Clinicopathologic and genetic features of primary bronchopulmonary mucoepidermoid carcinoma: the MD Anderson Cancer Center experience and comprehensive review of the literature

Salem, Alireza; Bell, Diana; Sepesi, Boris; Papadimitrakopoulou, Vassiliki A.; El‐Naggar, Adel K.; Moran, César A.; Kalhor, Neda

doi:10.1007/s00428-017-2104-4

Cited by 34 publications

(26 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The diagnosis of mucoepidermoid carcinoma can be supported by consistent labeling for p40/p63 in intermediate and squamous cells, positivity for intracytoplasmic mucin with mucin stains, and the demonstration of mastermind like transcriptional coactivator 2 gene (MAML2) rearrangements detected in tumors by FISH at a rate of 77% to 100%. [101][102][103] The result of staining for SRY-box 10 (SOX10) is usually negative in mucoepidermoid carcinoma but can be positive in a subset. 104 The main differential diagnosis in the lung is with adenosquamous carcinoma; expression of TTF1/napsin A in the glandular component would support the former, whereas MAML2 rearrangement would support the latter.…”

Section: Salivary Gland-type Tumorsmentioning

confidence: 99%

Best Practices Recommendations for Diagnostic Immunohistochemistry in Lung Cancer

Yatabe

Đačić

Borczuk

et al. 2019

Journal of Thoracic Oncology

256

221

View full text Add to dashboard Cite

Since the 2015 WHO classification was introduced into clinical practice, immunohistochemistry (IHC) has figured prominently in lung cancer diagnosis. In addition to distinction of small cell versus non-small cell carcinoma, patients' treatment of choice is directly linked to histologic subtypes of non-small cell carcinoma, which pertains to IHC results, particularly for poorly differentiated tumors. The use of IHC has improved diagnostic accuracy in the classification of lung carcinoma, but the interpretation of IHC results remains challenging in some instances. Also, pathologists must be aware of many interpretation pitfalls, and the use of IHC should be efficient to spare the tissue for molecular testing. The International Association for the Study of Lung Cancer Pathology Committee received questions on practical application and interpretation of IHC in lung cancer diagnosis. After discussions in several International Association for the Study of Lung Cancer Pathology Committee meetings, the issues and caveats were summarized in terms of 11 key questions covering common and important diagnostic situations in a daily clinical practice with some relevant challenging queries. The questions cover topics such as the best IHC markers for distinguishing NSCLC subtypes, differences in thyroid transcription factor 1 clones, and the utility of IHC in diagnosing uncommon subtypes of lung cancer and distinguishing primary from metastatic tumors. This article provides answers and explanations for the key questions about the use of IHC in diagnosis of lung carcinoma, representing viewpoints of experts in thoracic pathology that should assist the community in the appropriate use of IHC in diagnostic pathology.

show abstract

Section: Salivary Gland-type Tumorsmentioning

confidence: 99%

Best Practices Recommendations for Diagnostic Immunohistochemistry in Lung Cancer

Yatabe

Đačić

Borczuk

et al. 2019

Journal of Thoracic Oncology

256

221

View full text Add to dashboard Cite

show abstract

“…However, in contrast to adenosquamous carcinoma, these tumors occur at a relatively younger age, with a median of about 40 years, with about equal sex ratio, and are generally central large airway tumors ( Figure 5, A) rather than peripheral nodules. 70,71 In this regard, in many cases the location of tumor and the epidemiology is sufficiently distinct to favor mucoepidermoid carcinoma over adenosquamous carcinoma.…”

Section: Immunotherapymentioning

confidence: 99%

“…However, carcinoma in situ and overtly keratinizing areas are not seen. 71 Low-grade tumors are associated with improved outcome relative to high-grade tumors. 73 In low-grade tumors, neither the gland-forming nor the solid/squamous component is difficult to identify.…”

Section: Immunotherapymentioning

confidence: 99%

Uncommon Types of Lung Carcinoma With Mixed Histology: Sarcomatoid Carcinoma, Adenosquamous Carcinoma, and Mucoepidermoid Carcinoma

Borczuk

2018

Archives of Pathology &Amp; Laboratory Medicine

View full text Add to dashboard Cite

show abstract

“…The loss of fragile histidine triad protein (FHIT) is strongly related to pancreatic ductal adenocarcinomas [ 33 ]. MAML2-MECT1 fusion is a driver in salivary gland and bronchial gland mucoepidermoid carcinoma [ 34 , 35 ]. MYBL1 is a driver of adenoid cystic carcinoma when related to MYB [ 36 , 37 ].…”

Section: Resultsmentioning

confidence: 99%

Mut2Vec: distributed representation of cancerous mutations

et al. 2018

View full text Add to dashboard Cite

BackgroundEmbedding techniques for converting high-dimensional sparse data into low-dimensional distributed representations have been gaining popularity in various fields of research. In deep learning models, embedding is commonly used and proven to be more effective than naive binary representation. However, yet no attempt has been made to embed highly sparse mutation profiles into densely distributed representations. Since binary representation does not capture biological context, its use is limited in many applications such as discovering novel driver mutations. Additionally, training distributed representations of mutations is challenging due to a relatively small amount of available biological data compared with the large amount of text corpus data in text mining fields.MethodsWe introduce Mut2Vec, a novel computational pipeline that can be used to create a distributed representation of cancerous mutations. Mut2Vec is trained on cancer profiles using Skip-Gram since cancer can be characterized by a series of co-occurring mutations. We also augmented our pipeline with existing information in the biomedical literature and protein-protein interaction networks to compensate for the data insufficiency.ResultsTo evaluate our models, we conducted two experiments that involved the following tasks: a) visualizing driver and passenger mutations, b) identifying novel driver mutations using a clustering method. Our visualization showed a clear distinction between passenger mutations and driver mutations. We also found driver mutation candidates and proved that these were true driver mutations based on our literature survey. The pre-trained mutation vectors and the candidate driver mutations are publicly available at http://infos.korea.ac.kr/mut2vec.ConclusionsWe introduce Mut2Vec that can be utilized to generate distributed representations of mutations and experimentally validate the efficacy of the generated mutation representations. Mut2Vec can be used in various deep learning applications such as cancer classification and drug sensitivity prediction.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0349-7) contains supplementary material, which is available to authorized users.

show abstract

Clinicopathologic and genetic features of primary bronchopulmonary mucoepidermoid carcinoma: the MD Anderson Cancer Center experience and comprehensive review of the literature

Cited by 34 publications

References 29 publications

Best Practices Recommendations for Diagnostic Immunohistochemistry in Lung Cancer

Best Practices Recommendations for Diagnostic Immunohistochemistry in Lung Cancer

Uncommon Types of Lung Carcinoma With Mixed Histology: Sarcomatoid Carcinoma, Adenosquamous Carcinoma, and Mucoepidermoid Carcinoma

Mut2Vec: distributed representation of cancerous mutations

Contact Info

Product

Resources

About