AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

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.

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.

Immune infiltration, aggressive pathology, and poor survival outcomes in RECQL helicase deficient breast cancers.

RECQL is essential for genomic stability. Here, we evaluated RECQL in 449 pure ductal carcinomas in situ (DCIS), 152 DCIS components of mixed DCIS/invasive breast cancer (IBC) tumors, 157 IBC components of mixed DCIS/IBC and 50 normal epithelial terminal ductal lobular units (TDLUs). In 726 IBCs, CD8+, FOXP3+, IL17+, PDL1+, PD1+ T-cell infiltration (TILs) were investigated in RECQL deficient and proficient cancers. Tumor mutation burden (TMB) was evaluated in five RECQL germ-line mutation carriers with IBC by genome sequencing. Compared with normal epithelial cells, a striking reduction in nuclear RECQL in DCIS was evident with aggressive pathology and poor survival. In RECQL deficient IBCs, CD8+, FOXP3+, IL17+ or PDL1+ TILs were linked with aggressive pathology and shorter survival. In germline RECQL mutation carriers, increased TMB was observed in 4/5 tumors. We conclude that RECQL loss is an early event in breast cancer and promote immune cell infiltration.

Quantitative expression of oestrogen receptor in breast cancer: Clinical and molecular significance.

BACKGROUND: Oestrogen receptor (ER) positive breast cancer (BC) patients are eligible for endocrine therapy (ET), regardless of ER immunohistochemical expression level. There is a wide spectrum of ER expression and the response to ET is not uniform. This study aimed to assess the clinical and molecular consequences of ER heterogeneity with respect to ET-response. METHODS: ER expression, categorised by percentage and staining intensity in a large BC cohort (n = 7559) was correlated with clinicopathological parameters and patient ET response. The Cancer Genome Atlas Data BC cohort (n = 1047) was stratified by ER expression and transcriptomic analysis completed to better understand the molecular basis of ER heterogeneity. RESULTS: The quantitative proportional increase in ER expression was positively associated with favourable prognostic parameters. Tumours with 1-9% ER expression were characteristically similar to ER-negative (<1%) tumours. Maximum ET-response was observed in tumours with 100% ER expression, with responses significantly different to tumours exhibiting ER at < 100% and significantly decreased survival rates were observed in tumours with 50% and 10% of ER expression. The Histochemical-score (H-score), which considers both staining intensity and percentage, added significant prognostic value over ER percentage alone with significant outcome differences observed at H-scores of 30, 100 and 200. There was a positive correlation between ER expression and ESR1 mRNA expression and expression of ER-regulated genes. Pathway analysis identified differential expression in key cancer-related pathways in different ER-positive groups. CONCLUSION: ET-response is statistically proportionally related to ER expression with significant differences observed at 10%, 50% and 100%. The H-score adds prognostic and predictive information.

The expression of high mobility group protein 3 (HMGB3) in breast cancer with emphasis on its role in lymphovascular invasion.

Lymphovascular invasion (LVI) is a common phenomenon in breast cancer (BC), and it is correlated to poor outcome. However, the biomarkers that influence the development of LVI remain to be defined. Through rigorous bioinformatics analyses, high mobility group protein 3 (HMGB3) was revealed as a driver gene that is associated with the presence of LVI. The purpose of this study was to further investigate the role of HMGB3 in the pathogenesis of LVI in BC. In vitro functional assays were performed to investigate the effect of HMGB3 silencing on cell proliferation, migration, adherence and transmigration of BC cell lines with dermal lymphatic endothelial cells (DLECs) and human vascular endothelial cells (HUVECs). The correlation of HMGB3 expression with clinicopathological parameters was also assessed at the transcriptomic and the proteomic levels using large BC cohorts with well-characterised LVI status. Silencing HMGB3 reduced cell proliferation, migration, adherence and transmigration across endothelial cell lines. At the mRNA and protein levels, high HMGB3 expression was significantly correlated with LVI-positivity, higher tumour grade, lymph nodal stage, hormone receptor negativity, HER2 positivity and poor outcome. Moreover, high HMGB3 expression was an independent predictor of shorter breast cancer-specific survival. HMGB3 plays an oncogenic function and contributes to the development of LVI in BC. Results warrant further investigation as a potential target to inhibit LVI in BC.

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.

Characterisation of luminal and triple-negative breast cancer with HER2 Low protein expression.

BACKGROUND: Breast cancer (BC) expressing low levels of human epidermal growth factor receptor 2 (HER2 Low) is an emerging category that needs further refining. This study aims to provide a comprehensive clinico-pathological and molecular profile of HER2 Low BC including response to therapy and patient outcome in the adjuvant and neoadjuvant settings. METHODS: Two different independent and well-characterised BC cohorts were included. Nottingham cohort (A) (n = 5744) and The Cancer Genome Atlas (TCGA) BC cohort (B) (n = 854). The clinical, molecular, biological and immunological profile of HER2 Low BC was investigated. Transcriptomic and pathway enrichment analyses were performed on the TCGA BC cohort and validated through next-generation sequencing in a subset of Nottingham cases. RESULTS: Ninety percent of HER2 Low tumours were hormone receptor (HR) positive (HR+), enriched with luminal intrinsic molecular subtype, lacking significant expression of HER2 oncogenic signalling genes and of favourable clinical behaviour compared to HER2 negative (HER2-) BC. In HR+ BC, no significant prognostic differences were detected between HER2 Low and HER2- tumours. However, in HR- BC, HER2 Low tumours were less aggressive with longer patient survival. Transcriptomic data showed that the majority of HR- /HER2 Low tumours were of luminal androgen receptor (LAR) intrinsic subtype, enriched with T-helper lymphocytes, activated dendritic cells and tumour associated neutrophils, while most HR-/HER2- tumours were basal-like, enriched with tumour associated macrophages. CONCLUSION: HER2 Low BC is mainly driven by HR signalling in HR+ tumours. HR-/HER2 Low tumours tend to be enriched with LAR genes with a unique immune profile.

