Medicina Genomica en Tumores Sólidos
Medicina Genomica en Tumores Sólidos
Medicina Genomica en Tumores Sólidos
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Mutation A Drug A
Mutation A Drug A
Mutation B Drug B
Mutation C Drug C
Mutation N Drug N
Mutation B Drug B
Mutation C Drug C
Mutation N Drug N
Fig 3. Conceptual approaches to genomic medicine in oncology. FISH, uorescent in situ hybridization; PCR, polymerase chain reaction.
Dienstmann et al
1878 2013 by American Society of Clinical Oncology JOURNAL OF CLINICAL ONCOLOGY
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currently engendering an upfront multicategorical approach that can
be used to decide among different potential treatments. Genes that
predict sensitivity or resistance to cancer therapies are targeted for
sequencing using small amounts of tumor DNA.
84
The prospect is to
use massively parallel sequencing technologies, which allow evalua-
tion of a more comprehensive spectrum of alterations, including not
only point mutations but also copy number variations and fusion
events, by using a targeted capture-sequencing approach. At the pres-
ent time, clinical investigators are facing the alternative conceptual
model that pursues a personalized oncology paradigm through
comprehensive assessment of genomic data. Different from the
genomically stratied approach, it is hypothesis-generating by allow-
ing identication of novel drivers and matched targeted therapies in
the experimental setting. This omniscient model is grounded on
whole-genome, -exome, and -transcriptome sequencing platforms
but is ultimately reductionist and focused on actionable aberrations.
Bioinformatic algorithms based on available databases (eg, Cancer
Gene Census, Catalogue of Somatic Mutations inCancer, andMolec-
ular Signatures Database) can be used to selectively identify somatic
aberrations in high-priority cancer genes that might have immediate
implications for patient care (including the variants that have been
predicted to confer tumor sensitivity or resistance to approved or
experimental therapies) as well as those involving known biologically
relevant pathways intumor cells. The remaining variants of unknown
clinical signicance are also catalogued because they may ultimately
represent actionable aberrations after additional research in the eld.
Computational tools can help predict the effect of an amino acid
switch on protein structure and function,
85
although experimental
validation with transformation assays is the most powerful method.
International databases that include mutation type, patient demo-
graphics, and treatment and outcome data are needed to ensure ro-
bust statistical validity for these rare events.
2
GENOMIC-BASED CLINICAL TRIALS: MULTIPLE STEPS OF A
COMPLEX WORKFLOW
Using genomic proling to select patients for clinical trials with
matchedtreatments that are expectedtoprovide benet is timely. The
ultimate goal of such trials is to determine which mutation proles
correlate with sensitivity/lack of resistance to specic targeted thera-
pies and whether treatment outcomes are consistent among different
cancer histologies.
2
Inaddition, genomic-basedtrials canalsogenerate
valuable information regarding cancer biology, clinically qualify po-
tential predictive biomarkers, accelerate patient benet, and assist in
the decisiononwhether a novel targetedagent warrants further devel-
opment.
86
Some studies have shown that real-time molecular prol-
ing of tumors from actual patients and treatment with matched
Table 1. Conceptual Approaches to Genomic Medicine in Oncology
Genomically Stratied/Multicategorical Model Omniscient-Reductionist Model
Derivation from one test-one drug (companion diagnostics) to a
multiplexed, multicategorical approach.
Derivation from the Human Genome Project and the evolution of technology.
Comprehensive throughput.
Focused on unicausality whereby one aberration is effectively
targeted by one drug (or combinations of drugs).
Opened to multicausality of drivers but is reductionist because of current knowledge
and the available druggable options (typically used as monotherapies).
Diseases are categorized into different subsets by genomic analysis.
Aberration-drug pairs are predened. Occasional overlap among
categories may confound treatment options (colon cancer KRAS
plus PIK3CA mutation, Oncotype DX intermediate risk).
Diseases are dened by patterns of recurrent alterations in pathways (convergent
evolution). Each patient has a unique disease with a combination of alterations
that are unexpected before analysis: personalized medicine. Need for a
functional understanding of each alteration (systems biology) may complicate
decisions.
Hypothetical-deductive reasoning (preconceived theory). The
aberrations selected for analysis take into consideration
preclinical/clinical data.
