TP73 is a member of the TP53 gene family and produces N- and C-terminal protein isoforms through alternative promoters, alternative translation initiation and alternative splicing. Most notably, p73 protein isoforms may either contain a p53-like transactivation domain (TAp73 isoforms) or lack this domain (ΔTAp73 isoforms) and these variants have opposing or independent functions. To date, there is a lack of well-characterised isoform-specific p73 antibodies. Here, we produced polyclonal and monoclonal antibodies to N-terminal p73 variants and the C-terminal p73α isoform, the most common variant in human tissues. These reagents show that TAp73 is a marker of multiciliated epithelial cells, while ΔTAp73 is a marker of non-proliferative basal/reserve cells in squamous epithelium. We were unable to detect ΔNp73 variant proteins, in keeping with recent data that this is a minor form in human tissues. Most cervical squamous cell carcinomas (79%) express p73α, and the distribution of staining in basal cells correlated with lower tumour grade. TAp73 was found in 17% of these tumours, with a random distribution and no association with clinicopathological features. These data indicate roles for ΔTAp73 in maintaining a non-proliferative state of undifferentiated squamous epithelial cells and for TAp73 in the production of differentiated multiciliated cells.
- MeSH
- epitelové buňky metabolismus MeSH
- lidé MeSH
- monoklonální protilátky MeSH
- nádorové buněčné linie MeSH
- nádory děložního čípku metabolismus patologie genetika MeSH
- nádory metabolismus patologie genetika MeSH
- protein - isoformy * metabolismus genetika MeSH
- protein p73 * metabolismus genetika MeSH
- spinocelulární karcinom metabolismus patologie genetika MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Breast cancer is a highly heterogeneous disease. Its intrinsic subtype classification for diagnosis and choice of therapy traditionally relies on the presence of characteristic receptors. Unfortunately, this classification is often not sufficient for precise prediction of disease prognosis and treatment efficacy. The N-glycan profiles of 145 tumors and 10 healthy breast tissues were determined using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry. The tumor samples were classified into Mucinous, Lobular, No-Special-Type, Human Epidermal Growth Factor 2 + , and Triple-Negative Breast Cancer subtypes. Statistical analysis was conducted using the reproducibility-optimized test statistic software package in R, and the Wilcoxon rank sum test with continuity correction. In total, 92 N-glycans were detected and quantified, with 59 consistently observed in over half of the samples. Significant variations in N-glycan signals were found among subtypes. Mucinous tumor samples exhibited the most distinct changes, with 28 significantly altered N-glycan signals. Increased levels of tri- and tetra-antennary N-glycans were notably present in this subtype. Triple-Negative Breast Cancer showed more N-glycans with additional mannose units, a factor associated with cancer progression. Individual N-glycans differentiated Human Epidermal Growth Factor 2 + , No-Special-Type, and Lobular cancers, whereas lower fucosylation and branching levels were found in N-glycans significantly increased in Luminal subtypes (Lobular and No-Special-Type tumors). Clinically normal breast tissues featured a higher abundance of signals corresponding to N-glycans with bisecting moiety. This research confirms that histologically distinct breast cancer subtypes have a quantitatively unique set of N-glycans linked to clinical parameters like tumor size, proliferative rate, lymphovascular invasion, and metastases to lymph nodes. The presented results provide novel information that N-glycan profiling could accurately classify human breast cancer samples, offer stratification of patients, and ongoing disease monitoring.
Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that whole-tumor molecular descriptors depend on the morphological heterogeneity with significant impact on current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology represented by six morphological patterns (morphotypes): complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptome profiling by microarrays of 202 tumor regions (morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-α signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results show that morphotype-based tumor sampling allows the detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.
