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Published OnlineFirst August 6, 2010; DOI: 10.1158/1541-7786.MCR-10-0264

Molecular
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sonalized Medicine: Marking a New Epoch in Cancer

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Diamandis1, Nicole M.A. White1,2, and George M. Yousef1,2

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onalized medicine (PM) is defined as “a form of medicine that uses information about a person's genes,s, and environment to prevent, diagnose, and treat disease.” The promise of PM has been on us for years.uite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis,tion of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the strat-n of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminatene size fits all” model of medicine, which has centered on reaction to disease based on average responses toy dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for eachual according to disease aggressiveness and the ability to respond to a certain treatment. PM is alsog the emphasis of patient management from primary patient care to prevention and early interventionh-risk individuals. In addition to classic single molecular markers, high-throughput approaches can ber PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis,ass spectrometry. A common trend among these tools is their ability to analyze many targets simulta-y, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges needddressed 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 asignificant impact on our health care in the future. Mol Cancer Res; 8(9); 1175–87. ©2010 AACR.

genes,treat dyears ais to uotheran indtreatm“one steredon avpatienfor hithemvariatand reto fit

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field of medicine has seen many new advances int several decades. With the completion of the humane project (1), an enormous amount of new informa-as been gained about the human genome and thec variations between individuals. Added to this isergence of new technologies for global genomicis, including high-throughput sequencing, single-otide polymorphism (SNP) genotyping, and tran-profiling. The construction of haplotype maps ofnome (2, 3) is now allowing us to view DNA in abigger picture than ever before. This, coupled withous advances in computer systems and bioinfor-, has resulted in a revolutionary shift in medical careera of personalized medicine (PM).

d by the U.S. National Cancer Institute asicine that uses information about a person's

Theyet coPM aalso suapprocontroprovidical prtionsthe rea numPM, p

ions: 1Department of Laboratory Medicine andiversity of Toronto and 2Department of Laboratorye Keenan Research Centre in the Li Ka Shinge, St. Michael's Hospital, Toronto, Canada

thor: George M. Yousef, Department of Laboratoryhael's Hospital, 30 Bond Street, Toronto, Ontario,Phone: 416-864-6060, ext. 6129; Fax: 416-864-5648.h.ca

7786.MCR-10-0264

ssociation for Cancer Research.

ls.org

Research. on September 25, 202mcr.aacrjournals.org aded from

proteins, and environment to prevent, diagnose, andisease.” The promise of PM has been on us for somend is rapidly gaining momentum. The concept of PMse clinical, genomic, transcriptomic, proteomic, andinformation sources to plot the optimal course forividual in terms of disease risk assessment, prevention,ent, or palliation. Thus, PM aims to eliminate theize fits all”model of medicine, which has mainly cen-on reaction to a disease (treating the symptoms) basederage responses to care, by shifting the emphasis oft care to cancer prevention and early interventiongh-risk individuals (4, 5). Moreover, understandingolecular profiles of individuals and how these can causeions in disease susceptibility, symptoms, progression,sponses to treatment will lead to tailoring medical careeach individual patient (6-8).objectives of this review are to provide a simplifiedmprehensive overview of the scope of applications ofnd their effect on cancer patient management. Wemmarize the basic principles of the most promisingaches for PM. Finally, we address the challenges andversies that face our transition into an era of PM ande a vision of how PM can be incorporated into clin-actice. We will avoid technical details and sophistica-about specific applications when possible, referringader to more specialized articles in this regard. For
ber of excellent reviews covering many aspects oflease refer to the April 1, 2010 issue of Nature.
1175

0. © 2010 American Association for Cancer

http://mcr.aacrjournals.org/

The SPers

Canpathomentvariattype oimpormakeThe sencomthesesis, prsurgertion oing foInc

systemundertomsmeasuwhichwill ainto ucharacgetedwith tcomeswill b“nonrspondpersonwill anan effereducito res(10). Afor thPM

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Diamandis et al.

Mol C1176


cope of Clinical Applications ofonalized Medicine

cer is a heterogeneous group of diseases whose causes,genesis, metastatic potential, and responses to treat-can be very different among individuals (9). Greations exist, even between individuals with the samef cancer, suggesting that genetic factors play antant role in cancer pathogenesis. These differencescancer an ideal target for the application of PM.uite of clinical applications of PM in cancer is broad,passing a wide variety of fields. Constituting some ofclinical applications are screening, diagnosis, progno-ediction of treatment efficacy, patient follow-up aftery for early detection of recurrence, and the stratifica-f patients into specific smaller subgroups, thus allow-r individualization of treatment (ref. 10; Fig. 1).orporation of the concept of PM into our health carewill allow patients and doctors to become aware of anlying disease state, even before clinical signs and symp-become apparent. In turn, treatment or preventiveres could be taken, even before the disease manifests,can delay disease onset and symptom severity. PM

