Measurement of ER and PR status in breast cancer using the QuantiGene2.0 assay

Automated Classification of Breast Cancer Across the Spectrum of ERBB2 Expression Focusing on Heterogeneous Tumors With Low Human Epidermal Growth Factor Receptor 2 Expression

PURPOSE

Although pharmaceutical companies conduct clinical trials of novel human epidermal growth factor receptor 2 (HER2)-low-directed drugs, diagnosing HER2-low cancer by immunohistochemistry (IHC) and in situ hybridization (ISH) remains challenging. This study investigates the performance of first-in-kind computerized intelligence to classify samples across gene expression levels and differentiate HER2-low tumors.

MATERIALS AND METHODS

We classified 251 samples: 142 primary invasive breast cancers (IBCs), 75 ductal carcinomas in situ (DCIS), and 34 mammaplasties (reference) using mRNA expression data from the QuantiGene Plex 2.0 assay. We used g3mclass probabilistic software to assess the number of classes in the assay data, the mean and the variance in each class, diagnostic cutoffs, and the prevalence of each class in the study population.

RESULTS

HER2-low (IHC score of 1+ or 2+/ISH-) accounted for 31% of IBC. First, we discovered that HER2-low tumors were represented by cases with normal ERBB2 transcript levels that were expected to produce physiologic levels of HER2 (70%) and cases with abnormally upregulated unamplified ERBB2 (30%). We termed the latter cancers ERBB2-up as they do not meet the standard definitions for ERBB2 overexpression and amplification. Second, HER2-low IBC classified as ERBB2-up had not only abnormally increased luminal growth and adhesion markers (ERBB2, ESR1, PGR, IGF1R, VAV2, VAV3, KRT8, CDH1) but also downregulated myoepithelial marker (KRT5). The vascularization (RAP1 and C3G), immune cell infiltration (VAV1), and mesenchymal transition (CDH2) markers were dysregulated. Finally, in the independent cohort of DCIS, 40% of HER2-low DCIS shared similar traits with HER2-low IBC except for rare downregulation of KRT5 and no change in C3G, VAV1, and CDH2.

CONCLUSION

We demonstrated how innovative bioinformatic tools could help diagnose cancer across the spectrum of ERBB2 expression to aid decision making for HER2-low.

The g3mclass is a practical software for multiclass classification on biomarkers

The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output's accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.

ERβ Isoforms Have Differential Clinical Significance in Breast Cancer Subtypes and Subgroups

ERβ, an ER subtype first identified in 1996, is highly expressed in different types of BCa including ERα-negative BCa and TNBC. Many studies on ERβ expression investigated mostly on ERβ1 protein expression in ERα-positive and ERα-negative BCa combined. The results are conflicting. This may be due to the complexity of ERβ isoforms, subject heterogeneity, and various study designs targeting different ERβ isoforms and either ERβ protein or mRNA expression, as well as to the lack of a standardized testing protocol. Herein, we simultaneously investigated both mRNA and protein expression of ERβ isoforms 1, 2, and 5 in different BCa subtypes and clinical characteristics. Patient samples (138) and breast cancer cell lines (BCC) reflecting different types of BCa were tested for ERα and ERβ mRNA expression using quantitative real-time PCR, as well as for protein expression of ERα, ERβ1, ERβ2, and ERβ5 isoforms, PR, HER2/neu, Ki-67, CK 5/6, and p53 using immunohistochemistry. Associations of ERβ isoform expression with clinical characteristics and overall survival (OS) were analyzed. ERβ1, 2, and 5 isoforms are differentially expressed in different BCa subtypes including ERα-negative and TNBC. Each ERβ isoform seemingly plays a distinct role and is associated with clinical tumor characteristics and patient outcomes. ERβ isoform expression is significantly associated with >15% Ki-67 positivity and poor prognostic markers, and it predicts poorer OS, mostly in the subgroups. High ERβ2 and 5 isoform expression in ERα-negative BCa and TNBC is predictive of poor OS. Further investigation of ERβ isoforms in a larger cohort of BCa subgroups is needed to evaluate the role of ERβ for the potential usefulness of ERβ as a prognostic and predictive marker and for therapeutic use. The inconsistent outcomes of ERβ isoform mRNA or protein expression in many studies suggest that the standardization of ERβ testing would facilitate the use of ERβ in a clinical setting.

Higher ER load is not associated with better outcome in stage 1-3 breast cancer: a descriptive overview of quantitative HR analysis in operable breast cancer

PURPOSE

In breast cancer, hormone receptor (HR) status is generally a qualitative measure; positive or negative. Quantitatively measured oestrogen and progesterone receptors (ER and PR) are frequently proposed prognostic and predictive markers, some guidelines even provide different treatment options for patients with strong versus weak expression.

