Ubiquitin-conjugating enzyme 2C (UBE2C) is a poor prognostic biomarker in invasive breast cancer.

BACKGROUND: The Ubiquitin-conjugating enzyme 2C (UBE2C) is essential for the ubiquitin-proteasome system and is involved in cancer cell migration and apoptosis. This study aimed to determine the prognostic value of UBE2C in invasive breast cancer (BC). METHODS: UBE2C was evaluated using the Molecular Taxonomy of Breast Cancer International Consortium (n = 1980), The Cancer Genome Atlas (n = 854) and Kaplan-Meier Plotter (n = 3951) cohorts. UBE2C protein expression was assessed using immunohistochemistry in the BC cohort (n = 619). The correlation between UBE2C, clinicopathological parameters and patient outcome was assessed. RESULTS: High UBE2C mRNA and protein expressions were correlated with features of poor prognosis, including high tumour grade, large size, the presence of lymphovascular invasion, hormone receptor negativity and HER2 positivity. High UBE2C mRNA expression showed a negative association with E-cadherin, and a positive association with adhesion molecule N-cadherin, matrix metalloproteinases and cyclin-related genes. There was a positive correlation between high UBE2C protein expression and cell cycle-associated biomarkers, p53, Ki67, EGFR and PI3K. High UBE2C protein expression was an independent predictor of poor outcome (p = 0.011, HR = 1.45, 95% CI; 1.10-1.93). CONCLUSION: This study indicates that UBE2C is an independent prognostic biomarker in BC. These results warrant further functional validation for UBE2C as a potential therapeutic target in BC.

Nucleolar protein 10 (NOP10) predicts poor prognosis in invasive breast cancer.

PURPOSE: Nucleolar protein 10 (NOP10) is required for ribosome biogenesis and telomere maintenance and plays a key role in carcinogenesis. This study aims to evaluate the clinical and prognostic significance of NOP10 in breast cancer (BC). METHODS: NOP10 expression was assessed at mRNA level employing the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n = 1980) and Cancer Genome Atlas (TCGA) BC cohorts (n = 854). Protein expression was evaluated on tissue microarray of a large BC cohort (n = 1081) using immunohistochemistry. The correlation between NOP10 expression, clinicopathological parameters and patient outcome was assessed. RESULTS: NOP10 expression was detected in the nucleus and nucleolus of the tumour cells. At the transcriptomic and proteomic levels, NOP10 was significantly associated with aggressive BC features including high tumour grade, high nucleolar score and poor Nottingham Prognostic Index. High NOP10 protein expression was an independent predictor of poor outcome in the whole cohort and in triple-negative BC (TNBC) class (p = 0.002 & p = 0.014, respectively). In chemotherapy- treated patients, high NOP10 protein expression was significantly associated with shorter survival (p = 0.03) and was predictive of higher risk of death (p = 0.028) and development of distant metastasis (p = 0.02) independent of tumour size, nodal stage and tumour grade. CONCLUSION: High NOP10 expression is a poor prognostic biomarker in BC and its expression can help in predicting chemotherapy resistance. Functional assessments are necessary to decipher the underlying mechanisms and to reveal its potential therapeutic values in various BC subtypes especially in the aggressive TNBC class.

Artificial intelligence grading of breast cancer: a promising method to refine prognostic classification for management precision.

AIM: Artificial intelligence (AI)-based breast cancer grading may help to overcome perceived limitations of human assessment. Here, the potential value of AI grade was evaluated at the molecular level and in predicting patient outcome. METHODS AND RESULTS: A supervised convolutional neural network (CNN) model was trained on images of 612 breast cancers from The Cancer Genome Atlas (TCGA). The test set, obtained from the Cooperative Human Tissue Network (CHTN), comprised 1058 cancers with corresponding survival data. Upon reversal, a CNN was trained from images of 1537 CHTN cancers and tested on 397 TCGA cancers. In TCGA, mRNA models were trained using AI grade and Nottingham grade (NG) as labels. Performance of mRNA models in predicting patient outcome was evaluated using data from 1807 cancers from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. In selecting images for training, nucleolar prominence determined high- versus low-grade cancer cells. In CHTN, NG corresponded to significant survival stratification in stages 1, 2 and 3 cancers, while AI grade showed significance in stages 1 and 2 and borderline in stage 3 tumours. In METABRIC, the mRNA model trained from AI grade was not significantly different to the NG-based model. The gene which best described AI grade was TRIP13, a gene involved with mitotic spindle assembly. CONCLUSION: An AI grade trained from the morphologically distinctive feature of nucleolar prominence could transmit significant patient outcome information across three independent patient cohorts. AI grade shows promise in gene discovery and for second opinions.

The nucleolar-related protein Dyskerin pseudouridine synthase 1 (DKC1) predicts poor prognosis in breast cancer.

