Download Personalized Medicine: Marking a New Epoch in Cancer Patient

Published OnlineFirst August 6, 2010; DOI: 10.1158/1541-7786.MCR-10-0264
Molecular
Cancer
Research
Review
Personalized Medicine: Marking a New Epoch in Cancer
Patient Management
Maria Diamandis1, Nicole M.A. White1,2, and George M. Yousef1,2
Abstract
Personalized medicine (PM) is defined as “a form of medicine that uses information about a person's genes,
proteins, and environment to prevent, diagnose, and treat disease.” The promise of PM has been on us for years.
The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis,
prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate
the “one size fits all” model of medicine, which has centered on reaction to disease based on average responses to
care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each
individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also
shifting the emphasis of patient management from primary patient care to prevention and early intervention
for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be
used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis,
and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need
to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is
clear that PM will gradually continue to be incorporated into cancer patient management and will have a
significant impact on our health care in the future. Mol Cancer Res; 8(9); 1175–87. ©2010 AACR.
Introduction
The field of medicine has seen many new advances in
the last several decades. With the completion of the human
genome project (1), an enormous amount of new information has been gained about the human genome and the
genetic variations between individuals. Added to this is
the emergence of new technologies for global genomic
analysis, including high-throughput sequencing, singlenucleotide polymorphism (SNP) genotyping, and transcript profiling. The construction of haplotype maps of
the genome (2, 3) is now allowing us to view DNA in a
much bigger picture than ever before. This, coupled with
enormous advances in computer systems and bioinformatics, has resulted in a revolutionary shift in medical care
to the era of personalized medicine (PM).
PM is defined by the U.S. National Cancer Institute as
“a form of medicine that uses information about a person's
Authors' Affiliations: 1 Department of Laboratory Medicine and
Pathobiology, University of Toronto and 2 Department of Laboratory
Medicine and the Keenan Research Centre in the Li Ka Shing
Knowledge Institute, St. Michael's Hospital, Toronto, Canada
Corresponding Author: George M. Yousef, Department of Laboratory
Medicine, St. Michael's Hospital, 30 Bond Street, Toronto, Ontario,
Canada M5B 1W8. Phone: 416-864-6060, ext. 6129; Fax: 416-864-5648.
E-mail: [email protected]
doi: 10.1158/1541-7786.MCR-10-0264
©2010 American Association for Cancer Research.
genes, proteins, and environment to prevent, diagnose, and
treat disease.” The promise of PM has been on us for some
years and is rapidly gaining momentum. The concept of PM
is to use clinical, genomic, transcriptomic, proteomic, and
other information sources to plot the optimal course for
an individual in terms of disease risk assessment, prevention,
treatment, or palliation. Thus, PM aims to eliminate the
“one size fits all” model of medicine, which has mainly centered on reaction to a disease (treating the symptoms) based
on average responses to care, by shifting the emphasis of
patient care to cancer prevention and early intervention
for high-risk individuals (4, 5). Moreover, understanding
the molecular profiles of individuals and how these can cause
variations in disease susceptibility, symptoms, progression,
and responses to treatment will lead to tailoring medical care
to fit each individual patient (6-8).
The objectives of this review are to provide a simplified
yet comprehensive overview of the scope of applications of
PM and their effect on cancer patient management. We
also summarize the basic principles of the most promising
approaches for PM. Finally, we address the challenges and
controversies that face our transition into an era of PM and
provide a vision of how PM can be incorporated into clinical practice. We will avoid technical details and sophistications about specific applications when possible, referring
the reader to more specialized articles in this regard. For
a number of excellent reviews covering many aspects of
PM, please refer to the April 1, 2010 issue of Nature.
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The Scope of Clinical Applications of
Personalized Medicine
Cancer is a heterogeneous group of diseases whose causes,
pathogenesis, metastatic potential, and responses to treatment can be very different among individuals (9). Great
variations exist, even between individuals with the same
type of cancer, suggesting that genetic factors play an
important role in cancer pathogenesis. These differences
make cancer an ideal target for the application of PM.
The suite of clinical applications of PM in cancer is broad,
encompassing a wide variety of fields. Constituting some of
these clinical applications are screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after
surgery for early detection of recurrence, and the stratification of patients into specific smaller subgroups, thus allowing for individualization of treatment (ref. 10; Fig. 1).
