High USP6NL levels in breast cancer sustain chronic AKT phosphorylation and GLUT1 stability fueling aerobic glycolysis

USP6NL, also named RN-tre, is a GTPase activating protein (GAP) involved in control of endocytosis and signal transduction. Here we report that USP6NL is overexpressed in breast cancer (BC), mainly of the basal-like/integrative cluster 10 subtype. Increased USP6NL levels were accompanied by gene amplification and were associated with worse prognosis in the METABRIC dataset, retaining prognostic value in multivariable analysis. High levels of USP6NL in BC cells delayed endocytosis and degradation of the epidermal growth factor receptor (EGFR), causing chronic AKT activation. In turn, AKT stabilized the glucose transporter GLUT1 at the plasma membrane, increasing aerobic glycolysis. In agreement, elevated USP6NL sensitized BC cells to glucose deprivation, indicating that their glycolytic capacity relies on this protein. Depletion of USP6NL accelerated EGFR/AKT downregulation and GLUT1 degradation, impairing cell proliferation exclusively in BC cells that harbored increased levels of USP6NL. Overall, these findings argue that USP6NL overexpression generates a metabolic rewiring that is essential to foster the glycolytic demand of BC cells and promote their proliferation

Concurso sin masa. Algunas consideraciones generales

Breve exposición de la evolución doctrinal y legislativa de los concursos sin masa

Muscoid Diptera and their parasitoids collected from fish bait in Itumbiara, Goiás Dípteros muscóides e seus parasitóides coletados em isca de peixe em Itumbiara

<abstract language="por">Determinaram-se as espécies de parasitóides associados às moscas sinantrópicas coletados em iscas de peixe, em Itumbiara, Goiás. As pupas, obtidas pelo método de flutuação, foram individualizadas em cápsulas de gelatina até a emergência das moscas adultas ou de seus parasitóides. A porcentagem total de parasitismo foi 21,4%

Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms

Contains fulltext : 153904.pdf (publisher's version ) (Open Access)Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples

p120-catenin prevents multinucleation through control of MKLP1-dependent RhoA activity during cytokinesis

Cancer Signaling networks and Molecular Therapeutic