lso guide treatment decisions by stratifying patientsnique subgroups based on their specific molecularteristics. Subgroups would preferentially receive tar-therapies to which they are more likely to respond,he goal of improving response rates and survival out-(6). Unnecessary side effects and toxicity of treatmente reduced, especially in individuals predicted to beesponders” to a particular targeted therapy. Nonre-ers would be stratified into alternate subgroups foralized therapy. Furthermore, an expectation of whod will not respond to a particular treatment will havect on how clinical trials are designed and carried out,ng their costs and failures. Ultimately, PM is expectedult in an overall reduction in the cost of health caresummary of the currently available commercial tests

e different aspects of PM is shown in Table 1.can also benefit pharmaceutical companies lookingntify unique molecular targets for drug design. Suchies will reduce the cost of lead discovery, reduce the
ne and costs of clinical trials, and potentially revive On
FIGURE 1. Clinical applications of PM in canc

ancer Res; 8(9) September 2010

Research. on September 25, 202mcr.aacrjournals.org Downloaded from

enefits of PM for the pharmaceutical industry arearized in Fig. 2. Some of the methods currently be-ed for screening of cancer mutations are described incceeding paragraphs.

r screeningombination of genetic and environmental factors isht to contribute to an individual's predisposition tor (11). Knowledge of the precise nature of these con-ors is important because it can affect the course of ac-or disease prevention (modifications to behavior andle) and monitoring of high-risk patients (10). In somethe association between genetic factors and cancer islear and has a significant effect on clinical intervention.ample, individuals with mutations in the tumor sup-r genes breast cancer susceptibility gene 1 (BRCA1)reast cancer susceptibility gene 2 (BRCA2) have aicant risk of developing breast cancer (12) and maye to take early preventive surgical measures (e.g., pro-tic mastectomy; refs. 13, 14), undergo regular screen-r receive adjuvant therapies (15, 16). BRCA1 and2mutations are also associated with other cancers, in-g ovarian (17), hematologic (18), and early-onsette cancers (19), and knowledge about these mutationsffect strategies for clinical management of these can-e.g., prophylactic salpingo-oophorectomy; refs. 20,enetic testing has also been used to identify indivi-with inherited mutations in the DNAmismatch repairMLH1 and MSH2, who have a higher risk of devel-colon cancer (22). Knowledge of their predispositionsromote early screening colonoscopy for colon cancer,ore frequent testing, to enable early cancer detectioneatment. In addition, screening for RET gene muta-can identify individuals who are at a higher risk of de-ing multiple endocrine neoplasia type 2 (23) andfor closer follow-up or prophylactic thyroidectomy.

c databases are constantly being updated with newtions and polymorphisms associated with cancerhese databases can be useful resources for identifyingiomarkers for screening.

r classification and subtyping
e of the revolutionary aspects of PM is that it changes
that were previously thought to be ineffective (10). the traditional paradigm of classifying cancers. With PM,

er patient management.

Molecular Cancer Research



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logic classification can shift from the histologic scale,often gives little information on prognosis, individu-treatment options, and chance of recurrence, over-g the fact that many patients with similar histologicmight experience markedly different disease courses,molecular scale, which offers a highly detailed, globalctive of the disease process and promises superior per-nce over traditional classifications (25-28). Molecularses at the protein, DNA, RNA, or microRNAA) levels can contribute to the identification of novelsubclasses, each with unique prognostic outcome orse to treatment, which could not be identified by tra-al morphologic methods (10). Recently, molecularication has been used to identify unique subclassescers, including acute myeloid leukemia (29, 30),astoma (31), breast cancer (32, 33), and renal cell
oma (34, 35), and to differentiate between Burkitt'soma and diffuse B-cell lymphoma (36). Subclassifica-
ing threspon

acrjournals.org


f cancers into unique molecular groups with distinctosis or treatment options will significantly improvet management.

ted therapy and predictive markers forent efficiencyultimate goal of PM is to define the disease at the

ular level so that therapy can be directed at the rightation of patients (the predicted “responders”). Thus,ating the molecular differences between individualsave an impact on how new treatments are developedould be of great benefit to pharmaceutical companiesg to market new cancer drugs. It is now known thatn individuals with similar clinical phenotypes (clini-anifestations and pathologic type), drug therapy isy only effective in a fraction of those treated, suggest-

