Molecular diagnostic testing in breast cancer

Prognostic value of lncRNA AFAP1-AS1 in breast cancer: a meta-analysis and validated study in Chinese population

BACKGROUND

Long non encoding RNA (lncRNA) plays a crucial role in breast cancer. However, the prognostic role of AFAP1-AS1 in breast cancer remains unclear.

AIMS

To investigate the relationship between the expression of long non-coding RNA actin filament-associated protein1 antisense RNA1 (AFAP1-AS1) and prognosis of breast cancer.

METHODS AND RESULTS

Meta-analysis was performed to explore the correlation between AFAP1-AS1 and breast cancer. The AFAP1-AS1expression in patients with breast cancer tissue and adjacent normal tissue from 153 patients was determined by qRT-PCR. Bioinformatics and Cox proportional-hazards risk model were used to explore the relationship between expression of AFAP1-AS1 and prognosis. The combined analysis revealed a significant correlation between AFAP1-AS1 expression and both overall survival (hazard ratios, HR = 2.33, 95%Cl: 1.94-2.81, p < 0.001) as well as disease-free survival/progression-free survival (HR = 2.94, 95%CI: 2.35-3.67, p < 0.001). The relation between expression of AFAP1-AS1 and breast cancer was determined in 153 breast cancer and adjacent normal tissues. The findings revealed a significantly higher AFAP1-AS1expression levels in breast cancer tissues compared to adjacent normal tissues (p < 0.001). Additionally, patients exhibiting heightened levels of AFAP1-AS1 expression were correlated with an unfavorable prognosis (HR = 2.35, 95%CI: 1.47-3.74, p < 0.001), which aligns consistently with the findings of the pooled analysis. The subgroup analysis of clinical characteristics revealed a significant association between high expression of AFAP1-AS1 and TNM stage (HR = 1.72, 95%CI: 1.11-2.65, p = 0.015).

CONCLUSION

This study demonstrated that AFAP1-AS1 acts as an oncogene and may serve as a novel prognostic marker for breast cancer, particularly in the Chinese population.

Classic and New Markers in Diagnostics and Classification of Breast Cancer

Simple Summary

With ever-increasing incidence, breast cancer is considered a most diagnosed type of cancer among women worldwide. Breast cancer arises through malignant transformation of ductal or lobular cells in female (or male) breast and the genetic, phenotypic and morphological heterogeneity has an effect on tumour’s behaviour, thereby instigating a need for individual personalized therapy. A traditional assessment of tumour’s characteristics involves a biopsy and histological analysis of a tumour tissue, and in recent years has been accompanied by analysis of molecular biomarkers to enhance the results. In this work we aimed to thoroughly investigate the latest data in this field of study and give a comprehensive review of novel molecular biomarkers of breast cancer and methodologies used to analyse them.

Abstract

Breast cancer remains the most frequently diagnosed form of female’s cancer, and in recent years it has become the most common cause of cancer death in women worldwide. Like many other tumours, breast cancer is a histologically and biologically heterogeneous disease. In recent years, considerable progress has been made in diagnosis, subtyping, and complex treatment of breast cancer with the aim of providing best suited tumour-specific personalized therapy. Traditional methods for breast cancer diagnosis include mammography, MRI, biopsy and histological analysis of tumour tissue in order to determine classical markers such as estrogen and progesterone receptors (ER, PR), cytokeratins (CK5/6, CK14, C19), proliferation index (Ki67) and human epidermal growth factor type 2 receptor (HER2). In recent years, these methods have been supplemented by modern molecular methodologies such as next-generation sequencing, microRNA, in situ hybridization, and RT-qPCR to identify novel molecular biomarkers. MicroRNAs (miR-10b, miR-125b, miR145, miR-21, miR-155, mir-30, let-7, miR-25-3p), altered DNA methylation and mutations of specific genes (p16, BRCA1, RASSF1A, APC, GSTP1), circular RNA (hsa_circ_0072309, hsa_circRNA_0001785), circulating DNA and tumour cells, altered levels of specific proteins (apolipoprotein C-I), lipids, gene polymorphisms or nanoparticle enhanced imaging, all these are promising diagnostic and prognostic tools to disclose any specific features from the multifaceted nature of breast cancer to prepare best suited individualized therapy.

