Gene expression identifies heterogeneity of metastatic behavior among high-grade non-translocation associated soft tissue sarcomas

Skubitz, Keith M.; Skubitz, Amy P.N.; Xu, Wayne; Luo, Xianghua; Lagarde, Pauline; Coindre, Jean Michél; Chibon, Fréderic

doi:10.1186/1479-5876-12-176

Cited by 10 publications

(12 citation statements)

References 48 publications

(48 reference statements)

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“…Indeed, some studies suggest that gene signatures similar to that seen in DTF correlate with clinical outcome in some malignancies. 84,[185][186][187][188] Desmoid-type fibromatosis may also provide a model for other fibrosing diseases such as progressive pulmonary fibrosis.…”

Section: Desmoid-type Fibromatosis As a Model For The Role Of Tumor Smentioning

confidence: 99%

Biology and Treatment of Aggressive Fibromatosis or Desmoid Tumor

Skubitz

2017

Mayo Clinic Proceedings

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Aggressive fibromatosis, also known as desmoid-type fibromatosis (DTF) or desmoid tumor, is an uncommon locally invasive tumor. Because of its low incidence and variable behavior, DTF is often first seen by physicians who are not familiar with it, and recent advances in understanding this disease have led to changes in treatment approaches. The Wnt (β-catenin) pathway appears to play a key role in DTF pathogenesis, and recent studies of DTF biology suggest a possible model of DTF pathogenesis. Histologically, DTF shows a poorly circumscribed proliferation of myofibroblast-like cells with variable collagen deposition, similar to the proliferative phase of wound healing, and DTF has been associated with trauma and pregnancy. Desmoid-type fibromatosis may be a useful model of the tumor stroma in carcinomas as well as other fibrosing diseases such as progressive pulmonary fibrosis. The clinical course of DTF can vary greatly among patients, complicating the determination of the optimal treatment approach. Treatment options include surgery, nonsteroidal anti-inflammatory drugs with or without hormonal manipulation, chemotherapy, radiation therapy, and other forms of local therapy. Many treatments have been used, but these are not without toxicities. Because of the variable nature of the disease and the potential morbidity of treatment, some cases of DTF may do better without treatment; simple observation is often the best initial treatment. This review used a PubMed search from January 1, 1980, through October 31, 2016, using the terms fibromatosis and desmoid and discusses DTF disease characteristics, pathophysiology, and treatment options as well as examines several cases illustrating key points in the biology and treatment of this heterogeneous disease.

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Section: Desmoid-type Fibromatosis As a Model For The Role Of Tumor Smentioning

confidence: 99%

Biology and Treatment of Aggressive Fibromatosis or Desmoid Tumor

Skubitz

2017

Mayo Clinic Proceedings

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“…These gene sets have also been shown to separate sets of soft tissue sarcoma samples into groups with different metastatic behavior [ 25 , 26 ]. A gene set of 67 genes involved in mitosis and control of chromosome integrity, termed the complexity index in sarcomas (CINSARC), also predicts metastasis outcome in non-translocation dependent soft tissue sarcomas [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…Gene expression patterns have been used to predict the development of metastases in soft tissue sarcoma [ 23 – 26 ]. Differences in the gene expression profiles of GISTs with different KIT- or PDGFRA-mutant tumors have been reported [ 27 , 28 ], and several recent studies have explored the use of gene expression patterns to predict recurrence rate of GIST [ 29 – 35 ].…”

Section: Introductionmentioning

confidence: 99%

“…We previously used a gene set derived from these three studies to separate 73 high grade soft tissue sarcoma into 2 or 4 groups with different propensities of metastasis [ 25 ]. In an independent study, these gene sets were used to separate 309 high-grade soft tissue sarcoma into 2 or 4 groups with different propensity of metastasis [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Gene expression identifies heterogeneity of metastatic behavior among gastrointestinal stromal tumors