Incorporation of the concept of PM into our health care
system will allow patients and doctors to become aware of an
underlying disease state, even before clinical signs and symptoms become apparent. In turn, treatment or preventive
measures could be taken, even before the disease manifests,
which can delay disease onset and symptom severity. PM
will also guide treatment decisions by stratifying patients
into unique subgroups based on their specific molecular
characteristics. Subgroups would preferentially receive targeted therapies to which they are more likely to respond,
with the goal of improving response rates and survival outcomes (6). Unnecessary side effects and toxicity of treatment
will be reduced, especially in individuals predicted to be
“nonresponders” to a particular targeted therapy. Nonresponders would be stratified into alternate subgroups for
personalized therapy. Furthermore, an expectation of who
will and will not respond to a particular treatment will have
an effect on how clinical trials are designed and carried out,
reducing their costs and failures. Ultimately, PM is expected
to result in an overall reduction in the cost of health care
(10). A summary of the currently available commercial tests
for the different aspects of PM is shown in Table 1.
PM can also benefit pharmaceutical companies looking
to identify unique molecular targets for drug design. Such
strategies will reduce the cost of lead discovery, reduce the
timeline and costs of clinical trials, and potentially revive
drugs that were previously thought to be ineffective (10).
The benefits of PM for the pharmaceutical industry are
summarized in Fig. 2. Some of the methods currently being used for screening of cancer mutations are described in
the succeeding paragraphs.
Cancer screening
A combination of genetic and environmental factors is
thought to contribute to an individual's predisposition to
cancer (11). Knowledge of the precise nature of these contributors is important because it can affect the course of action for disease prevention (modifications to behavior and
lifestyle) and monitoring of high-risk patients (10). In some
cases, the association between genetic factors and cancer is
very clear and has a significant effect on clinical intervention.
For example, individuals with mutations in the tumor suppressor genes breast cancer susceptibility gene 1 (BRCA1)
and breast cancer susceptibility gene 2 (BRCA2) have a
significant risk of developing breast cancer (12) and may
choose to take early preventive surgical measures (e.g., prophylactic mastectomy; refs. 13, 14), undergo regular screening, or receive adjuvant therapies (15, 16). BRCA1 and
BRCA2 mutations are also associated with other cancers, including ovarian (17), hematologic (18), and early-onset
prostate cancers (19), and knowledge about these mutations
may affect strategies for clinical management of these cancers (e.g., prophylactic salpingo-oophorectomy; refs. 20,
21). Genetic testing has also been used to identify individuals with inherited mutations in the DNA mismatch repair
genes, MLH1 and MSH2, who have a higher risk of developing colon cancer (22). Knowledge of their predispositions
can promote early screening colonoscopy for colon cancer,
with more frequent testing, to enable early cancer detection
and treatment. In addition, screening for RET gene mutations can identify individuals who are at a higher risk of developing multiple endocrine neoplasia type 2 (23) and
allows for closer follow-up or prophylactic thyroidectomy.
Public databases are constantly being updated with new
mutations and polymorphisms associated with cancer
(24). These databases can be useful resources for identifying
new biomarkers for screening.
Tumor classification and subtyping
One of the revolutionary aspects of PM is that it changes
the traditional paradigm of classifying cancers. With PM,
FIGURE 1. Clinical applications of PM in cancer patient management.
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Personalized Medicine in Cancer Management
Table 1. A partial list of commercial tests currently available for PM in cancer patients
Test
Company
Cancer type Test type
Technique Application
Oncotype Dx Breast
Cancer Assay
Genomic
Health
Breast
RT-PCR
Oncotype Dx Colon
Cancer Assay
Genomic
Health
Colon
MammaPrint
Agendia
Breast
HercepTest
Dako
Breast
Ventana Pathway
Ventana
Breast
TheraScreen: EGFR29
DxS
NSCLC
Expression profile
of a panel of
21 genes
Expression profile
of a panel of
12 genes
Expression of a
panel of 70 genes
c-erbB-2
overexpression
c-erbB-2
overexpression
EGFR29 mutation
TheraScreen: KRAS
Mutation Kit
DxS
mCRC
KRAS mutation
RT-PCR
CYP450 Test
Amplichip
Breast
Microarray
BCR-ABL Mutation
Analysis Test
EGFR Amplification
Test
EGFR Amplification
Test
ALK Gene
Rearrangement Test
EGFR Mutation
Genzyme
Genetics
Genzyme
Genetics
Genzyme
Genetics
Genzyme
Genetics
Genzyme
Genetics
CML
Identify CYP2D6
and CYP2C19
genotype
T315I mutation
RT-PCR
CRC
EGFR amplification
FISH
NSCLC
EGFR amplification
FISH
NSCLC
ALK gene
rearrangement
EGFR Mutation
FISH
NSCLC
RT-PCR
Microarray
IHC
IHC
RT-PCR
RT-PCR
Predicts risk of recurrence
and guides chemotherapy
treatment decision
Predicts recurrence and
assists treatment decision
in stage II colon cancer
Predicts risk of recurrence
Predicts response to
trastuzumab (Herceptin)
Predicts response to
trastuzumab (Herceptin)
Predicts response to gefitinib
(Iressa) and erlotinib (Tarceva)
Predicts response to
panitumumab (Vectibix)
and cetuximab (Erbitux)
Predicts response to tamoxifen
and determines optimal
treatment dose
Predicts response to imatinib
(Gleevec)
Predicts response to cetuximab
(Erbitux) and panitumumab (Vectibix)
Predicts response to gefitinib
(Iressa) and erlotinib (Tarceva)
Predicts response to
erlotinib (Tarceva)
Predicts response to tyrosine
kinase inhibitors
Abbreviations: RT-PCR, real-time PCR; IHC, immunohistochemistry; NSCLC, non–small cell lung cancer; mCRC, metastatic
colorectal cancer; CML, chronic myelogenous leukemia; CRC, colorectal cancer; FISH, fluorescence in situ hybridization.