Inductive reasoning can be applied (when data generate a new theory). Deductive
reasoning is also used to simplify and prioritize data analysis and to identify
actionable aberrations.
Attitude: Ask questions you want to know the answers to. Attitude: Ask all questions, but listen only to answers that please you. Describe
reality as it is but reduce it later to correspond with your theory.
The paradigms are embedded in the question and established prior to
analysis (ie, PI3K inhibitors work well in PIK3CA-mutated tumors).
Decision after the analysis is straightforward (if BRAF
V600
-
mutated gene is present, treat with a BRAF inhibitor).
No preconceived notion before analysis. A comprehensive analysis follows (quasi-
omniscient) with subsequent reductionism to what we can understand.
Decision after the analysis is not straightforward because one could identify 10
alterations in known oncogenes/tumor suppressor genes, but the patient is
treated with only one drug (ie, a PI3K inhibitor based on current knowledge or
best guess).
If patient fails to respond, genomic aberrations that may represent
primary resistance mechanisms cannot be dened. Therefore, it
is not hypothesis-generating. Targeted assays may evade
opportunities for discoveries of new drivers and predictive
biomarkers.
If patient fails to respond, potential genomic aberrations that may represent primary
resistance mechanisms can be identied after re-analysis of original assay results
(considering repetition of the same event in other patients). Therefore, it is
hypothesis-generating but not easily reproducible. By allowing identication of
novel drivers and hypothesis-driven therapies, a single patient can provide many
lessons for investigators when an identiable, druggable aberration has a good
response to treatment.
Analysis can be performed locally at the patients facility or in a
centralized laboratory.
Analysis has to be done centrally since few sites can afford the cost of analysis or
interpretation of data.
Quick, relatively low performance but applicable solutions, including
enrollment in clinical trials with enrichment strategies.
Slow, high performance, hard to interpret and apply to the individual. Applicable to
clinical trials for enrichment strategies but has limitations such as turnaround
time. Fits well with the research/experimental setting and relies on bioinformatics
expertise.
If multiple platforms are used to identify different aberrations (such as
multiplexed genotyping for mutation detection plus uorescent in
situ hybridization for copy number variations and
rearrangements), the cost may become high.
Current platforms of comprehensive genomic analysis are becoming less expensive.
Genomic Medicine in Solid Tumors: Prospects and Challenges
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targeted agents can increase response rates and improve time to pro-
gression compared with unselected therapies.
87,88
Recently, academic
groups have demonstrated the feasibility of a targeted, massively par-
allel sequencing approach to assist in determining mutation-driven
inclusioncriteria for clinical trials.
89
By usinga combinationof whole-
genome and-exome sequencing plus sequencing of transcribedRNA,
others have proven that informative mutations in tumors of selected
patients can be identied within 3 to 4 weeks, a time that is short
enough to be clinically useful.
90
Although the potential benets of
high-throughput assays for individual patients with cancer have al-
ready been demonstrated,
91
the next logical step is to facilitate larger
clinical trials in oncology with biomarker-informed therapies.
As targeted therapeutics are developed to treat small subsets of
individual disease populations, changes will be required in current
clinical trial designs and research frameworks. Early-phase clinical
trials will best be served by taking advantage of enrichment strategies,
witha focus onpatients who are unlikely to benet fromstandard-of-
care chemotherapy lines and more likely to benet from a novel
therapy because of the presence or absence of specic tumor genetic
abnormalities.
92
Patients with excellent responses to targeted thera-
pies based on novel driver mutations should be reported to advance
theeldbeyondanecdotal observation. PhaseII trials will benet from
exploiting novel designs, including studies that subclassify a specic
disease into discrete molecular categories to test different agents in
separate cohorts (eg, the I-SPY 2 [Investigation of Serial Studies to
Predict Your Therapeutic Response With Imaging and Molecular
Analysis] trial in breast cancer
93
and the BATTLE [Biomarker-
Integrated Approaches of Targeted Therapy for Lung Cancer Elimi-
nation] trial in NSCLC
94
), as well as histology-independent trials (eg,
the MO28072trial withvemurafenibinmultiple solidtumors harbor-
ing BRAF
V600
mutations). In genomic trials of selected populations,
many patients need to be proled for sufcient accrual to provide
appropriate statistical power. If the magnitude of treatment effect is
greater than expected with an unselected approach, the number of
patients required per arm to demonstrate improved outcomes is sig-
nicantly reduced. Conversely, when the predictive value of a specic
molecular aberration is unknown, large randomized trials with strat-
ication strategies are still required, with biomarker-positive and
-negative patients being allocated to the standard-of-care or targeted
therapy arm.