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reviations: RT-PCR, real-time PCR; IHC, immunohistochemistry; NSCLC, non–small cell lung cancer; mCRC, metastatic

es account for individual drugto predict which patients will

l Cancer Res; 8(9) September 2010 1177

Association for Cancer


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Mol C1178


it from a drug would promote a shift to identifyative therapies to improve outcomes in the predictedesponders,” in turn reducing the administration ofand potentially toxic treatments to them. Further-clinical trials of new drugs could focus on enrollingt populations in which treatment is predicted to beefficacious, thus increasing the likelihood of positivemes (7).concept of PM gained much positive momentum

the development of highly successful U.S. Food andAdministration–approved targeted therapies, such asib mesylate (Gleevec) for chronic myeloid leukemiaastrointestinal stromal tumor (37, 38) and trastuzu-Herceptin) for breast cancer (39). Using specific mo-r characteristics of these cancers as predictive markersatment response (abnormal protein tyrosine kinase ac-in chronic myeloid leukemia and gastrointestinal stro-mor and overexpression of the HER-2 receptor incancer), only those individuals who harbor the appro-molecular alterations are eligible for treatment. It isbclassification of cancers, and the consequent custo-ion of therapy, that has resulted in the remarkablein survival rates for some cancers from zero to 70% (7).s application of PM has been further shown by the usetation screening in the treatment of patients with lungwho received the tyrosine kinase inhibitors gefitinib) or erlotinib (Tarceva; ref. 40). Non–small cell lungs with mutations in the kinase domain of epidermalh factor receptor (EGFR) are much more responsive toent with gefitinib/erlotinib, and more specifically,drugs are especially effective in subgroups of Asiane never-smokers with adenocarcinomas (41, 42). Inst, individuals with mutations in the downstream ef-KRAS are resistant to erlotinib (43). Activating muta-in KRAS also cause resistance to the drugs cetuximabux) and panitumumab (Vectibix) in colon cancer pa-whereas these drugs are effective in cancers with wild-RAS (44, 45). Specific responses to drugs based onmolecular profile has led to the recent recommenda-y the American Society of Clinical Oncology thatular testing ofKRAS be done before the administrationximab or panitumumab to colon cancer patients (46).urance companies have even offered to pay foration for those individuals with wild-type KRASing they will respond well to therapy), adding a layerplexity to the concept of PM. Targeted therapy, as

FIGURE 2. Benefits of PM for the

cases of lung and colon cancers, highlights the po-l for PM to guide patient care and gives hope for the

(discubeen



pment of new or combination treatments for otherrs based on an understanding of their pathogenesisAdditionally, old drugs that may have been dismissedffective in a broader category of cancer patients, orbeing used to treat other diseases, could be reintro-into clinical trials that are focused on a specific

r subgroup to investigate novel effects.

acogenomics and treatment safetyetic variations can also predict treatment dose andin different subgroups of cancer patients. Variationses that encode drug-metabolizing enzymes, drugorters, or drug targets can have detrimental effectstient outcomes following treatment (48). For exam-olymorphisms in cytochrome P450 (CYP) enzymessult in too slow or very fast metabolism of drugs,g patients to exhibit symptoms of overdose or haveug response at all (49, 50) by altering the pharmaco-cs of drug metabolism and distribution (48), andalso account for the common occurrence of adversereactions (51). Thus, these polymorphisms could beto predict optimal drug dosage, minimize harmfulffects, reduce the costs of “trial-and-error dosing,”nsure more successful outcomes (50). In familialcancer, for example, genetic variations in CYP2D6onfer “poor metabolizer” status have been shown toe survival in individuals treated with the chemother-ic drug tamoxifen (52). Genetic variability in P-rotein, adrug transporter, affects thepharmacokineticsantineoplastic drug paclitaxel in ovarian cancer and isssociated with clinical outcomes (53, 54). The transi-o PM already has some pharmaceutical companiesg information about genetic testing (including testingP450 polymorphisms) on drug labels (8, 55).

osistil recently, the clinicopathologic parameters of thet (mainly tumor type, grade, and stage), along withemical testing for few tumor markers, were the soletors for prognosis and tumor aggressiveness and werequently used for therapeutic decision-making for can-tients. However, it has become increasingly clear thatostic accuracy may greatly be improved by under-ng the molecular basis of the different cancer sub-. Genotyping or gene expression profiling byarray (56) and protein analysis by mass spectrometry

aceutical industry.

ssed in the following sections; ref. 57) have alreadyused to identify numerous prognostic biomarkers.




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markers can help, either alone or in combinationlassic parameters, to substratify patients into smallerct prognostic risk groups and guide therapy decisions.tissue microarray analysis, Kim et al. (58) con-ed a combined molecular and clinical prognosticl for survival that was significantly more accuratetandard clinical parameters for patients with renalrcinoma. The Oncotype Dx Breast Cancer Assay,gives a gene expression profile of 21 genes known
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oaches and Tools for Personalizedcine

re 3 summarizes the different approaches and toolsle for molecular PM testing. A large array of techni-can be used for PM. Commonly used techniquese PCR, fluorescence in situ hybridization, immuno-hemistry, and sequencing. More recently, the com-n of the human genome project opened a newn for PM analysis by using high-throughput analysishese include microarray, mass spectrometry, second-tion sequencing, array comparative genomic hybrid-, and high-throughput SNP analysis, among others.mon trend among these tools is their ability to si-neously analyze hundreds or thousands of targets.multiparametric approach is likely to improve theivity, specificity, and accuracy of new biomarkersIn addition to acceleration of biomarker discovery,hroughput analysis allows a better understanding ofross-talk” or interaction between different moleculespathogenesis of cancer. In the following discussions,
ll highlight some of the commonly used techniquesbal analysis.
expec

e PCR (qRT-PCR).