pathologic classification can shift from the histologic scale,
which often gives little information on prognosis, individualized treatment options, and chance of recurrence, overlooking the fact that many patients with similar histologic
types might experience markedly different disease courses,
to the molecular scale, which offers a highly detailed, global
perspective of the disease process and promises superior performance over traditional classifications (25-28). Molecular
analyses at the protein, DNA, RNA, or microRNA
(miRNA) levels can contribute to the identification of novel
tumor subclasses, each with unique prognostic outcome or
response to treatment, which could not be identified by traditional morphologic methods (10). Recently, molecular
classification has been used to identify unique subclasses
of cancers, including acute myeloid leukemia (29, 30),
glioblastoma (31), breast cancer (32, 33), and renal cell
carcinoma (34, 35), and to differentiate between Burkitt's
lymphoma and diffuse B-cell lymphoma (36). Subclassifica-
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tion of cancers into unique molecular groups with distinct
prognosis or treatment options will significantly improve
patient management.
Targeted therapy and predictive markers for
treatment efficiency
The ultimate goal of PM is to define the disease at the
molecular level so that therapy can be directed at the right
population of patients (the predicted “responders”). Thus,
elucidating the molecular differences between individuals
will have an impact on how new treatments are developed
and would be of great benefit to pharmaceutical companies
looking to market new cancer drugs. It is now known that
even in individuals with similar clinical phenotypes (clinical manifestations and pathologic type), drug therapy is
usually only effective in a fraction of those treated, suggesting that other differences account for individual drug
responses (7). Being able to predict which patients will
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FIGURE 2. Benefits of PM for the pharmaceutical industry.
benefit from a drug would promote a shift to identify
alternative therapies to improve outcomes in the predicted
“nonresponders,” in turn reducing the administration of
costly and potentially toxic treatments to them. Furthermore, clinical trials of new drugs could focus on enrolling
patient populations in which treatment is predicted to be
most efficacious, thus increasing the likelihood of positive
outcomes (7).
The concept of PM gained much positive momentum
from the development of highly successful U.S. Food and
Drug Administration–approved targeted therapies, such as
imatinib mesylate (Gleevec) for chronic myeloid leukemia
and gastrointestinal stromal tumor (37, 38) and trastuzumab (Herceptin) for breast cancer (39). Using specific molecular characteristics of these cancers as predictive markers
for treatment response (abnormal protein tyrosine kinase activity in chronic myeloid leukemia and gastrointestinal stromal tumor and overexpression of the HER-2 receptor in
breast cancer), only those individuals who harbor the appropriate molecular alterations are eligible for treatment. It is
this subclassification of cancers, and the consequent customization of therapy, that has resulted in the remarkable
surge in survival rates for some cancers from zero to 70% (7).
This application of PM has been further shown by the use
of mutation screening in the treatment of patients with lung
cancer who received the tyrosine kinase inhibitors gefitinib
(Iressa) or erlotinib (Tarceva; ref. 40). Non–small cell lung
cancers with mutations in the kinase domain of epidermal
growth factor receptor (EGFR) are much more responsive to
treatment with gefitinib/erlotinib, and more specifically,
these drugs are especially effective in subgroups of Asian
female never-smokers with adenocarcinomas (41, 42). In
contrast, individuals with mutations in the downstream effector KRAS are resistant to erlotinib (43). Activating mutations in KRAS also cause resistance to the drugs cetuximab
(Erbitux) and panitumumab (Vectibix) in colon cancer patients, whereas these drugs are effective in cancers with wildtype KRAS (44, 45). Specific responses to drugs based on
one's molecular profile has led to the recent recommendation by the American Society of Clinical Oncology that
molecular testing of KRAS be done before the administration
of cetuximab or panitumumab to colon cancer patients (46).
