Identification of cancer subtype specific vulnerability genes as novel therapeutic targets

Abstract

Outcome for many types of cancer has improved significantly in recent years. However, most current cancer treatments are highly toxic and result in significant side-effects. This can lead to treatment failure and to severe long-term health problems among survivors. Thus, there is an urgent need for more cancer-specific, less toxic cancer therapies. Identification of cancerspecific vulnerability genes is one of the most promising approaches for developing more effective and less toxic cancer treatments. Cancer genomes exhibit thousands of changes in DNA methylation and gene expression, with the vast majority likely to be passenger changes. We hypothesised that, through integration of genome-wide DNA methylation/expression data, we could exploit this inherent variability to identify cancer subtype-specific vulnerability genes that would represent novel therapeutic targets that could allow cancer-specific cell killing. We developed a bioinformatics pipeline integrating genome-wide DNA methylation/gene expression data to identify true signal in the noise and identify candidate subtype-specific vulnerability partner genes for the genetic drivers of individual genetic/molecular subtypes. We demonstrated our method was applicable to multiple childhood cancers (Acute Lymphoblastic Leukaemia and Medulloblastoma and common B-lymphocyte derived malignancies) and was able to identify subtype-specific vulnerability genes in almost all subtypes assessed. Furthermore, for most tested candidates (7/9) targeting the genes with siRNA was able to inhibit proliferation and induce apoptosis specifically in the corresponding cancer subtypes. Additionally, in cancer lacking well defined molecular subtypes (Neuroblastoma), we utilised DNA methylation data for deriving novel molecular subtypes and used as basis for identification of candidate subtype vulnerable genes. We further show that utilising the newly identified methylation-based subgroups allowed identification of subtypespecific vulnerability gene candidates in four of the five subgroups. We, therefore, presents a 7 novel approach that integrates genome-wide DNA methylation/expression data to identify cancer subtype-specific vulnerability genes as novel therapeutic targets. A key underlying aspect of our initial analysis is that altered methylation observed in cancer cells was predominantly linked to cell proliferation (i.e., was replicated in both transformed and normal cells after extensive proliferation). Thus, to extend the original approach we assessed comparative methylation changes across multiple sites of malignant and normal Blymphocyte subsets to allowing mapping of the derivation of all methylation changes in the cells. This analysis identified that only a tiny fraction of methylation changes (1-2%) occurring in B-cell malignancies appear to be disease specific and furthermore that interrogation of these specific methylation changes could be used to identify cancer-specific vulnerability and tumour suppressor candidates of relevance for all molecular subtypes of specific B-cell derived malignancies. Overall, we show that a detailed analysis of the origin of methylation changes across cancer cell types can allow identification of key methylation changes and identify potential new cancer specific therapeutic targets.