Novel 2 Gene Signatures Associated With Breast Cancer Proliferation: Insights From Predictive Differential Gene Expression Analysis.

The use of proliferation markers provides valuable information about the rate of tumor growth, which can guide treatment decisions. However, there is still a lack of consensus regarding the optimal molecular markers or tests to use in clinical practice. Integrating gene expression data with clinical and histopathologic parameters enhances our understanding of disease processes, facilitates the identification of precise prognostic predictors, and supports the development of effective therapeutic strategies. The purpose of this study was to apply an integrated approach that combines morphologic, clinical, and bioinformatic data to reveal effective regulators of proliferation. Whole-slide images generated from hematoxylin-and-eosin-stained sections of The Cancer Genome Atlas (TCGA) breast cancer (BC) database (n = 1053) alongside their transcriptomic and clinical data were used to identify genes differentially expressed between tumors with high and low mitotic scores. Genes enriched in the cell-cycle pathway were used to predict the protein-protein interaction (PPI) network. Ten hub genes (ORC6, SKP2, SMC1B, CDKN2A, CDC25B, E2F1, E2F2, ORC1, PTTG1, and CDC25A) were identified using CytoHubba a Cytoscape plugin. In a multivariate Cox regression model, ORC6 and SKP2 were predictors of survival independent of existing methods of proliferation assessment including mitotic score and Ki67. The prognostic ability of these genes was validated using the Molecular Taxonomy of Breast Cancer International Consortium, Nottingham cohort, Uppsala cohort, and a combined multicentric cohort. The protein expression of these 2 genes was investigated on a large cohort of BC cases, and they were significantly associated with poor prognosis and patient outcome. A positive correlation between ORC6 and SKP2 mRNA and protein expression was observed. Our study has identified 2 gene signatures, ORC6 and SKP2, which play a significant role in BC proliferation. These genes surpassed both mitotic scores and Ki67 in multivariate analysis. Their identification provides potential opportunities for the development of targeted treatments for patients with BC.

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application.

In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.

Nottingham prognostic x (NPx): a risk stratification tool in ER-positive HER2-negative breast cancer: a validation study.

AIMS: In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC. MATERIALS AND METHODS: Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC. RESULTS: In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx. CONCLUSION: NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

The clinical value of progesterone receptor expression in luminal breast cancer: A study of a large cohort with long-term follow-up.

BACKGROUND: The routine assessment of progesterone receptor (PR) expression in breast cancer (BC) remains controversial. This study aimed to evaluate the role of PR expression in luminal BC, with emphasis on the definition of positivity and its prognostic significance as compared to Ki67 expression. METHODS: A large cohort (n = 1924) of estrogen receptor (ER)-positive/HER2-negative BC was included. PR was immunohistochemically (IHC) stained on full face sections and core needle biopsies (CNB) where the optimal scoring cutoff was evaluated. In addition, the association of PR with other clinicopathological factors, cellular proliferation, disease outcome, and response to adjuvant therapy were analyzed. RESULTS: Although several cutoffs showed prognostic significance, the optimal cutoff to categorize PR expression into two clinically distinct prognostic groups on CNB was 10%. PR negativity showed a significant association with features of aggressive tumor behavior and poor outcome. Multivariate analyses indicated that the association between PR negativity and poor outcome was independent of tumor grade, size, node stage, and Ki67. PR negativity showed independent association with shorter survival in patients who received endocrine therapy whereas Ki67did not. CONCLUSION: PR IHC expression provides independent prognostic value superior to Ki67. Routine assessment of PR expression in BC using 10% cutoff in the clinical setting is recommended. PLAIN LANGUAGE SUMMARY: In this study, we have established an optimal approach to determine the prognostic value of progesterone receptor expression in estrogen receptor-positive breast cancer patients. To do this, the levels of progesterone receptor were measured in a large cohort of estrogen receptor-positive breast cancer patients. We have refined the definition of progesterone receptor positivity in estrogen receptor-positive breast cancer. We show that progesterone receptor expression adds prognostic and predictive value of endocrine therapy in estrogen receptor-positive breast cancer patients, and our results show that the absence of progesterone receptor is associated with poorer outcomes independent of tumor grade, size, node stage, and Ki67 expression.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

The clinical significance of cyclin B1 (CCNB1) in invasive breast cancer with emphasis on its contribution to lymphovascular invasion development.

