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1.
Cancer ; 130(S8): 1424-1434, 2024 Apr 15.
Article in English | MEDLINE | ID: mdl-38217532

ABSTRACT

BACKGROUND: Immunohistochemistry (IHC) is an essential technique in surgical and clinical pathology for detecting diagnostic, prognostic, and predictive biomarkers for personalized cancer therapy. However, the lack of standardization and reference controls results in poor reproducibility, and a reliable tool for IHC quantification is urgently required. The objective of this study was to describe a novel approach in which H3F3B (histone H3, family 3B) can be used as an internal reference standard to quantify protein expression levels using IHC. METHODS: The authors enrolled 89 patients who had human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). They used a novel IHC-based assay to measure protein expression using H3F3B as the internal reference standard. H3F3B was uniformly expressed at the protein level in all tumor regions in cancer tissues. HER2 expression levels were measured with the H-score using HALO software. RESULTS: Kaplan-Meier analysis indicated that, among patients who had HER2-positive BC in The Cancer Genome Atlas data set and the authors' data set, the subgroup with low HER2 expression had a significantly better prognosis than the subgroup with high HER2 expression. Furthermore, the authors observed that HER2 expression levels were precisely evaluated using the proposed method, which can classify patients who are at higher risk of HER2-positive BC to receive trastuzumab-based adjuvant therapy. Dual-color IHC with H3F3B is an excellent tool for internal and external quality control of HER2 expression assays. CONCLUSIONS: The proposed IHC-based quantification method accurately assesses HER2 expression levels and provides insights for predicting clinical prognosis in patients with HER2-positive BC who receive trastuzumab-based adjuvant therapy.


Subject(s)
Breast Neoplasms , Humans , Female , Breast Neoplasms/pathology , Histones , Immunohistochemistry , Reproducibility of Results , Receptor, ErbB-2/genetics , Trastuzumab/therapeutic use , Reference Standards , Biomarkers, Tumor/metabolism
2.
Anticancer Drugs ; 33(1): e500-e506, 2022 01 01.
Article in English | MEDLINE | ID: mdl-34321420

ABSTRACT

Phosphoglycerate mutase (PGAM) is a critical enzyme in glycolysis. PGAM2 is abundant in several types of tissues and malignant tumours. However, there is limited information regarding their clinicopathological significance in dysplastic nodules and hepatocellular carcinoma (HCC). This study aims to investigate the prognostic value of PGAM2 as a new biomarker for HCC. The PGAM2 expression level was evaluated by immunohistochemistry in liver cirrhosis (n = 10), low-grade dysplastic nodules (n = 15), high-grade dysplastic nodules (n = 15) and HCCs (n = 20) and 178 pairs of HCC and adjacent peritumoral liver tissues. We selected X-tile software for counting cut-point based on the outcomes for prognosis analysis, and used Kaplan-Meier analysis and Cox regression analysis can assess the prognosis of clinicopathologic parameters. Nuclear PGAM2 was significantly overexpressed in peritumoral liver tissues compared with HCC tissues (P = 0.0010). Kaplan-Meier analyses of 178 HCC samples revealed that nuclear PGAM2's high expression level, but not cytoplasmic PGAM2, was significantly related to good overall survival rate (OS). In addition, univariate and multivariate Cox analyses indicated nuclear PGAM2 expression could be regarded as valuable predictors for OS in HCC. PGAM2 was highly expressed in HCC tissues than liver cirrhosis tissues, and nuclear PGAM2's high expression might demonstrate HCC patients have poor postoperative results. Thus, nuclear PGAM2 can be regarded as valuable predictors for OS in HCC patients after surgery.


Subject(s)
Carcinoma, Hepatocellular/pathology , Liver Neoplasms/pathology , Phosphoglycerate Mutase/biosynthesis , Biomarkers, Tumor , Carcinoma, Hepatocellular/mortality , Female , Humans , Kaplan-Meier Estimate , Liver Cirrhosis/pathology , Liver Neoplasms/mortality , Male , Middle Aged , Prognosis , Regression Analysis
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