Tumor classification: molecular analysis meets Aristotle

BACKGROUND: Traditionally, tumors have been classified by their morphologic appearances. Unfortunately, tumors with similar histologic features often follow different clinical courses or respond differently to chemotherapy. Limitations in the clinical utility of morphology-based tumor classifications have prompted a search for a new tumor classification based on molecular analysis. Gene expression array data and proteomic data from tumor samples will provide complex data that is unobtainable from morphologic examination alone. The growing question facing cancer researchers is, "How can we successfully integrate the molecular, morphologic and clinical characteristics of human cancer to produce a helpful tumor classification?" DISCUSSION: Current efforts to classify cancers based on molecular features ignore lessons learned from millennia of experience in biological classification. A tumor classification must include every type of tumor and must provide a unique place for each tumor within the classification. Groups within a classification inherit the properties of their ancestors and impart properties to their descendants. A classification was prepared grouping tumors according to their histogenetic development. The classification is simple (reducing the complexity of information received from the molecular analysis of tumors), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. The clinical and research value of this historical approach to tumor classification is discussed. SUMMARY: This manuscript reviews tumor classification and provides a new and comprehensive classification for neoplasia that preserves traditional nomenclature while incorporating information derived from the molecular analysis of tumors. The classification is provided as an open access XML document that can be used by cancer researchers to relate tumor classes with heterogeneous experimental and clinical tumor databases

BCL10 is rarely mutated in human prostate carcinoma, small-cell lung cancer, head and neck tumours, renal carcinoma and sarcomas

We have used single-strand conformation polymorphism (SSCP) analysis to screen for mutations in the BCL 10 gene in 81 primary prostate carcinomas, 20 squamous cell cancers of the head and neck, 15 small-cell lung cancer cell lines, 24 renal carcinoma cell lines and 13 sarcoma cell lines. We failed to find evidence of somatically acquired mutations of the BCL10 gene suggesting that BCL 10 does not play a major role in the development of these malignancies

Inactivation of the FLCN Tumor Suppressor Gene Induces TFE3 Transcriptional Activity by Increasing Its Nuclear Localization

Germline mutations in a tumor suppressor gene FLCN lead to development of fibrofolliculomas, lung cysts and renal cell carcinoma (RCC) in Birt-Hogg-Dubé syndrome. TFE3 is a member of the MiTF/TFE transcription factor family and Xp11.2 translocations found in sporadic RCC involving TFE3 result in gene fusions and overexpression of chimeric fusion proteins that retain the C-terminal DNA binding domain of TFE3. We found that GPNMB expression, which is regulated by MiTF, was greatly elevated in renal cancer cells harboring either TFE3 translocations or FLCN inactivation. Since TFE3 is implicated in RCC, we hypothesized that elevated GPNMB expression was due to increased TFE3 activity resulting from the inactivation of FLCN.TFE3 knockdown reduced GPNMB expression in renal cancer cells harboring either TFE3 translocations or FLCN inactivation. Moreover, FLCN knockdown induced GPNMB expression in FLCN-restored renal cancer cells. Conversely, wildtype FLCN suppressed GPNMB expression in FLCN-null cells. FLCN inactivation was correlated with increased TFE3 transcriptional activity accompanied by its nuclear localization as revealed by elevated GPNMB mRNA and protein expression, and predominantly nuclear immunostaining of TFE3 in renal cancer cells, mouse embryo fibroblast cells, mouse kidneys and mouse and human renal tumors. Nuclear localization of TFE3 was associated with TFE3 post-translational modifications including decreased phosphorylation.Increased TFE3 activity is a downstream event induced by FLCN inactivation and is likely to be important for renal tumor development. This study provides an important novel mechanism for induction of TFE3 activity in addition to TFE3 overexpression resulting from Xp11.2 translocations, suggesting that TFE3 may be more broadly involved in tumorigenesis

Gene expression profiling of alveolar soft-part sarcoma (ASPS)