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 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.

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.

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.

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.

The KDM5B and KDM1A lysine demethylases cooperate in regulating androgen receptor expression and signalling in prostate cancer.

Histone H3 lysine 4 (H3K4) methylation is key epigenetic mark associated with active transcription and is a substrate for the KDM1A/LSD1 and KDM5B/JARID1B lysine demethylases. Increased expression of KDM1A and KDM5B is implicated in many cancer types, including prostate cancer (PCa). Both KDM1A and KDM5B interact with AR and promote androgen regulated gene expression. For this reason, there is great interested in the development of new therapies targeting KDM1A and KDM5B, particularly in the context of castrate resistant PCa (CRPC), where conventional androgen deprivation therapies and androgen receptor signalling inhibitors are no longer effective. As there is no curative therapy for CRPC, new approaches are urgently required to suppress androgen signalling that prevent, delay or reverse progression to the castrate resistant state. While the contribution of KDM1A to PCa is well established, the exact contribution of KDM5B to PCa is less well understood. However, there is evidence that KDM5B is implicated in numerous pro-oncogenic mechanisms in many different types of cancer, including the hypoxic response, immune evasion and PI3/AKT signalling. Here we elucidate the individual and cooperative functions of KDM1A and KDM5B in PCa. We show that KDM5B mRNA and protein expression is elevated in localised and advanced PCa. We show that the KDM5 inhibitor, CPI-455, impairs androgen regulated transcription and alternative splicing. Consistent with the established role of KDM1A and KDM5B as AR coregulators, we found that individual pharmacologic inhibition of KDM1A and KDM5 by namoline and CPI-455 respectively, impairs androgen regulated transcription. Notably, combined inhibition of KDM1A and KDM5 downregulates AR expression in CRPC cells. Furthermore, combined KDM1A and KDM5 inhibition impairs PCa cell proliferation and invasion more than individual inhibition of KDM1A and KDM5B. Collectively our study has identified individual and cooperative mechanisms involving KDM1A and KDM5 in androgen signalling in PCa. Our findings support the further development of KDM1A and KDM5B inhibitors to treat advanced PCa. Further work is now required to confirm the therapeutic feasibility of combined inhibition of KDM1A and KDM5B as a novel therapeutic strategy for targeting AR positive CRPC.

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.

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.

RANK is a poor prognosis marker and a therapeutic target in ER-negative postmenopausal breast cancer.

Despite strong preclinical data, the therapeutic benefit of the RANKL inhibitor, denosumab, in breast cancer patients, beyond the bone, is unclear. Aiming to select patients who may benefit from denosumab, we hereby analyzed RANK and RANKL protein expression in more than 2,000 breast tumors (777 estrogen receptor-negative, ER- ) from four independent cohorts. RANK protein expression was more frequent in ER- tumors, where it associated with poor outcome and poor response to chemotherapy. In ER- breast cancer patient-derived orthoxenografts (PDXs), RANKL inhibition reduced tumor cell proliferation and stemness, regulated tumor immunity and metabolism, and improved response to chemotherapy. Intriguingly, tumor RANK protein expression associated with poor prognosis in postmenopausal breast cancer patients, activation of NFKB signaling, and modulation of immune and metabolic pathways, suggesting that RANK signaling increases after menopause. Our results demonstrate that RANK protein expression is an independent biomarker of poor prognosis in postmenopausal and ER- breast cancer patients and support the therapeutic benefit of RANK pathway inhibitors, such as denosumab, in breast cancer patients with RANK+ ER- tumors after menopause.

Unravelling the clinicopathological and functional significance of replication protein A (RPA) heterotrimeric complex in breast cancers.

Replication Protein A (RPA), a heterotrimeric complex consisting of RPA1, 2, and 3 subunits, is a single-stranded DNA (ssDNA)-binding protein that is critically involved in replication, checkpoint regulation and DNA repair. Here we have evaluated RPA in 776 pure ductal carcinomas in situ (DCIS), 239 DCIS that co-exist with invasive breast cancer (IBC), 50 normal breast tissue and 4221 IBC. Transcriptomic [METABRIC cohort (n = 1980)] and genomic [TCGA cohort (n = 1090)] evaluations were completed. Preclinically, RPA deficient cells were tested for cisplatin sensitivity and Olaparib induced synthetic lethality. Low RPA linked to aggressive DCIS, aggressive IBC, and shorter survival outcomes. At the transcriptomic level, low RPA tumours overexpress pseudogene/lncRNA as well as genes involved in chemical carcinogenesis, and drug metabolism. Low RPA remains linked with poor outcome. RPA deficient cells are sensitive to cisplatin and Olaparib induced synthetic lethality. We conclude that RPA directed precision oncology strategy is feasible in breast cancers.

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.