36 features. The GA ensemble yielded an Az of 0.851 (0.775-0.907). The proposed two-step algorithm produced a maximum Az value of 0.889 (0.823-0.936) when the GA ensemble was used to completely remove less relevant features from the second RSM step, with similar results obtained when the GA-LDA results were used to reduce but not eliminate the occurrence of certain features. After accounting for correlations in the data, the leave-one-out Az in the two-step method was significantly higher than in the RSM and the GA-LDA. CONCLUSIONS We have developed a CADx system for evaluation of pulmonary nodule based on a two-step feature selection and ensemble classifier algorithm. We have shown that by combining classifier ensemble algorithms in this two-step manner, it is possible to predict the malignancy for solitary pulmonary nodules with a performance exceeding that of either of the individual steps.

STRUCTURAL EMPHYSEMA DOES NOT CORRELATE WITH LUNG COMPLIANCE: LESSONS FROM THE MOUSE SMOKING MODEL

The murine smoke-induced model produces histologic emphysema. The authors sought to assess whether the structural emphysema that occurred correlated with the development of compliance changes. The study exposed 2 strains of mice (CBA/J/J × C57BL/6J and A/J) to chronic cigarette smoke. Lung compliance and morphometry were measured. The smoking model generated significant emphysema in A/J mice in the absence of changes in compliance, lung matrix, or apoptosis. Importantly, there was no correlation between the emphysema measured by lung morphometry and pulmonary compliance. This lack of correlation suggests that the mechanisms involved in anatomic emphysema may be distinct from those that cause the loss of elastic recoil.

Effectiveness of Radiation Therapy for Elderly Patients with Unresected Stage I and II Non-Small Cell Lung Cancer

RATIONALE Radiotherapy (RT) is considered the standard treatment for patients with stage I or II non-small lung cancer who are not surgical candidates because of comorbities or preferences against surgery. OBJECTIVES To compare the outcomes of patients treated with RT alone with those who were untreated to assess the effect of RT on survival. METHODS Using the Surveillance, Epidemiology and End Results (SEER) registry linked to Medicare files, we identified 6,065 unresected patients with histologically confirmed stage I and stage II non-small cell lung cancer, diagnosed between 1992 and 2002. We used propensity score methods and instrumental variable analysis to control for the possible effects of known as well as unmeasured confounders. MEASUREMENTS AND MAIN RESULTS Overall, 59% of patients received RT. The overall and lung cancer-specific survival of unresected patients treated with RT was significantly better compared with the untreated cases (P < 0.0001 for both comparisons). RT was associated with a 6-month improvement in median overall survival. Propensity score analyses showed that RT was associated with improved overall (hazard ratio, 0.74; 95% confidence interval, 0.70-0.78) and lung cancer-specific survival (hazard ratio, 0.73; 95% confidence interval, 0.69-0.78). Instrumental variable analysis also indicated improved outcomes among patients treated with RT. CONCLUSIONS RT improves survival of elderly patients with unresected stage I or II lung cancer. These results should be confirmed in prospective trials.

A 10-gene classifier for distinguishing head and neck squamous cell carcinoma and lung squamous cell carcinoma.

Purpose: The risk of developing metastatic squamous cell carcinoma for patients with head and neck squamous cell carcinoma (HNSCC) is very high. Because these patients are often heavy tobacco users, they are also at risk for developing a second primary cancer, with squamous cell carcinoma of the lung (LSCC) being the most common. The distinction between a lung metastasis and a primary LSCC is currently based on certain clinical and histologic criteria, although the accuracy of this approach remains in question. Experimental Design: Gene expression patterns derived from 28 patients with HNSCC or LSCC from a single center were analyzed using penalized discriminant analysis. Validation was done on previously published data for 134 total subjects from four independent Affymetrix data sets. Results: We identified a panel of 10 genes (CXCL13, COL6A2, SFTPB, KRT14, TSPYL5, TMP3, KLK10, MMP1, GAS1, and MYH2) that accurately distinguished these two tumor types. This 10-gene classifier was validated on 122 subjects derived from four independent data sets and an average accuracy of 96% was shown. Gene expression values were validated by quantitative reverse transcription-PCR derived on 12 independent samples (seven HNSCC and five LSCC). The 10-gene classifier was also used to determine the site of origin of 12 lung lesions from patients with prior HNSCC. Conclusions: The results suggest that penalized discriminant analysis using these 10 genes will be highly accurate in determining the origin of squamous cell carcinomas in the lungs of patients with previous head and neck malignancies.