Insurance companies have even offered to pay for
medication for those individuals with wild-type KRAS
(knowing they will respond well to therapy), adding a layer
of complexity to the concept of PM. Targeted therapy, as
in the cases of lung and colon cancers, highlights the potential for PM to guide patient care and gives hope for the
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development of new or combination treatments for other
cancers based on an understanding of their pathogenesis
(47). Additionally, old drugs that may have been dismissed
as ineffective in a broader category of cancer patients, or
drugs being used to treat other diseases, could be reintroduced into clinical trials that are focused on a specific
cancer subgroup to investigate novel effects.
Pharmacogenomics and treatment safety
Genetic variations can also predict treatment dose and
safety in different subgroups of cancer patients. Variations
in genes that encode drug-metabolizing enzymes, drug
transporters, or drug targets can have detrimental effects
on patient outcomes following treatment (48). For example, polymorphisms in cytochrome P450 (CYP) enzymes
can result in too slow or very fast metabolism of drugs,
causing patients to exhibit symptoms of overdose or have
no drug response at all (49, 50) by altering the pharmacokinetics of drug metabolism and distribution (48), and
could also account for the common occurrence of adverse
drug reactions (51). Thus, these polymorphisms could be
used to predict optimal drug dosage, minimize harmful
side effects, reduce the costs of “trial-and-error dosing,”
and ensure more successful outcomes (50). In familial
breast cancer, for example, genetic variations in CYP2D6
that confer “poor metabolizer” status have been shown to
reduce survival in individuals treated with the chemotherapeutic drug tamoxifen (52). Genetic variability in Pglycoprotein, a drug transporter, affects the pharmacokinetics
of the antineoplastic drug paclitaxel in ovarian cancer and is
also associated with clinical outcomes (53, 54). The transition to PM already has some pharmaceutical companies
placing information about genetic testing (including testing
for CYP450 polymorphisms) on drug labels (8, 55).
Prognosis
Until recently, the clinicopathologic parameters of the
patient (mainly tumor type, grade, and stage), along with
biochemical testing for few tumor markers, were the sole
indicators for prognosis and tumor aggressiveness and were
consequently used for therapeutic decision-making for cancer patients. However, it has become increasingly clear that
prognostic accuracy may greatly be improved by understanding the molecular basis of the different cancer subtypes. Genotyping or gene expression profiling by
microarray (56) and protein analysis by mass spectrometry
(discussed in the following sections; ref. 57) have already
been used to identify numerous prognostic biomarkers.
Molecular Cancer Research
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Personalized Medicine in Cancer Management
Such markers can help, either alone or in combination
with classic parameters, to substratify patients into smaller
distinct prognostic risk groups and guide therapy decisions.
Using tissue microarray analysis, Kim et al. (58) constructed a combined molecular and clinical prognostic
model for survival that was significantly more accurate
than standard clinical parameters for patients with renal
cell carcinoma. The Oncotype Dx Breast Cancer Assay,
which gives a gene expression profile of 21 genes known
to be involved in breast cancer, has also been shown to
be a good predictor of the risk of tumor recurrence (59).
Approaches and Tools for Personalized
Medicine
Figure 3 summarizes the different approaches and tools
available for molecular PM testing. A large array of techniques can be used for PM. Commonly used techniques
include PCR, fluorescence in situ hybridization, immunohistochemistry, and sequencing. More recently, the completion of the human genome project opened a new
horizon for PM analysis by using high-throughput analysis
(60). These include microarray, mass spectrometry, secondgeneration sequencing, array comparative genomic hybridization, and high-throughput SNP analysis, among others.
A common trend among these tools is their ability to simultaneously analyze hundreds or thousands of targets.
This multiparametric approach is likely to improve the
sensitivity, specificity, and accuracy of new biomarkers
(35). In addition to acceleration of biomarker discovery,
high-throughput analysis allows a better understanding of
the “cross-talk” or interaction between different molecules
in the pathogenesis of cancer. In the following discussions,
we will highlight some of the commonly used techniques
for global analysis.
High-throughput whole genome sequencing
The completion of the human genome project has motivated the development of “next-generation sequencing” technologies (61). High-throughput analyzers from Roche (454
Genome Analyzer), Life Technologies (SOLiD analyzer),
and Illumina (Genome Analyzer IIe and HiSeq 2000) now
make it possible to analyze millions of nucleotides at once,
at a lower cost compared with the traditional Sanger method.
These analyzers use pyrosequencing (62) or sequencing by
ligation (61), rather than the traditional Sanger end-chain
termination method. Table 2 summarizes the commonly used
high-throughput sequencers with their features and applications for PM. The advantages of high-throughput sequencing
include the capacity to perform multiplex reactions, reduced
operating costs, and very fast acquisition of data.
