Abstract P4-07-06: Breast cancer subtype classification using NanoString and RNAseq technologies
Authors: Rocca Andrea , Ravaioli Sara , Fonzi Eugenio , Barozzi Iros , Perone Ylenia , Magnani Luca , Pirini Francesca , Martinelli Giovanni , Bravaccini Sara 
Published: March 2020

Background: Immunohistochemistry (IHC) is conventionally used in clinical practice to define Breast Cancer (BC) subtypes, while PAM50 is useful in specific cases. The former is partly subjective and semi-quantitative, the latter is based on gene expression profile. We compared gene expression data obtained by NanoString BC 360™ panel with those obtained by RNAseq on 12 Formalin-Fixed Paraffin-Embedded (FFPE) BC samples.

Methods: RNA was isolated from FFPE tumors and the quality was checked before performing NanoString BC 360™ assay and preparing RNAseq library (NEBNext Ultra II RNA Library Prep Kit, Illumina) following the manufacturers’ instructions. Libraries were sequenced on NextSeq500 (Illumina). Reads were aligned using Kallisto and raw read counts were normalized as Transcripts Per Million (TPM). PAM50 and Tumor Inflammation Signature (TIS) were determined on NanoString data. Two-sided t-test on TPM and on normalized NanoString counts was used to compare luminal and Triple Negative (TN) BCs (p &lt;0.05 statistically significant), considering the 734 genes shared by the 2 assays. Deseq2 (1.22.1) was used to perform a differential gene expression analysis on raw RNAseq counts. Enrichment tests were performed on KEGG pathways (FDR &lt; 0.05). Python and R softwares were used for statistical analyses.

Results: Of the 12 BC samples, 3 were luminal A-like, 3 luminal B (HER2+)-like and 6 TN by IHC (Table 1). Pearson’s correlation coefficient between NanoString counts and RNAseq TPM was 0.72.

Out of the 734 genes shared by both methods, those differentially expressed between luminal and TN BCs, defined based on IHC, were 135 according to RNAseq TPM, 155 according to NanoString counts and 88 according to both methods. These 88 genes belonged to “cell cycle”, “pathways in cancer”, “HIF-1 signaling”, “progesterone-mediated oocyte maturation”, “prostate cancer” and “basal cell carcinoma” KEGG pathways.

When considering the whole transcriptome analyzed by RNAseq, the differential gene expression analysis between luminal and TN/basal BCs was performed according to both IHC and PAM50 classifications. Considering the former, there were 1143 differentially expressed genes out of a total of 20936 (5.5%) and the pathways involved were “fatty acid elongation”, “cell cycle”, “oocyte meiosis”, “p53 signaling” and “alcoholism”. The analysis on the latter classification showed 1608 differentially expressed genes out of 21048 (7.6%), and the enriched pathways were “cell cycle”, “p53 signaling”, “DNA replication”, “alcoholism” and “systemic lupus erythematosus”.

Regarding the TIS, no significant differences were found between TN/basal and luminal BCs, comparing both IHC and the PAM50 subtypes. An expected heterogeneity was found among TNBCs, that showed 3 distinct signatures: basal-like immune activated, basal-like immune-suppressed and luminal androgen receptor.

Conclusions: This preliminary analysis shows a good concordance between NanoString and RNAseq data. The choice of the method must take into account important factors such as the costs, the number of genes analyzed, the requirement of bioinformatic support, the working time. Further analyses on pathway enrichment will be presented.

Table 1. Breast cancer samples characteristics.Sample ID Age Grade T N IHC subtype PAM50 Subtype TIS scoreTNBC signature 0172222aLumALumB7,0NA026031c2aTNBasal7,2BLIA037221cxTNLumA7,4LAR045231c0LumALumA7,5NA053531c1aTNBasal5,5BLIS0675320TNBasal6,2BLIS077931cxLumB HER2+LumB6,7NA0878220LumALumB7,7NA097431a2aTNBasal8,5BLIA106031c1LumB HER2+LumA8,2NA116621b0LumB HER2+LumA6,1NA1241321aTNBasal7,5BLIATIS: Tumor Inflammation Signature; BLIA: Basal-like Immune Activated; BLIS: Basal-like Immune-Suppressed; LAR: Luminal Androgen Receptor.

Citation Format: Andrea Rocca, Sara Ravaioli, Eugenio Fonzi, Iros Barozzi, Ylenia Perone, Luca Magnani, Francesca Pirini, Giovanni Martinelli, Sara Bravaccini. Breast cancer subtype classification using NanoString and RNAseq technologies [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-07-06.