Tumor tissue protein signatures reflect histological grade of breast cancer

Assessment of Breast Cancer Immunohistochemical Properties with Demographics and Pathological Features; A Retrospective Study

Background: Breast cancer is considered the most common malignant disease in the female population. It is known as an emerging epidemy with a great burden on women's health, which can be associated with poor outcomes. Some factors including histological type, immunohistochemistry (IHC), tumor grade, and tumor size can have effects on breast cancer. Objectives: This study aimed at assessing the effects of mentioned factors on IHC type of breast cancer. Methods: This retrospective cross-sectional study was conducted on 142 patients, who were referred to one of the referral centers for breast cancer in Mashhad. Information including age, histological type, familial history, menopause status, tumor grade, tumor size, and IHC properties was collected from the patient’s medical records. Allred score was used for reporting hormonal status. The data were analyzed by version 26 of SPSS software. Results: The mean age of patient was 50.2 ± 12.7. The frequency of luminal A and luminal B type was calculated as 29.7 and 18.9%, respectively. In addition, triple-negative IHC type has a prevalence of 24.3% and HER2 had a prevalence of 27%. There were no significant differences between age (P = 0.34), familial history (P = 0.42), menopause (P = 0.36), histological type (invasive: P = 0.11, in situ: P = 0.45), and IHC properties. However, tumor diameter (P = 0.0001) and tumor grading (P = 0.002) had significant association with IHC properties. Conclusions: Factors including tumor size and pathological grade can have effects on the gene expression properties of breast cancers. Luminal IHC type A is more common in breast cancer and is associated with better outcomes. However, age, histological type, familial history, and menopause status had no effects on the IHC properties of breast cancer.

Proteomic Data Analysis for Differential Profiling of the Autoimmune Diseases SLE, RA, SS, and ANCA-Associated Vasculitis

Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren’s syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test, and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting each disease (raw data set available at https://figshare.com/s/3bd3848a28ef6e7ae9a9.). In this study, we were able to classify the included individual IRDs with high accuracy, as demonstrated by the ROC area under the curve (ROC AUC) values ranging between 0.96 and 0.80. In addition, the groups of IRDs could be separated from healthy controls at an ROC AUC value of 0.94. Disease-specific candidate biomarker signatures and general autoimmune signature were identified, including several deregulated analytes. This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial complex diseases, such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ- and tissue-related damage.

The potential of cystatin C as a predictive biomarker in breast cancer

INTRODUCTION

Breast cancer (BCa) is the leading cause of cancer-related deaths among women. Numerous efforts are being directed toward identifying novel tissue and/or circulating molecular markers that may help clinicians in detecting early-stage BCa patients and in providing an accurate estimation of the prognosis and prediction of response to clinical treatments. In this setting, emerging evidence has indicated Cystatin C (Cyst C), as the most potent endogenous inhibitor of cysteine cathepsins, as a possible useful marker in the clinical management of BCa patients.

AREAS COVERED

This review analyzes the results of emerging studies underpinning a potential clinical role of Cyst C, as additional marker in BCa.

EXPERT OPINION

Cyst C expression levels have been reported to be altered in tumor tissues and/or in biological fluids of BCa patients. Furthermore, clinical evidence has highlighted a significant correlation between altered Cyst C levels in tumor tissues and/or biological fluids and some clinco-biological parameters of BCa progression. These findings provide evidence for a potential clinical use of Cyst C as a novel marker to improve the clinical and therapeutic management of BCa patients and as a gauge for better clarifying the role of cysteine proteinases in the various steps of BCa progression.

Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

BACKGROUND

Endoscopic ultrasound-guided fine-needle aspiration fails to diagnose up to 25% of patients with pancreatic ductal adenocarcinoma (PDAC). Proteomics can help to overcome this clinical dilemma. We hypothesized that soluble protein signatures can differentiate PDAC from benign tissues.

STUDY DESIGN

Tissues were obtained from resected surgical specimens, lysed, and homogenates collected for analysis with a 41-protein multiplex assay. Analyte concentrations were normalized to total protein. Statistical analysis was performed to evaluate for differences in PDAC vs benign tissue.

RESULTS

Tissues were obtained from 159 patients, 82 patients with PDAC naïve to therapy and 77 with benign pancreatic pathology. Fourteen analytes had a receiver operating characteristic curve area of >0.75 for predicting PDAC vs benign tissue. A recursive partitioning model using only 2 analytes, interleukin 1 receptor antagonist and transforming growth factor-α, provided an accuracy, sensitivity, and specificity of 91.2%, 90.2%, and 92.2%, respectively. A penalized logistic regression model found 12 analytes that provide diagnostic value to a protein signature. The mean area under the receiver operating characteristic after 50 tenfold cross-validations was 0.951. Accuracy, sensitivity, and specificity of this model were 91.2%, 87.8%, and 94.8%, respectively. Applying the scenario of 80% disease prevalence in patients undergoing endoscopic ultrasound with fine-needle aspiration for a pancreatic head mass, positive predictive value is 98.5% (95% CI 93.0% to 99.7%) and negative predictive value is 66.0% (95% CI 54.9% to 75.6%).

CONCLUSIONS

Protein signatures from pancreatic specimens can differentiate PDAC from benign tissue. Additional work to validate these findings in a unique sample set is warranted.

Identification of a 3-mRNA signature as a novel potential prognostic biomarker in patients with ovarian serous cystadenocarcinoma in G2 and G3

The use of mRNAs as biomarkers serves to diagnose, treat, as well as aid the prognosis of cancer. The present study involved an analysis of mRNAs in the cell cycle at the G2 and G3 tumor grades for the prognosis of ovarian serous cystadenocarcinoma (OSC) using 364 clinical samples (G2:G3=42:322). Statistics aided the identification of NPFFR2, XPNPEP2 and CELA3B; the 3-mRNA model that allows for classification of patients into high- and low-risk groups using a median value of 0.9580745. The rates of survival varied (P=0.00149) and the independent detection of stratification of the risk of this disease was validated with success using the 3-mRNA signature, which was demonstrated to be more successful than the weight model. This approach was revealed to provide the prognosis of grade G2 and G3 in patients with OSC compared with factors used traditionally. Compared with traditional factors, this 3-mRNA model was demonstrated to be the only and independent prognostic factor for patients with G2 and G3 stage OSC. A literature survey was also performed in the present study in order to assess the role of the 3 genes and indirectly prove their effectiveness. The establishment of this new genetic model will enhance prospective prognosis and treatment for patients with OSC.

Current applications of antibody microarrays

The concept of antibody microarrays is one of the most versatile approaches within multiplexed immunoassay technologies. These types of arrays have increasingly become an attractive tool for the exploratory detection and study of protein abundance, function, pathways, and potential drug targets. Due to the properties of the antibody microarrays and their potential use in basic research and clinical analytics, various types of antibody microarrays have already been developed. In spite of the growing number of studies utilizing this technique, few reviews about antibody microarray technology have been presented to reflect the quality and future uses of the generated data. In this review, we provide a summary of the recent applications of antibody microarray techniques in basic biology and clinical studies, providing insights into the current trends and future of protein analysis.