Thermo ebook on eQTL - 30

That would be an amazing application of SNVs, and
ongoing studies could bring that optimism to clinics.

false-negatives can be equally problematic to
false-positives," Townsend says. "So, there is no easy
way out."

Identify to apply

Assessing the effect size

To make the most of SNVs in clinical uses, scientists
must be able to identify these gene changes and
what they do. As Stefano Lise, bioinformatics core
leader at The Institute of Cancer Research, states,
"Depending on the approach you are taking, you
can identify SNVs that have a frequency of just
0.1%." So, finding an SNV can be easier than assessing its function.

With the right data, scientists can determine which
mutated genes drive cancer and which ones don't.
Sequencing data can also reveal the prevalence of
the cancer-driving mutations. What scientists haven't been able to do is compute "the cancer effect
size-how important one mutation is compared to
another," as Townsend describes it.
Townsend and his colleagues identified the cancer
effect size of specific SNVs for 22 kinds of cancer.2
That is, they determined, "how much each somatic
variant contributes to proliferation and survival of a
cancer lineage," Townsend explains.

In terms of what SNVs do, Davuluri points out that
one challenge is the "functional annotation of impact of SNVs on loss of gene/protein function and
risk of disease." His team is developing ways to identify functional SNVs and their target genes in prostate cancer. "The proposed informatics methodology integrates multi-omics data from prostate cancer
tumor samples and prostate cancer cell-lines," he
explains. "We identified 38 regulatory SNVs and
their targeted genes."1 Further research showed that
some of these SNVs could enhance the molecular
pathways involved in prostate cancer.

So, how could this information help a clinician? Imagine that a patient has a tumor with mutations in two
genes related to that cancer, and there are two drugs
known to target those mutations, but no head-tohead comparison of these drugs. What should a clinician do? Give the drug that treats the mutation with
the largest cancer effect size. That's just one way that
the cancer effect size can be used in the clinic.

To apply SNVs to precision medicine more broadly, scientists need to reduce the odds of identifying an SNV as clinically significant when it's not. As
Townsend says, "The issue of false-positive variant
calls still remains problematic." Using very strict criteria to connect an SNV to a clinical result will reduce false positives, but that could tip the balance
to increasing the false negatives. "Now that we are
moving toward clinical applications of next-generation sequencing and quantitation of the effects
of variants-instead of just discovery of variants-

Subtracting the noise
Despite the great improvements in sequencing
technology, the results aren't perfect. Plus, the biology makes sequencing challenging. At The Institute
of Cancer Research in London, scientists often study
SNVs that are very rare. Plus, samples can include
tumor and healthy tissue. So with some sequencing
error, a scientist can't tell if a very rare SNV is real or
an artefact. Lise and his colleagues came up with a
way to tell the difference.

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Thermo ebook on eQTL

Table of Contents for the Digital Edition of Thermo ebook on eQTL

Contents
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