acrjournals.org


throughput whole genome sequencingcompletion of the human genome project has moti-

the development of “next-generation sequencing” tech-ies (61). High-throughput analyzers from Roche (454me Analyzer), Life Technologies (SOLiD analyzer),lumina (Genome Analyzer IIe and HiSeq 2000) nowit possible to analyze millions of nucleotides at once,wer cost compared with the traditional Sanger method.analyzers use pyrosequencing (62) or sequencing byn (61), rather than the traditional Sanger end-chaination method. Table 2 summarizes the commonly usedhroughput sequencers with their features and applica-or PM. The advantages of high-throughput sequencinge the capacity to perform multiplex reactions, reduceding costs, and very fast acquisition of data.t-generation sequencing is now being used for globalis of the genome. Previously, some cancer alleles coulde detected by Sanger sequencing because they weret in extremely low levels in cells. Now, the use of “deepncing” (extensive repeated coverage of the sequence ofst; ref. 63) and paired-end sequencing (64) has madeidentification possible, thus expanding our under-ng of the cancer genome. Laser capture microdissec-65) has also been useful for isolating and enrichingr cell DNA and RNA obtained from a tissue sample,can then be used for targeted sequencing. Theseds enabled the identification of novel mutations, re-ements, and other genomic alterations (66-68) thato tumorigenesis in acute myeloid leukemia (69, 70),ic myeloid leukemia (67), and other cancers (71-74).thods for high-throughput sequencing continuelve. Biotechnology companies such as Completemics, Helicos, and Pacific Biosciences are now work-“third-generation sequencing” methods, which are

ted to further reduce cost, increase accuracy and length
of reads, and provide even higher-throughput analysis of
E 3. A schematic representing differentches available for PM molecular testing.er of available biological materialsg DNA, RNA, miRNA, and protein canted from either fresh-frozen or-fixed paraffin-embedded (FFPE), blood, or other bodily fluids and usedecular testing. Common techniquestissue microarray (TMA), SNP analysis,mparative genomic hybridization (CGH),histochemistry (IHC), fluorescenceybridization (FISH), and quantitative

Mol Cancer Res; 8(9) September 2010 1179



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Diamandis et al.

Mol C1180


es (61, 75). As the reality of faster and cheaper high-hput analyses is becoming closer, several biotechnol-ompanies are racing to commercialize full genomencing, which is expected within the next few years5). Although currently too expensive (∼$48,000
for a full genome by Illumina), it is estimated thata of the “$1,000” genome is fast approaching (76).
is oneforwa



re is no doubt that whole genome sequencing is aful tool for digging deep into the genetic code toor influences on disease; however, with such a toolthe challenge of determining what information isimportant for analysis and interpretation, and this

2. Commer
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nalysis and haplotype mappinghuman genome is estimated to have more than 30n SNPs, which are essentially the “fingerprints” ofnetic code (77, 78). Some of these have been thor-y characterized by the International Haplotype Map-roject (2, 3) in various populations and made publiclyle (79). These databases have provided the necessaryor researchers to discover associations between diseased common SNPs (80). The advent of commerciallyle microarrays (SNP chips; ref. 81) has resulted in arom studying disease by linkage analysis to using ge--wide association studies (82). SNP arrays use allele-ic oligonucleotide probes that produce a fluorescentwhen a specific allele of a SNP is present and are ca-of analyzing up to 1 million SNPs in a single sample4). Alternatively, SNP haplotypes, rather than single, can be analyzed. SNP arrays can also be used foring for common features of cancer genomes, such asimbalance, copy number variation, or loss of hetero-ty. SNP arrays have been used extensively to assess var-spects of cancer, including risk assessment (85-87),osis (88), survival (89), response to therapy (90),rogression and metastasis (91-93).

array analysisroarrays are widely used for global analysis of gene ex-n in cancer because they are cost-effective and havehroughput (94). Microarrays are chips with immobi-apture molecules (such as oligonucleotides or cDNAs)rve as probes for binding fluorescently labeled targetscDNA) prepared from the two specimens to beared (e.g., normal versus cancer). Using microarrays,ossible to assess the expression levels of thousands ofin a single experiment. There are several platformscroarray analysis, including the most popular mRNAarray, miRNA microarrays, DNA arrays (array com-ve genomic hybridization), and protein arrays.roarrays have been extensively used to study the differ-pects of malignancy, especially at the discovery phase.expression profiling has been successfully used for theion of cancer to categorize different subtypes of a can-5-98), identify invasive versus noninvasive phenotypespredict prognosis (95, 96, 100-102), and predictses to treatment (103-106) and early recurrence (107).er microarray platforms, such as miRNA microar-re also showing encouraging preliminary data for theirtial use as cancer biomarkers (108-113). MiRNA sig-s have been used to stratify patients into prognosticts and treatment subgroups. Finally, microarrays cand to assess epigenetic changes in cancer (DNA meth-, histone acetylation, serine phosphorylation), whichplicated in tumorigenesis, and can be used to classifys and direct patient management (114).

