Clinical OMICs - Issue 4 - (Page 23)
Clinical OMICs CASE STUDY
Detection and Interpretation of
Rare Sub-Clonal Cancer Driver Mutations
Anika Jöcker, Rupert Yip, and Salim Essakali
W
ithin the last decade, our understanding of the molecular biology of cancer and its underlying
complexity has experienced unparalleled and exponential growth (Hanahan et al. 2000, Hanahan et al. and
2011)1, 2. Both patients and clinicians
have started benefiting from this new
level of understanding, which can also
be seen in the FDA approval within
the last two years of approximately 20
new drugs that target a specific mutation for cancer.
With next-generation sequencing
technologies, pathologists and clinicians are now empowered to test for
several actionable mutations, as well
as identify potential drug targets in
parallel. This can significantly improve
the diagnostic rate, as well as the
drug treatment. Furthermore, data
from large patient cohorts can be
used to develop even better biomarkers, which can be used for prognosis,
diagnosis, and drug treatment.
However, the bioinformatics analysis tools required to identify and interpret the plethora of variants-which
in some cancers may amount to 50
variants per megabase (Vogelstein
www.clinicalomics.com
et al. 2013)3-still remain highly fragmented and require multiple experts.
Two new bioinformatics platforms
have been specifically designed to target and streamline the identification
(CLC Cancer Research Workbench)
and the interpretation (Ingenuity®
Variant Analysis) of somatic variants
and driver mutations in cancer. This
case study was aimed at testing both
platforms in combination to identify
cancer driver mutations that are present in a low percentage of tumor cells.
These mutations are of special interest in a clinical setting as they can
drive tumor spread and recurrence.
Materials, Methods,
and Discussion
For this study, we used a publicly
available tumor/normal dataset from
a patient with massive acinic cell carcinoma, which was published last
year by Nichols et al. in Case Reports
in Oncological Medicine4. The data is
targeted whole exome sequencing
prepared using Agilent's Sure Select
for the enrichment of exonic regions
in the genome then sequenced using
Illumina's HiSeq® 2000.
Raw reads from the HiSeq 2000
were imported into CLC Cancer
Research Workbench for streamlined
data analysis and visualization of
results. The sequencing reads from
the patient's tumor and normal tissues were mapped to the human reference genome and locally realigned
to improve sensitivity and specificity
of variant calling. Furthermore, variants were called down to an allele
frequency of 5%, while potential false
positives were filtered out. In addition,
a detailed and summary coverage
report was generated to check for coverage and uniformity of the targeted
regions. All these steps were facilitated
(continued on next page)
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May 29, 2014 Clinical OMICs
23
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Table of Contents for the Digital Edition of Clinical OMICs - Issue 4
Contents
Clinical OMICs - Issue 4
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