Skubitz

Geschwind

et al. 2016

J Transl Med

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BackgroundAdjuvant imatinib is useful in patients with gastrointestinal stromal tumors (GIST) at high risk of recurrence. At present, the risk of recurrence is determined based on tumor size, mitotic rate, tumor site, and tumor rupture. Previous studies using various biochemical pathways identified gene expression patterns that distinguish two subsets of aggressive fibromatosis (AF), serous ovarian carcinoma (OVCA), and clear cell renal cell carcinoma (RCC). These gene sets separated soft tissue sarcomas into two groups with different probabilities of developing metastatic disease. The present study used these gene sets to identify GIST subgroups with different probabilities of developing metastatic disease.MethodsWe utilized these three gene sets, hierarchical clustering, and Kaplan–Meier analysis, to examine 60 primary resected GIST samples using Agilent chip expression profiling.ResultsHierarchical clustering using both the combined and individual AF-, OVCA-, and RCC- gene sets identified differences in probabilities of developing metastatic disease between the clusters defined by the first branch point of the clustering dendrograms (p = 0.029 for the combined gene set, p = 0.003 for the AF-gene set, p < 0.001 for the OVCA-gene set, and p = 0.003 for the RCC-gene set).ConclusionsHierarchical clustering using these gene sets identified at least two subsets of GIST with distinct clinical behavior and risk of metastatic disease. The use of gene expression analysis along with other known prognostic factors may better predict the long-term outcome following surgery, and thus restrict the use of adjuvant therapy to high-risk GIST, and reduce heterogeneity among groups in clinical trials of new drugs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-016-0802-3) contains supplementary material, which is available to authorized users.

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“…Several clustering techniques have been previously described for identifying closely interconnected gene expression networks from large scale expression data. These include K-means clustering [ 21 ], hierarchical clustering [ 22 ], model-based clustering (MCLUST) [ 23 ], self-organizing-maps [ 24 ], and weighted gene co-expression analysis (WGCNA) [ 25 ] among others. The performance of these methods has been extensively discussed and WGCNA and MCLUST are thought to be two of the best performing clustering algorithms available [ 26 – 28 ].…”

Section: Introductionmentioning

confidence: 99%

A novel approach identifies the first transcriptome networks in bats: a new genetic model for vocal communication

et al. 2015

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BackgroundBats are able to employ an astonishingly complex vocal repertoire for navigating their environment and conveying social information. A handful of species also show evidence for vocal learning, an extremely rare ability shared only with humans and few other animals. However, despite their potential for the study of vocal communication, bats remain severely understudied at a molecular level. To address this fundamental gap we performed the first transcriptome profiling and genetic interrogation of molecular networks in the brain of a highly vocal bat species, Phyllostomus discolor.ResultsGene network analysis typically needs large sample sizes for correct clustering, this can be prohibitive where samples are limited, such as in this study. To overcome this, we developed a novel bioinformatics methodology for identifying robust co-expression gene networks using few samples (N=6). Using this approach, we identified tissue-specific functional gene networks from the bat PAG, a brain region fundamental for mammalian vocalisation. The most highly connected network identified represented a cluster of genes involved in glutamatergic synaptic transmission. Glutamatergic receptors play a significant role in vocalisation from the PAG, suggesting that this gene network may be mechanistically important for vocal-motor control in mammals.ConclusionWe have developed an innovative approach to cluster co-expressing gene networks and show that it is highly effective in detecting robust functional gene networks with limited sample sizes. Moreover, this work represents the first gene network analysis performed in a bat brain and establishes bats as a novel, tractable model system for understanding the genetics of vocal mammalian communication.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2068-1) contains supplementary material, which is available to authorized users.

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Gene expression identifies heterogeneity of metastatic behavior among high-grade non-translocation associated soft tissue sarcomas

Cited by 10 publications

References 48 publications

Biology and Treatment of Aggressive Fibromatosis or Desmoid Tumor

Biology and Treatment of Aggressive Fibromatosis or Desmoid Tumor

Gene expression identifies heterogeneity of metastatic behavior among gastrointestinal stromal tumors

A novel approach identifies the first transcriptome networks in bats: a new genetic model for vocal communication

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