omics by mass spectrometrynges in the protein profiles of cancer cells can be im-
t for determining new diagnostic biomarkers andmayd in the classification of tumors into unique subtypes
foundfor th

acrjournals.org


. Proteomic analysis can be advantageous over mRNArements because proteins are the final effector mole-and their levels do not always correlate with mRNAdue to posttranscriptional modifications (116). Fur-ore, protein-protein interactions are important contri-s to cellular pathways and mechanisms that contributeinogenesis. Inmass spectrometry, proteins are ionizedmaller molecules with unique mass-to-charge ratiosan be used to quantify proteins (117). This has ledidentification of several new cancer biomarkers forent malignancies, including breast (118), ovarian, prostate (120), and kidney (121, 122) cancers. Pro-cs can contribute to tumor classification (123), treat-selection, pharmacoproteomics, and identification ofrug targets and may also be used for therapeutic drugtoring (124-126). Details of the principles of massometry are beyond the scope of this review and havely been covered in a number of excellent reviews.

me-wide association studiesent technological advances have allowed the exam-n of genetic variations on a much larger scale thanefore. Genome-wide association studies (GWAS)for a complete picture of the genome rather thanaller sections to which we were previously limited.

-throughput approaches allow for an unbiasedination of genetic variations in many differentrs. There are a number of ongoing GWAS andof them are listed in Table 3. The Cancer Geneticrs of Susceptibility project, an initiative of the Na-Cancer Institute, aims to identify genes involvedast and prostate cancers by SNP analysis. The Hu-ancer Genome Project will examine not only mu-

s but also other genetic abnormalities, such as geneing, through methylation and other epigenetic me-sms, as well as gene translocation, amplifications,eletions (127).AS are all driven by the quest for future PM.t results of GWAS have identified 6q25.1 as atibility locus for breast cancer (128) and two inde-nt loci within 8q24 that contribute to prostater in men of European ancestry (129, 130). GWASalso indicated clear differences in susceptibility be-cancer subtypes. For example, a lung cancer locusfied on 5p15.33 was strongly associated with adeno-oma but not squamous or other subtypes (131).aberrations in the ovarian cancer locus BNC2 wereated with the serous subtype only (132). Recentgs indicate that certain mutations can predict pa-response to treatment, as was shown for certainmutations that can predict patient response to

rug gefitinib (Iressa). Recently, a genome-wide scanPs found 20 SNPs that were associated with theiveness of platinum-based chemotherapy in small-ng cancer patients (133). Also, a genome-widelation analysis in mantle cell lymphoma patients
that differentially methylated genes can be targetederapeutic benefit (134). Although the use of GWAS



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scovered many genetic loci and SNPs that are relat-cancer, more studies are required to identify howalterations contribute to disease risk (135).

ases/bioinformaticsincreasingly important new tool to achieve PM is thebility of repositories of freely accessible databases.ation obtained from genomic, transcriptomic, andmic analyses is now deposited into large databasesobal access and data processing. Some of thesee the Cancer Genome Anatomy Project (136),ngle Nucleotide Polymorphism Database (79), theogue of Somatic Mutations in Cancer (24), theeman Database of Chromosomal Aberrations inr (137), the Stanford Microarray Database (138),oche Cancer Genome Database (139), the Proteination Resource (140), and the Human Proteinnce Database (141), in addition to many others.ormatic algorithms can then be used to integrate at's clinical information and the genetic profiles ofumor to predict the relationships of certain molecu-anges to cancer, as discussed earlier.