Next-generation sequencing is now being used for global
analysis of the genome. Previously, some cancer alleles could
not be detected by Sanger sequencing because they were
present in extremely low levels in cells. Now, the use of “deep
sequencing” (extensive repeated coverage of the sequence of
interest; ref. 63) and paired-end sequencing (64) has made
their identification possible, thus expanding our understanding of the cancer genome. Laser capture microdissection (65) has also been useful for isolating and enriching
cancer cell DNA and RNA obtained from a tissue sample,
which can then be used for targeted sequencing. These
methods enabled the identification of novel mutations, rearrangements, and other genomic alterations (66-68) that
lead to tumorigenesis in acute myeloid leukemia (69, 70),
chronic myeloid leukemia (67), and other cancers (71-74).
Methods for high-throughput sequencing continue
to evolve. Biotechnology companies such as Complete
Genomics, Helicos, and Pacific Biosciences are now working on “third-generation sequencing” methods, which are
expected to further reduce cost, increase accuracy and length
of reads, and provide even higher-throughput analysis of
FIGURE 3. A schematic representing different
approaches available for PM molecular testing.
A number of available biological materials
including DNA, RNA, miRNA, and protein can
be isolated from either fresh-frozen or
formalin-fixed paraffin-embedded (FFPE)
tissues, blood, or other bodily fluids and used
for molecular testing. Common techniques
include tissue microarray (TMA), SNP analysis,
array comparative genomic hybridization (CGH),
immunohistochemistry (IHC), fluorescence
in situ hybridization (FISH), and quantitative
real-time PCR (qRT-PCR).
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Table 2. Commercial analyzers for second-generation sequencing
Analyzer
Company
Sequencing method
Throughput
Applications
454 Genome
Sequencer
Roche
Pyrosequencing
De novo sequencing of
whole genomes and
transcriptomes of any size
Genome
Analyzer IIe
Illumina
Reversible terminatorbased sequencing
>1 million high-quality
reads per run and read
lengths of 400 bases
per 10-h instrument run
Combination of 2 × 100 bp
read length and up to 500
million reads per flow cell
HiSeq 2000
Illumina
Reversible terminatorbased sequencing
∼30× coverage of two
human genomes in a
single run
SOLiD
Applied Biosystems
Sequential ligation
100 Gb and 1.4 billion
tags per run (extendable
to 300 Gb and
2.4 billion tags per run)
Genome
DNA sequencing
Gene regulation analysis
Epigenome
ChIP-Seq
Methylation analysis
Transcriptome
Transcriptome analysis
SNP discovery and
structural variation analysis
Small RNA discovery
and analysis
Cytogenetic analysis
Genome
DNA sequencing
Gene regulation analysis
Epigenome
ChIP-Seq
Methylation analysis
Transcriptome
Transcriptome analysis
SNP discovery and
structural variation analysis
Small RNA discovery
and analysis
Cytogenetic analysis
Genome
De novo sequencing
Targeted resequencing
Whole genome sequencing
Epigenome
ChIP-Seq
Methylation analysis
Transcriptome
Gene expression profiling
Small RNA analysis
Whole transcriptome
profiling
Abbreviation: ChIP-Seq, chromatin immunoprecipitation sequencing.
genomes (61, 75). As the reality of faster and cheaper highthroughput analyses is becoming closer, several biotechnology companies are racing to commercialize full genome
sequencing, which is expected within the next few years
(68, 75). Although currently too expensive (∼$48,000
USD for a full genome by Illumina), it is estimated that
the era of the “$1,000” genome is fast approaching (76).
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There is no doubt that whole genome sequencing is a
powerful tool for digging deep into the genetic code to
look for influences on disease; however, with such a tool
comes the challenge of determining what information is
most important for analysis and interpretation, and this
is one challenge that will have to be addressed as we move
forward into the era of PM, as discussed later.
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SNP analysis and haplotype mapping
The human genome is estimated to have more than 30
million SNPs, which are essentially the “fingerprints” of
our genetic code (77, 78). Some of these have been thoroughly characterized by the International Haplotype Mapping Project (2, 3) in various populations and made publicly
available (79). These databases have provided the necessary
tools for researchers to discover associations between disease
risk and common SNPs (80). The advent of commercially
available microarrays (SNP chips; ref. 81) has resulted in a
shift from studying disease by linkage analysis to using genome-wide association studies (82). SNP arrays use allelespecific oligonucleotide probes that produce a fluorescent
signal when a specific allele of a SNP is present and are capable of analyzing up to 1 million SNPs in a single sample
(83, 84). Alternatively, SNP haplotypes, rather than single
SNPs, can be analyzed. SNP arrays can also be used for
screening for common features of cancer genomes, such as
allelic imbalance, copy number variation, or loss of heterozygosity. SNP arrays have been used extensively to assess various aspects of cancer, including risk assessment (85-87),
prognosis (88), survival (89), response to therapy (90),
and progression and metastasis (91-93).