Plasma carbonyls do not correlate with lung function or computed tomography measures of lung density in older smokers

Abstract Oxidative stress and inflammation are hallmarks of chronic obstructive pulmonary disease (COPD). A critical byproduct of oxidative damage is the introduction of carbonyl groups into amino acid residues. We hypothesize that plasma carbonyl content is inversely correlated with lung function and computed tomography (CT) measures of lung density among smokers and is elevated in COPD. Carbonyl was measured in plasma of participants aged 60 years and older by ELISA. Generalized linear and additive models were used to adjust for potential confounders. Among 541 participants (52% male, mean age 67 years, 41% current smokers), mean plasma carbonyl content was 17.9±2.9 nmol ml−1 and mean forced expiratory volume in one second (FEV1) was 80.7±20.9% of predicted. Plasma carbonyl content was inversely associated with FEV1, but this relationship was largely explained by age. Multivariate analyses ruled out clinically meaningful associations of plasma carbonyl content with FEV1, FEV1/FVC (forced vital capacity) ratio, severity of airflow obstruction, and CT lung density. Plasma carbonyl content is a poor biomarker of oxidative stress in COPD and emphysema.

The Epithelial Cell in Lung Health and Emphysema Pathogenesis.

Cigarette smoking is the primary cause of the irreversible lung disease emphysema. Historically, inflammatory cells such as macrophages and neutrophils have been studied for their role in emphysema pathology. However, recent studies indicate that the lung epithelium is an active participant in emphysema pathogenesis and plays a critical role in the lungs response to cigarette smoke. Tobacco smoke increases protease production and alters cytokine expression in isolated epithelial cells, suggesting that these cells respond potently even in the absence of a complete inflammatory program. Tobacco smoke also acts as an immunosuppressant, reducing the defense function of airway epithelial cells and enhancing colonization of the lower airways. Thus, the paradigm that emphysema is strictly an inflammatory-cell based disease is shifting to consider the involvement of resident epithelial cells. Here we review the role of epithelial cells in lung development and emphysema. To better understand tobacco-epithelial interactions we performed microarray analyses of RNA from human airway epithelial cells exposed to smoke extract for 24 hours. These studies identified differential regulation of 425 genes involved in diverse biological processes, such as apoptosis, immune function, cell cycle, signal transduction, proliferation, and antioxidants. Some of these genes, including VEGF, glutathione peroxidase, IL-13 receptor, and cytochrome P450, have been previously reported to be altered in the lungs of smokers. Others, such as pirin, cathepsin L, STAT1, and BMP2, are shown here for the first time to have a potential role in smoke-associated injury. These data broaden our understanding of the importance of epithelial cells in lung health and cigarette smoke-induced emphysema.

A Two-Step Approach for Feature Selection and Classifier Ensemble Construction in Computer-Aided Diagnosis

Accurate classification methods are critical in computer-aided diagnosis and other clinical decision support systems. Previous research has studied methods for combining genetic algorithms for feature selection with ensemble classifier systems in an effort to increase classification accuracy. We propose a two-step approach that first uses genetic algorithms to reduce the number of features used to characterize the data, then applies the random subspace method on the remaining features to create a set of diverse but high performing classifiers. These classifiers are combined using ensemble learning techniques to yield a final classification. We demonstrate this approach for computer-aided diagnosis of solitary pulmonary nodules from CT scans, in which the proposed method outperforms several previously described methods.