onalized Medicine in the Clinic: Challengesontroversies

e move into the era of PM, several challenges have toressed. The first is to establish a significant clinicalthat is superior to our existing parameters, and thisad to a “real” improvement in cancer patient care. Thises a team approach, with collaborative efforts betweenans, research scientists, computer experts, and biosta-ns, to achieve a clinically meaningful outcome. Fullarency in reporting results (especially the negativeshould be emphasized to avoid selection bias for pos-esults reporting (35). It also has to be noted in this re-hat statistically significant results in a research settingt always equivalent to clinically significant results.
ective trials on large patient cohorts are needed to so-the
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ts. Care must be taken when interpreting publisheds due to the heterogeneity of the analyzed material,can be obtained from tissues, cell lines, and biological, and when comparing different histologic types,, and grades of tumors.ther important issue is the need for standardizationting. Standardization encompasses several aspectsing the type of specimen to be analyzed, the appro-methods of specimen collection and storage, theof the target genes/proteins to be tested, the plat-to be used, optimal experimental conditions, andinical interpretation of tests (cutoff values for positiveegative results). There will be a need for qualityards for different laboratories, and test validationse required with external quality assurance protocols.also be necessary to implement Clinical Laboratoryvement Amendments–certified specialized laborato-perform particular tests to ensure quality. A recentby the National Academy of Clinical Biochemistryatory Medicine Practice Guidelines expounded onain technologies commonly implemented for PM,array and mass spectrometry, and developed recom-ations on what must be done before for their appli-into the clinical realm.e of the most important considerations will be ther cost of incorporating new, advanced technologiesill provide more detailed predictive and prognostication about each individual patient. Setting up thetructure for PM will require significant spending.are, however, becoming much more affordable com-with earlier years, as technology becomes more wide-. Following an initial capital investment, additionalng costs will become much less. The increased costse advanced diagnostic systems will be partially com-ted for by savings in patient care cost, such as reduceds of unnecessary chemotherapy or radiotherapy anded hospital stays.ical issues, including who will have access to this new
d information, especially those at risk of developing
clinical utility of new tests being offered to cancer cancer, should be adequately addressed. Individuals should

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e to accept or decline susceptibility testing. Thefor insurance companies, other family members,yers, and other agents will need to be clearly statedew laws may have to be passed to ensure fair treat-protection, and privacy of the tested individuals.e of genetic information could take place at these of those tested. Patients with known risks forping cancer, those who are expected to have poormes, or those who will not respond well to treat-may be discriminated against or denied employmenttunities or health insurance coverage. Individualsre expected to not respond to treatment may alsonied certain medications. Also, the cost of targetedies is high. In this case, although genetic testingndicate one would receive benefits from a certainthey may have limited access due to wealth or insur-tatus. In this case, PM will add to the problem ofconomic divisions. Furthermore, genetic testingeveal traits for other serious illnesses (regardless ofer an individual chooses to find out about them orhich may also affect how they are treated by others.policy decisions will be crucial to reap the benefits of42). Luckily, in the United States, laws are already into protect patients from these types of issues (8).

onalized Medicine: Fantasy or Reality?

introduction of the concept of PM initially created apromise in the scientific community. There was op-

rs in high-risk groups may develop malignancy.

for an imminent revolution in our medical practiceby a molecular “fingerprint” for each cancer patient

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acrjournals.org


replace the clinicopathologic designation system andprovide many pieces of information related to treat-simultaneously. This was, however, followed by aant period when promising research results did notthe bridge to be adopted into clinical applications.important reason for this is that the new classifiersd new “categories” of patients for which the clinicalcance was unknown. One famous example of this issal-like phenotype of breast cancer that was createdcroarray analysis (143). There are important reasonswhy there has not been a rapid change in theach to cancer with the introduction of PM. Thes of developing a tumor marker for “clinical” use isous and multistep process requiring extensive testing,ization, validation, and establishment of usefulnessother currently used methods (57).r years of experience, we developed a more practicalof using molecular genetic information to comple-rather than replace, clinicopathologic parameters.ular profiling will be able to answer specific focusedons related to patient management for each particu-ncer. In some respects, great leaps have been takenhe introduction of PM approaches to cancer. Severalrkers have been very useful and are currently beingted, as described earlier.

ture Prospective

clear from recent literature that the concept of PM

E 4. A schematic representing the future use of PM through the course of a cancer patients' disease. Individual molecular analysis can be

ave a significant impact on medical practice and isally being included as an integral component of our




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management plans. It will inevitably result in moreve treatment and fewer side effects for patients andduce a proactive and participatory role for the patientir own health care decisions. Patients will have the in-e to engage in lifestyle choices and health maintenancepensate for their genetic susceptibilities.

ore the era of PM, cancer diagnosis, prognosis, anduent treatment decisions were based on histopatho-arameters including the tissue of origin and the stageade of the tumor. Experience has shown morpholog-sification to be deficient in many aspects and thatts with the same histopathologic diagnosis can havelained variable outcomes. Individual molecular mar-ave been slowly added to ameliorate the accuracy ofting prognosis and treatment efficiency. We now wit-he accumulation of enormous amounts of genomicenerated by high-throughput analyses, which arented by tremendous advances in computer analytics. PM is a revolutionary concept that challengesaditional classification and management of cancer.ugh very appealing, it is unlikely that it will com-replace traditional approaches, at least in the near

. It will be essential to confirm the validity of newrkers generated by PM technologies using prospec-inical trials in independent patient cohorts. This willongoing process that is expected to extend for manyto come.introduction of PM requires a very big initial invest-but with it comes the promise of a rewarding andffective future for medical practice. Figure 4 showsossible future scenario where molecular analysis can
orporated with the various steps of cancer patient Rece
adouri L, Hubert A, Rotenberg Y, et al. Cancer risks in carriers ofe BRCA1/2 Ashkenazi founder mutations. J Med Genet 2007;44:7–71.

