Molecular Cancer Research - Personalized Medicine: Marking a New Epoch … · 2010. 8. 6. · Personalized Medicine: Marking a New Epoch in Cancer Patient Management Maria Diamandis1,

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Copyright © 2010 American Association for Cancer Research

Personalized Medicine: Marking a New Epoch in Cancer Patient Management

Maria Diamandis1, Nicole MA White1,2, George M Yousef1,2,3

1. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto,

Canada

2. Department of Laboratory Medicine, and the Keenan Research Centre in the Li Ka Shing

Knowledge Institute, St. Michael’s Hospital, Toronto, Canada

3. To whom correspondence should be addressed.

Running Title: Personalized medicine in cancer management

Correspondence and reprints:

George M. Yousef, MD, PhD, FRCPC (Path). MSc Department of Laboratory Medicine St. Michael’s Hospital 30 Bond Street Toronto, ON M5B 1W8, Canada Tel: 416-864-6060 ext. 6129 Fax: 416-864-5648 Email: [email protected]

Published OnlineFirst on August 6, 2010

on February 2, 2021. © 2010 American Association for Cancer Research. mcr.aacrjournals.org Downloaded from

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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 upon 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.





Non-standard abbreviations

CGAP; Cancer Genome Anatomy Project, CGEMS; Cancer Genetic Markers of Susceptibility

Project, CLIA; Clinical Laboratory Improvement Amendments, CML; chronic myeloid

leukemia, CGH; comparative genomic hybridization, CTP; cytochrome P450; dbSNP; Single

Nucleotide Polymorphism Database, EGFR; epidermal growth factor receptor, FISH;

fluorescence in situ hybridization, FDA; United States Food and Drug Administration, GIST;

gastrointestinal stromal tumour, IHC; immunohistochemistry, miRNA; microRNA, MEN-2;

multiple endocrine neoplasia type 2, PCR; polymerase chain reaction, PM; personalized

medicine, SNP; single nucleotide polymorphism, TMA; tissue microarray, qRT-PCR;

quantitative real time polymerase chain reaction.

Key Words

Cancer management; cancer susceptibility; deep sequencing; mass spectrometry; microarray;

microRNA; molecular biology; molecular profiling; next generation sequencing; personalized

medicine; SNPs; tumour markers.





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, single nucleotide 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 United States National Cancer Institute 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 upon us for some years, and is rapidly gaining

momentum. The concept of PM is to utilize 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 impact 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.

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 [10]. (Box 1).

Incorporation of the concept of PM into our healthcare 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 ‘non-responders’ to a particular targeted therapy. Non-responders 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 healthcare [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 Box 2.

Some of the methods currently being used for screening of cancer mutations are described

below.

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 as it can impact the course of action for disease prevention (modifications to

behaviour 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 impact on clinical





intervention. For example, individuals with mutations in the tumour 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) [13, 14], undergo regular screening, or

administer 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 impact strategies for clinical management of these

cancers (e.g. prophylactic salpingo-oophorectomy) [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, in order 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 (MEN-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.

Tumour Classification and Sub-Typing

One of the revolutionary aspects of PM is that it changes the traditional paradigm of

classifying cancers. With PM, pathological classification can shift from the histological scale-

that often gives little information on prognosis, individualized treatment options, and chance of

recurrence, overlooking the fact that many patients with similar histological 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 tumour subclasses, each with

unique prognostic outcome or response to treatment, that could not be identified by traditional

morphological methods [10]. Recently, molecular classification has been used to identify unique

subclasses of cancers including acute myeloid leukemia (AML) [29, 30], glioblastoma [31],

breast cancer [32, 33], renal cell carcinoma [34, 35], and to differentiate between Burkitt’s

lymphoma and diffuse B-cell lymphoma [36]. Sub-classification 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 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 pathological 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 benefit from a drug would promote a shift to identify alternative

therapies to improve outcomes in the predicted “non-responders,” 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 therefore increasing the likelihood of positive outcomes [7].

The concept of PM gained much positive momentum from the development of highly

successful, United States Food and Drug Administration (FDA)-approved, targeted therapies like

imatinib mesylate (Gleevec®) for chronic myeloid leukemia (CML) and gastrointestinal stromal

tumour (GIST) [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 CML and GIST and over expression of the HER-2 receptor in

breast cancer), only those individuals who harbour the appropriate molecular alterations are

eligible for treatment. It is this sub-classification of cancers, and 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 demonstrated 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®) [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 wild type 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 lung and colon cancers, highlights the potential for PM to

guide patient care, and gives hope for the development of new or combination treatments for

other cancers based on understanding 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 re-introduced into clinical trials 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 P-glycoprotein, a drug transporter,

affects the pharmacokinetics of the anti-neoplastic 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 clinicopathological parameters of the patient (mainly tumour type,

grade, and stage), along with biochemical testing for few tumour markers, were the sole

indicators for prognosis and tumour 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 below) [57] have already been used to identify numerous

prognostic biomarkers. Such markers can help, either alone or in combination with classical

parameters, to sub-stratify 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 tumour recurrence [59].