A review on methods for diagnosis of breast cancer cells and tissues

Breast cancer has seriously been threatening physical and mental health of women in the world, and its morbidity and mortality also show clearly upward trend in China over time. Through inquiry, we find that survival rate of patients with early‐stage breast cancer is significantly higher than those with middle‐ and late‐stage breast cancer, hence, it is essential to conduct research to quickly diagnose breast cancer. Until now, many methods for diagnosing breast cancer have been developed, mainly based on imaging and molecular biotechnology examination. These methods have great contributions in screening and confirmation of breast cancer. In this review article, we introduce and elaborate the advances of these methods, and then conclude some gold standard diagnostic methods for certain breast cancer patients. We lastly discuss how to choose the most suitable diagnostic methods for breast cancer patients. In general, this article not only summarizes application and development of these diagnostic methods, but also provides the guidance for researchers who work on diagnosis of breast cancer.

In silico analyses identify lncRNAs: WDFY3-AS2, BDNF-AS and AFAP1-AS1 as potential prognostic factors for patients with triple-negative breast tumors

BACKGROUND

Long non-coding RNAs (lncRNAs) are characterized as having 200 nucleotides or more and not coding any protein, and several been identified as differentially expressed in several human malignancies, including breast cancer.

METHODS

Here, we evaluated lncRNAs differentially expressed in triple-negative breast cancer (TNBC) from a cDNA microarray data set obtained in a previous study from our group. Using in silico analyses in combination with a review of the current literature, we identify three lncRNAs as potential prognostic factors for TNBC patients.

RESULTS

We found that the expression of WDFY3-AS2, BDNF-AS, and AFAP1-AS1 was associated with poor survival in patients with TNBCs. WDFY3-AS2 and BDNF-AS are lncRNAs known to play an important role in tumor suppression of different types of cancer, while AFAP1-AS1 exerts oncogenic activity.

CONCLUSION

Our findings provided evidence that WDFY3-AS2, BDNF-AS, and AFAP1-AS1 may be potential prognostic factors in TNBC development.

Identification of the copy number variant biomarkers for breast cancer subtypes

Breast cancer is a common and threatening malignant disease with multiple biological and clinical subtypes. It can be categorized into subtypes of luminal A, luminal B, Her2 positive, and basal-like. Copy number variants (CNVs) have been reported to be a potential and even better biomarker for cancer diagnosis than mRNA biomarkers, because it is considerably more stable and robust than gene expression. Thus, it is meaningful to detect CNVs of different cancers. To identify the CNV biomarker for breast cancer subtypes, we integrated the CNV data of more than 2000 samples from two large breast cancer databases, METABRIC and The Cancer Genome Atlas (TCGA). A Monte Carlo feature selection-based and incremental feature selection-based computational method was proposed and tested to identify the distinctive core CNVs in different breast cancer subtypes. We identified the CNV genes that may contribute to breast cancer tumorigenesis as well as built a set of quantitative distinctive rules for recognition of the breast cancer subtypes. The tenfold cross-validation Matthew's correlation coefficient (MCC) on METABRIC training set and the independent test on TCGA dataset were 0.515 and 0.492, respectively. The CNVs of PGAP3, GRB7, MIR4728, PNMT, STARD3, TCAP and ERBB2 were important for the accurate diagnosis of breast cancer subtypes. The findings reported in this study may further uncover the difference between different breast cancer subtypes and improve the diagnosis accuracy.

Expression and functionality of TRPV1 in breast cancer cells

Transient receptor potential (TRP) channels contribute to the regulation of intracellular calcium, which can promote cancer hallmarks in cases of dysregulation of gene transcription and calcium-dependent pro-proliferative or anti-apoptotic mechanisms. Several studies have begun to elucidate the roles of TRPV1, TRPV6, TRPM8, and TRPC1 in cancer progression; however, no study has examined the expression profiles of human TRP channels in breast cancer on a large scale. This study focused on the expression and functionality of TRPV1, a nonselective cation channel that was found to be expressed in different carcinoma tissues. Next-generation sequencing analyses revealed the expression of TRPV1 in several native breast cancer tissues, which was subsequently validated via reverse transcriptase-polymerase chain reaction. Activation of TRPV1 by its ligand capsaicin was associated with the growth inhibition of some cancer cell types; however, the signaling components involved are complex. In this study, stimulation by the TRPV1 agonist, capsaicin, of SUM149PT cells, a model system for the most aggressive breast cancer subtype, triple-negative breast cancer, led to intracellular calcium signals that were diminished by the specific TRPV1 antagonist, capsazepin. Activation of TRPV1 by capsaicin caused significant inhibition of cancer cell growth and induced apoptosis and necrosis. In conclusion, the current study revealed the expression profiles of human TRP channels in 60 different breast cancer tissues and cell lines and furthermore validated the antitumor activity of TRPV1 against SUM149PT breast cancer cells, indicating that activation of TRPV1 could be used as a therapeutic target, even in the most aggressive breast cancer types.

