Lung cancer is one of the most frequent and lethal malignant neoplasms, but knowledge regarding the molecular basis of its pathogenesis is far from complete due to the striking diversity of different forms. The current lung cancer classification (World Health Organization 2004) can efficiently distinguish clinically relevant major subtypes (small cell and non-small cell carcinomas), but its results are partly inadequate when facing prognostic and therapeutic decisions for non-small cell carcinomas, especially for the group of tumors classified as adenocarcinoma. Lung adenocarcinoma comprises a heterogeneous group of tumors characterized by diverse morphologic features and molecular pathogenesis. The category of mixed adenocarcinomas includes most adenocarcinomas (approximately 80%) and, according to World Health Organization criteria, is defined by the occurrence of a mixed array of different patterns (acinar, papillary, bronchioloalveolar, solid with mucin). The histologic recognition of mixed adenocarcinoma is subjective and cannot consistently discriminate between responders and nonresponders to new targeted therapies (eg, tyrosine kinase inhibitors). Diagnostic problems are mainly related to the poor reproducibility of histologic criteria, especially when applied in small biopsies and cytology, and to the difficulty in assigning each form to a precisely defined entity, as needed by updated therapeutic approaches. In this evolving scenario, pathologists face new challenging diagnostic roles that include not only the precise morphologic definition of carcinoma subtypes but also their molecular characterization.
To use a comprehensive critical analysis reconciling the overwhelming variety of biologic, morphologic, molecular, and clinical data to define new classification schemes for lung adenocarcinoma.
Scientific literature and personal data were used.
A new classification approach should redefine lung adenocarcinoma heterogeneity reconciling classic morphology, immunophenotypic and molecular features of neoplastic cells, and also relevant information provided by stem cell biology. This approach, which has been already successfully applied in World Health Organization classification of other tumors, could improve the recognition of new reproducible profiles for adenocarcinomas, more closely and reproducibly related to clinical features and response to specific therapies, limiting the use of "wastebasket" categories such as mixed adenocarcinoma.