Microarray analysis
Microarrays are widely used for global analysis of gene expression in cancer because they are cost-effective and have
high throughput (94). Microarrays are chips with immobilized capture molecules (such as oligonucleotides or cDNAs)
that serve as probes for binding fluorescently labeled targets
(e.g., cDNA) prepared from the two specimens to be
compared (e.g., normal versus cancer). Using microarrays,
it is possible to assess the expression levels of thousands of
genes in a single experiment. There are several platforms
for microarray analysis, including the most popular mRNA
microarray, miRNA microarrays, DNA arrays (array comparative genomic hybridization), and protein arrays.
Microarrays have been extensively used to study the different aspects of malignancy, especially at the discovery phase.
Gene expression profiling has been successfully used for the
detection of cancer to categorize different subtypes of a cancer (95-98), identify invasive versus noninvasive phenotypes
(99), predict prognosis (95, 96, 100-102), and predict
responses to treatment (103-106) and early recurrence (107).
Newer microarray platforms, such as miRNA microarrays, are also showing encouraging preliminary data for their
potential use as cancer biomarkers (108-113). MiRNA signatures have been used to stratify patients into prognostic
cohorts and treatment subgroups. Finally, microarrays can
be used to assess epigenetic changes in cancer (DNA methylation, histone acetylation, serine phosphorylation), which
are implicated in tumorigenesis, and can be used to classify
cancers and direct patient management (114).
Proteomics by mass spectrometry
Changes in the protein profiles of cancer cells can be important for determining new diagnostic biomarkers and may
also aid in the classification of tumors into unique subtypes
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(115). Proteomic analysis can be advantageous over mRNA
measurements because proteins are the final effector molecules and their levels do not always correlate with mRNA
levels due to posttranscriptional modifications (116). Furthermore, protein-protein interactions are important contributors to cellular pathways and mechanisms that contribute
to carcinogenesis. In mass spectrometry, proteins are ionized
into smaller molecules with unique mass-to-charge ratios
that can be used to quantify proteins (117). This has led
to the identification of several new cancer biomarkers for
different malignancies, including breast (118), ovarian
(119), prostate (120), and kidney (121, 122) cancers. Proteomics can contribute to tumor classification (123), treatment selection, pharmacoproteomics, and identification of
new drug targets and may also be used for therapeutic drug
monitoring (124-126). Details of the principles of mass
spectrometry are beyond the scope of this review and have
recently been covered in a number of excellent reviews.
Genome-wide association studies
Recent technological advances have allowed the examination of genetic variations on a much larger scale than
ever before. Genome-wide association studies (GWAS)
allow for a complete picture of the genome rather than
the smaller sections to which we were previously limited.
High-throughput approaches allow for an unbiased
examination of genetic variations in many different
tumors. There are a number of ongoing GWAS and
some of them are listed in Table 3. The Cancer Genetic
Markers of Susceptibility project, an initiative of the National Cancer Institute, aims to identify genes involved
in breast and prostate cancers by SNP analysis. The Human Cancer Genome Project will examine not only mutations but also other genetic abnormalities, such as gene
silencing, through methylation and other epigenetic mechanisms, as well as gene translocation, amplifications,
and deletions (127).
GWAS are all driven by the quest for future PM.
Recent results of GWAS have identified 6q25.1 as a
susceptibility locus for breast cancer (128) and two independent loci within 8q24 that contribute to prostate
cancer in men of European ancestry (129, 130). GWAS
have also indicated clear differences in susceptibility between cancer subtypes. For example, a lung cancer locus
identified on 5p15.33 was strongly associated with adenocarcinoma but not squamous or other subtypes (131).
Also, aberrations in the ovarian cancer locus BNC2 were
associated with the serous subtype only (132). Recent
findings indicate that certain mutations can predict patient response to treatment, as was shown for certain
EGFR mutations that can predict patient response to
the drug gefitinib (Iressa). Recently, a genome-wide scan
of SNPs found 20 SNPs that were associated with the
effectiveness of platinum-based chemotherapy in smallcell lung cancer patients (133). Also, a genome-wide
methylation analysis in mantle cell lymphoma patients
found that differentially methylated genes can be targeted
for therapeutic benefit (134). Although the use of GWAS
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has discovered many genetic loci and SNPs that are related to cancer, more studies are required to identify how
these alterations contribute to disease risk (135).
Databases/bioinformatics
An increasingly important new tool to achieve PM is the
availability of repositories of freely accessible databases.