BACKGROUND: Lymphovascular invasion (LVI) is regulated through complex molecular mechanisms. Cyclin B1 (CCNB1) was previously determined as being associated with LVI using large cohorts of breast cancer (BC) and artificial neural network (ANN) technique. In this study, we aimed to assess the association between CCNB1 and LVI, other clinicopathological and other LVI-related biomarkers at the molecular (RNA transcriptomic) and proteomic levels in BC. METHODS: Two transcriptomic BC cohorts (n = 2834) were used to assess the association between the expression of CCNB1 at the mRNA level and clinicopathological characteristics and patient outcome. Tissue microarrays (TMAs) from a well-characterised BC cohort (n = 2480) with long-term outcome were also used to assess the clinical significance of CCNB1 protein expression using immunohistochemistry. RESULTS: High CCNB1 mRNA expression was associated with aggressive tumour behaviour, including LVI, larger size, higher tumour grade, high lymph nodal stage, hormonal receptor negativity, HER2 positivity and poor clinical outcome (all p < 0.0001). Similarly, high CCNB1 protein expression was associated with higher tumour grade, hormonal receptor negativity and HER2 positivity (all p < 0.0001). Additionally, there was a significant association between CCNB1- and LVI-related biomarkers including N-cadherin, P-cadherin and TWIST2 at the transcriptomic and proteomic level. Multivariate analysis revealed that CCNB1 was an independent predictor of shorter BC-specific survival (HR = 1.3; 95% CI 1.2-1.5; p = 0.010). CONCLUSION: CCNB1 is a key gene associated with LVI in BC and has prognostic value. More functional studies are warranted to unravel the mechanistic role of CCNB1 in the development of LVI.

The Clinical and Biological Significance of Estrogen Receptor-Low Positive Breast Cancer.

Estrogen receptor (ER) status in breast cancer (BC) is determined using immunohistochemistry (IHC) with nuclear expression in ≥1% of cells defined as ER-positive. BC with 1%-9% expression (ER-low-positive), is a clinically and biologically unique subgroup. In this study, we hypothesized that ER-low-positive BC represents a heterogeneous group with a mixture of ER-positive and ER-negative tumor, which may explain their divergent clinical behavior. A large BC cohort (n = 8171) was investigated and categorized into 3 groups: ER-low-positive (1%-9%), ER-positive (≥10%), and ER-negative (<1%) where clinicopathological and outcome characteristics were compared. A subset of ER-low-positive cases was further evaluated using IHC, RNAscope, and RT-qPCR. PAM50 subtyping and ESR1 mRNA expression levels were assessed in ER-low-positive cases within The Cancer Genome Atlas data set. The reliability of image analysis software in assessment of ER expression in the ER-low-positive category was also assessed. ER-low-positive tumors constituted <2% of BC cases examined and showed significant clinicopathological similarity to ER-negative tumors. Most of these tumors were nonluminal types showing low ESR1 mRNA expression. Further validation of ER status revealed that 45% of these tumors were ER-negative with repeated IHC staining and confirmed by RNAscope and RT-qPCR. ER-low-positive tumors diagnosed on needle core biopsy were enriched with false-positive ER staining. BCs with 10% ER behaved similar to ER-positive, rather than ER-negative or low-positive BCs. Moderate concordance was found in assessment of ER-low-positive tumors, and this was not improved by image analysis. Routinely diagnosed ER-low-positive BC includes a proportion of ER-negative cases. We recommend repeat testing of BC showing 1%-9% ER expression and using a cutoff ≥10% expression to define ER positivity to help better inform treatment decisions.

Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence.

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.

Evaluation oncotype DX® 21-gene recurrence score and clinicopathological parameters: a single institutional experience.

AIMS: Oncotype DX recurrence score (RS) is a clinically validated assay, which predicts the likelihood of disease recurrence in oestrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer (BC). In this study we aimed to compare the performance of Oncotype DX against the conventional clinicopathological parameters using a large BC cohort diagnosed in a single institution. METHODS AND RESULTS: A cohort (n = 430) of ER+/HER2- BC patients who were diagnosed at the Nottingham University Hospitals NHS Trust and had Oncotype DX testing was included. Correlation with the clinicopathological and other biomarkers, including the proliferation index, was analysed. The median Oncotype DX RS was 17.5 (range = 0-69). There was a significant association between high RS and grade 3 tumours. No grade 1 BC or grade 2 tumours with mitosis score 1 showed high RS. Low RS was significantly associated with special tumour types where none of the patients with classical lobular or tubular carcinomas had a high RS. There was an inverse association between RS and levels of ER and progesterone receptor (PR) expression and a positive linear correlation with Ki67 labelling index. Notably, six patients who developed recurrence had an intermediate RS; however, four of these six cases (67%) were identified as high-risk disease when the conventional clinical and molecular parameters were considered. CONCLUSION: Oncotype DX RS is correlated strongly with the conventional clinicopathological parameters in BC. Some tumour features such as tumour grade, type, PR status and Ki67 index can be used as surrogate markers in certain scenarios.

