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.
Current clinicopathological parameters are useful predictors of breast ductal carcinoma in situ behavior, but they are insufficient to define high-risk patients for disease progression precisely. ...Thioredoxin-interacting protein (TXNIP) is a key player of oxidative stress. This study aims to evaluate the role of TXNIP as a predictor of ductal carcinoma in situ progression. Tissue microarrays from 776 pure ductal carcinoma in situ and 239 mixed ductal carcinoma in situ and invasive tumors were constructed. All patients were treated at a single institution with a long-term follow-up and TXNIP expression was assessed using immunohistochemistry. TXNIP expression was investigated in terms of associations with clinicopathological and molecular features and patient outcome. Loss/reduced cytoplasmic expression of TXNIP was associated with features of aggressiveness including high nuclear grade (p = 1.6 × 10
), presence of comedo necrosis (p = 0.001), and estrogen receptor negative (ER-)/HER2- ductal carcinoma in situ (p = 4.6 × 10
). Univariate analysis showed an inverse association between TXNIP expression and outcome in terms of shorter local recurrence-free survival (p = 0.009). Multivariable analyses showed that independent predictors of ductal carcinoma in situ recurrence were low TXNIP expression (p = 0.005, HR = 0.51, and 95% CI: 0.32-0.81), larger ductal carcinoma in situ size, and high nuclear grade. TXNIP functions as a tumor suppressor gene with loss of its expression associated with ductal carcinoma in situ recurrence. TXNIP can be used as a potentially useful marker in prognostic stratification of ductal carcinoma in situ for management decisions.
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).
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.
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.
High INCENP expression is a poor prognostic biomarker in BC with potential therapeutic benefits.
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.
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
cells to the Top1 poison SN-38, it does not sensitise nuclease-dead
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) (
= 0.005) and overall survival (OS) (
= 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.
Aims
Although evaluation of nuclear morphology is important for the diagnosis and categorisation of breast lesions, the criteria used to assess nuclear atypia rely upon the subjective evaluation of ...several features that may result in inter‐ and intraobserver variation. This study aims to refine the definitions of cytonuclear features in various breast lesions.
Methods and results
ImageJ was used to assess the nuclear morphological features including nuclear diameter, axis length, perimeter, area, circularity and roundness in 160 breast lesions comprising ductal carcinoma in situ (DCIS), invasive breast carcinoma of no special type (IBC‐NST), tubular carcinoma, usual ductal hyperplasia (UDH), columnar cell change (CCC) and flat epithelial atypia (FEA). Reference cells included normal epithelial cells, red blood cells (RBCs) and lymphocytes. Reference cells showed size differences not only between normal epithelial cells and RBCs but also between RBCs in varied‐sized blood vessels. Nottingham grade nuclear pleomorphism scores 1 and 3 cut‐offs in IBC‐NST, compared to normal epithelial cells, were < ×1.2 and > ×1.4 that of mean maximum Feret’s diameter and < ×1.6 and > ×2.4 that of mean nuclear area, respectively. Nuclear morphometrics were significantly different in low‐grade IBC‐NST versus tubular carcinoma, low‐grade DCIS versus UDH and CCC versus FEA. No differences in the nuclear features between grade‐matched DCIS and IBC‐NST were identified.
Conclusion
This study provides a guide for the assessment of nuclear atypia in breast lesions, refines the comparison with reference cells and highlights the potential diagnostic value of image analysis tools in the era of digital pathology.
Aims
Breast adenomyoepitheliomas (AMEs) are uncommon tumours. Most oestrogen receptor (ER)‐positive AMEs have mutations in phosphoinositide 3‐kinase (PI3K) pathway genes, whereas ER‐negative AMEs ...usually harbour concurrent mutations affecting the HRAS Q61 hotspot and PI3K pathway genes. Here, we sought to determine the sensitivity and specificity of RAS Q61R immunohistochemical (IHC) analysis for detection of HRAS Q61R mutations in AMEs.
Methods and results
Twenty‐six AMEs (14 ER‐positive; 12 ER‐negative) previously subjected to massively parallel sequencing (n = 21) or Sanger sequencing (n = 5) of the HRAS Q61 hotspot locus were included in this study. All AMEs were subjected to IHC analysis with a monoclonal (SP174) RAS Q61R‐specific antibody, in addition to detailed histopathological analysis. Nine ER‐negative AMEs harboured HRAS mutations, including Q61R (n = 7) and Q61K (n = 2) mutations. Five of seven (71%) AMEs with HRAS Q61R mutations were immunohistochemically positive, whereas none of the AMEs lacking HRAS Q61R mutations (n = 17) were immunoreactive. RAS Q61R immunoreactivity was restricted to the myoepithelium in 80% (4/5) of cases, whereas one case showed immunoreactivity in both the epithelial component and the myoepithelial component. RAS Q61R immunohistochemically positive AMEs were associated with infiltrative borders (P < 0.001), necrosis (P < 0.01) and mitotic index in the epithelial (P < 0.05) and myoepithelial (P < 0.01) components. RAS Q61R IHC assessment did not reveal Q61K mutations (0/2).
Conclusions
IHC analysis of RAS Q61R shows high specificity (100%) and moderate sensitivity (71%) for detection of HRAS Q61R mutations in breast AMEs, and appears not to detect HRAS Q61K mutations. IHC analysis of RAS Q61R may constitute a useful technique in the diagnostic workup of ER‐negative AMEs.
Centrosome amplification (CA) has been implicated in the progression of various cancer types. Although studies have shown that overexpression of PLK4 promotes CA, the effect of tumor microenvironment ...on polo-like kinase 4 (PLK4) regulation is understudied. The aim of this study was to examine the role of hypoxia in promoting CA via PLK4. We found that hypoxia induced CA via hypoxia-inducible factor-1α (HIF1α). We quantified the prevalence of CA in tumor cell lines and tissue sections from breast cancer, pancreatic ductal adenocarcinoma (PDAC), colorectal cancer, and prostate cancer and found that CA was prevalent in cells with increased HIF1α levels under normoxic conditions. HIF1α levels were correlated with the extent of CA and PLK4 expression in clinical samples. We analyzed the correlation between PLK4 and HIF1A mRNA levels in The Cancer Genome Atlas (TCGA) datasets to evaluate the role of PLK4 and HIF1α in breast cancer and PDAC prognosis. High HIF1A and PLK4 levels in patients with breast cancer and PDAC were associated with poor overall survival. We confirmed PLK4 as a transcriptional target of HIF1α and demonstrated that in PLK4 knockdown cells, hypoxia-mimicking agents did not affect CA and expression of CA-associated proteins, underscoring the necessity of PLK4 in HIF1α-related CA. To further dissect the HIF1α-PLK4 interplay, we used HIF1α-deficient cells overexpressing PLK4 and showed a significant increase in CA compared with HIF1α-deficient cells harboring wild-type PLK4. These findings suggest that HIF1α induces CA by directly upregulating PLK4 and could help us risk-stratify patients and design new therapies for CA-rich cancers.
Hypoxia drives CA in cancer cells by regulating expression of PLK4, uncovering a novel HIF1α/PLK4 axis.
Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known ...about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2–4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.
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