Approaches and Tools for Personalized Medicine

Figure 1 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

polymerase chain reaction (PCR), fluorescence in situ hybridization (FISH),

immunohistochemistry (IHC), 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, second generation sequencing, array comparative

genomic hybridization (CGH), 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 multi-parametric approach is likely to improve 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. Below, 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

to 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. 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) [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 AML [69, 70], CML [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 genomes [61, 75]. As the

reality of faster and cheaper high-throughput 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 “$1000” genome is fast-approaching [76].

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.

SNP Analysis and Haplotype Mapping

The human genome is estimated to have over 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 publically

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) [81], has resulted in a shift from studying disease by linkage analysis,

to using genome-wide association studies [82]. SNP arrays employ allele-specific

oligonucleotide probes that produce a fluorescent signal when a specific allele of a SNP is

present, and are capable of analyzing up to one 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 like 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 high-throughput [94]. Microarrays are chips with immobilized capture

molecules (such as oligonucleotides or cDNAs), that serve as probes for binding fluorescently-





labelled targets (for example, cDNA) prepared from the two specimens to be compared (e.g.

normal vs. cancer). Using microarrays, it is possible to assess 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-CGH), and protein

arrays.

Microarrays have been extensively used to study 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

non-invasive phenotypes [99], predict prognosis [95, 96, 100-102], and to predict responses to

treatment [103-106] and early recurrence [107].

Newer microarray platforms, such as miRNA microarrays are also showing encouraging

preliminary data for the 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 tumours into unique subtypes

[115]. Proteomic analysis can be advantageous over mRNA measurements as proteins are the

final effector molecules and their levels do not always correlate with mRNA levels due to post-

transcriptional modifications [116]. Furthermore, protein-protein interactions are important





contributors to cellular pathways and mechanism 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 tumour classification [123], treatment

selection, pharmacoproteomics, 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 (GWAS)

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 is a number of ongoing GWAS and some of them are listed in

Table 3. The National Cancer Institute’s initiative the Cancer Genetic Markers of Susceptibility

Project (CGEMS) 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 was associated with the

serous subtype only [132]. Recent findings indicate 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 were

associated with the effectiveness of platinum-based chemotherapy in small-cell 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 have 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 are now deposited into large databases for global access and data processing. Some of

these include: the Cancer Genome Anatomy Project (CGAP) [136], the Single Nucleotide

Polymorphism Database (dbSNP) [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





genetic profiles of their tumour to predict the relationships of certain molecular changes to

cancer, as discussed above.

PM 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 that 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

histological 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

(cut-off 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 (CLIA)-certified, specialized laboratories to perform particular tests in order to

ensure quality. A recent report by the National Academy of Clinical Biochemistry Laboratory

Medicine Practice Guidelines expounded upon 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 to 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 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 USA, laws are already in place to protect patients from

these types of issues [8].

Personalized medicine: Fantasy or reality?

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 would replace the clinicopathological 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 multi-step 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, where molecular genetic

information is used to complement, rather than replace, clinicopathological 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 above.





A Future Prospective

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 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 of the patient in their own healthcare

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 tumour. Experience has shown morphological 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 prediction of treatment efficiency. We now witness the accumulation of

enormous amounts of genomic data generated by high-throughput analyses, and augmented by

tremendous advances in computer analytical 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 2 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 tumour “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 genotype-phenotype map

[145-147]. Although this may be a reality in years to come, we need to approach PM in a step-

wise manner and remember the long-tem benefits are yet to come.





Acknowledgements

This work was supported by grants to George M Yousef from the Canadian Institute of Health

Research (CIHR grant # 86490), Canadian Cancer Society (CCS grant # 20185), and the

Ministry of Research and Innovation, Government of Ontario.





Table 1. A partial list of commercial tests currently available for personalized medicine in cancer patients

Test Company Cancer Type

Test Type Technique Application

Oncotype Dx™ Breast Cancer Assay

Genomic Health

Breast Expression profile of a panel of 21 genes

RT-PCR1 Predicts risk of recurrence and guides chemotherapy treatment decision

Oncotype Dx™ Colon Cancer Assay

Genomic Health

Colon Expression profile of a panel of 12 genes

RT-PCR1 Predicts recurrence and assists treatment decision in stage II colon cancer

Mammaprint™

Agendia Breast Expression of a panel of 70 genes

Microarray Predicts risk of recurrence

HercepTest™ Dako Breast c-erbB-2 overexpression

IHC2 Predicts response to trastuzumab (Herceptin®)

Ventana Pathway®

Ventana Breast c-erbB-2 overexpression

IHC2 Predicts response to trastuzumab (Herceptin®)

TheraScreen: EGFR29

DxS NSCLC3 EGFR29 mutation

RT-PCR1 Predicts response to gefitinib (Iressa®) and erlotinib (Tarceva®)

TheraScreen: K-RAS mutation kit

DxS mCRC4 K-RAS mutation RT-PCR1 Predicts response to panitumumab (Vectibix®)and cetuximab (Eribtux®)