BACKGROUND: Hypertrophy of the nucleolus is a distinctive cytological feature of malignant cells and corresponds to aggressive behaviour. This study aimed to identify the key gene associated with nucleolar prominence (NP) in breast cancer (BC) and determine its prognostic significance. METHODS: From The Cancer Genome Atlas (TCGA) cohort, digital whole slide images identified cancers having NP served as label and an information theory algorithm was applied to find which mRNA gene best explained NP. Dyskerin Pseudouridine Synthase 1 (DKC1) was identified. DKC1 expression was assessed using mRNA data of Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, n = 1980) and TCGA (n = 855). DKC1 protein expression was assessed using immunohistochemistry in Nottingham BC cohort (n = 943). RESULTS: Nuclear and nucleolar expressions of DKC1 protein were significantly associated with higher tumour grade (p < 0.0001), high nucleolar score (p < 0.001) and poor Nottingham Prognostic Index (p < 0.0001). High DKC1 expression was associated with shorter BC-specific survival (BCSS). In multivariate analysis, DKC1 mRNA and protein expressions were independent risk factors for BCSS (p < 0.01). CONCLUSION: DKC1 expression is strongly correlated with NP and its overexpression in BC is associated with unfavourable clinicopathological characteristics and poor outcome. This has been a detailed example in the correlation of phenotype with genotype.

Prognostic significance of nucleolar assessment in invasive breast cancer.

AIMS: Nucleolar morphometric features have a potential role in the assessment of the aggressiveness of many cancers. However, the role of nucleoli in invasive breast cancer (BC) is still unclear. The aims of this study were to investigate the optimal method for scoring nucleoli in IBC and their prognostic significance, and to refine the grading of breast cancer (BC) by incorporating nucleolar score. METHODS AND RESULTS: Digital images acquired from haematoxylin and eosin-stained sections from a large BC cohort were divided into training (n = 400) and validation (n = 1200) sets for use in this study. Four different assessment methods were evaluated in the training set to identify the optimal method associated with the best performance and significant prognostic value. These were: (i) a modified Helpap method; (ii) counting prominent nucleoli (size ≥2.5 µm) in 10 field views (FVs); (iii) counting prominent nucleoli in five FVs; and (iv) counting prominent nucleoli in one FV. The optimal method was applied to the validation set and to an external validation set, i.e. data from The Cancer Genome Atlas (n = 743). Scoring prominent nucleoli in five FVs showed the highest interobserver concordance rate (intraclass correlation coefficient of 0.8) and a significant association with BC-specific survival (P < 0.0001). A high nucleolar score was associated with younger age, larger tumour size, and higher grade. Incorporation of nucleolar score in the Nottingham grading system resulted in a higher significant association with survival than the conventional grade. CONCLUSIONS: Quantification of nucleolar prominence in five FVs is a cost-efficient and reproducible morphological feature that can predict BC behaviour and can provide an alternative to pleomorphism to improve BC grading performance.

Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer: a morphomolecular study.

AIMS: Tumour genotype and phenotype are related and can predict outcome. In this study, we hypothesised that the visual assessment of breast cancer (BC) morphological features can provide valuable insight into underlying molecular profiles. METHODS AND RESULTS: The Cancer Genome Atlas (TCGA) BC cohort was used (n = 743) and morphological features, including Nottingham grade and its components and nucleolar prominence, were assessed utilising whole-slide images (WSIs). Two independent scores were assigned, and discordant cases were utilised to represent cases with intermediate morphological features. Differentially expressed genes (DEGs) were identified for each feature, compared among concordant/discordant cases and tested for specific pathways. Concordant grading was observed in 467 of 743 (63%) of cases. Among concordant case groups, eight common DEGs (UGT8, DDC, RGR, RLBP1, SPRR1B, CXorf49B, PSAPL1 and SPRR2G) were associated with overall tumour grade and its components. These genes are related mainly to cellular proliferation, differentiation and metabolism. The number of DEGs in cases with discordant grading was larger than those identified in concordant cases. The largest number of DEGs was observed in discordant grade 1:3 cases (n = 1185). DEGs were identified for each discordant component. Some DEGs were uniquely associated with well-defined specific morphological features, whereas expression/co-expression of other genes was identified across multiple features and underlined intermediate morphological features. CONCLUSION: Morphological features are probably related to distinct underlying molecular profiles that drive both morphology and behaviour. This study provides further evidence to support the use of image-based analysis of WSIs, including artificial intelligence algorithms, to predict tumour molecular profiles and outcome.

Mechanistic and Clinical Evidence Supports a Key Role for Cell Division Cycle Associated 5 (CDCA5) as an Independent Predictor of Outcome in Invasive Breast Cancer.

BACKGROUND: Cell Division Cycle Associated 5 (CDCA5) plays a role in the phosphoinositide 3-kinase (PI3K)/AKT/mTOR signalling pathway involving cell division, cancer cell migration and apoptosis. This study aims to assess the prognostic and biological value of CDCA5 in breast cancer (BC). METHODS: The biological and prognostic value of CDCA5 were evaluated at mRNA (n = 5109) and protein levels (n = 614) utilizing multiple well-characterized early stage BC cohorts. The effects of CDCA5 knockdown (KD) on multiple oncogenic assays were assessed in vitro using a panel of BC cell lines. RESULTS: this study examined cohorts showed that high CDCA5 expression was correlated with features characteristic of aggressive behavior and poor prognosis, including the presence of high grade, large tumor size, lymphovascular invasion (LVI), hormone receptor negativity and HER2 positivity. High CDCA5 expression, at both mRNA and protein levels, was associated with shorter BC-specific survival independent of other variables (p = 0.034, Hazard ratio (HR) = 1.6, 95% CI; 1.1-2.3). In line with the clinical data, in vitro models indicated that CDCA5 depletion results in a marked decrease in BC cell invasion and migration abilities and a significant accumulation of the BC cells in the G2/M-phase. CONCLUSIONS: These results provide evidence that CDCA5 plays an important role in BC development and metastasis and could be used as a potential biomarker to predict disease progression in BC.