Diagnostic agreement in the histopathological evaluation of lung cancer tissue in a population-based case-control study

Stang, Andreas; Pohlabeln, Hermann; Müller, Klaus; Jahn, Ingeborg; Giersiepen, Klaus; Jöckel, Karl‐Heinz

doi:10.1016/j.lungcan.2005.11.012

Cited by 80 publications

(67 citation statements)

References 25 publications

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“…Unlike prior validation studies, rigorous adjudication was used to establish diagnosis in all cases: previous reports of other gene expression-based classifiers did not include independent validation of the diagnoses used to assess accuracy (24,31,32). In tumor bank studies of archival specimens, corresponding pathology reports may contain inaccurate diagnoses, leading to over-or underestimation of test performance (33,34). The increased rigor provided by peer adjudication allows a more precise characterization of performance, and therefore a more substantiated clinical indication for the 92-gene assay.…”

Section: Discussionmentioning

confidence: 99%

Multisite Validation Study to Determine Performance Characteristics of a 92-Gene Molecular Cancer Classifier

Kerr

Schnabel

Sullivan

et al. 2012

Clinical Cancer Research

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Purpose: Accurate tumor classification is essential for cancer management as patient outcomes improve with use of site-and subtype-specific therapies. Current clinicopathologic evaluation is varied in approach, yet standardized diagnoses are critical for determining therapy. While gene expression-based cancer classifiers may potentially meet this need, imperative to determining their application to patient care is validation in rigorously designed studies. Here, we examined the performance of a 92-gene molecular classifier in a large multi-institution cohort.Experimental Design: Case selection incorporated specimens from more than 50 subtypes, including a range of tumor grades, metastatic and primary tumors, and limited tissue samples. Formalin-fixed, paraffinembedded tumors passed pathologist-adjudicated review between three institutions. Tumor classification using a 92-gene quantitative reverse transcriptase polymerase chain reaction (RT-PCR) assay was conducted on blinded tumor sections from 790 cases and compared with adjudicated diagnoses.Results: The 92-gene assay showed overall sensitivities of 87% for tumor type [95% confidence interval (CI), 84-89] and 82% for subtype (95% CI, 79-85). Analyses of metastatic tumors, high-grade tumors, or cases with limited tissue showed no decrease in comparative performance (P ¼ 0.16, 0.58, and 0.16). High specificity (96%-100%) was showed for ruling in a primary tumor in organs commonly harboring metastases. The assay incorrectly excluded the adjudicated diagnosis in 5% of cases.Conclusions: The 92-gene assay showed strong performance for accurate molecular classification of a diverse set of tumor histologies. Results support potential use of the assay as a standardized molecular adjunct to routine clinicopathologic evaluation for tumor classification and primary site diagnosis.

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Section: Discussionmentioning

confidence: 99%

Multisite Validation Study to Determine Performance Characteristics of a 92-Gene Molecular Cancer Classifier

Kerr

Schnabel

Sullivan

et al. 2012

Clinical Cancer Research

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“…10 Concordance rates as low as 81% between pathologists have been reported in subtyping NSCLC on H&E alone. 11,12 The use of immunohistochemistry (IHC) has been well documented as an important ancillary tool in diagnosing lung carcinoma. Immunohistochemistry is also a valuable tool in the distinction of metastatic lesions from primary lung carcinomas.…”

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confidence: 99%

“…[15][16][17] The role of napsin A in differentiating primary from metastatic ACA of the lung has previously been reported. 12,15,18,19 Positive immunohistochemical staining shows intense granular cytoplasmic reactivity. 1,18,20 The reported sensitivity of napsin A for lung ACA is 74% to 87%.…”

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confidence: 99%

Evaluation of Napsin A, Cytokeratin 5/6, p63, and Thyroid Transcription Factor 1 in Adenocarcinoma Versus Squamous Cell Carcinoma of the Lung

