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Mar 21, 2013
 |  BC Innovations  |  Cover Story

Applying high throughput to CLL

Boston researchers have provided a detailed look at how cancer genome evolution alters clinical outcomes in chronic lymphocytic leukemia,1 and a Michigan team has identified a new transcriptional fusion that could broadly contribute to the pathogenesis of CLL.2 Both studies provide insights that could guide the development of new diagnostics and therapeutics for patients with CLL.

CLL is the second most common type of adult leukemia and has highly variable clinical progression. To better understand the origin of this variability, multiple groups have set out to conduct high throughput DNA sequencing of CLL tumor samples from patients. In the last two years, whole-exome analysis of hundreds of these tumors has shown that the disease is associated with a large number of mutations, with no one predominant mutation or pathway.3-5

"The mutational landscape of CLL that is emerging from sequencing studies is extremely challenging, in that most recurrent mutations are present at low frequencies," said Victor Quesada, a postdoctoral fellow at the University of Oviedo who led one of those sequencing studies. "At this point, we need to gather information to characterize one of at least two scenarios: either there is an underlying mechanism that connects all of the driver mutations or there are many independent mechanisms that may lead to CLL. So far, the second scenario has seemed more likely."

Now, two teams have applied new high throughput sequencing data analysis methods to gain further insight into the mechanisms underlying CLL disease progression.

A joint team from the Dana-Farber Cancer Institute and the Broad Institute of MIT and Harvard focused on tumor DNA and used whole-exome sequencing to measure mutational heterogeneity in patient samples taken before and after chemotherapy. The findings suggest that identifying driver mutations present in only small fractions of cells could help predict the clinical course of CLL.

The team expects that its computational tools could be broadly applied to measure tumor heterogeneity in a variety of cancers.

University of Michigan Medical School researchers instead focused on tumor RNA and used a recently developed approach for whole-transcriptome data analysis to identify new chimeric transcripts comprised of normally disconnected genes. The group found one such transcript in more than 95% of CLL samples and presented preliminary data showing that the truncated protein phosphatase it encodes could be oncogenic.

However, no DNA mutations or translocations were found that could explain how the chimera is generated, and...

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