Combined proliferation and apoptosis index provides better risk stratification in breast cancer.

AIMS: Breast cancer (BC) risk stratification is critical for predicting behaviour and guiding management decision-making. Despite the well-established prognostic value of cellular proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study, we hypothesised that the combined proliferation and apoptosis indices can provide a more accurate in-vivo growth rate measure and a precise prognostic predictor. METHODS AND RESULTS: Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin-stained sections of 1545 BC cases derived from two well-defined BC cohorts. Counts were carried out visually within defined areas. There was a significant correlation between mitosis and apoptosis scores. High apoptotic counts were associated with features of aggressive behaviour, including high grade, high pleomorphism score and hormonal receptor negativity. Although the mitotic index (MI) and apoptotic index (AI) were independent prognostic indicators, the prognostic value was synergistically higher when combined. BC patients with a high combined AI and MI had the shortest survival. Replacing the mitosis score with the mitosis-apoptosis index in the Nottingham grading system revealed that the modified grade with the new score had a higher significant association with BC-specific survival with a higher hazard ratio. CONCLUSION: Apoptotic figures count provides additional prognostic value in BC when combined with MI; such a combination can be implemented to assess the behaviour of BC and provides an accurate prognostic indicator. This can be considered when using artificial intelligence algorithms to assess proliferation in BC.

Improving mitotic cell counting accuracy and efficiency using phosphohistone-H3 (PHH3) antibody counterstained with haematoxylin and eosin as part of breast cancer grading.

BACKGROUND: Mitotic count in breast cancer is an important prognostic marker. Unfortunately, substantial inter- and intraobserver variation exists when pathologists manually count mitotic figures. To alleviate this problem, we developed a new technique incorporating both haematoxylin and eosin (H&E) and phosphorylated histone H3 (PHH3), a marker highly specific to mitotic figures, and compared it to visual scoring of mitotic figures using H&E only. METHODS: Two full-face sections from 97 cases were cut, one stained with H&E only, and the other was stained with PHH3 and counterstained with H&E (PHH3-H&E). Counting mitoses using PHH3-H&E was compared to traditional mitoses scoring using H&E in terms of reproducibility, scoring time, and the ability to detect mitosis hotspots. We assessed the agreement between manual and image analysis-assisted scoring of mitotic figures using H&E and PHH3-H&E-stained cells. The diagnostic performance of PHH3 in detecting mitotic figures in terms of sensitivity and specificity was measured. Finally, PHH3 replaced the mitosis score in a multivariate analysis to assess its significance. RESULTS: Pathologists detected significantly higher mitotic figures using the PHH3-H&E (median ± SD, 20 ± 33) compared with H&E alone (median ± SD, 16 ± 25), P < 0.001. The concordance between pathologists in identifying mitotic figures was highest when using the dual PHH3-H&E technique; in addition, it highlighted mitotic figures at low power, allowing better agreement on choosing the hotspot area (k = 0.842) in comparison with standard H&E (k = 0.625). A better agreement between image analysis-assisted software and the human eye was observed for PHH3-stained mitotic figures. When the mitosis score was replaced with PHH3 in a Cox regression model with other grade components, PHH3 was an independent predictor of survival (hazard ratio [HR] 5.66, 95% confidence interval [CI] 1.92-16.69; P = 0.002), and even showed a more significant association with breast cancer-specific survival (BCSS) than mitosis (HR 3.63, 95% CI 1.49-8.86; P = 0.005) and Ki67 (P = 0.27). CONCLUSION: Using PHH3-H&E-stained slides can reliably be used in routine scoring of mitotic figures and integrating both techniques will compensate for each other's limitations and improve diagnostic accuracy, quality, and precision.

Characteristics and prognostic significance of polo-like kinase-1 (PLK1) expression in breast cancer.