Information obtained from genomic, transcriptomic, and
proteomic analyses is now deposited into large databases
for global access and data processing. Some of these
include the Cancer Genome Anatomy Project (136),
the Single Nucleotide Polymorphism Database (79), the
Catalogue of Somatic Mutations in Cancer (24), the
Mittleman Database of Chromosomal Aberrations in
Cancer (137), the Stanford Microarray Database (138),
the Roche Cancer Genome Database (139), the Protein
Information Resource (140), and the Human Protein
Reference Database (141), in addition to many others.
Bioinformatic algorithms can then be used to integrate a
patient's clinical information and the genetic profiles of
their tumor to predict the relationships of certain molecular changes to cancer, as discussed earlier.
Personalized Medicine in the Clinic: Challenges
and Controversies
As we move into the era of PM, several challenges have to
be addressed. The first is to establish a significant clinical
utility that is superior to our existing parameters, and this
will lead to a “real” improvement in cancer patient care. This
requires a team approach, with collaborative efforts between
clinicians, research scientists, computer experts, and biostatisticians, to achieve a clinically meaningful outcome. Full
transparency in reporting results (especially the negative
ones) should be emphasized to avoid selection bias for positive results reporting (35). It also has to be noted in this regard that statistically significant results in a research setting
are not always equivalent to clinically significant results.
Prospective trials on large patient cohorts are needed to solidify the clinical utility of new tests being offered to cancer
patients. Care must be taken when interpreting published
results due to the heterogeneity of the analyzed material,
which can be obtained from tissues, cell lines, and biological
fluids, and when comparing different histologic types,
stages, and grades of tumors.
Another important issue is the need for standardization
of testing. Standardization encompasses several aspects
including the type of specimen to be analyzed, the appropriate methods of specimen collection and storage, the
choice of the target genes/proteins to be tested, the platform to be used, optimal experimental conditions, and
the clinical interpretation of tests (cutoff values for positive
and negative results). There will be a need for quality
standards for different laboratories, and test validations
will be required with external quality assurance protocols.
It may also be necessary to implement Clinical Laboratory
Improvement Amendments–certified specialized laboratories to perform particular tests to ensure quality. A recent
report by the National Academy of Clinical Biochemistry
Laboratory Medicine Practice Guidelines expounded on
two main technologies commonly implemented for PM,
microarray and mass spectrometry, and developed recommendations on what must be done before for their application into the clinical realm.
One of the most important considerations will be the
higher cost of incorporating new, advanced technologies
that will provide more detailed predictive and prognostic
information about each individual patient. Setting up the
infrastructure for PM will require significant spending.
Prices are, however, becoming much more affordable compared with earlier years, as technology becomes more widespread. Following an initial capital investment, additional
running costs will become much less. The increased cost
of these advanced diagnostic systems will be partially compensated for by savings in patient care cost, such as reduced
courses of unnecessary chemotherapy or radiotherapy and
reduced hospital stays.
Ethical issues, including who will have access to this new
detailed information, especially those at risk of developing
cancer, should be adequately addressed. Individuals should
Table 3. A partial list of genome-wide association studies
Project
Objectives
Reference
Cancer Genetic Markers of Susceptibility
Identify inherited susceptibility to prostate
and breast cancers by examining SNPs
Systemic genome-wide scan of the coding
sequence of breast and colorectal cancers
Identify all functional gene mutations and
other abnormalities in common tumor types
Systematic study of more than 25,000
cancer genomes at the genomic, epigenomic,
and transcriptomic levels
Catalogue and characterize all proteins
in the human body
(128-130, 148)
The Consensus Coding Sequence of
Human Breast and Colorectal Cancers
The Cancer Genome Atlas
The International Cancer Genome Consortium
Human Proteome Project
1182
Mol Cancer Res; 8(9) September 2010
(149)
(127)
(150)
(151)
Molecular Cancer Research
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Personalized Medicine in Cancer Management
FIGURE 4. A schematic representing the future use of PM through the course of a cancer patients' disease. Individual molecular analysis can be
applied at each step to ensure that the patient receives the most appropriate and optimal treatment. The benefits of PM can also help predict which family
members in high-risk groups may develop malignancy.
be free to accept or decline susceptibility testing. The
access for insurance companies, other family members,
employers, and other agents will need to be clearly stated
and new laws may have to be passed to ensure fair treatment, protection, and privacy of the tested individuals.
Misuse of genetic information could take place at the
expense of those tested. Patients with known risks for
developing cancer, those who are expected to have poor
outcomes, or those who will not respond well to treatments may be discriminated against or denied employment
opportunities or health insurance coverage. Individuals
who are expected to not respond to treatment may also
be denied certain medications. Also, the cost of targeted
therapies is high. In this case, although genetic testing
may indicate one would receive benefits from a certain
drug, they may have limited access due to wealth or insurance status. In this case, PM will add to the problem of
socioeconomic divisions. Furthermore, genetic testing
may reveal traits for other serious illnesses (regardless of
whether an individual chooses to find out about them or
not), which may also affect how they are treated by others.