AIM

To evaluate quantitative HR load assessed by immunohistochemistry as a prognostic and predictive measure in stage 1-3 breast cancer.

METHODS

We reviewed all the available literature on quantitatively measured HRs using immunohistochemistry.

RESULTS

All included studies (n = 19) comprised a cohort of 30,754 patients. Only 2 out of 17 studies found a clear correlation between higher quantitative ER and better disease outcome. Only one trial examined quantitative ER both as prognostic and predictive marker and found no association between ER% and survival. Ten studies examined quantitative PR load, only two of those found a significant correlation between higher PR load and better disease outcome. Two trials examined quantitative PR both as prognostic and predictive marker, neither found any association between PR% and disease outcome.

CONCLUSIONS

There is no clear evidence for using quantitatively assessed ER and PR as prognostic nor predictive marker in patients with stage 1-3 breast cancer. We recommend only using a qualitative HR status in future guidelines and treatment considerations.

Gaussian Mixture Models for Probabilistic Classification of Breast Cancer

In the era of omics-driven research, it remains a common dilemma to stratify individual patients based on the molecular characteristics of their tumors. To improve molecular stratification of patients with breast cancer, we developed the Gaussian mixture model (GMM)-based classifier. This probabilistic classifier was built on mRNA expression data from more than 300 clinical samples of breast cancer and healthy tissue and was validated on datasets of ESR1, PGR, and ERBB2, which encode standard clinical markers and therapeutic targets. To demonstrate how a GMM approach could be exploited for multiclass classification using data from a candidate marker, we analyzed the insulin-like growth factor I receptor (IGF1R), a promising target, but a marker of uncertain importance in breast cancer. The GMM defined subclasses with downregulated (40%), unchanged (39%), upregulated (19%), and overexpressed (2%) IGF1R levels; inter- and intrapatient analyses of IGF1R transcript and protein levels supported these predictions. Overexpressed IGF1R was observed in a small percentage of tumors. Samples with unchanged and upregulated IGF1R were differentiated tumors, and downregulation of IGF1R correlated with poorly differentiated, high-risk hormone receptor-negative and HER2-positive tumors. A similar correlation was found in the independent cohort of carcinoma in situ, suggesting that loss or low expression of IGF1R is a marker of aggressiveness in subsets of preinvasive and invasive breast cancer. These results demonstrate the importance of probabilistic modeling that delves deeper into molecular data and aims to improve diagnostic classification, prognostic assessment, and treatment selection. SIGNIFICANCE: A GMM classifier demonstrates potential use for clinical validation of markers and determination of target populations, particularly when availability of specimens for marker development is low.

A novel ViewRNA in situ hybridization method for the detection of the dynamic distribution of Classical Swine Fever Virus RNA in PK15 cells

BACKGROUND

Classical swine fever (CSF) is a highly contagious fatal infectious disease caused by classical swine fever virus (CSFV). A better understanding of CSFV replication is important for the study of pathogenic mechanism of CSF. With the development of novel RNA in situ Hybridization method, quantitatively localization and visualization of the virus RNA molecular in cultured cell or tissue section becomes very important tool to address these pivotal pathogenic questions. In this study, we established ViewRNA ISH method to reveal the dynamic distribution of CSFV RNA in PK15 cells.

METHODS

We designed several specific probes of CSFV RNA and reference gene β-actin for host PK15 cells to monitor the relative location of CSFV RNA and house-keeping gene in the infected cells. After determining the titer of reference strain CSFV (HeBHH1/95) with the 50% tissue culture infective dose (TCID50), we optimized the protease K concentration and formalin fixation time to analyze the hybridization efficiency, fluorescence intensity and repeatability. In order to measure the sensitivity of this assay, we compared it with the fluorescent antibody test (FAT) and immunohistochemical(IHC) method. Specificity of the ViewRNA ISH was tested by detecting several sub genotypes of CSFV (sub genotype 1.1, 2.1, 2.2 and 2.3) which are present in China and other normal pig infectious virus (bovine viral diarrhea virus (BVDV), porcine parvovirus (PPV), porcine pseudorabies virus (PRV) and porcine circovirusII(PCV-2).

RESULTS

The lowest detection threshold of the ViewRNA ISH method was 10^-8, while the sensitivity of FAT and IHC were 10^-5 and 10^-4, respectively. The ViewRNA ISH was specific for CSFV RNA including 1.1, 2.1, 2.2 and 2.3 subtypes, meanwhile, there was no cross-reaction with negative control and other viruses including BVDV, PPV, PRV and PCV-2. Our results showed that after infection at 0.5 hpi (hours post inoculation, hpi), the CSFV RNA can be detected in nucleus and cytoplasm; during 3-9 hpi, RNA was mainly distributed in nucleus and reached a maximum at 12hpi, then RNA copy number was gradually increased around the cell nucleus during 24-48 hpi and reached the peak at 72hpi.