<p>Abstract</p> <p>Background</p> <p>Alveolar soft-part sarcoma (ASPS) is an extremely rare, highly vascular soft tissue sarcoma affecting predominantly adolescents and young adults. In an attempt to gain insight into the pathobiology of this enigmatic tumor, we performed the first genome-wide gene expression profiling study.</p> <p>Methods</p> <p>For seven patients with confirmed primary or metastatic ASPS, RNA samples were isolated immediately following surgery, reverse transcribed to cDNA and each sample hybridized to duplicate high-density human U133 plus 2.0 microarrays. Array data was then analyzed relative to arrays hybridized to universal RNA to generate an unbiased transcriptome. Subsequent gene ontology analysis was used to identify transcripts with therapeutic or diagnostic potential. A subset of the most interesting genes was then validated using quantitative RT-PCR and immunohistochemistry.</p> <p>Results</p> <p>Analysis of patient array data versus universal RNA identified elevated expression of transcripts related to angiogenesis (ANGPTL2, HIF-1 alpha, MDK, c-MET, VEGF, TIMP-2), cell proliferation (PRL, IGFBP1, NTSR2, PCSK1), metastasis (ADAM9, ECM1, POSTN) and steroid biosynthesis (CYP17A1 and STS). A number of muscle-restricted transcripts (ITGB1BP3/MIBP, MYF5, MYF6 and TRIM63) were also identified, strengthening the case for a muscle cell progenitor as the origin of disease. Transcript differentials were validated using real-time PCR and subsequent immunohistochemical analysis confirmed protein expression for several of the most interesting changes (MDK, c-MET, VEGF, POSTN, CYP17A1, ITGB1BP3/MIBP and TRIM63).</p> <p>Conclusion</p> <p>Results from this first comprehensive study of ASPS gene expression identifies several targets involved in angiogenesis, metastasis and myogenic differentiation. These efforts represent the first step towards defining the cellular origin, pathogenesis and effective treatment strategies for this atypical malignancy.</p

The Mitotic Arrest Deficient Protein MAD2B Interacts with the Clathrin Light Chain A during Mitosis

Contains fulltext : 87811.pdf (publisher's version ) (Open Access)BACKGROUND: Although the mitotic arrest deficient protein MAD2B (MAD2L2) is thought to inhibit the anaphase promoting complex (APC) by binding to CDC20 and/or CDH1 (FZR1), its exact role in cell cycle control still remains to be established. METHODOLOGY/PRINCIPAL FINDINGS: Using a yeast two-hybrid interaction trap we identified the human clathrin light chain A (CLTA) as a novel MAD2B binding protein. A direct interaction was established in mammalian cells via GST pull-down and endogenous co-immunoprecipitation during the G2/M phase of the cell cycle. Through subsequent confocal laser scanning microscopy we found that MAD2B and CLTA co-localize at the mitotic spindle. Clathrin forms a trimeric structure, i.e., the clathrin triskelion, consisting of three heavy chains (CLTC), each with an associated light chain. This clathrin structure has previously been shown to be required for the function of the mitotic spindle through stabilization of kinetochore fibers. Upon siRNA-mediated MAD2B depletion, we found that CLTA was no longer concentrated at the mitotic spindle but, instead, diffusely distributed throughout the cell. In addition, we found a marked increase in the percentage of misaligned chromosomes. CONCLUSIONS/SIGNIFICANCE: Previously, we identified MAD2B as an interactor of the renal cell carcinoma (RCC)-associated protein PRCC. In addition, we found that fusion of PRCC with the transcription factor TFE3 in t(X;1)(p11;q21)-positive RCCs results in an impairment of this interaction and a concomitant failure to shuttle MAD2B to the nucleus. Our current data show that MAD2B interacts with CLTA during the G2/M phase of the cell cycle and that depletion of MAD2B leads to a marked increase in the percentage of misaligned chromosomes and a redistribution of CLTA during mitosis

deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes

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

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