AIM: Polo-like kinase-1 (PLK1) plays a crucial role in cell cycle progression, and it is considered a potential therapeutic target in many cancers. Although the role of PLK1 is well established in triple-negative breast cancer (TNBC) as an oncogene, its role in luminal BC is still controversial. In this study, we aimed to evaluate the prognostic and predictive role of PLK1 in BC and its molecular subtypes. METHODS: A large BC cohort (n = 1208) were immunohistochemically stained for PLK1. The association with clinicopathological, molecular subtypes, and survival data was analysed. PLK1 mRNA was evaluated in the publicly available datasets (n = 6774), including The Cancer Genome Atlas and the Kaplan-Meier Plotter tool. RESULTS: 20% of the study cohort showed high cytoplasmic PLK1 expression. High PLK1 expression was significantly associated with a better outcome in the whole cohort, luminal BC. In contrast, high PLK1 expression was associated with a poor outcome in TNBC. Multivariate analyses indicated that high PLK1 expression is independently associated with longer survival in luminal BC, and in poorer prognosis in TNBC. At the mRNA levels, PLK1 expression was associated with short survival in TNBC consistent with the protein expression. However, in luminal BC, its prognostic value significantly varies between cohorts. CONCLUSION: The prognostic role of PLK1 in BC is molecular subtype-dependent. As PLK1 inhibitors are introduced to clinical trials for several cancer types, our study supports evaluation of the pharmacological inhibition of PLK1 as an attractive therapeutic target in TNBC. However, in luminal BC, PLK1 prognostic role remains controversial.

Preoperative diagnosis of ductal carcinoma in situ of the breast over a 24-year period.

AIMS: The method of diagnosis of ductal carcinoma in situ (DCIS) has changed since the 1980s. The aim of this audit was to assess changes in the preoperative diagnosis of DCIS since the introduction of needle core biopsy, particularly the proportion with a preoperative biopsy diagnosis of DCIS. METHODS AND RESULTS: The preoperative diagnoses of patients with a final diagnosis of DCIS in the surgical specimen were reviewed (i) in 809 patients who presented through breast screening from 1997 to 2021, and (ii) in all patients in 5 individual years at 5-year intervals from 2000 to 2020 (254 in total). For screening-detected DCIS the proportion with a preoperative diagnosis of DCIS increased from 75% to 98% over the study period. In a detailed analysis of all cases of DCIS in 5 separate years the proportion with a preoperative diagnosis of DCIS increased from 68% in 2000 to 96% in 2020. For high-grade DCIS the proportion increased from 87% to 97%, and for low- or intermediate-grade DCIS from 48% to 93%. The proportion of women who had vacuum-assisted biopsy increased from 7% in 2000 to 58% in 2015. There was a small increase in the number of biopsies that had basal cytokeratin and oestrogen receptor immunohistochemistry to aid diagnosis. CONCLUSION: There has been an increase in the preoperative diagnosis of DCIS, particularly of low- or intermediate-grade, over the last two decades. The increasing use of vacuum-assisted biopsy is likely to be a major contributory factor to this increase.

High Inner Centromere Protein Expression Correlates with Aggressive Features and Predicts Poor Prognosis in Patients with Invasive Breast Cancer.

INTRODUCTION: Inner centromere protein (INCENP) is a member of the chromosomal passenger complex and plays a key role in mitosis and cell proliferation. This study aimed to evaluate the clinical and prognostic significance of INCENP in invasive breast cancer (BC). METHODS: INCENP expression was evaluated on a tissue microarray of a large BC cohort (n = 1,295) using immunohistochemistry. At the mRNA level, INCENP expression was assessed using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n = 1,980) and The Cancer Genome Atlas (TCGA) BC cohorts (n = 854). The correlations between INCENP expression, clinicopathological parameters, and patient outcome were investigated. RESULTS: INCENP expression was detected in the nucleus and cytoplasm of the tumour cells. Its expression was significantly associated with features characteristic of aggressive BC behaviour including high tumour grade, larger tumour size, and high Nottingham prognostic index scores. High INCENP nuclear expression was a predictor of shorter BC-specific survival in the whole cohort, as well as in the luminal subtype (p < 0.001). High INCENP nuclear expression was predictive of poor prognosis in BC patients who received hormone treatment or chemotherapy. CONCLUSION: High INCENP expression is a poor prognostic biomarker in BC with potential therapeutic benefits.

Selective Killing of BRCA2-Deficient Ovarian Cancer Cells via MRE11 Blockade.

