Abstract 5255: Breast cancer subtype classification using a multi-gene expression profile
Authors: Ravaioli Sara , Pirini Francesca , Rocca Andrea , Puccetti Maurizio , Bonafè Massimiliano , Martinelli Giovanni , Bravaccini Sara 
Published: August 2019

Background: Immunohistochemistry (IHC) is the method conventionally used in clinical practice to define breast cancer (BC) subtypes. This technique is subjective and semi-quantitative. Molecular approaches like multigene panels are increasingly used in clinical practice for their reliability and accuracy. This preliminary study was performed to assess the applicability of the NanoString Breast Cancer 360™ panel, which analyze 770 transcripts of genes involved in a number of signatures useful to define the molecular subtype. We evaluated the correlation between the gene expression levels with the IHC values of conventional biomarkers on 12 BC formalin-fixed paraffin-embedded (FFPE) samples.

Methods: Immunostaining was performed by using Ventana Benchmark Ultra system. Anti-ER, -PR, -Ki67 and -HER2 Ventana antibodies were used. Tumors were classified according to the most recent St. Gallen classification. RNA was isolated from FFPE tumor with AllPrep DNA/RNA FFPE Kit and quantified by Nanodrop. The Breast Cancer 360™ panel assay was performed according to manufacturer’s instructions. The normalized count for each gene analyzed using nSolver analysis software was compared with the respective IHC biomarker status by Mann-Whitney test to assess the correlation between the two methods. The mean mRNA count was compared between luminal and triple negative BC by unpaired T test. All statistical analyses were performed by Graphpad Prism software 5.

Results: The 12 BC samples were classified in 3 luminal A, 3 luminal B (HER2+) and 6 triple negative tumors by IHC. The expression level of ESR1, PGR, MKI67 and ERBB2 genes showed a statistically significant correlation with the corresponding protein expression (p=0.0022, p=0.0054, p=0.0025 and p=0.0091, respectively).

A significant difference in the expression levels of ERBB2 gene between HER2+ and HER2- BC was observed (mean mRNA counts 16848 vs 1307, p=0,035). Furthermore, ESR1 expression was significantly higher in ER+ than in ER- BC (mean mRNA counts 9105 vs 195, p=0,021). We also evaluated the differential expression between luminal and triple negative BC for AR, EGFR, HIF1A, MKI67, MYC, NOTCH1, RAD51 genes. The expression levels for all these genes were significantly higher in triple negative cases than luminal BC, except for AR that was higher in luminal tumors (p&lt;0.05).

Conclusions: In this study Breast Cancer 360™ panel showed high concordance with IHC data. We are aware that our preliminary results require validation in a larger case series to establish the real value of this multi-gene expression profile in BC. However, our results highlight the potential of this molecular approach to properly identify tumor subtypes, allowing a better management of BC patients.

Citation Format: Sara Ravaioli, Francesca Pirini, Andrea Rocca, Maurizio Puccetti, Massimiliano Bonafè, Giovanni Martinelli, Sara Bravaccini. Breast cancer subtype classification using a multi-gene expression profile [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5255.