Exploiting the genome and transcriptome for individualized cancer diagnosis and treatment stratification

Despite major improvements in elucidating the genetic changes underlying the initiation and progression of colorectal cancer (CRC), there remains a clinical need to implement novel targeted therapeutic strategies. Proteins that are highly overexpressed in tumor cells have the potential to be selective therapeutic targets. We focused in our analyses on genes on chromosome 13 that were consistently overexpressed. Using cell based model systems that recapitulate the genomic and gene expression changes we have previously observed in primary CRC we applied a functional genomics strategy to identify such anti-CRC targets. We identified 69 genes within the amplified regions that were over-expressed in the tumors compared to matching normal mucosa samples. Next, we validated the expression levels of these 69 genes in 25 colorectal cancer cell lines using real-time PCR, and confirmed over-expression of 44 genes out of these 69 genes. Subsequently, we conducted an RNAi screen in the colorectal cancer cell lines SW480 and HT29. For 15 out of these 44 genes, we observed a decreased cellular viability as a consequence of mRNA silencing. Our experimental strategy led to the identification of genes that were amplified and/or over-expressed in primary colorectal cancers. We surmise that some of these genes represent potential oncogenes residing on chromosome 13q. In order to identify the underlying signaling pathways involved in reduction of viability, we subsequently analyzed the global transcriptomic changes following RNAi using whole-genome microarrays, and could identify the disruption of a variety of pathways. Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome.