Expression of cell cycle markers is predictive of the response to primary systemic therapy of locally advanced breast cancer

MCM2 in human cancer: functions, mechanisms, and clinical significance

Background

Aberrant DNA replication is the main source of genomic instability that leads to tumorigenesis and progression. MCM2, a core subunit of eukaryotic helicase, plays a vital role in DNA replication. The dysfunction of MCM2 results in the occurrence and progression of multiple cancers through impairing DNA replication and cell proliferation.

Conclusions

MCM2 is a vital regulator in DNA replication. The overexpression of MCM2 was detected in multiple types of cancers, and the dysfunction of MCM2 was correlated with the progression and poor prognoses of malignant tumors. According to the altered expression of MCM2 and its correlation with clinicopathological features of cancer patients, MCM2 was thought to be a sensitive biomarker for cancer diagnosis, prognosis, and chemotherapy response. The anti-tumor effect induced by MCM2 inhibition implies the potential of MCM2 to be a novel therapeutic target for cancer treatment. Since DNA replication stress, which may stimulate anti-tumor immunity, frequently occurs in MCM2 deficient cells, it also proposes the possibility that MCM2 targeting improves the effect of tumor immunotherapy.

Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains

In triple negative breast cancer (TNBC) treatment, early prediction of pathological complete response (PCR) from chemotherapy before surgical operations is crucial for optimal treatment planning. We propose a novel deep learning-based system to predict PCR to neoadjuvant chemotherapy for TNBC patients with multi-stained histopathology images of serial tissue sections. By first performing tumor cell detection and recognition in a cell detection module, we produce a set of feature maps that capture cell type, shape, and location information. Next, a newly designed spatial attention module integrates such feature maps with original pathology images in multiple stains for enhanced PCR prediction in a dedicated prediction module. We compare it with baseline models that either use a single-stained slide or have no spatial attention module in place. Our proposed system yields 78.3% and 87.5% of accuracy for patch-, and patient-level PCR prediction, respectively, outperforming all other baseline models. Additionally, the heatmaps generated from the spatial attention module can help pathologists in targeting tissue regions important for disease assessment. Our system presents high efficiency and effectiveness and improves interpretability, making it highly promising for immediate clinical and translational impact.

Prognostic and Clinicopathological Correlations of Cell Cycle Marker Expressions before and after the Primary Systemic Therapy of Breast Cancer

We aimed to analyze the expression of cell-cycle regulation markers - minichromosome maintenance protein 2 (MCM2), Ki-67, Cyclin-A and phosphohistone-H3 (PHH3) - in pre-treatment core-biopsy samples of breast carcinomas in correlation with known predictive and prognostic factors. Totally 52 core biopsy samples obtained prior to neoadjuvant therapy were analyzed. Immunohistochemistry was performed to analyze the expression of MCM2, Ki-67, Cyclin A and PHH3, which were correlated with the following clinicopathological parameters: clinical TNM, tumor grade, biological subtype, the presence of tumor infiltrating lymphocytes (TIL), pathological tumor response rate to the neoadjuvant therapy and patient survival. All investigated markers showed higher expression in high grade and in triple negative tumors (p < 0.01 and p < 0.05, respectively). Hormone receptor negative tumors showed significantly higher expression of Ki-67 (p < 0.01), MCM2 (p < 0.01) and Cyclin A (p < 0.01) than hormone receptor positive ones. Tumors with increased TIL showed significantly higher Ki-67 expression (p = 0.04). Pattern analysis suggested that novel cell-cycle marker-based subgrouping reveals predictive and prognostic potential. Tumors with high MCM2, Cyclin A or PHH3 expression showed significantly higher rate of pathological complete remission. Tumors with early relapse (progression-free survival ≤2 years) and shortened overall survival also show a higher rate of proliferation. Our cell cycle marker (Ki-67, MCM2, Cyclin A, PHH3) based testing could identify tumors with worse prognosis, but with a favorable response to primary systemic therapy. The pattern of cell-cycle activity could also be useful for predicting early relapse, but our findings need to be further substantiated in larger patient cohorts.

Artificial intelligence in digital breast pathology: Techniques and applications

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.

Fluoro-Chromogenic Labelling for Detection of MCM2 to Assess Proliferation Activity in HER2-amplified Breast Carcinomas

Minichromosome Maintenance Protein 2 (MCM2) is critical in initiating DNA replication during the cell division process. As expressed intensively in all phases of the active cell cycle, MCM2 has been proposed as a novel biomarker to determine cellular proliferation. We aimed at clarifying the prevalence and clinical significance of MCM2 in HER2-amplified breast cancer subtype. MCM2 expression was studied in 142 primary HER2-amplified breast carcinomas by applying a novel fluoro-chromogenic immunohistochemistry and tailored digital image analysis to determine labelling index (MCM2-LI). The presence of MCM2 was detected with HRP-conjugated polymer and visualized with 3, 3'-diaminobenzidine tetrahydrochloride, in cytokeratin (CK)-positive and Cy2-IgG-labelled breast cancer cells of epithelial origin. Stained slides were digitized by scanning sequentially under bright field (for MCM2) and fluorescence (for CK) illumination. Multilayer JPEG2000 images were analyzed with ImmunoRatio 2.5 (accessory in SlideVantage 1.2 software) utilizing its bright field and fluorescence image-blending mode to display MCM2-CK dual-positive cells. MCM2-LI was retrospectively compared with histopathologic characteristics and patients' clinical outcome. MCM2 protein-expressing cells (median MCM2-LI, 63.5%) were more frequent than those of Ki67 (median Ki67 labelling index, 33%). Significant correlations were found between high MCM2-LI, high Ki67 labelling index, negative hormone receptor (ER, PR) statuses, high grade of malignancy, and high cyclin E expression. MCM2-LI was not shown to be predictive of disease recurrence during the median follow-up of 5.3 years but was shown to be useful to distinguish aggressive-type HER2-amplified breast carcinomas with high malignancy grade and hormone receptor negativity. The fluoro-chromogenic double-labelling immunohistochemistry accompanied with digital image analysis provides an accurate carcinoma-specific determination of MCM2-LI on a single tumor section.

Minichromosome maintenance protein 2 correlates with the malignant status and regulates proliferation and cell cycle in lung squamous cell carcinoma

Background

Minichromosome maintenance protein 2 (MCM2), which is a member of MCM family, has been found to be a relevant marker for progression and prognosis in a variety of human cancers. Due to lack of effective therapeutic target in lung squamous cell carcinoma (LUSC) patients, the aim of our study was to screen and identify biomarkers which are associated to clinicopathological characteristics including prognosis in LUSC patients.

Methods

The expression status of MCM2 in lung cancer was analyzed using the publicly available Gene Expression Omnibus databases (GSE3268 and GSE10245). The mRNA and protein expression of MCM2 in lung cancer tissues and cell lines was detected by quantitative real-time PCR and Western blot, and the association between MCM2 expression and clinicopathological factors was analyzed by immunohistochemistry. The loss-of-function study of MCM2 was conducted in LUSC cell lines.

Results

In our study, we found MCM2 expression was increased in LUSC tissues compared with paired adjacent normal lung tissues or lung adenocarcinoma tissues through analyzing microarray data sets (GSE3268 and GSE10245), which confirmed that MCM2 mRNA and protein were overexpressed in LUSC tissues and cell lines. Meanwhile, we analyzed the association between MCM2 protein expression and clinicopathological characteristics of LUSC patients, and found high expression of MCM2 protein was obviously associated with malign differentiated degree, advanced clinical stage, large tumor size, more lymph node metastasis and present distant metastasis. The survival analysis showed MCM2 overexpression was an independent unfavorable prognostic factor for LUSC patients.

Conclusion

MCM2 is involved in the development and progression of LUSC as an oncogene, and thereby may act as a potential therapeutic target for LUSC patients.

The mitosis-specific marker phosphohistone-H3 (PHH3) is an independent prognosticator in uterine smooth muscle tumours: an outcome-based study

AIMS

Accurate mitosis counting, which is important in the diagnosis of uterine smooth muscle tumours (USMTs), is often difficult and subjective. The mitosis-specific immunohistochemical marker phosphohistone-H3 (PHH3) has been shown to be diagnostically useful, but its expression, in relation to outcome, has not been thoroughly investigated. The aim of this study is to evaluate PHH3 as a diagnostic and prognostic marker in USMTs.

METHODS AND RESULTS

PHH3 expression was evaluated in 55 leiomyosarcomas (LMSs), 26 smooth muscle tumours of uncertain malignant potential (STUMPs), 18 leiomyomas with bizarre nuclei (LBN), and 12 leiomyomas (LMs). Scores were expressed as counts per 10 high-power fields (HPFs). Median follow-up durations of patients with LMS, STUMP, LBN and LM were, respectively, 39, 78, 65.5 and 49.5 months. Twenty-eight patients with LMSs (50.9%) died, and two (7.7%) patients with STUMPs experienced recurrence. The median PHH3 scores for LMSs were significantly higher than those for other categories of tumour. A score of ≥29/10 HPFs was also independently associated with a poor outcome. To test whether the PHH3 score could distinguish between benign USMTs with atypical histology and those that were clinically malignant, two biological groups were further delineated. Patients in group 1 (18 LBNs and 24 STUMPs) all had an uneventful outcome, whereas patients in group 2 (two recurrent STUMPs and 32 LMSs) all had a recurrence or tumour-related death. Median PHH3 scores for the two groups were, respectively, 2/10 HPFs and 27/10 HPFs. A PHH3 score of ≥7/10 HPFs was highly associated with malignancy.

CONCLUSION

PHH3 is useful in evaluation of the biological behaviour of USMTs, and may serve as a prognostic indicator for LMSs.