95
Many unsolved biologic questions, scientic concerns regarding
platformselection, and logistical issues related to prescreening strate-
gies andtrial allocation, inadditionto challenges inthe economic and
ethical domains, will need to be overcome before genomic-driven
trials and personalized cancer medicine can become a reality and
supplant currently accepted modalities, as summarized in Table
2.
2,11,92,96
Importantly, academic institutions, pharmaceutical compa-
nies (that control which new agents are available for clinical testing),
and third-party payers (for provision of marketed drugs) will need to
work in a collaborative environment to allowrational drug combina-
tions tobe readilytestedandavoidduplicationof efforts. Clinical trials
that test matched targeted agents in the setting of rare molecular
aberrations or that integrate biomarker development can only be
accomplished with multi-institutional cooperative networks that fa-
cilitate data sharing and technology exchange. Finally, global efforts
are needed to prospectively validate genomic tools. Successful initia-
tives include adjuvant clinical trials in early breast and colon cancer
that evaluate gene expression proling platforms, such as Oncotype
DX (Genomic Health, Redwood City, CA) and MammaPrint/Colo-
Print (Agendia, Amsterdam, the Netherlands), which are expected to
assist in clinical decision making by enhancing the prediction of out-
come compared with traditional pathology.
FUTURE DIRECTIONS AND CONCLUSIONS
The ability to prole patients with each cancer for actionable aberra-
tions in a cost-effective way provides unprecedented possibilities for
matched therapies in a selected patient population. The major chal-
lenge will be to integrate and make biologic sense of the massive
amount of genomic data derived from multiple platforms. The low
frequency of many signicantly mutated genes represents another
important limitation of correlative analyses. Therefore, to obtain a
complete picture of the biology underlying eachcancer subtype, it will
be mandatory to map specic patterns of somatic mutations into
cellular pathways linkedtotumor biology. Inaddition, because cancer
genomes are not exclusively disrupted at the DNA sequence level but
are also driven by various permutations in genetic regulation, it is
imperative to investigate other genomic dimensions, such as DNA
methylation status, to understand the phenotypic heterogeneity dis-
played by most solid tumors.
97
Studies that use massively parallel
sequencing of primary tumor samples have identied recurrent mu-
tations in genes related to histone modication, proteins involved in
chromatin remodeling, and transcription factors, known targets of
epigenetic modiers.
65,98-102
Several international projects are in the
process of assessing epigenomic events in human cancers and how
genetic mechanisms affect epigenetic effectors.
103,104
To date, it re-
mains tobe shownwhether data obtainedfromepigenetic prolingby
using deep sequencing can predict drug response. Furthermore, to
implement true personalizedmedicine, one shouldconsider the char-
acteristics of the tumor and also the host-tumor relationship (tumor
microenvironment and immune response) and host-drug relation-
ship (metabolism genes and pharmacogenomics). The signicant
clinical activity of immune-checkpoint-pathway inhibitors such as
antiCTLA-4 (ipilimumab)
105
and antiprogrammed death 1 (PD-
1)
106
mAbs in a variety of solid tumors is a practical example that
shows that not all actionable/druggable cancer targets are products of
genetic aberrations. Germline genetic aberrations inproteins involved
in drug metabolism and apoptosis, which may modify toxicity and
response to targeted agents, should also be assessed.
We envisiona future inwhichgenomics-drivencancer medicine
will address treatment of many solid tumor subsets. Novel potential
targets in selected malignancies are increasingly being described. Ulti-
mately, however, solid tumors will not be universally impacted with
genomic proling alone. More likely, the management of rare subsets
of commoncancers andsome relatively rare tumor types withspecic
aberrations will change dramatically with the widespread availability
of genomic tools. Examples include NRAS-mutated melanomas and
benet with MEKinhibitors
107
or CDK4-amplied liposarcomas and
antitumor activity of CDK4 inhibitors.
108
In addition, the develop-
ment and validation of predictive markers will certainly guide the
reappropriation of existing therapies, such as histone deacetylase,
DNAmethyltransferases, and proteasome inhibitors.
Key issues related to clonal evolution and tumor heterogeneity
must be identiedandaddressed. Systematic analyses of the evolution
of cancer clonal architecture during therapy will identify ubiquitous
Dienstmann et al
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driver events present in all regions of the tumor that could be more
efciently targeted.
57
Because therapy-induced selection of drug-
resistant mutations limits the efcacy of targeted therapies, methods
that allow massively parallel sequencing from small samples, such as
circulating tumor cells or free ctDNA from serum (also known as
liquid biopsies), are likely to be the mainstay technologies for clinical
laboratory testing in the future and may also provide a cost-effective
alternative to biopsy. Other advantages include lack of invasiveness
andlackof spatial sampling errors. Indeed, ultrasensitive ctDNAanal-
ysis platforms have demonstrated sufcient accuracy in reecting
aberrations present in primary and metastatic breast tumors
109,110
as
well as detecting mutant KRAS clones in colorectal cancer.
75,76
These
advances may greatly enhance successful application of genomic test-
ing in cancer management by more efciently monitoring disease
status over time and allowing real-time resequencing and molecular
proling at multiple points during the disease course, particularly of
patients progressing to targeted treatment approaches.
In conclusion, the genomic information derived from modern
sequencing technologies provides a newparadigmfor clinical investi-
gation. Treatment decisions for each individual patient are dened by
Table 2. Challenges for Implementing Genomic-Driven Trials and Personalized Cancer Therapy
Prescreening strategies
Population: The focus is on chemorefractory patients with common malignancies who are considering clinical trials versus those with rare diseases with no
standard treatment options.
Timing: Timing choice is one of the following: immediately before considering clinical trials versus a broader prescreening program for patients with
metastatic disease and high possibility of actionable aberrations.
Location: Local analysis is done in academic centers (need for secondary verication?) versus centralized analysis in reference institutions.
Tissue for analysis
Quantity and quality of DNA: Recommendation for tissue is 70% tumor cellularity with 10% necrotic tumor tissue.
Fresh frozen versus FFPE: Snap freezing tumor tissues in liquid nitrogen is the optimal method for nucleic acid preservation. There is good correlation with
mutational output between matched fresh-frozen and FFPE tumor samples with next-generation sequencing results. RNA extracted from FFPE tissue is
often of poor quality.
Primary tumor versus real-time biopsies: Clonal evolution, selection pressure from prior therapies, and tumor heterogeneity may affect the results.
Need for standardization: Collection, handling, and storage of specimens needs to be standardized.
Platform selection
Sensitivity: Deep sequencing technology increases the sensitivity of mutation detection, particularly in the setting in which there is a high stromal admixture.
Reliability, precision, accuracy, and interlaboratory reproducibility of data: Bioinformatic analysis and stringent quality control can reduce errors.
Turnaround time: Sequencing analysis is completed in a clinically relevant time frame.
Laboratory CLIA/GCLP certication: Results that affect clinical decision-making must be validated.
Type of analysis: The genome is comprehensively assessed for structural rearrangements, copy number alterations, point mutations, insertions, deletions,
and gene expression proles versus focusing on actionable aberrations with immediate clinical implications (such as druggable kinases or selected tumor
suppressor genes). There is balance between analytical coverage (ie, genome coverage), statistical power, and cost-effectiveness when high-throughput
platforms are used.
Multidisciplinary treatment decision
Results can be grouped into three categories: (1) those that may have a direct impact on care of the patient with the cancer of interest, (2) those that may
be biologically important but are not currently actionable, and (3) those of unknown importance.
1. How is a driver distinguished from a passenger genetic alteration?
2. How does one dene which mutations engender sensitivity to specic targeted therapeutics?
3. How are nondruggable aberrations managed?
4. How are aberrations of unknown biologic/clinical signicance dealt with?
5. How is therapy selected in the case of multiple aberrations?
Data integration among multiple platforms: Bioinformatics pipelines for data analysis are required.
Trial allocation
There is a need for multi-institutional trial networks to assess novel agents that target specic aberrations; there are potential geographical limitations.
What should be done when eligibility criteria are too stringent or slots are not available in genomic-driven early clinical trials? And when patients present
with clinical deterioration at the time of recruitment?
Is it possible to offer treatment outside clinical trials with matched targeted agents approved for another indication (such as off-label vemurafenib in
BRAF
V600
-mutated lung cancer) or to prescribe marketed drugs not previously tested in combination regimens?
Reimbursement and nancial issues
Cost of prescreening and diagnostic approaches (eg, expenditures for training personnel with appropriate expertise and setting up certied laboratories) and
the resulting therapeutic implications (when analysis indicates off-label treatments) must be addressed.
From a societal point of view, the benets (in terms of cost savings) of avoiding empiric prescription of expensive drugs when a personalized cancer
approach is adopted must be taken into consideration.
From an economic point of view, patients receiving targeted therapies are expected to remain on treatment for longer periods, with benets for
pharmaceutical companies.
Ethical issues
Privacy and condentiality should be addressed at all times.
Informed consent form should discuss how to deal with incidental ndings, such as germline mutations associated with risk for other diseases (eg, long QT
syndrome) and those that provide risk information relevant to family members (such as mutations in BRCA1/2 and cystic brosis genes).
Results should be disclosed to patients but only those associated with sufcient risk and established clinical utility should be communicated, ensuring
proper understanding of health and social implications.
Clinicians have an undened obligation to nd suitable on-trial or off-trial options for patients whose tumors have undergone molecular proling.
Abbreviations: CLIA, Clinical Laboratory Improvement Amendments; FFPE, formalin xed, parafn embedded; GCLP, Good Clinical Laboratory Practice.
Genomic Medicine in Solid Tumors: Prospects and Challenges
www.jco.org 2013 by American Society of Clinical Oncology 1881
Information downloaded from jco.ascopubs.org and provided by at ASCO on March 6, 2014 from 158.232.241.130
Copyright 2013 American Society of Clinical Oncology. All rights reserved.
the evolving knowledge of specic molecular aberrations and their
possible consequences. Nevertheless, the extreme genomic complex-
ity and mutability of cancer mandates the use of comprehensive se-
quencing and gene expression platforms as well as analysis of
functional protein pathway activation patterns so that a personalized
treatment strategy in human solid tumors can best be implemented.
AUTHORS DISCLOSURES OF POTENTIAL CONFLICTS
OF INTEREST
Although all authors completed the disclosure declaration, the following
author(s) and/or an authors immediate family member(s) indicated a
nancial or other interest that is relevant to the subject matter under
consideration in this article. Certain relationships marked with a U are
those for which no compensation was received; those relationships marked
with a C were compensated. For a detailed description of the disclosure
categories, or for more information about ASCOs conict of interest policy,
please refer to the Author Disclosure Declaration and the Disclosures of
Potential Conicts of Interest section in Information for Contributors.
Employment or Leadership Position: Jordi Barretina, Novartis (C)
Consultant or Advisory Role: Josep Tabernero, Amgen (C), Genentech
(C), Merck Serono (C), Novartis (C), Roche (C), sano-aventis (C),
Bayer Pharmaceuticals (C) Stock Ownership: None Honoraria: None
Research Funding: None Expert Testimony: None Other
Remuneration: None
AUTHOR CONTRIBUTIONS
Conception and design: Rodrigo Dienstmann, Jordi Rodon, Jordi
Barretina, Josep Tabernero
Provision of study materials or patients: All authors
Manuscript writing: All authors
Final approval of manuscript: All authors
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Acknowledgment
We thank Josep Corco (Universitat Internacional de Catalunya) for his invaluable help in conceptual denitions of genomic approaches and
Joann Aaron for English editing.
Genomic Medicine in Solid Tumors: Prospects and Challenges
www.jco.org 2013 by American Society of Clinical Oncology
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Copyright 2013 American Society of Clinical Oncology. All rights reserved.