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ill be performed hand in hand with the usual histo-logic evaluation to bring together more specific de-bout every individual cancer, including assessment, aggressiveness, and treatment options. This tumorrprint” will reduce unnecessary treatments and couldxtend to other family members who are at risk ofping the same or a related malignancy.lowing the establishment of molecular fingerprints,amination and incorporation of one's phenotype intodual disease assessment must be considered (144). Of, this comes with its own challenges including theement of super-computers that can analyze theseous amounts of data for one person. Integratingrom cancer genetics and functional data into molec-etworks could potentially approach a genotype-type map (145-147). Although this may be a realityrs to come, we need to approach PM in a stepwiseer and remember that the long-tem benefits arecome.

osure of Potential Conflicts of Interest

otential conflicts of interest were disclosed.

Support

. Yousef was supported by grants from the Canadian Institute of Health(grant 86490), the Canadian Cancer Society (grant 20185), and theof Research and Innovation, Government of Ontario.costs of publication of this article were defrayed in part by the payment ofarges. This article must therefore be hereby marked advertisement ince with 18 U.S.C. Section 1734 solely to indicate this fact.

ived 06/15/2010; revised 07/30/2010; accepted 08/02/2010; published
ement, from early detecti on to time of treatment. OnlineFirst 08/06/2010.
rencesinishing the euchromatic sequence of the human genome. Nature04;431:931–45.haplotypemap of the human genome. Nature 2005;437:1299–320.razer KA, Ballinger DG, Cox DR, et al. A second generation humanplotype map of over 3.1 million SNPs. Nature 2007;449:851–61.brahams E, Ginsburg GS, Silver M. The Personalized Medicineoalition: goals and strategies. Am J Pharmacogenomics 2005;5:45–55.brahams E, Silver M. The case for personalized medicine. J Dia-etes Sci Technol 2009;3:680–4.insburg GS, McCarthy JJ. Personalized medicine: revolutionizingrug discovery and patient care. Trends Biotechnol 2001;19:491–6.llison M. Is personalized medicine finally arriving? Nat Biotechnol08;26:509–17.insburg GS, Willard HF. Genomic and personalized medicine:undations and applications. Transl Res 2009;154:277–87.idler IJ. Tumor heterogeneity and the biology of cancer invasiond metastasis. Cancer Res 1978;38:2651–60.verdevest JB, Theodorescu D, Lee JK. Utilizing the molecularateway: the path to personalized cancer management. Clin Chem009;55:684–97.erera FP. Environment and cancer: who are susceptible? Science97;278:1068–73.

iogas D, Roukos DH. Genetics and personal genomics for person-lized breast cancer surgery: progress and challenges in researchnd clinical practice. Ann Surg Oncol 2009;16:1771–82.oukos DH, Briasoulis E. Individualized preventive and therapeuticanagement of hereditary breast ovarian cancer syndrome. Natlin Pract Oncol 2007;4:578–90.ronwald J, Tung N, Foulkes WD, et al. Tamoxifen and contralateralreast cancer in BRCA1 and BRCA2 carriers: an update. Int J Can-er 2006;118:2281–4.yrski T, Huzarski T, Dent R, et al. Response to neoadjuvant ther-py with cisplatin in BRCA1-positive breast cancer patients. Breastancer Res Treat 2009;115:359–63.arod SA, Boyd J. Current understanding of the epidemiology andlinical implications of BRCA1 and BRCA2 mutations for ovarianancer. Curr Opin Obstet Gynecol 2002;14:19–26.riedenson B. The BRCA1/2 pathway prevents hematologic can-ers in addition to breast and ovarian cancers. BMC Cancer007;7:152.allagher DJ, Gaudet MM, Pal P, et al. Germline BRCA mutationsenote a clinicopathologic subset of prostate cancer. Clin Canceres 2010;16:2115–21.auff ND, Domchek SM, Friebel TM, et al. Risk-reducing salpingo-ophorectomy for the prevention of BRCA1- and BRCA2-associated
reast and gynecologic cancer: a multicenter, prospective study.Clin Oncol 2008;26:1331–7.an S, Meng Q, Auborn K, Carter T, Rosen EM. BRCA1 and BRCA2



