An Animal Model of Cutaneous Cyst Development Enables the Identification of Three Quantitative Trait Loci, Including the Homologue of a Human Locus (TRICY1)

Brief Summary Using inbred BN and LE/Stm rats susceptible and resistant, respectively, to chemically induced cutaneous cyst development we were able to further unveil the genetic architecture of inherited multiple cyst formation. N-methyl-N-nitrosourea-treated (BN x LE) F2 intercross rats proved to develop differential numbers of cutaneous cysts, demonstrating epidermal, trichilemmal and verrucous keratinization types. Male rats developed significantly more cysts per animal than females. QTL interval mapping yielded three loci on rat chromosomes 1, 8 and 11 (Ccd1, Ccd2, Ccd3) linked to cutaneous cyst formation. Ccd2 proved to be homologous to the human TRICY1 region which could further be narrowed down by genome comparison in both species. It contains 11 genes with evidence of expression in human keratinocytes.Non peer reviewe

PLZF induces an intravascular surveillance program mediated by long-lived LFA-1–ICAM-1 interactions

PLZF-expressing NKT cells establish residence at intravascular locations, failing to enter the circulation because of constitutive interactions with LFA-1 and ICAM-1

Genome-wide methylation profiling and copy number analysis in atypical fibroxanthomas and pleomorphic dermal sarcomas indicate a similar molecular phenotype

Background: Atypical fibroxanthomas (AFX) and pleomorphic dermal sarcomas (PDS) are lesions of the skin with overlapping histologic features and unspecific molecular traits. PDS behaves aggressive compared to AFX. Thus, a precise delineation, although challenging in some instances, is relevant. Methods: We examined the value of DNA-methylation profiling and copy number analysis for separating these tumors. DNA-methylation data were generated from 17 AFX and 15 PDS using the Illumina EPIC array. These were compared with DNA-methylation data generated from 196 tumors encompassing potential histologic mimics like cutaneous squamous carcinomas (cSCC; n = 19), basal cell carcinomas (n = 10), melanoma metastases originating from the skin (n = 11), leiomyosarcomas (n = 11), angiosarcomas of the skin and soft tissue (n = 11), malignant peripheral nerve sheath tumors (n = 19), dermatofibrosarcomas protuberans (n = 13), extraskeletal myxoid chondrosarcomas (n = 9), myxoid liposarcomas (n = 14), schwannomas (n = 10), neurofibromas (n = 21), alveolar (n = 19) and embryonal (n = 17) rhabdomyosarcomas as well as undifferentiated pleomorphic sarcomas (n = 12). Results: DNA-methylation profiling did not separate AFX from PDS. The DNA-methylation profiles of the other cases, however, were distinct from AFX/PDS. They reliably assigned to subtype-specific DNA-methylation clusters, although overlap occurred between some AFX/PDS and cSCC. Copy number profiling revealed alterations in a similar frequency and distribution between AFX and PDS. They involved losses of 9p (22/32) and 13q (25/32). Gains frequently involved 8q (8/32). Notably, a homozygous deletion of CDKN2A was more frequent in PDS (6/15) than in AFX (2/17), whereas amplifications were non-recurrent and overall rare (5/32). Conclusions: Our findings support the concept that AFX and PDS belong to a common tumor spectrum. We could demonstrate the diagnostic value of DNA-methylation profiling to delineating AFX/PDS from potential mimics. However, the assessment of certain histologic features remains crucial for separating PDS from AFX

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications