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DNA-Guided Precision Medicine for Cancer: A Case
of Irrational Exuberance?
Emile E. Voest1 and Rene Bernards2
Summary: Precision treatment with targeted cancer drugs requires the selection of patients who are most likely
to benefit from a given therapy. We argue here that the use of a combination of both DNA and transcriptome
analyses will significantly improve drug response prediction. Cancer Discov; 6(2); 130–2. ©2016 AACR.
The identification of recurrent mutations in the genomes
of cancer cells has enabled the development of a series of targeted cancer drugs. Use of these drugs requires the selection of
patients who are most likely to respond through the identification of their corresponding genomic alterations. Indeed, most
targeted cancer drugs mandate the use of a companion diagnostic test based on these genomic alterations to identify eligible patients. These genotype–drug response relationships are
most readily explained by “oncogene addiction,” a term coined
by the late Bernhard Weinstein to describe the critical dependence of cancer cells on oncogenic “driver” mutations (1). Thus,
BRAF V600E-mutant melanomas critically depend on increased
MAPK pathway signaling, and hence such tumors respond
well to selective inhibition of BRAF and/or MEK kinases (2–4).
These and other encouraging results with targeted agents have
sparked a number of histology-agnostic studies in which the
presence of the oncogenic driver mutations is the sole selection criterion for treatment with the corresponding targeted
agent in so-called basket studies (5–7). A first disappointment
of these studies is that the number of “actionable” mutations (mutations for which a targeted drug is available as an
approved indication or through a clinical trial) is relatively low.
In a recent study at the MD Anderson Cancer Center, of 2,000
patients whose cancers were subjected to DNA sequencing,
789 (39%) were found to have an actionable mutation. Even
though MD Anderson is one of the largest oncology centers in terms of clinical studies, only 83 of these 798 patients
(some 4% of the total) could be treated in a genotype-matched
trial targeting these alterations. Similarly, in the SHIVA trial,
a genotype-matched “off-label” drug could be identified for
only 40% of the patients. A second disappointment was that
the responses of the patients treated with molecularly targeted
agents, defined by progression-free survival, were no better
than those of patients treated by “physician’s choice,” leading
the authors to conclude that the “off-label use of molecularly
targeted agents should be discouraged” (7).
1
Division of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands. 2Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
Corresponding Author: Rene Bernards, Division of Molecular Carcinogenesis,
The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam,
the Netherlands. Phone: 31-20-5126973; Fax: 31-20-5121954; E-mail:
[email protected]
doi: 10.1158/2159-8290.CD-15-1321
©2016 American Association for Cancer Research.
That the genotype alone may not predict drug responses
reliably suggests that the tissue in which the cancer mutation occurs can be a major factor in determining response to
therapy (8). As one example, BRAFV600E-mutant colon cancers
have modest responses to BRAF inhibition compared to
melanomas harboring the same oncogenic driver mutation
(9, 10). This discrepancy can be explained by the expression
of the EGFR gene in colon cancer, a receptor that is activated
upon BRAF inhibition in these cancers (11, 12). On the other
hand, PARP inhibitors are active not only in BRCA-deficient
breast and ovarian cancers but also in BRCA2-deficient prostate cancers, indicating that some genotype–drug response
relationships have only limited tissue dependence (13).
Responses to actionable mutations may also be limited
because the term “actionable” is used rather loosely in the
context of treatment selection. For instance, KRAS mutations
are frequent in human cancer and are generally considered
to be “actionable,” as the oncogene addiction model predicts
that treatment of such patients with a MEK inhibitor (a
kinase downstream of KRAS) would deliver clinical benefit.
However, in both preclinical models of KRAS-mutant cancers and in early clinical studies, responses of KRAS-mutant
tumors to MEK inhibitors have proven to be modest (14–16).
It will therefore be important to define more unambiguously
what the term “actionable” means. These examples highlight
the notion that the genotype alone may, in many cases, not be
sufficient to choose a targeted therapy for a patient and point
toward the need to consider additional factors beyond the
actionable mutations to select patients for targeted therapies.
COMBINING DNA AND RNA ANALYSES FOR
TREATMENT SELECTION
As stated above, the differing responses of BRAF-mutant
colon cancers appear to be due to differences in EGFR expression, pointing to the relevance of the gene expression context
in which a mutation is present. Even within a single cancer
type, drug responses can be highly variable, depending on
gene expression patterns. For example, using hierarchical
clustering analysis, three major intrinsic subtypes of breast
cancer can be identified: basal, luminal, and HER2 type.
Although one would expect all HER2-amplified tumors to
be of the HER2 type, a significant fraction of the HER2amplified breast tumors are of the luminal subtype by gene
expression analysis. Importantly, the neoadjuvant responses
of these luminal-type tumors with HER2 amplification to
chemotherapy and trastuzumab are much less impressive
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than the HER2-amplified tumors that are of the HER2 type
by gene expression also. This highlights that even within cancers of a single tissue of origin, gene expression patterns can
modulate responses to targeted drugs (17).
Along similar lines, it has been shown that loss of the
BRCA1 or BRCA2 tumor suppressor genes confers sensitivity
to drugs that inhibit PARP (18, 19). Based on these observations, olaparib is now an approved drug for BRCA-mutant
ovarian cancers. Genome-wide copy-number analysis studies
have shown that BRCA1-mutant breast tumors have a distinctive pattern of copy-number gains and losses. Interestingly,
the group of breast cancers that has this distinctive pattern of
genomic aberrations includes a number of tumors that lack
a BRCA1 gene mutation and are referred to as BRCA1-like.