Whithaus

Fukuoka

Prihoda³

et al. 2012

Archives of Pathology &Amp; Laboratory Medicine

110

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N Context.-The distinction of lung adenocarcinoma from other types of primary lung malignancies is important clinically. Accurate morphologic classification is often hindered because 70% of lung cancers are diagnosed on limited fine-needle aspiration or transbronchial biopsy specimens. Although thyroid transcription factor 1 (TTF-1) has historically been the most specific marker for lung adenocarcinoma, a relatively new marker, napsin A, has recently been shown to be more sensitive and specific than TTF-1.Objective.-To find the most cost-effective panel to reliably distinguish lung adenocarcinoma from squamous cell carcinoma.Design.-A total of 291 lung cancers were evaluated morphologically (197 adenocarcinomas [75%]; 66 squamous cell carcinomas [25%]; 28 cases could not be classified into either and were dropped). Immunohistochemistry for napsin A, Cytokeratin 5/6, p63, and TTF-1 was performed on a formalin-fixed tissue microarray obtained from Toyama, Japan. Cases were scored as positive or negative against a negative control.Results.-Napsin A had 83% sensitivity and 98% specificity and TTF-1 had 60% sensitivity and 98% specificity for adenocarcinoma. Cytokeratin 5/6 had 53% sensitivity and 96% specificity and p63 had 95% sensitivity and 86% specificity for squamous cell carcinoma. A panel of napsin A and p63 has a specificity of 94% and a sensitivity of 96% for distinguishing adenocarcinoma from squamous cell carcinoma.Conclusions.-The source of the antibody is important in avoiding false-negative results. The most cost-effective tissue-preserving panel for small biopsy specimens in the differential diagnosis of lung adenocarcinoma versus squamous cell carcinoma is a combination of p63 and napsin A.

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“…In one study addressing the reproducibility of histopathologic classification of resected lung cancers, the central expert pathologist disagreed with the regional pathologists in one third of the cases (5). The difficulty is vastly inflated in the preoperative setting where attempts at tumor classification in small diagnostic samples (e.g., fine-needle aspirates) are further handicapped by the paucity of tumor cells and the absence of tissue architecture (6,8,9).…”

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confidence: 99%

Accurate Classification of Non–Small Cell Lung Carcinoma Using a Novel MicroRNA-Based Approach

Bishop

Benjamin

Cholakh

et al. 2010

Clinical Cancer Research

218

154

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Purpose: Advances in targeted lung cancer therapy now demand accurate classification of non-small cell lung cancer (NSCLC). MicroRNAs (miRNA) are recently discovered short, noncoding genes that play essential roles in tissue differentiation during normal development and tumorigenesis. For example, hsamiR-205 is a miRNA that is highly expressed in lung squamous cell carcinomas (SqCC) but not in lung adenocarcinomas. The differential expression of miRNAs could be exploited to distinguish these tumor types.Experimental Design: One hundred and two resected NSCLCs were classified as SqCC or adenocarcinoma based on their histologic features and immunohistochemical profiles. Corresponding preoperative biopsies/aspirates that had been originally diagnosed as poorly differentiated NSCLCs were available for 21 cases. A quantitative reverse transcription-PCR diagnostic assay that measures the expression level of hsa-miR-205 was used to classify the carcinomas as SqCC or adenocarcinoma based solely on expression levels. The two sets of diagnoses were compared.Results: Using standard pathologic methods of classification (i.e., microscopy and immunohistochemistry), 52 resected lung carcinomas were classified as SqCCs and 50 as adenocarcinomas. There was 100% concordance between the diagnoses established by conventional and miRNA-based methods. MiRNA profiling also correctly classified 20 of the 21 preoperative biopsy specimens.Conclusions: MiRNA profiling is a highly reliable strategy for classifying NSCLCs. Indeed, classification is consistently accurate even in small biopsies/aspirates of poorly differentiated tumors. Confirmation of its reliability across the full range of tumor grades and specimen types represents an important step toward broad application. Clin Cancer Res; 16(2); 610-9. ©2010 AACR.Lung cancer is the leading cause of cancer-related death in both men and women worldwide. In the United States, there are over 215,000 new cases of lung cancer each year resulting in over 160,000 deaths annually (1). Eighty-five percent of these lung cancers are non-small cell lung cancers (NSCLC), a heterogeneous group comprised of mostly squamous cell carcinomas (SqCC) and adenocarcinomas. In the past, the only diagnostic branch point in the classification of lung cancers that carried any relevance was the distinction between small cell carcinoma and NSCLC. Further subclassification of the NSCLCs has not been important from a therapeutic or prognostic standpoint. The recent emergence of targeted lung cancer therapies directed against specific cellular alterations, however, now demands the most precise classification possible for NSCLCs. For example, lung adenocarcinomas are more likely to harbor activating mutations of epidermal growth factor receptor and, in turn, are more likely to respond to epidermal growth factor receptor tyrosine kinase inhibitors (2). Similarly, certain angiogenesis inhibitors such as bevacizumab are more effective in the treatment of adenocarcinomas. Not only is bevacizumab less effective in t...

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Diagnostic agreement in the histopathological evaluation of lung cancer tissue in a population-based case-control study

Cited by 80 publications

References 25 publications

Multisite Validation Study to Determine Performance Characteristics of a 92-Gene Molecular Cancer Classifier

Multisite Validation Study to Determine Performance Characteristics of a 92-Gene Molecular Cancer Classifier

Evaluation of Napsin A, Cytokeratin 5/6, p63, and Thyroid Transcription Factor 1 in Adenocarcinoma Versus Squamous Cell Carcinoma of the Lung

Accurate Classification of Non–Small Cell Lung Carcinoma Using a Novel MicroRNA-Based Approach

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