Breast cancers are a heterogeneous group of tumors classified according to their histological growth patterns and receptor expression characteristics. Intratumor heterogeneity also exists, with subpopulations of cells with different phenotypes found in individual cancers, including cells with stem or progenitor cell properties. At least two types of breast cancer stem cells (CSCs) exist, the epithelial and the basal/mesenchymal subtypes, although how these phenotypes are controlled is unknown. ΔNp63 is a basal cell marker and regulator of stem/progenitor cell activities in the normal mammary gland and is expressed in the basal-like CSC subpopulation in some estrogen receptor-positive (ER+) and/or human epidermal growth factor receptor 2-positive (HER2+) breast adenocarcinomas. Whilst p63 is known to directly impart CSC properties in luminal breast cancer cells, how p63 is regulated and induced in these cells is unknown. We initially confirmed the existence of a small subpopulation of ΔNp63+ cells in lymph node metastases of ER+ human ductal adenocarcinomas, indicating together with previous reports that ΔNp63+ tumor cells are present in approximately 40% of these metastases. Notably, ΔNp63+ cells show a preferential location at the edge of tumor areas, suggesting possible regulation of ΔNp63 by the tumor microenvironment. Subsequently, we showed that the high levels of ΔNp63 in basal non-transformed MCF-10A mammary epithelial cells rely on insulin in their culture medium, whilst ΔNp63 levels are increased in MCF-7 ER+ luminal-type breast cancer cells treated with insulin or insulin-like growth factor 1 (IGF-1). Mechanistically, small molecule inhibitors and siRNA gene knockdown demonstrated that induction of ΔNp63 by IGF-1 requires PI3K, ERK1/2, and p38 MAPK activation, and acts through FOXO transcriptional inactivation. We also show that metformin inhibits ΔNp63 induction. These data reveal an IGF-mediated mechanism to control basal-type breast CSCs, with therapeutic implications to modify intratumor breast cancer cell heterogeneity and plasticity.
- Publikační typ
- abstrakt z konference MeSH
The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large-scale guidance of AI methods to identify the epithelial component. The workflow is based on re-staining existing hematoxylin and eosin (H&E) formalin-fixed paraffin-embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin-rich regions, using cell nuclei positions that are visible in both the original and the re-stained slide. The registration method has been compared to state-of-the-art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U-Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real-world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI-assisted tumor assessment directly from scanned H&E sections. In addition, the re-staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.
- MeSH
- barvení a značení MeSH
- deep learning * MeSH
- eosin MeSH
- epitelové buňky MeSH
- hematoxylin MeSH
- keratiny * MeSH
- lidé MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Publikační typ
- abstrakt z konference MeSH
Signal transducer and activator of transcription 3 (Stat3) is responsible for many aspects of normal development and contributes to the development and progression of cancer through regulating epithelial cell identity and cancer stem cells. In breast cancer, Stat3 is associated with triple-negative breast cancers (TNBC) and its function has been related to the activation of p63, itself a marker of basal-like TNBC and a master regulator of stem cell activities. Stat3 activation is controlled by dual phosphorylation at tyrosine 705 (pTyr705) and serine 727 (pSer727), although it is unclear whether these have equivalent effects, and whether they are related or independent events. To address these issues, we investigated Stat3 phosphorylation at the two sites by immunohistochemistry in 173 patients with TNBC. Stat3 phosphorylation was assessed by automated quantitative measurements of digitized scanned images and classified into four categories based on histoscore. The results were analyzed for associations with multiple markers of tumor phenotype, proliferation, BRCA status, and clinicopathological characteristics. We show that the levels of pTyr705- and pSer727-Stat3 were independent in 34% of tumors. High pTyr705-Stat3 levels were associated with the luminal differentiation markers ERβ/AR and MUC1, whereas tumors with high levels of pSer727-Stat3 were more likely to be positive for the basal marker CK5/6, but were independent of p63 and were EGFR negative. Combined high pSer727- and low Tyr705-Stat3 phosphorylation associated with basal-like cancer. Although high Stat3 phosphorylation levels were associated with less aggressive tumor characteristics, they did not associate with improved survival, indicating that Stat3 phosphorylation is an unfavorable indicator for tumors with an otherwise good prognosis according to clinicopathological characteristics. These findings also show that pTyr705-Stat3 and pSer727-Stat3 associate with specific breast tumor phenotypes, implying that they exert distinct functional activities in breast cancer.
- MeSH
- fenotyp MeSH
- fosforylace MeSH
- lidé MeSH
- nádorové buněčné linie MeSH
- serin genetika MeSH
- transkripční faktor STAT3 metabolismus MeSH
- triple-negativní karcinom prsu * patologie MeSH
- tyrosin genetika MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Publikační typ
- abstrakt z konference MeSH
- Publikační typ
- abstrakt z konference MeSH