Good policy decisions will be crucial to reap the benefits of
PM (142). Luckily, in the United States, laws are already in
place to protect patients from these types of issues (8).
would replace the clinicopathologic designation system and
would provide many pieces of information related to treatment simultaneously. This was, however, followed by a
dormant period when promising research results did not
cross the bridge to be adopted into clinical applications.
One important reason for this is that the new classifiers
created new “categories” of patients for which the clinical
significance was unknown. One famous example of this is
the basal-like phenotype of breast cancer that was created
by microarray analysis (143). There are important reasons
as to why there has not been a rapid change in the
approach to cancer with the introduction of PM. The
process of developing a tumor marker for “clinical” use is
a tedious and multistep process requiring extensive testing,
optimization, validation, and establishment of usefulness
above other currently used methods (57).
After years of experience, we developed a more practical
view of using molecular genetic information to complement, rather than replace, clinicopathologic parameters.
Molecular profiling will be able to answer specific focused
questions related to patient management for each particular cancer. In some respects, great leaps have been taken
with the introduction of PM approaches to cancer. Several
biomarkers have been very useful and are currently being
marketed, as described earlier.
Personalized Medicine: Fantasy or Reality?
A Future Prospective
The introduction of the concept of PM initially created a
lot of promise in the scientific community. There was optimism for an imminent revolution in our medical practice
whereby a molecular “fingerprint” for each cancer patient
www.aacrjournals.org
It is clear from recent literature that the concept of PM
will have a significant impact on medical practice and is
gradually being included as an integral component of our
Mol Cancer Res; 8(9) September 2010
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1183
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Diamandis et al.
cancer management plans. It will inevitably result in more
effective treatment and fewer side effects for patients and
will induce a proactive and participatory role for the patient
in their own health care decisions. Patients will have the incentive to engage in lifestyle choices and health maintenance
to compensate for their genetic susceptibilities.
Before the era of PM, cancer diagnosis, prognosis, and
subsequent treatment decisions were based on histopathologic parameters including the tissue of origin and the stage
and grade of the tumor. Experience has shown morphologic classification to be deficient in many aspects and that
patients with the same histopathologic diagnosis can have
unexplained variable outcomes. Individual molecular markers have been slowly added to ameliorate the accuracy of
predicting prognosis and treatment efficiency. We now witness the accumulation of enormous amounts of genomic
data generated by high-throughput analyses, which are
augmented by tremendous advances in computer analytic
systems. PM is a revolutionary concept that challenges
our traditional classification and management of cancer.
Although very appealing, it is unlikely that it will completely replace traditional approaches, at least in the near
future. It will be essential to confirm the validity of new
biomarkers generated by PM technologies using prospective clinical trials in independent patient cohorts. This will
be an ongoing process that is expected to extend for many
years to come.
The introduction of PM requires a very big initial investment, but with it comes the promise of a rewarding and
cost-effective future for medical practice. Figure 4 shows
one possible future scenario where molecular analysis can
be incorporated with the various steps of cancer patient
management, from early detection to time of treatment.
PM will be performed hand in hand with the usual histopathologic evaluation to bring together more specific details about every individual cancer, including assessment
of risk, aggressiveness, and treatment options. This tumor
“fingerprint” will reduce unnecessary treatments and could
also extend to other family members who are at risk of
developing the same or a related malignancy.
Following the establishment of molecular fingerprints,
the examination and incorporation of one's phenotype into
individual disease assessment must be considered (144). Of
course, this comes with its own challenges including the
requirement of super-computers that can analyze these
enormous amounts of data for one person. Integrating
data from cancer genetics and functional data into molecular networks could potentially approach a genotypephenotype map (145-147). Although this may be a reality
in years to come, we need to approach PM in a stepwise
manner and remember that the long-tem benefits are
yet to come.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Grant Support
G.M. Yousef was supported by grants from the Canadian Institute of Health
Research (grant 86490), the Canadian Cancer Society (grant 20185), and the
Ministry of Research and Innovation, Government of Ontario.
The costs of publication of this article were defrayed in part by the payment of
page charges. This article must therefore be hereby marked advertisement in
accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 06/15/2010; revised 07/30/2010; accepted 08/02/2010; published
OnlineFirst 08/06/2010.
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Personalized Medicine: Marking a New Epoch in Cancer
Patient Management
Maria Diamandis, Nicole M.A. White and George M. Yousef
Mol Cancer Res 2010;8:1175-1187. Published OnlineFirst August 6, 2010.
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