Do More With Less: Tips and Techniques for Maximizing Small Biopsy and Cytology Specimens for Molecular and Ancillary Testing: The University of Colorado Experience

Context .- In an era in which testing of patient tumor material for molecular and other ancillary studies is of increasing clinical importance for selection of therapy, the ability to test on small samplings becomes critical. Often, small samplings are rapidly depleted in the diagnostic workup or are insufficient for multiple ancillary testing approaches. Objective .- To describe technical methodologies that can be implemented to preserve and maximize tissue for molecular and other ancillary testing. Data Sources .- Retrospective analysis of a case cohort from the University of Colorado, description of techniques used at the University of Colorado, and published literature. Conclusions .- Numerous techniques can be deployed to maximize molecular and other ancillary testing, even when specimens are from small samplings. A dedicated process for molecular prioritization has a high success rate, but also increases workload, which must be factored into establishing such a process. Additionally, establishing high-fidelity communication strings is critical for success of dedicated molecular prioritization of samples. Numerous approaches can be deployed for alternative specimen types, and several technical approaches can also aid in maximizing small specimens.

PINCAGE: probabilistic integration of cancer genomics data for perturbed gene identification and sample classification

MOTIVATION

Cancer development and progression is driven by a complex pattern of genomic and epigenomic perturbations. Both types of perturbations can affect gene expression levels and disease outcome. Integrative analysis of cancer genomics data may therefore improve detection of perturbed genes and prediction of disease state. As different data types are usually dependent, analysis based on independence assumptions will make inefficient use of the data and potentially lead to false conclusions.

MODEL

Here, we present PINCAGE (Probabilistic INtegration of CAncer GEnomics data), a method that uses probabilistic integration of cancer genomics data for combined evaluation of RNA-seq gene expression and 450k array DNA methylation measurements of promoters as well as gene bodies. It models the dependence between expression and methylation using modular graphical models, which also allows future inclusion of additional data types.

RESULTS

We apply our approach to a Breast Invasive Carcinoma dataset from The Cancer Genome Atlas consortium, which includes 82 adjacent normal and 730 cancer samples. We identify new biomarker candidates of breast cancer development (PTF1A, RABIF, RAG1AP1, TIMM17A, LOC148145) and progression (SERPINE3, ZNF706). PINCAGE discriminates better between normal and tumour tissue and between progressing and non-progressing tumours in comparison with established methods that assume independence between tested data types, especially when using evidence from multiple genes. Our method can be applied to any type of cancer or, more generally, to any genomic disease for which sufficient amount of molecular data is available.

AVAILABILITY AND IMPLEMENTATION

R scripts available at http://moma.ki.au.dk/prj/pincage/

CONTACT

: michal.switnicki@clin.au.dk or jakob.skou@clin.au.dk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MicroRNA-338-3p functions as tumor suppressor in breast cancer by targeting SOX4

MicroRNA-338-3p (miR‑338-3p), a recently discovered miRNA, has been reported to be downregulated and play tumor suppressor roles in gastric cancer, ovarian cancer, colorectal carcinoma and lung cancer by targeting several genes. However, the role and potential mechanism of miR‑338-3p in breast cancer (BC) is still unclear. In the present study, we investigated the roles and mechanisms of miR‑338-3p in human breast cancer. miR‑338-3p expression was determined by qRT-PCR in human BC cell lines, and clinical significantly of miR‑338-3p expression was further evaluated. Furthermore, the function of miR‑338-3p in breast cancer also was investigated by several in vitro approaches and in nude mouse models. Luciferase assay and western blot analysis were performed to validate the potential targets of miR‑338-3p after the preliminary screening by employing open access software. It was found that miR‑338-3p was significantly downregulated in both BC tissues and cell lines and the low expression of miR‑338-3p was inversely correlated with lymph node metastatic and TNM stage status (P<0.01). Function assay showed that the overexpression of miR‑338-3p in BC cells significantly inhibited cell proliferation, colony formation, migration and invasion, and induced cell apoptosis and cell cycle arrest at G1/G0 stage, as well as suppressed tumor growth in the nude mouse model. Luciferase assay and western blot analysis identified sex-determining region Y-box 4 (SOX4) as a direct and functional target of miR‑338-3p. These findings revealed that miR‑338-3p may act as a tumor suppressor in breast cancer by targeting SOX4, suggesting miR‑338-3p as a novel strategy for breast cancer treatment.