Clinical OMICs - Issue 7 - (Page 10)
in defined tumor types.
Further, the successful identification (and targeted therGlobal Oncogenomics Findings
apy against) a driver mutation in one tumor type does not
A systematic analysis of 3,281 tumors from 12 cancer types guarantee it will work in another type. Other factors-tisby Kandoth et al., offered a global picture of the genomics sue specificity, genetic environment, and tumor microof common human cancers. Many tumor types had muta- environment-must be considered as well.
tions in chromatin remodeling genes (MLL2, MLL4, or the
In many current clinical trials, gene expression and
ARID gene family). TP53 was the most
mutation data are being concomicommon mutated gene overall. Mutatantly assessed for insight into patient
tions in that gene and six others (BAP1,
stratification and therapeutic response.
Most targeted therapies
DNMT3A, HGF, KDM5C, FBX7, and
These sorts of trials are necessary to
didn't emerge from large-scale
BRCA2) were significantly associated
close the gap between new knowledge
genomics studies, but from
with poor survival. Large alterations
from large-scale cancer genomics and
a deep understanding of
(CNAs, SVs), clearly have an important
its application in the clinic. The feedspecific pathways involved in
role in tumor biology, and gene/miRNA
back loop needs to work both ways:
expression profiling allows stratification
clinical trial results should inform future
defined tumor types.
of tumors into subtypes, often ones that
oncogenomics studies as well.
correlate with clinical outcomes. Even
It's clear that we will require both crewithin one tumor type, the mutational profiles suggested ativity and cross-discipline expertise to carry the mission
that few driver genes were shared across subtypes.
forward from here. Specifically, we'll need:
The broader conclusion from these and from so-called * Continued efforts to develop large, high-resolution,
pan-can studies is that cancer represents a wide variety
clinical-genomics datasets
of diseases originating from different organs. Clustering * Better and earlier access to drugs
genomic data across organs will therefore allow a biology- * Cross-discipline expertise in cancer, genomics, and
driven approach, focusing more on key genes and cellular
informatics ("onco-bioinformaticians")
pathways and less on simple tumor morphology.
* Integration of genomic data into clinical tumor board
discussions
Clinical Translation of Cancer Genomics
(continued from previous page)
The real question, now that we've made considerable progress, is how to make use of that information in the clinic.
Many institutions have launched personalized oncology
programs which consider tumor mutation and/or gene
expression profiling. Early reports suggest that 30-70%
of cases will harbor mutations that are "actionable" for targeted therapy or patient stratification. The poster child for
this might be the identification of BRAF as a driver gene
in melanoma, which led to the use of BRAF inhibitors in
melanomas that harbor the V600E mutation. It's a wonderful story, but the simple fact is that most targeted therapies didn't emerge from large-scale genomics studies, but
from a deep understanding of specific pathways involved
10
Clinical OMICs July 16, 2014
Beating cancer is an important but incredibly difficult
mission, and it won't be solved by one scientific discipline
alone. Collaborative efforts by cross-discipline teams are
going to be necessary. Let's get going.
DAN KOBOLDT
leads the human genetics analysis group
of the Genome Institute at Washington
University. He started the Mass Genomics
blog in 2008 to write about nextgeneration sequencing and medical
genomics in the post-HGP era.
(www.massgenomics.org)
www.clinicalomics.com
http://www.massgenomics.org
http://www.clinicalomics.com
Table of Contents for the Digital Edition of Clinical OMICs - Issue 7
Contents
Clinical OMICs - Issue 7
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss9
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss8
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss7
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss6
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss5
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss4
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss3
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss2
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol3iss1
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss12
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss11
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss10
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss9
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss8
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss7
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss6
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss5
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss4
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss3
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss2
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_vol2iss1
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue15
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue14
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue13
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https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue9
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue8
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue7
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue6
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue5
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue4
https://www.nxtbook.com/nxtbooks/gen/clinical_omics_issue3
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