Abstract

The use of gene expression profiling (GEP) in cancer management is rising, as GEP can be used for disease classification and diagnosis, tailoring treatment to underlying genetic determinants of pharmacological response, monitoring of therapy response, and prognosis. However, the reliability of GEP heavily depends on the input of RNA in sufficient quantity and quality. This highlights the need for standard procedures to ensure best practices for RNA extraction from often small tumor biopsies with variable tissue handling. We optimized an RNA extraction protocol from fresh-frozen (FF) core needle biopsies (CNB) from breast cancer patients and from formalin-fixed paraffin-embedded (FFPE) tissue when FF CNB did not yield sufficient RNA. Methods to avoid ribonucleases andto homogenize or to deparaffinize tissues and the impact of tissue composition on RNA extraction were studied. Additionally, RNA’s compatibility with the nanoString nCounter® technology was studied. This technology platform enables GEP using small RNA fragments. After optimization of the protocol, RNA of high quality and sufficient quantity was obtained from FF CNB in 92% of samples. For the remaining 8% of cases, FFPE material prepared by the pathology department was used for RNA extraction. Both resulting RNA end products are compatible with the nanoString nCounter® technology.

Highlights

  • We showed that the extraction of high-yield and high-quality RNA was feasible for >90% of FF core needle biopsies (CNB) samples and that formalin-fixed paraffin-embedded (FFPE) samples served as an adequate back-up source for RNA extraction in cases where RNA extraction failed

  • We hypothesized that FF CNB as starting material would result in higher amounts of intact

  • Most studies focus on the use of clinically available FFPE specimens as starting material to perform downstream gene expression profiling (GEP) [31]

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Summary

Introduction

Gene expression profiling (GEP) has proven a valuable strategy to advance our understanding of the molecular landscape and drug resistance mechanisms of several cancer types, including breast cancer (BC) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20].With respect to BC, GEP is increasingly performed in routine practice, guiding patient management [19]. Biomolecules 2021, 11, 621 transcriptional signatures for estrogen receptor+ (ER+ , luminal), human epidermal growth factor receptor 2+ (HER2+ ) ERBB2-amplified, and ER− , progesterone receptor− (PR− ). HER2− (basal) BC [21] This has resulted in the development of kits for GEP of BC samples, such as Mammaprint, Oncotype Dx Breast, Prosigna PAM50 Breast Cancer. Prognostic Gene Signature Assay, Breast Cancer Index, and EndoPredict [22]. More detailed BC molecular subclasses have been defined. These are associated with therapy outcome [23,24]

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