Adding to the complexity of data is the discovery of “driver” genes and their role in cancer biology. Elli Papaemmunuil and colleague, reporting on behalf of Chronic Myeloid Disorders Working Group of the International Cancer Genome, recently described their analysis of oncogenic mutations in large, well-characterized patient cohorts of myelodysplastic syndromes (MDS), characterized by dysplasia, ineffective hematopoiesis, and a variable risk of progression to acute myeloid leukemia.
Using previously identified mutations in genes implicated in RNA splicing, DNA modification, chromatin regulation, and cell signaling, the investigators sequenced 111 genes across 738 patients with MDS or closely related neoplasms to explore the role of acquired mutations in MDS biology and clinical phenotype.
The scientists reported that 78% of patients had one or more oncogenic mutations and that they could identify complex patterns of pairwise association between genes, indicative of epistatic interactions involving components of the spliceosome machinery and epigenetic modifiers.
This data suggests a hypothesis of genetic “predestination,” in which early driver mutations, typically affecting genes involved in RNA splicing, “dictate future trajectories of disease evolution with distinct clinical phenotypes.” Driver mutations had equivalent prognostic significance, whether clonal or subclonal, and leukemia-free survival deteriorated steadily as the number of driver mutations increased. The authors concluded that analysis of oncogenic mutations in large, well-characterized cohorts of patients illustrates the interconnections between the cancer genome and disease biology, with considerable potential for clinical application.
Further illustrating the complexity confronting scientists and clinicians in translating OMICs into clinically useful information is the recent development by Dutta et al., of a data-integration method to identify gene networks that drive the biology of breast cancer clinical subtypes.
The key objective of their work, the team said, was to shift the focus away from driver genes, derived from a long list of amplified genes, to identifying driver-networks. This strategy, they noted, not only includes driver genes but also reveals the associated deregulated networks/pathways. They argue that targeting individual driver genes is often difficult as not all driver genes are appropriate drug targets, and that finding a suitable drug target from members of a driver-network might be more feasible.
Their computational method simultaneously overlays gene expression and gene copy number data on protein–protein interaction, transcriptional-regulatory, and signaling networks by identifying coincident genomic and transcriptional disturbances in local network “neighborhoods.”
The scientists identified distinct driver-networks for each of the three common clinical breast cancer subtypes: estrogen receptor (ER)þ, human epidermal growth factor receptor 2 (HER2)þ, and triple receptor-negative breast cancers (TNBC) from patient and cell-line datasets.
In one example of clinical relevance, the scientists’ TNBC analysis identified the LYN kinase as an important hub of the main driver-network in addition to EGFR in both patent tumor cells and cell-line datasets. Although EGFR was identified as the main hub gene and has been shown to inhibit the growth of TNBC cell lines, EGFR inhibitors alone or combined with carboplatin chemotherapy showed very little activity in the clinic in past studies.
Their network analysis suggests potential combination therapy approaches, such as inhibiting LYN and other network EGFR partners in order to improve efficacy.