NEW YORK (GenomeWeb) – Next-generation sequencing technology as applied to various cancers has led to the discovery of several DNA mutations thought to be responsible in some way for the development or proliferation of those cancers. This, in turn, has led to the development of several treatments targeted at those specific mutations. But what can be done to treat cancers in the same manner if they don't have actionable mutations that can be targeted?
Some groups are increasingly turning to transcriptomics to help fill in the gaps, and they're finding that the combination of DNA and RNA analysis is allowing for more precision in matching cancer patients to available treatments. For example, last June, researchers from the WIN Consortium found that adding RNA-based testing to DNA analysis in 303 cancer patients increased the treatment match rate to 35 percent from 23 percent with DNA-based analysis alone.
The issue of targetable somatic mutations is especially problematic in pediatric cancers, as these cancers are known to be lacking in druggable DNA targets. In order to solve that problem, researchers with the Treehouse Childhood Cancer Initiative are instead focusing on using RNA sequencing data to analyze entire genetic pathways. Their hypothesis is that because cancer drugs are aimed at these specific pathways, they could be effective in treating pediatric cancers, even if they don't have somatic mutations.
Even more specifically, researchers from the University of California, Santa Cruz — which is part of the Treehouse Initiative — have developed what they're calling gene expression outlier analysis to see if they can identify overexpressed genes that can then be targeted with available cancer treatments. Much like the WIN Consortium, they're also aiming to determine if this kind of RNA-based analysis can bolster efforts to match patients to drugs as opposed to using tumor mutation analysis only.
"What we really want is to identify genes that are significantly overexpressed and significantly underexpressed in the given patient. There are no good methods for that," UCSC Assistant Professor of Molecular, Cell, and Developmental Biology Olena Vaske said. "The standard differential expression analysis is comparing two groups of samples to each other and finding differences and similarities between groups. But, in a clinical setting we really have the one patient in question. And so those group-type of approaches don't work. Because of that, we developed an approach that uses outlier analysis, which is more suitable for a single patient."
Vaske and her group are currently conducting a study of 11,340 pediatric cancer patients, who have submitted clinical and omics data, including transcriptomic data, to the Treehouse Initiative.
"The hypothesis is that overexpressed oncogenes are cancer drivers in addition to mutations. So, our goal is to find overexpressed oncogenes in individual patients," Vaske said. "There are some drugs that target specific mutation and those would not work for our purposes. But there are also drugs that just target the pathway and they can target a wild-type protein, and so those are the drugs that we can hypothesize would be effective in these cases."
Once the researchers have identified a potentially actionable overexpressed gene, they try to match it to an available drug. They then communicate their findings to the patient's clinician, who ultimately makes the treatment decisions.
"What we do is present the analysis," Isabel Bjork, director of operations and pediatric programs at the UC Santa Cruz Genomics Institute, added. "Olena goes through it and reports on what the team has found with the clinical team, but the clinical team is responsible for making any treatment decisions."
Importantly, an analysis of 75 people in the study showed that the researchers were able to match 28 patients to a drug by the overexpressed genes they found in their transcriptomes compared to only six patients matched to a treatment by DNA mutation only. Another 28 patients were matched to a treatment by both a druggable DNA mutation and a druggable overexpressed gene, and 11 patients did not have any druggable targets at all.
Further, Vaske added, as the researchers continue to analyze the data they've collected, overexpression patterns are starting to emerge in certain tumors.
"We have been doing this now for a few years and we've done it on probably close to 200 cases now. … We're definitely seeing patterns," Vaske said. "We're working on trying to define some of them. There are certain genes we call recurrently overexpressed, which is exciting because they might represent potential targets for a specific group of tumors."
The researchers are currently working on a paper in which they will describe these patterns in greater detail, she added.
Because these efforts are bearing fruit for patients, Vaske and her team are also looking at ways to make the information easier for those patients and their doctors to understand. The idea is to eventually make gene expression outlier analysis a ubiquitous part of the cancer omics testing that patients receive, in order to find treatments as quickly as possible and make them as precise as possible.
To that end, UCSC is currently conducting a clinical registry trial with Stanford University, a component of which is to determine how patients, their families, and clinicians respond to this type of novel genomic analysis, Vaske said.
The team is also developing a scoring system that presents these gene expression findings in a systematized way, prioritizing the findings and putting them in context with therapies for the patients.
"One of the things that we've really realized is a barrier to translational research is understanding and acceptance on the clinical side of what's being done," Bjork added. "The genomic analysis is actually quite difficult to explain and for others to understand. So, a big part of what we're trying to do is assess not only the efficacy from the point of view of science but the actual efficacy in terms of adoption in the clinic and how it affects whether we can get the science translated to actual care of kids and what the barriers are."
In the Stanford study, for example, a nurse oncologist is working with the researchers to do evaluations of clinical decision-making and family response to genomic analysis, she said. They're specifically trying to look at what needs are there for education on these types of genomic analyses, as well as what concerns patients and clinicians may have so that they can incorporate those parameters into future genomic reports they might provide to doctors.
It is precisely because gene expression outlier analysis seems to work so well for matching pediatric cancer patients to treatments that Vaske and her colleagues want to make sure that doctors know how to use it.
Eventually, she said, gene expression outlier analysis will likely be used alongside tumor mutation analysis, adding, "Tumor mutation analysis is very important and there are targets that can be found by that analysis. The issue is that for pediatric cancers, only a minority of patients would have a DNA target. So, our vision is to have this used in conjunction with DNA analysis."
This type of approach could also be helpful even for patients who do have DNA mutations, Vaske said. Gene expression outlier analysis could help in finding treatments for patients who do have DNA mutations, but for whom first-line treatments weren't effective. It could also corroborate findings from DNA analysis, or even provide clinicians with additional therapeutic options, which is also useful in precision cancer treatments.
Part of the UCSC researchers' effort is creating a report that incorporates the RNA analysis they're doing in the context of DNA analysis to help clinicians understand what both of those mean in unison, Bjork said.
"So, a lot of our work with clinicians and clinical teams right now is trying to assess what they're understanding, what they're not understanding, and where the questions are for them so that we can address them as well as possible in the material we provide," she added.