Identification of a Notch transcriptomic signature for breast cancer [dataset1]
Dysregulated Notch signalling contributes to breast cancer development and progression, but validated tools to measure the level of Notch signalling in breast cancer subtypes and in response to systemic therapy are largely lacking. A transcriptomic signature of Notch signalling would thus be warranted, and in this report, we have established such a classifier. To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated and reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by ?-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and an optimal 20-gene signature was selected from analysis of the transcriptomes of a broader breast cancer cell line cohort. We validated the signature on two independent patient datasets (METABRIC and Oslo2) and it showed an improved coherence score and tumour specificity compared with previously published signatures. Furthermore, the signature score was particularly high for basal-like breast cancer, indicating an enhanced level of Notch signalling in this subtype. The signature score was increased after neoadjuvant treatment in the PROMIX and BEAUTY patient cohorts, and a lower signature score generally correlated with better clinical outcome. Collectively, the 20-gene transcriptional signature has the potential to better stratify patients and to evaluate the response of future Notch-based therapies for breast cancer. Overall design: To generate the signature, we first identified Notch-regulated genes from six basal-like breast cancer cell lines subjected to elevated and reduced Notch signalling by culturing on immobilized Notch ligand Jagged1 or blockade of Notch by g-secretase inhibitors, respectively. From this cadre of Notch-regulated genes, we developed candidate transcriptomic signatures that were trained on a breast cancer patient dataset (the TCGA-BRCA cohort) and a broader breast cancer cell line cohort. To explore the accuracy and robustness of the three signatures further, we generated a novel transcriptomic dataset using the same principal experimental set up for activation (Jagged1) and blockade (DAPT) of Notch signalling as described,but this time from a larger panel of  breast cancer cell lines from different subtypes of breast cancer. Untreated samples (FC) as well as additional control samples (Jag1+DAPT) were sequenced as well. Clustering was done by 'cBot' and samples were sequenced on HiSeq2500 (HiSeq Control Software 2.2.58/RTA 1.18.64) with a 1x51 setup using 'HiSeq SBS Kit v4' chemistry. The Bcl to FastQ conversion was performed using None from the CASAVA software suite. The quality scale used is Sanger / phred33 / Illumina 1.8+.  Library preparation, Sequencing; Data Processing Validated under ISO accreditation 17025:2005 The authors would like to acknowledge support from Science for Life Laboratory, the National Genomics Infrastructure, NGI, and Uppmax for providing assistance in massive parallel sequencing and computational infrastructure. MultiQC report, library info TPM and Limma R object analysis files ( (supplied as compressed files)