Title |
Application of high-resolution genomic profiling in the differential diagnosis of liposarcoma
|
---|---|
Published in |
Molecular Cytogenetics, March 2017
|
DOI | 10.1186/s13039-017-0309-5 |
Pubmed ID | |
Authors |
Magdalena Koczkowska, Beata Stefania Lipska-Ziętkiewicz, Mariola Iliszko, Janusz Ryś, Markku Miettinen, Jerzy Lasota, Wojciech Biernat, Agnieszka Harazin-Lechowska, Anna Kruczak, Janusz Limon |
Abstract |
Rarity and heterogeneity of liposarcomas (LPS) make their diagnosis difficult even for sarcoma-experts pathologists. The molecular mechanism underlying the development and progression of liposarcomas (LPS) remains only partially known. In order to identify and compare the genomic profiles, we analyzed array-based comparative genomic hybridization (array-CGH) profiles of 66 liposarcomas, including well-differentiated (WDLPS), dedifferentiated (DDLPS) and myxoid (MLPS) subtypes. Copy number aberrations (CNAs) were identified in 98% of WDLPS and DDLPS and in 95% of MLPS cases. The minimal common region of amplification at 12q14.1q21.1 was observed in 96% of WDLPS and DDLPS cases. Four regions of CNAs, including losses of chromosome 6, 11 and 13 and gains of chromosome 14 were classified as recurrent in DDLPS; at least one was identified in 74% of DDLPS tumors. The DDLPS-associated losses were much more common in tumors with increased genomic complexity. In MLPS, the most frequent CNAs were losses of chromosome 6 (40%) and gains of chromosome 1 (30%), with the minimal overlapping regions 6q14.1q22.31 and 1q25.1q32.2, respectively. Our findings show that the application of array-CGH allows to delineate clearly the genomic profiles of WDLPS, DDLPS and MLPS that reflect biological differences between these tumors. Although CNAs varied widely, the subtypes of tumors have characteristic genomic profiles that could facilitate the differential diagnosis of LPS subtypes, especially between WDLPS and DDLPS. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 2 | 17% |
Student > Master | 2 | 17% |
Lecturer > Senior Lecturer | 1 | 8% |
Student > Doctoral Student | 1 | 8% |
Other | 1 | 8% |
Other | 2 | 17% |
Unknown | 3 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 6 | 50% |
Biochemistry, Genetics and Molecular Biology | 2 | 17% |
Agricultural and Biological Sciences | 1 | 8% |
Unknown | 3 | 25% |