CONCLUSIONS

To our knowledge, this is the first to reveal the dynamic distribution of medium virulence CSFV RNA in PK15 cells using the ViewRNA ISH method. The sensitivity of the ViewRNA ISH was three to four orders of magnitude higher than that of FAT and IHC methods. The specificity experiment showed that the ViewRNA ISH was highly specific for CSFV and no cross-reaction occurred to negative control and other pig infectious virus. This assay is more suitable for studying the CSFV RNA life cycle in cell nucleus. The results proved that CSFV RNA enters into PK15 cells earlier than 0.5hpi, relative to the eclipse period of cytoplasm is 6-9 hpi and CSFV RNA has ever existed in nucleus.

In situ hybridisation: Technologies and their application to understanding disease

In situ hybridisation (ISH) is unique amongst molecular analysis methods in providing for the precise microscopic localisation of genes, mRNA and microRNA in metaphase spreads, cell and tissue preparations. The method is well established as a tool to guide appropriate therapeutic intervention in breast, gastric and lung cancer. With the description of ultrasensitive ISH technologies for low copy mRNA demonstration and the relative ease by which microRNA can be visualised, the applications for research and diagnostic purposes is set to increase dramatically. In this review ISH is considered with emphasis on recent technological developments and surveyed for present and future applications in the context of the demonstration of genes, mRNA and microRNA in health and disease.

Clinicopathological significance of PTPN12 expression in human breast cancer

Protein tyrosine phosphatase non-receptor type 12 (PTPN12) is a recently identified tumor suppressor gene (TSG) that is frequently compromised in human triple-negative breast cancer. In the present study, we investigated the expression of PTPN12 protein by patients with breast cancer in a Chinese population and the relationship between PTPN12 expression levels and patient clinicopathological features and prognosis. Additionally, we explored the underlying down-regulation mechanism from the perspective of an epigenetic alteration. We examined PTPN12 mRNA expression in five breast cancer cell lines using semi-quantitative reverse-transcription PCR, and detected PTPN12 protein expression using immunohistochemistry in 150 primary invasive breast cancer cases and paired adjacent non-tumor tissues. Methylation-specific PCR was performed to analyze the promoter CpG island methylation status of PTPN12. PTPN12 was significantly down-regulated in breast cancer cases (48/150) compared to adjacent noncancerous tissues (17/150; P < 0.05). Furthermore, low expression of PTPN12 showed a significant positive correlation with tumor size (P = 0.047), lymph node metastasis (P = 0.001), distant metastasis (P = 0.009), histological grade (P = 0.012), and survival time (P = 0.019). Additionally, promoter CpG island hypermethylation occurs more frequently in breast cancer cases and breast cancer cell lines with low PTPN12 expression. Our findings suggest that PTPN12 is potentially a methylation-silenced TSG for breast cancer that may play an important role in breast carcinogenesis and could potentially serve as an independent prognostic factor for invasive breast cancer patients.

Prognostic Significance of High Expression of ER-beta in Surgically Treated ER-Positive Breast Cancer Following Endocrine Therapy

PURPOSE

This study evaluated estrogen receptor (ER)-beta mRNA and ER-beta protein expression and its prognostic implications in hormone receptor-positive breast cancer.

METHODS

Paraffin sections from 139 hormone receptor-positive breast cancer cases were prepared. The expression of ER-beta mRNA and protein were analyzed by branched-chain assay and immunohistochemistry (IHC), respectively.

RESULTS

The Allred score of ER-beta IHC was correlated with smaller tumor size (p=0.043), the Allred score of ER-alpha IHC (p<0.001), and the Allred score of progesterone receptor (PR) IHC (p=0.022) but not with the HER2 IHC score. ER-beta mRNA level was correlated with PR mRNA levels (p<0.001) but not with the Allred score of ER-beta IHC, ER-alpha IHC, and PR IHC, nor with the HER2 IHC score and ER-alpha mRNA level. In survival analysis, high expression of ER-beta mRNA was associated with worse disease-free survival along with poor differentiation, lymph node metastasis and absence of PR protein expression in univariate analysis (p=0.040, p=0.002, p=0.018, and p=0.007, respectively) and multivariate analysis (p=0.044, p=0.002, p=0.035, and p=0.007, respectively).

CONCLUSION

High expression of ER-beta mRNA is an independent predictor of disease recurrence in hormone-receptor-positive breast cancer.