The MRE11 nuclease is essential during DNA damage recognition, homologous recombination, and replication. BRCA2 plays important roles during homologous recombination and replication. Here, we show that effecting an MRE11 blockade using a prototypical inhibitor (Mirin) induces synthetic lethality (SL) in BRCA2-deficient ovarian cancer cells, HeLa cells, and 3D spheroids compared to BRCA2-proficient controls. Increased cytotoxicity was associated with double-strand break accumulation, S-phase cell cycle arrest, and increased apoptosis. An in silico analysis revealed Mirin docking onto the active site of MRE11. While Mirin sensitises DT40 MRE11+/- cells to the Top1 poison SN-38, it does not sensitise nuclease-dead MRE11 cells to this compound confirming that Mirin specifically inhibits Mre11 nuclease activity. MRE11 knockdown reduced cell viability in BRCA2-deficient PEO1 cells but not in BRCA2-proficient PEO4 cells. In a Mirin-resistant model, we show the downregulation of 53BP1 and DNA repair upregulation, leading to resistance, including in in vivo xenograft models. In a clinical cohort of human ovarian tumours, low levels of BRCA2 expression with high levels of MRE11 co-expression were linked with worse progression-free survival (PFS) (p = 0.005) and overall survival (OS) (p = 0.001). We conclude that MRE11 is an attractive SL target, and the pharmaceutical development of MRE11 inhibitors for precision oncology therapeutics may be of clinical benefit.

Centrosome amplification: a quantifiable cancer cell trait with prognostic value in solid malignancies.

Numerical and/or structural centrosome amplification (CA) is a hallmark of cancers that is often associated with the aberrant tumor karyotypes and poor clinical outcomes. Mechanistically, CA compromises mitotic fidelity and leads to chromosome instability (CIN), which underlies tumor initiation and progression. Recent technological advances in microscopy and image analysis platforms have enabled better-than-ever detection and quantification of centrosomal aberrancies in cancer. Numerous studies have thenceforth correlated the presence and the degree of CA with indicators of poor prognosis such as higher tumor grade and ability to recur and metastasize. We have pioneered a novel semi-automated pipeline that integrates immunofluorescence confocal microscopy with digital image analysis to yield a quantitative centrosome amplification score (CAS), which is a summation of the severity and frequency of structural and numerical centrosome aberrations in tumor samples. Recent studies in breast cancer show that CA increases across the disease progression continuum, while normal breast tissue exhibited the lowest CA, followed by cancer-adjacent apparently normal, ductal carcinoma in situ and invasive tumors, which showed the highest CA. This finding strengthens the notion that CA could be evolutionarily favored and can promote tumor progression and metastasis. In this review, we discuss the prevalence, extent, and severity of CA in various solid cancer types, the utility of quantifying amplified centrosomes as an independent prognostic marker. We also highlight the clinical feasibility of a CA-based risk score for predicting recurrence, metastasis, and overall prognosis in patients with solid cancers.

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.

The characteristics and clinical significance of atypical mitosis in breast cancer.

Atypical mitosis is considered a feature of malignancy, however, its significance in breast cancer (BC) remains elusive. Here, we aimed to assess the clinical value of atypical mitoses in BC and to explore their underlying molecular features. Atypical and typical mitotic figures were quantified and correlated with clinicopathological variables in a large cohort of primary BC tissue sections (n = 846) using digitalized hematoxylin and eosin whole-slide images (WSIs). In addition, atypical mitoses were assessed in The Cancer Genome Atlas (TCGA) BC dataset (n = 1032) and were linked to the genetic alterations and pathways. In this study, the median of typical mitoses was 17 per 3 mm2 (range 0-120 mitoses), while the median of atypical mitoses was 4 (range 0-103 mitoses). High atypical mitoses were significantly associated with parameters characteristic of aggressive tumor behavior. The total number of mitoses, and a high atypical-to-typical mitoses ratio (>0.27) were associated with poor BC specific survival (BCSS), (p = 0.04 and 0.01, respectively). The atypical-to-typical mitoses ratio dichotomized triple negative-BC (TNBC) patients into two distinct groups in terms of the association with the outcome, while the overall number of mitoses was not. Moreover, TNBC patients with high atypical-to-typical mitoses ratio treated with adjuvant chemotherapy were associated with shorter survival (p = 0.003). Transcriptomic analysis of the TCGA-BRCA cohort dichotomized based on atypical mitoses identified 2494 differentially expressed genes. These included genes linked to pathways involved in chromosomal localization and segregation, centrosome assembly, spindle and microtubule formation, regulation of cell cycle and DNA repair. To conclude, the atypical-to-typical mitoses ratio has prognostic value independent of the overall mitotic count in BC patients and could predict the response to chemotherapy in TNBC.

Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice.

Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1-70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.

Defining the area of mitoses counting in invasive breast cancer using whole slide image.

Although counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.