Such BRCA1-like tumors are very sensitive to agents inducing DNA double-strand breaks, an established property of
BRCA1-mutated tumors (20). It will therefore be of considerable interest to investigate whether such BRCA1-like tumors
lacking a mutation in the BRCA1 gene itself will also be sensitive to PARP inhibitors.
The above-mentioned example indicates that cancer development can display convergent evolution, leading to a situation in which the resulting phenotype of tumors in terms
of gene expression is similar, but the underlying genotype
is different. This notion is also echoed in studies of colon
cancer. Microsatellite instability (MSI) in colon cancer is
associated with a higher mutation rate and a good prognosis.
A gene expression signature trained on MSI colon tumors
also identified a group of colon cancers that are not MSI by
the clinical diagnostic assay, but resemble MSI tumors in
terms of gene expression pattern. Importantly, such MSIlike tumors also have the higher mutation rate and the good
prognosis of true MSI tumors (21). These observations are
of interest in light of recent findings that MSI colon cancers
have good responses to checkpoint immunotherapy (22). As
a final example, BRAF-mutant colon cancers have a distinctive gene expression pattern and have a poor prognosis upon
relapse. However, some 10% of colon cancers do not have
a BRAF mutation but share the gene expression pattern of
BRAF-mutant tumors and also have a poor prognosis after
relapse (23).
In summary, these studies highlight two important points.
First, tumors having the same oncogenic driver mutations
can differ significantly in their responses to targeted cancer
drugs. Second, tumors that lack a specific oncogenic driver
mutation may nevertheless display very similar responses to
therapy due to similarity in gene expression patterns. These
two findings may explain why basket trials, in which patients
are matched based on the genotype of the tumor only, are to
date only moderately successful.
BIOMARKERS OF RESPONSE TO
CHEMOTHERAPEUTIC AGENTS
Although there is much excitement about targeted therapies, chemotherapy will remain the mainstay of drug-based
cancer therapies for the foreseeable future. One of the main
drawbacks of the use of chemotherapy is that it remains
difficult to predict who might benefit from such therapies.
Two large-scale drug-screening efforts using cancer cell line
panels have failed to identify recurrent genomic alterations
associated with responses to chemotherapeutic agents (24,
25). Gene expression analyses may also offer solutions here.
In colon cancer, gene expression–based clustering analyses
have identified multiple intrinsic subtypes, of which the
mesenchymal subtype has the worst outcome (26). Unfortunately, these mesenchymal tumors also show poor responses
to chemotherapy and EGFR-based therapies (27, 28). There
are no mutations unique to mesenchymal colon cancer, but
such tumors can be readily identified through transcriptome
analyses. That gene expression–based subtyping can identify
subgroups having different responses to chemotherapies is
also seen in glioblastoma (29) and in breast cancer, where
basal cancers are more chemotherapy-responsive than luminal tumors (17).
THE NEED FOR CLINICAL STANDARDS FOR
TRANSCRIPTOMIC ANALYSIS
The first studies using genotype-directed patient selection
in nonapproved indications suggest that we may have been
overly optimistic about the contribution of cancer genotype
analysis to the prediction of responses to therapy. It is likely
that prospective studies that include transcriptome profiling
in addition to mutation/copy-number variation analyses will
yield important new insight into the interplay between the
mutations in the cancer genome and the modulation of these
effects by patterns of gene expression. Such studies combining DNA and RNA analyses in relation to patient outcome are
urgently needed to improve response prediction in oncology,
but are hampered by a lack of clinical standards in performing
genome-scale transcriptome analyses in a clinical diagnostic
setting. Large cancer centers increasingly use next-generation
sequencing of cancer gene panels to genotype cancers in a routine diagnostic setting, and the FDA has taken an active role
in the regulatory oversight of these DNA-sequencing technologies. However, the variable use of germline sequencing as an
intrapatient control, the use of patient material without quality control, and the use of different bioinformatics pipelines
make it difficult to compare studies. Standardization will be
essential for future data comparison and sharing.
Even more challenging is comprehensive gene expression
analysis of cancers through transcriptome sequencing (RNAseq). It is hardly used outside a research setting for diagnostic
purposes, and a highly standardized protocol for isolation
of RNA and performing RNA-seq in a quality-controlled
diagnostic setting is lacking. One additional limitation of
RNA-based diagnostics appears to be that, unlike DNA,
cell-free tumor RNA cannot be analyzed from the blood of
tumor-bearing patients. However, a recent study suggests
that blood platelets accumulate RNA molecules from cancer
cells, enabling transcriptomic analyses for diagnostic purposes potentially using as little as a single drop of patient
blood (30). Such technologies, when further validated, may
be valuable to further elucidate the interplay between gene
expression and oncogenic driver mutations in cancer.
Although DNA sequencing has kick-started an era of precision medicine by providing biomarkers of response to targeted agents, we begin to realize that cross-talk between
signaling pathways and gene expression patterns in cancers
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can obscure simple genotype–drug response relationships.
RNA sequencing represents a first additional dimension to
understand better how tumors respond to treatment, and its
implementation in clinical studies should be a priority of the
translational research community.
Disclosure of Potential Conflicts of Interest
R. Bernards is founder and employee of Agendia and has ownership interest (including patents) in the same. No potential conflicts
of interest were disclosed by the other author.
Grant Support
The work of the authors is supported by a Stand Up To Cancer-Dutch
Cancer Society Translational Research Grant (SU2C-AACR-0912). Stand
Up To Cancer is a program of the Entertainment Industry Foundation
administered by the American Association for Cancer Research.
Published online February 5, 2016.
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DNA-Guided Precision Medicine for Cancer: A Case of
Irrational Exuberance?
Emile E. Voest and Rene Bernards
Cancer Discov 2016;6:130-132.
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