Abstract PD01-08: Decoding the Transcriptional Landscape of Triple-Negative Breast Cancer Using Next-Generation Whole Transcriptome Sequencing

Background: Triple-negative breast cancer (TNBC) has been plagued by the absence of targeted therapies. Discovery of therapeutic targets in TNBC has in part, been hampered by an inadequate understanding of the transcriptional biology of the normal breast as an optimal comparator. Using next-generation sequencing, we embarked on a study to compare the transcriptomes of TNBC and normal breast to comprehensively identify novel targets by analyzing all full length transcripts expressed in these tissues.

Methods: Normal breast tissues from healthy pre-menopausal volunteers with no history of disease were procured from the Susan G. Komen for the Cure® Tissue Bank at the IU Simon Cancer Center. To eliminate bias from stromal tissue, normal tissues were laser capture microdissected for ductal epithelium. cDNA libraries from 10 TNBC tumors and 10 normal breast tissues were sequenced on an Applied Biosystems (AB) SOLiD3 sequencer using 50bp fragment runs. For gene expression, mapping of reads to the genome was performed using the AB BioScope 1.2 Pipeline and outputs imported into Partek Genomics Suite for analysis. In Partek, mapped reads were cross-referenced against known genes from the UCSC database followed by statistical comparison of RPKM values for each gene between TNBC and normal. Dimensionality reduction analyses (PCA & Hierarchical clustering) and identification of Novel Transcribed Regions were also performed in Partek, whereas construction of gene networks was performed using Ingenuity Pathway Analysis. To identify gene fusions, partially mapped reads were interrogated utilizing a novel algorithm that searched for reads spanning exons from two different genes. Fusions that were supported by at least 3 reads (of which 2 had to be unique) were considered candidates and were subsequently validated. Results/Discussion: Sequencing produced 1.1 billion reads equaling 57.3GB of data of which 36.0GB (63%) mapped to the human genome. In comparing RPKM values between TNBC and Normal, we report 7140 RefSeq Genes, 22 pre-miRNAs, 109 lincRNA exons, and 15 ultraconserved regions that were differentially expressed between these tissues (FDR<0.01). Biological interpretation of these results reveals upregulation of genes and miRNAs involved in DNA repair, angiogenesis, and inhibitors of Estrogen Receptor-alpha. Some previous drug targets (e.g. EGFR and c-kit) were not found to be upregulated here which may explain lack of clinical success to date. Conversely, PARP was significantly upregulated and early trial results suggest a strong signal for efficacy with inhibition of PARP. We also surveyed the genome for Novel Transcribed Regions (NTRs), defined as areas of significant transcription where no annotated gene is present. When comparing between TNBC and Normal, we report 6408 NTRs to be differentially expressed (FDR<0.01). Lastly, when analyzing the dataset for gene fusions, we identified several gene fusions in the TNBC samples, though no individual fusion was present in more than one sample.

Conclusion: We report an extensive comparison of the transcriptomes of TNBC and normal ductal epithelium. We identified numerous genes previously unknown to be dysregulated in TNBC that can be utilized for therapeutic discovery.

Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr PD01-08.

Next-Generation Whole Transcriptome Sequencing of Triple-Negative Breast Tumors and Normal Tissues

Background: Triple-negative breast cancer predominately affects pre-menopausal women and women of African-American descent and has been plagued by the absence of targeted therapies leading to poor survival. Using a new cutting edge technology, next-generation sequencing, we embarked on a study to analyze the whole transcriptomes of triple-negative tumors and normal tissues from pre-menopausal women in order to comprehensively identify new targets by analyzing all full length transcripts expressed in these tissues. This approach is independent of pre-determined gene selection as is common with microarrays, and allows for the analysis of RNA species that have not been previously profiled in breast cancer.Methods: cDNA libraries were created from RNA isolated from 8 triple-negative tumors and 2 normal breast tissues. Triple negative tumors were procured from Origene Technologies and normal breast tissues were procured from the Susan G. Komen for the Cure tissue bank at Indiana University. Normal samples were from healthy pre-menopausal volunteers with no history of disease. In order to eliminate bias from stromal tissue, normal samples were laser capture microdissected for ductal cells and RNA extracted from the excised tissue. cDNA libraries were prepared and subsequently sequenced on an Applied Biosystems (ABI) SOLiD3 sequencer using a 50bp fragment run. Mapping of whole reads to the human genome was performed using the SOLiD Analysis Pipeline Tool software (ABI) followed by a split-read alignment in order to map reads crossing exon-exon junctions. Gene expression profiles for each sample were then created and statistically compared to identify the most differentially expressed genes. In order to analyze for fusion genes, a split-read alignment of non-mapping reads to a composite transcriptome formed from previously mapped reads (clusters) was performed.Results: Sequencing of the 10 samples produced 513 million filtered reads equaling 25.66GB of data. Mapping of the reads to the genome revealed 1.14 million transcribed regions (exons). A preliminary analysis of gene expression shows 55.2% of the transcribed loci to have significant differential expression between tumor and normal. In a further analysis for gene fusions, several candidate fusions were bioinformatically detected. These are currently being reviewed and validated.Discussion: Herein we present a preliminary analysis of the transcriptomes of triple-negative breast cancers in comparison to normal tissues. A multitude of analyses are ongoing, including but not limited to: gene fusions, differentially expressed novel genes, novel transcripts, alternative splicing, intrinsic subtyping, and presence of viral genes. In addition 2 more triple-negative tumors and 8 normal samples will also be sequenced. In the current analysis, differentially expressed non-coding RNAs was highly pervasive among the samples indicating a major role of this RNA species in tumorigenesis. In addition, triple-negative breast cancers may contain fusion genes that could be “drivers” of this malignancy. Further validation of these differentially expressed RNAs and fusion genes in a larger set of samples with subsequent functional studies is planned.

Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6134.