scholarly journals Molecular Classification of Tumors

2020 ◽  
Author(s):  
Keyword(s):  
Molecular Classification ◽  
Classification Of Tumors

Morphological and Molecular Classification of Human Cancer

2017 ◽  
Author(s):  
Mark E. Sherman ◽  
Melissa A. Troester ◽  
Katherine A. Hoadley ◽  
William F. Anderson
Keyword(s):  
Human Cancer ◽  
Molecular Classification ◽  
Tumor Stage ◽  
Cancer Surveillance ◽  
Molecular Profile ◽  
Malignant Neoplasms ◽  
Anatomic Site ◽  
Molecular Events ◽  
Classification Of Tumors

Accurate and reproducible classification of tumors is essential for clinical management, cancer surveillance, and studies of pathogenesis and etiology. Tumor classification has historically been based on the primary anatomic site or organ in which the tumor occurs and on its morphologic and histologic phenotype. While pathologic criteria are useful in predicting the average behavior of a group of tumors, histopathology alone cannot accurately predict the prognosis and treatment response of individual cancers. Traditional measures such as tumor stage and grade do not take into account molecular events that influence tumor aggressiveness or changes in the tumor composition during treatment. This chapter provides a primer on approaches that use pathology and molecular biology to classify and subclassify cancers. It describes the features of carcinomas, sarcomas, and malignant neoplasms of the immune system and blood, as well as various high-throughput genomic platforms that characterize the molecular profile of tumors.

scholarly journals Molecular Classification of Soft Tissue and Bone Tumors

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2326
Author(s):  
David Creytens
Keyword(s):  
Soft Tissue ◽  
World Health Organization ◽  
Bone Tumors ◽  
Who Classification ◽  
Molecular Classification ◽  
World Health ◽  
The World ◽  
Classification Of Tumors ◽  
Health Organization

Soft tissue and bone tumors constitute a large and heterogeneous group of tumors comprising >100 distinct histological types and subtypes, which are diagnosed and classified using criteria from the World Health Organization (WHO) Classification of Tumors [...]

Molecular classification of pituitary adenomas

2015 ◽  
Author(s):  
Antonio Pico ◽  
Laura Sanchez-Tejada ◽  
Ruth Sanchez-Ortiga ◽  
Rosa Camara ◽  
Cristina Lamas ◽  
...  
Keyword(s):  
Pituitary Adenomas ◽  
Molecular Classification

scholarly journals Normal tissue content impact on the GBM molecular classification

2020 ◽  
Author(s):  
Rodrigo Madurga ◽  
Noemí García-Romero ◽  
Beatriz Jiménez ◽  
Ana Collazo ◽  
Francisco Pérez-Rodríguez ◽  
...  
Keyword(s):  
Normal Tissue ◽  
In Silico Analysis ◽  
Molecular Classification ◽  
Gene Signature ◽  
Normal Brain ◽  
Normal Cells ◽  
Final Model ◽  
Tumor Subtypes ◽  
Tissue Content

Abstract Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

scholarly journals Druggable targets meet oncogenic drivers: opportunities and limitations of target-based classification of tumors and the role of Molecular Tumor Boards

ESMO Open ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 100040 ◽  
Author(s):  
R. Danesi ◽  
S. Fogli ◽  
S. Indraccolo ◽  
M. Del Re ◽  
A.P. Dei Tos ◽  
...  
Keyword(s):  
Tumor Boards ◽  
Classification Of Tumors ◽  
Druggable Targets

scholarly journals Molecular Classification and Tumor Microenvironment Characterization of Gallbladder Cancer by Comprehensive Genomic and Transcriptomic Analysis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 733
Author(s):  
Nobutaka Ebata ◽  
Masashi Fujita ◽  
Shota Sasagawa ◽  
Kazuhiro Maejima ◽  
Yuki Okawa ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Gallbladder Cancer ◽  
Epithelial To Mesenchymal Transition ◽  
Molecular Classification ◽  
Mesenchymal Transition ◽  
Elevated Expression ◽  
Fresh Frozen ◽  
Therapeutic Decision Making

Gallbladder cancer (GBC), a rare but lethal disease, is often diagnosed at advanced stages. So far, molecular characterization of GBC is insufficient, and a comprehensive molecular portrait is warranted to uncover new targets and classify GBC. We performed a transcriptome analysis of both coding and non-coding RNAs from 36 GBC fresh-frozen samples. The results were integrated with those of comprehensive mutation profiling based on whole-genome or exome sequencing. The clustering analysis of RNA-seq data facilitated the classification of GBCs into two subclasses, characterized by high or low expression levels of TME (tumor microenvironment) genes. A correlation was observed between gene expression and pathological immunostaining. TME-rich tumors showed significantly poor prognosis and higher recurrence rate than TME-poor tumors. TME-rich tumors showed overexpression of genes involved in epithelial-to-mesenchymal transition (EMT) and inflammation or immune suppression, which was validated by immunostaining. One non-coding RNA, miR125B1, exhibited elevated expression in stroma-rich tumors, and miR125B1 knockout in GBC cell lines decreased its invasion ability and altered the EMT pathway. Mutation profiles revealed TP53 (47%) as the most commonly mutated gene, followed by ELF3 (13%) and ARID1A (11%). Mutations of ARID1A, ERBB3, and the genes related to the TGF-β signaling pathway were enriched in TME-rich tumors. This comprehensive analysis demonstrated that TME, EMT, and TGF-β pathway alterations are the main drivers of GBC and provides a new classification of GBCs that may be useful for therapeutic decision-making.

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