CYP450 Test Amplichip Breast Identify CYP2D6 and CYP2C19 genotype

Microarray Predicts response to tamoxifen and determine optimal treatment dose

BCR-ABL Mutation Analysis Test

Genzyme Genetics

CML5 T315I mutation RT-PCR1 Predicts response to imatinib (Gleevac®)

EGFR Amplification Test

Genzyme Genetics

CRC6 EGFR amplification

FISH7 Predicts response to cetuximab (Erbitux®) and panitumumab (Vectibix®)

EGFR Amplification Test

Genzyme Genetics

NSCLC3 EGFR amplification

FISH7 Predicts response to gefitinib (Iressa®) and erlotinib (Tarceva®)

ALK Gene Rearrangement Test

Genzyme Genetics

NSCLC3 ALK gene rearrangement

FISH7 Predicts response to erlotinib (Tarceva®)

EGFR Mutation Genzyme Genetics

NSCLC3 EGFR Mutation RT-PCR1 Predicts response to tyrosine kinase inhibitors

1. RT-PCR, real time polymerase chain reaction 2. IHC, immunohistochemistry 3. NSCLS, non-small cell lung cancer 4. mCRC, metastatic colorectal cancer 5. CML, chronic myelogenous kinase 6. CRC, colorectal cancer 7. FISH, fluorescence in situ hybridization





Table 2. Commercial analyzers for second generation sequencing.

Analyzer

Company Sequencing Method Throughput Applications

454 Genome Sequencer

Roche Pyrosequencing

>1 million high-quality reads per run and read lengths of 400 bases, per 10-hour instrument run.

De novo sequencing of whole genomes and transcriptomes of any size

Genome Analyzer IIe

Illumina

Reversible terminator-based sequencing

Combination of 2 x 100 bp read length and up to 500 million reads per flow cell

Genome DNA Sequencing Gene Regulation Analysis

Epigenome ChIP-Seq1

Methylation analysis Transcriptome

Transcriptome Analysis SNP Discovery and Structural Variation Analysis Small RNA Discovery and Analysis

Cytogenetic Analysis

HiSeq 2000

Illumina

Reversible terminator-based sequencing

~30x coverage of two human genomes in a single run

Genome DNA Sequencing Gene Regulation Analysis

Epigenome ChIP-Seq1


Transcriptome Analysis SNP Discovery and Structural Variation Analysis Small RNA Discovery and Analysis

Cytogenetic Analysis SOLiD

Applied Biosystems

Sequential ligation 100 gigabases and 1.4 billion tags per run (extendable to 300 GB and 2.4 Billion tags per run)

Genome De-novo sequencing Targeted re-sequencing Whole genome sequencing

Epigenome

on February 2, 2021. ©

2010 Am

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ChIP-Seq1


Gene expression profiling Small RNA analysis Whole transcriptome profiling

1. ChIP-Seq, chromatin immunoprecipitation sequencing

on February 2, 2021. ©

2010 Am

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ancer Research.

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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 SNPs1

[128-130, 148]

The Consensus Coding Sequence of Human Breast and Colorectal Cancers

Systemic genome-wide scan of the coding sequence of breast and colorectal cancers

[149]

The Cancer Genome Atlas

Identify all functional gene mutations and other abnormalities in common tumor types

[127]

The International Cancer Genome Consortium

Systematic study of more than 25,000 cancer genomes at the genomic, epigenomic and transcriptomic levels

[150]

Human Proteome Project

Catalogue and characterize all proteins in the human body

[151]

1. SNPs, single nucleotide polymorphisms





Box 1. Clinical Applications of Personalized Medicine in Cancer Patient Management Screening and risk assessment Lifestyle modifications for risk reduction Diagnosis and subclassification Prognosis Prediction of treatment efficacy Pharmacogenomics and dose adjustments Post-surgical follow-up for early detection of recurrence Stratification of patients into smaller cancer subtypes for targeted therapy Box 2. Benefits of Personalized Medicine for the Pharmaceutical Industry Reduce time and cost of lead discovery Development of targeted therapies for specific cancer subgroups Improved patient selection for clinical trials Reduced timelines and costs of clinical trials Novel applications for old drugs





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Figure Legends

Figure 1. A schematic representing different approaches available for personalized medicine

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), single nucleotide polymorphism (SNP), array comparative genomic

hybridization (CGH), immunohistochemistry (IHC), fluorescence in situ hybridization (FISH),

and quantitative real-time polymerase chain reaction (qRT-PCR).

Figure 2. A schematic representing the future use of personalized medicine through the course

of a cancer patients’ disease. Individual molecular analysis can be applied at each step to ensure

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.










Published OnlineFirst August 6, 2010.Mol Cancer Res Maria Diamandis, Nicole MA White and George M Yousef ManagementPersonalized Medicine: Marking a New Epoch in Cancer Patient

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Molecular Cancer Research - Personalized Medicine: Marking a New Epoch … · 2010. 8. 6. · Personalized Medicine: Marking a New Epoch in Cancer Patient Management Maria Diamandis1,