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molecular targets for phytochemicals indole-3-carbinol andenistein in breast and prostate cancer cells. Br J Cancer 2006;:407–26.eltomaki P, de la Chapelle A. Mutations predisposing to hereditarynpolyposis colorectal cancer. Adv Cancer Res 1997;71:93–119.ps CJ, Landsvater RM, Hoppener JW, et al. Clinical screening asmpared with DNA analysis in families with multiple endocrineoplasia type 2A. N Engl J Med 1994;331:828–35.orbes SA, Tang G, Bindal N, et al. COSMIC (the Catalogue ofomatic Mutations in Cancer): a resource to investigate acquiredutations in human cancer. Nucleic Acids Res 2010;38:D652–7.irard N, Ostrovnaya I, Lau C, et al. Genomic and mutationalrofiling to assess clonal relationships between multiple non-smallell lung cancers. Clin Cancer Res 2009;15:5184–90.ordes B, Williams MD, Tirado Y, et al. Molecular and phenotypicalysis of poorly differentiated sinonasal neoplasms: an integratedproach for early diagnosis and classification. Hum Pathol 2009;:283–92.avicioni E, Anderson MJ, Finckenstein FG, et al. Molecular classi-ation of rhabdomyosarcoma-genotypic and phenotypic determi-nts of diagnosis: a report from the Children's Oncology Group.m J Pathol 2009;174:550–64.e Leval L, Gaulard P. Pathobiology and molecular profiling oferipheral T-cell lymphomas. Hematol Am Soc Hematol Educrogram 2008:272–9.ullinger L, Valk PJ. Gene expression profiling in acute myeloid leu-mia. J Clin Oncol 2005;23:6296–305.ilva FP, Swagemakers SM, Erpelinck-Verschueren C, et al. Genepression profiling of minimally differentiated acute myeloid leuke-ia: M0 is a distinct entity subdivided by RUNX1 mutation status.lood 2009;114:3001–7.erhaak RG, Hoadley KA, Purdom E, et al. Integrated genomicnalysis identifies clinically relevant subtypes of glioblastomaharacterized by abnormalities in PDGFRA, IDH1, EGFR, NF1.ancer Cell 2010;17:98–110.iechmann L, Sampson M, Stempel M, et al. Presenting features ofreast cancer differ by molecular subtype. Ann Surg Oncol 2009;16:05–10.ormanno N, De Luca A, Carotenuto P, Lamura L, Oliva I, D'Alessio. Prognostic applications of gene expression signatures in breastncer. Oncology 2009;77 Suppl 1:2–8.ahinden C, Ingold B, Wild P, et al. Mining tissue microarray data toncover combinations of biomarker expression patterns thatprove intermediate staging and grading of clear cell renal cellncer. Clin Cancer Res 2010;16:88–98.rsanious A, Bjarnason GA, Yousef GM. From bench to bedside:rrent and future applications of molecular profiling in renal cellrcinoma. Mol Cancer 2009;8:20.ave SS, Fu K, Wright GW, et al. Molecular diagnosis of Burkitt'smphoma. N Engl J Med 2006;354:2431–42.ruker BJ, Talpaz M, Resta DJ, et al. Efficacy and safety of a spe-fic inhibitor of the BCR-ABL tyrosine kinase in chronic myeloidukemia. N Engl J Med 2001;344:1031–7.uveson DA, Willis NA, Jacks T, et al. STI571 inactivation of theastrointestinal stromal tumor c-KIT oncoprotein: biological andinical implications. Oncogene 2001;20:5054–8.aselga J, Norton L, Albanell J, Kim YM, Mendelsohn J. Recombi-nt humanized anti-HER2 antibody (Herceptin) enhances the anti-mor activity of paclitaxel and doxorubicin against HER2/neuverexpressing human breast cancer xenografts. Cancer Res98;58:2825–31.aez JG, Janne PA, Lee JC, et al. EGFR mutations in lung cancer:rrelation with clinical response to gefitinib therapy. Science 2004;4:1497–500.ao W, Miller V, Zakowski M, et al. EGF receptor gene mutationse common in lung cancers from “never smokers” and are associ-ed with sensitivity of tumors to gefitinib and erlotinib. Proc Natl

5

5

5

5

5

5

5

5

6

6

6

6

cad Sci U S A 2004;101:13306–11.nne PA, Johnson BE. Effect of epidermal growth factor receptorrosine kinase domain mutations on the outcome of patients with

64. Ss2

acrjournals.org


on-small cell lung cancer treated with epidermal growth factor re-eptor tyrosine kinase inhibitors. Clin Cancer Res 2006;12:4416–20s.assarelli E, Varella-Garcia M, Tang X, et al. KRAS mutation is anportant predictor of resistance to therapy with epidermal growthctor receptor tyrosine kinase inhibitors in non-small-cell lungancer. Clin Cancer Res 2007;13:2890–6.mado RG, Wolf M, Peeters M, et al. Wild-type KRAS is required foranitumumab efficacy in patients with metastatic colorectal cancer.Clin Oncol 2008;26:1626–34.ievre A, Bachet JB, Le CD, et al. KRAS mutation status is predic-ve of response to cetuximab therapy in colorectal cancer. Canceres 2006;66:3992–5.llegra CJ, Jessup JM, