Modeling the genomic landscape of HER2-positive breast cancer
We are in the process of deep-sequence analysis of the transcriptome of HER2-positive cell lines and tumors using paired end-tagged RNA sequence (RNA-seq) technology.
We have completed RNA-seq analysis of a survey panel of tumors, including ER positive, HER2 positive and triple negative. In addition, we have carried out RNA-seq analysis of a panel of benign breast tumors, which we used to build a HER2 transcriptome map.
In parallel, we have isolated and characterized trastuzumab-resistant HER2 cell lines (BT474 and SKBR3). We recently completed RNA-seq analysis of both sensitive and resistant cell lines after treatment with trastuzumab and following ERBB2 knockdown with two independent shRNA constructs. These data are being analyzed to identify HER2-regulated genes, which will be evaluated for mechanistic insight, as biomarkers of HER2 activity, and as potential therapeutic targets.
Also in parallel, we are carrying out deep sequence analysis of genomic rearrangements in HER2-positive tumors and patient-matched normal DNA using mate-pair tagged genomic sequence technology. We have invested a considerable amount of time and effort into developing analytical workflows to identify genomic rearrangements using mate-pair genomic sequence analysis.
Two major conclusions
Two major conclusions are derived from these studies:
- First, the majority of our fusion transcripts arise due to genomic rearrangements. This outcome substantiates our view that fusion transcripts can be used as surrogate markers of chromosomal instability.
- Second, we have satisfied ourselves that mate-pair sequencing does not produce sufficient depth of coverage for detailed mapping of genomic rearrangements. Our future directions in this regard will therefore focus on whole-genome sequencing, which is becoming increasingly cost-effective.
The ability to integrate various types of genomic data remains a major challenge in cancer genomics.
Developing new analytic tools
We recently completed the construction of genomic interactome maps of HER2-positive breast cancer. The evolution of this landscape model has required that we develop new analytical tools for quantifying splice variants and for identifying and quantifying expressed single nucleotide sequence variants (eSNVs) in tumor RNA.
Using these tools, we have been able to build a landscape model that incorporates differential mRNA expression, alternative splicing and HER2-specific eSNVs. Components of this pathway have been linked to therapeutic response, using our genomic data from the North Central Cancer Treatment Group N9831 HER2 adjuvant trial.
These data are also being used to interrogate the functional HER2 signaling model that we are building using data from sensitive and resistant cell lines treated with trastuzumab and following ERBB2 knockdown.
In parallel, we also are interrogating phosphoproteomic data (generated in collaboration with Emanuel Petricoin) in an effort to link genomic changes to changes in protein phosphorylation status. These studies are currently exploratory.