The combinatorial approach of laser-captured microdissection and reverse transcription quantitative polymerase chain reaction accurately determines HER2 status in breast cancer

Establishment of Rapid and Accurate Screening System for Molecular Target Therapy of Osteosarcoma

Introduction Comprehensive analyses using clinical sequences subcategorized osteosarcoma (OS) into several groups according to the activated signaling pathways. Mutually exclusive co-occurrences of gene amplification (PDGFRA/KIT/KDR, VEGFA/CCND3, and MDM2/CDK4) have been identified in approximately 40% of OS, representing candidate subsets for clinical evaluation of additional therapeutic options. Thus, it would be desirable to evaluate the specific gene amplification before starting therapy in patients with OS. Materials and Methods This is a retrospective study. We examined 13 cases of clinical OS samples using NanoString-based copy number variation (CNV) analysis. Decalcification and chemotherapeutic effects on this analysis were also assessed. Results First, the accuracy of this system was validated by showing that amplification/deletion data obtained from this system using various types of cancer cell lines almost perfectly matched to that from the Cancer Cell Line Encyclopedia (CCLE). We identified potentially actionable alterations in CDK4/MDM2 amplification in 10% of samples and potential additional therapeutic targets (PDGFRA/KIT/KDR and VEGFA/CCND3) in 20% of samples, which is consistent with the reported frequencies. Furthermore, this assay could identify these potential therapeutic targets regardless of the sample status (frozen vs formalin-fixed paraffin-embedded [FFPE] tissues). Conclusion We established a NanoString-based rapid and cost-effective method with a short turnaround time (TAT) to examine gene amplification status in OS. This CNV analysis using FFPE samples is recommended where the histological evaluation of viable tumor cells is possible, especially for tumors after chemotherapy with higher chemotherapeutic effects.

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.

Comparing protein and mRNA expressions of the human epidermal growth factor receptor family in estrogen receptor-positive breast cancer

The human epidermal growth factor receptor (HER) family plays a vital role in the development of resistance to treatments in estrogen receptor (ER)-positive breast cancer. This study investigated the correlation between protein and mRNA expressions of the HER family in ER-positive breast cancer. We dissected regions of invasive cancer from the frozen tissues of 34 patients with ER-positive breast cancer using laser-capture microdissection, followed by evaluation of the mRNA levels of the ER and HER family (EGFR, HER2, HER3, and HER4) using the quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. In addition, we assessed the protein expressions of the ER and HER family using an immunohistochemical (IHC) assay. A significant correlation was observed between the ER protein and mRNA expressions. For HER2, HER3, and HER4, protein expressions significantly correlated with mRNA levels. We established significant correlations of the mRNA level between EGFR versus HER2, as well as EGFR versus HER3. Furthermore, a significant correlation of the mRNA level between HER2 and HER3 was illustrated. In conclusion, IHC evaluation may be reliable and representable for mRNA. Hence, this study established a marked correlation between the mRNA expressions of HER family members in patients with ER-positive breast cancer.

Site-specific gene expression analysis of implant-near cells in a soft tissue infection model - Application of laser microdissection to study biomaterial-associated infection

Analysis of the implant-tissue interface is important for an understanding of the cellular response to biomaterials with different surface characteristics. However, inaccessibility to the site has restricted the detailed evaluation of the tissue surrounding the implant. Laser microdissection enables the isolation of specific cells and tissues for subsequent DNA, RNA, or protein analysis. The present experimental study employed laser microdissection to analyze tissue-specific differences in gene expression in cells around infected or control titanium implants 72 h after subcutaneous implantation in a rat model. Three different tissue zones located 0-800 μm away from the implant-tissue interface were analyzed. Implant sites challenged with a dose of 10⁶  CFU Staphylococcus epidermidis demonstrated higher gene expression of selected markers for inflammation (TNF-α, IL-6), cell recruitment (MCP-1, IL-8, IL-8 R), infection (TLR2), and tissue remodeling (MMP-9) compared with control implants. Furthermore, the gene expression analysis of the three extracted tissue zones revealed marked spatial differences, depending on the distance to the implant. Control implants continuously induced higher cell gene expression in the implant-tissue interface compared with cells 200-800 μm away from the implant, whereas the sites inoculated with S. epidermidis resulted in high gene expression further away from the implant as well. In conclusion, this study demonstrates that laser microdissection is an interesting tool, revealing both gene- and site-specific gene expression patterns in the implant-tissue interface. The technique provides an opportunity for detailed molecular dissection of the biological events related to the implant but occurring at different distances from the implant. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 2210-2217, 2017.

ErbB Family Signalling: A Paradigm for Oncogene Addiction and Personalized Oncology

ErbB family members represent important biomarkers and drug targets for modern precision therapy. They have gained considerable importance as paradigms for oncoprotein addiction and personalized medicine. This review summarizes the current understanding of ErbB proteins in cell signalling and cancer and describes the molecular rationale of prominent cases of ErbB oncoprotein addiction in different cancer types. In addition, we have highlighted experimental technologies for the development of innovative cancer cell models that accurately predicted clinical ErbB drug efficacies. In the future, such cancer models might facilitate the identification and validation of physiologically relevant novel forms of oncoprotein and non-oncoprotein addiction or synthetic lethality. The identification of genotype-drug response relationships will further advance personalized oncology and improve drug efficacy in the clinic. Finally, we review the most important drugs targeting ErbB family members that are under investigation in clinical trials or that made their way already into clinical routine. Taken together, the functional characterization of ErbB oncoproteins have significantly increased our knowledge on predictive biomarkers, oncoprotein addiction and patient stratification and treatment.