Dataset Information

Modern classification of neoplasms: reconciling differences between morphologic and molecular approaches.

ABSTRACT: BACKGROUND: For over 150 years, pathologists have relied on histomorphology to classify and diagnose neoplasms. Their success has been stunning, permitting the accurate diagnosis of thousands of different types of neoplasms using only a microscope and a trained eye. In the past two decades, cancer genomics has challenged the supremacy of histomorphology by identifying genetic alterations shared by morphologically diverse tumors and by finding genetic features that distinguish subgroups of morphologically homogeneous tumors. DISCUSSION: The Developmental Lineage Classification and Taxonomy of Neoplasms groups neoplasms by their embryologic origin. The putative value of this classification is based on the expectation that tumors of a common developmental lineage will share common metabolic pathways and common responses to drugs that target these pathways. The purpose of this manuscript is to show that grouping tumors according to their developmental lineage can reconcile certain fundamental discrepancies resulting from morphologic and molecular approaches to neoplasm classification. In this study, six issues in tumor classification are described that exemplify the growing rift between morphologic and molecular approaches to tumor classification: 1) the morphologic separation between epithelial and non-epithelial tumors; 2) the grouping of tumors based on shared cellular functions; 3) the distinction between germ cell tumors and pluripotent tumors of non-germ cell origin; 4) the distinction between tumors that have lost their differentiation and tumors that arise from uncommitted stem cells; 5) the molecular properties shared by morphologically disparate tumors that have a common developmental lineage, and 6) the problem of re-classifying morphologically identical but clinically distinct subsets of tumors. The discussion of these issues in the context of describing different methods of tumor classification is intended to underscore the clinical value of a robust tumor classification. SUMMARY: A classification of neoplasms should guide the rational design and selection of a new generation of cancer medications targeted to metabolic pathways. Without a scientifically sound neoplasm classification, biological measurements on individual tumor samples cannot be generalized to class-related tumors, and constitutive properties common to a class of tumors cannot be distinguished from uninformative data in complex and chaotic biological systems. This paper discusses the importance of biological classification and examines several different approaches to the specific problem of tumor classification.

SUBMITTER: Berman J

PROVIDER: S-EPMC1208861 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modern classification of neoplasms: reconciling differences between morphologic and molecular approaches.

Berman Jules J

BMC cancer 20050810

<h4>Background</h4>For over 150 years, pathologists have relied on histomorphology to classify and diagnose neoplasms. Their success has been stunning, permitting the accurate diagnosis of thousands of different types of neoplasms using only a microscope and a trained eye. In the past two decades, cancer genomics has challenged the supremacy of histomorphology by identifying genetic alterations shared by morphologically diverse tumors and by finding genetic features that distinguish subgroups of ...[more]

PMID: 16092965

Similar Datasets

Project description:Osteogenesis imperfecta (imperfect osteogenesis in the Russian literature) is the most common hereditary form of bone fragility, it is a genetically and clinically heterogeneous disease with a wide range of clinical severity, often leading to disability from early childhood. It is based on genetic disorders leading to a violation of the structure of bone tissue, which leads to frequent fractures, impaired growth and posture, with the development of characteristic disabling bone deformities and associated problems, including respiratory, neurological, cardiac, renal impairment, hearing loss. Osteogenesis imperfecta occurs in both men and women, the disease is inherited in both autosomal dominant and autosomal recessive types, there are sporadic cases of the disease due to de novo mutations, as well as X-linked forms. The term "osteogenesis imperfecta" was coined by W. Vrolick in the 1840s. The first classification of the disease was made in 1979 and has been repeatedly reviewed due to the identification of the molecular cause of the disease and the discovery of new mechanisms for the development of osteogenesis imperfecta. In the early 1980s, mutations in two genes of collagen type I (COL1A1 and COL1A2) were first associated with an autosomal dominant inheritance type of osteogenesis imperfecta. Since then, 18 more genes have been identified whose products are involved in the formation and mineralization of bone tissue. The degree of genetic heterogeneity of the disease has not yet been determined, researchers continue to identify new genes involved in its pathogenesis, the number of which has reached 20. In the last decade, it has become known that autosomal recessive, autosomal dominant and X-linked mutations in a wide range of genes, encoding proteins that are involved in the synthesis of type I collagen, its processing, secretion and post-translational modification, as well as in proteins that regulate the differentiation and activity of bone-forming cells, cause imperfect osteogenesis. A large number of causative genes complicated the classical classification of the disease and, due to new advances in the molecular basis of the disease, the classification of the disease is constantly being improved. In this review, we systematized and summarized information on the results of studies in the field of clinical and genetic aspects of osteogenesis imperfecta and reflected the current state of the classification criteria for diagnosing the disease.

Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.

Dataset Information

Modern classification of neoplasms: reconciling differences between morphologic and molecular approaches.

Publications

Modern classification of neoplasms: reconciling differences between morphologic and molecular approaches.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure