Breast cancer is a heterogeneous disease comprising a diversity of tumor subtypes that manifest themselves in a wide variety of clinical, pathological, and molecular features. One important subset, luminal breast cancers, comprises two clinically distinct subtypes luminal A and B each of them endowed with its own genetic program of differentiation and proliferation. Luminal breast cancers were operationally defined as follows: Luminal A: ER+, PR+, HER2-, Ki-67<14% and Luminal B: ER+ and/or PR+, HER2-,Ki-67≥14% or, alternatively ER+ and/or PR+, HER2+, any Ki-67. There is currently a need for a clinically robust and validated immunohistochemical assay that can help distinguish between luminal A and B breast cancer. MCM2 is a family member of the minichromosome maintenance protein complex whose role in DNA replication and cell proliferation is firmly established. As MCM2 appears to be an attractive alternative to Ki-67, we sought to study the expression of MCM2 and Ki-67 in different histological grades and molecular subtypes of breast cancer focusing primarily on ER-positive tumors. MCM2 and Ki-67 mRNA expression were studied using in silico analysis of available DNA microarray and RNA-sequencing data of human breast cancer. We next used immunohistochemistry to evaluate protein expression of MCM2 and Ki-67 on tissue microarrays of invasive breast carcinoma. We found that MCM2 and Ki-67 are highly expressed in breast tumors of high histological grades, comprising clinically aggressive tumors such as triple-negative, HER2-positive and luminal B subtypes. MCM2 expression was detected at higher levels than that of Ki-67 in normal breast tissues and in breast cancers. The bimodal distribution of MCM2 scores in ER+/HER2- breast tumors led to the identification of two distinct subgroups with different relapse-free survival rates. In conclusion, MCM2 expression can help sorting out two clinically important subsets of luminal breast cancer whose treatment and clinical outcomes are likely to diverge.
Currently, emphasis must be put on standardization of procedures, internal and external quality control assessment, and competency evaluation of already existing methods to ensure accurate, reliable, and clinically meaningful test results. Development of new robust and accurate diagnostic assays should also be encouraged. In addition, large clinical trials are warranted to identify the technique that most reliably predicts a positive response to anti-HER2 drugs.
Amplification of the human epidermal growth factor receptor 2 (HER2) is a prognostic marker for poor clinical outcome and a predictive marker for therapeutic response to targeted therapies in breast cancer patients. With the introduction of anti-HER2 therapies, accurate assessment of HER2 status has become essential. Fluorescence in situ hybridization (FISH) is a widely used technique for the determination of HER2 status in breast cancer. However, the manual signal enumeration is time-consuming. Therefore, several companies like MetaSystem have developed automated image analysis software. Some of these signal enumeration software employ the so called “tile-sampling classifier”, a programming algorithm through which the software quantifies fluorescent signals in images on the basis of square tiles of fixed dimensions. Considering that the size of tile does not always correspond to the size of a single tumor cell nucleus, some users argue that this analysis method might not completely reflect the biology of cells. For that reason, MetaSystems has developed a new classifier which is able to recognize nuclei within tissue sections in order to determine the HER2 amplification status on nuclei basis. We call this new programming algorithm “nuclei-sampling classifier”. In this study, we evaluated the accuracy of the “nuclei-sampling classifier” in determining HER2 gene amplification by FISH in nuclei of breast cancer cells. To this aim, we randomly selected from our cohort 64 breast cancer specimens (32 nonamplified and 32 amplified) and we compared results obtained through manual scoring and through this new classifier. The new classifier automatically recognized individual nuclei. The automated analysis was followed by an optional human correction, during which the user interacted with the software in order to improve the selection of cell nuclei automatically selected. Overall concordance between manual scoring and automated nuclei-sampling analysis was 98.4% (100% for nonamplified cases and 96.9% for amplified cases). However, after human correction, concordance between the two methods was 100%. We conclude that the nuclei-based classifier is a new available tool for automated quantitative HER2 FISH signals analysis in nuclei in breast cancer specimen and it can be used for clinical purposes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.