Abstract P1-05-22: The value of RNA-Seq for the detection of clinically actionable targets in breast cancer - A small cohort analysis
Authors: Meissner T , Amallraja A , Mark A , Andrews A , Connolly C , Young B , De P , Williams C , Leyland-Jones B 
Published: March 2017

Abstract
Introduction
Next generation sequencing has facilitated the understanding of pathogenesis and molecular heterogeneity of breast cancer (BC) as well as accelerated the path towards precision medicine. DNA sequencing (DNA-Seq) based assays for the detection of mutations and alterations in solid and hematologic cancers are finding their way into clinical practice and are readily available as clinical products. RNA sequencing (RNA-Seq), so far being vastly applied in the research context, promises to expand the diagnostic, prognostic and therapeutic use of this technology in cancer. Beyond mutational status, RNA-Seq enables the detection of fusions, quantification of gene expression level, detection of differentially expressed genes, molecular based subtyping, and risk-stratification. In this study we analyzed RNA-Seq and copy number data from BC patients that had undergone DNA-Seq based diagnostics through commercial providers with the goal to detect additional actionable targets.
Materials and Methods
We included 18 BC patients (5/18 triple negative) that had previously undergone DNA-based targeted (321 genes) sequencing. RNA-Seq to a minimum of 75M reads (75pb) was performed using 100 ng of total RNA on the Illumina NextSeq 500 platform. STAR was used for alignment (hg19) and gene expression quantification (RefSeq). Fusions were detected using STAR-Fusion. DESeq2 was utilized to identify patient specific differentially expressed genes by analyzing samples individually against a set of 13 controls from healthy breast tissue generated in-house. Copy number variations (CNVs) were detected using the Nanostring CNV Cancer panel (89 genes) on the Nanostring nCounter platform. Differentially upregulated or amplified genes were queried against DGIdb and Gene Drug Knowledge database for suitable drug matches, limiting the queries to clinically actionable antineoplastic drugs.
Results
Analyzing the cohort of 18 BC patients, we detected on average 26 BC relevant genes (526 total, log2 FC &gt; 2) to be upregulated per patient. Querying the upregulated genes against DGIdb, we found a total of 18 genes that had drug matches and fulfilled the criteria of being actionable antineoplastic drugs, with 17/18 samples having a minimum of two gene targets (avg: 4). Most frequent upregulated genes were TOP2A (83%), AURKA (61%), AURKB (56%), RET (39%)and FGFR3 (28%). In the case of CNVs, 12/18 patients showed at least one gene target with clinically actionable drugs associated. This was observed across 12 gene targets that were amplified (avg: 3) and 4 gene targets that underwent deletions (avg: 1). Most frequent CNVs included MYC (14%) and CCND1 (12%). 4/7 patients having an AURKA overexpression also showed an AURKA amplification on the CNV assay. 10/18 patients had fusions events, with an average of three fusions per patient, including GAB2-WNT11, PAK1-TENM4 and FGFR2-CEP55 fusions.
Conclusions
We show that RNA-Seq and copy number assays provide additional clinical value by detecting suitable drug targets beyond traditional DNA-based approaches. We are conducting further analysis on how these additionally derived drug targets could improve the current treatment schedule of those patients.
Citation Format: Meissner T, Amallraja A, Mark A, Andrews A, Connolly C, Young B, De P, Williams C, Leyland-Jones B. The value of RNA-Seq for the detection of clinically actionable targets in breast cancer - A small cohort analysis [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-05-22.