Workflow For Whole Cancer Genome Sequencing
Dr. Ideker in his interview with Decoded Science explained it thus: “When you look at patients’ data at the level of genes, everybody looks different. But groupings start to appear, when you look at impacted biological networks. In other words, no genes are mutated in exactly the same place, but the mutations do appear in the same genetic pathways.”
Specifically, the authors utilized a method, network-based stratification (NBS) to form subtypes of patients suffering from lung, uterine and ovarian cancers.
As an example, in ovarian cancer, patients classified as subtype 1 exhibited mutated genes belonging to the FGF (fibroblast growth factor) signaling pathway. This served as a nice validation of the stratification system, since members of FGF family have been previously shown to be involved in poor survival and clinical development of ovarian cancer. A second subtype of patients had mutations in genes involved in DNA-damage response.
Similar analyses in uterine and lung cancers yielded subtypes of patients with unique and characteristic biological networks. The grouping together of somatic mutation profiles into patient subtypes were also seen to show good association with clinical outcomes like patient survival times and resistance to drug treatments.
Secrets to Understanding Cancer: Biological Pathways
So far, scientists have focused largely on the commonly mutated genes, and that is in large part, due to the limited sensitivity of technologies. With the advent of whole genome sequencing, the ease at which individual cancer genomes are completely sequenced is very apparent. With the cartload of information available, it is easy to just focus on one or a few genes, but Dr. Ideker’s study suggests that stepping back and looking at the larger picture will help.
The approach of finding treatments in cancer based on one particular gene mutation, instead of pathways, is likened to just concentrating on fixing one traffic light. Instead, one needs to step back, look at all the defective traffic lights on the whole street, and possibly find a common thread to fix all the traffic lights at one time.
Targeting Signaling Pathways, Rather Than Individual Genes: Key to Treating Cancer?
The methodology of subtyping patients, as used in this study is much appreciated, because it’s a well established fact that cancer is driven by combination of genes working concertedly in a molecular network.
An explosion of research has led to an understanding of cancer at multiple levels: the DNA (gene mutations and copy number), RNA (microRNA, mRNA expression) and epigenetic (DNA methylation). Albeit a complex proposition, the time is ripe to layer on these additional misregulated levels of gene expression to the stratification system and tailor treatment accordingly.
A very interesting futuristic analyses proposed by the authors, is to perform a pan-cancer analyses, where one would be able to stratify all different cancers on this network of pathways, and learn more about the development and commonality of all cancers.
Hofree M, Shen JP, Carter H, Gross A and Ideker T. Network-based stratification of tumor mutations. (2013). Nature Methods. Accessed September 26, 2013.
Wood et al. The genomic landscapes of human breast and colorectal cancers. (2007). Science. Accessed September 26, 2013.
Salzman DW & Weidhaas JB. miRNAs in the spotlight: making ‘silent’ mutations speak up. (2011). Nat. Med. Accessed September 26, 2013.
Vogelstein, B., Papadopoulos, N., et al. Cancer Genome Landscapes. (2003). Science. Accessed September 27, 2013.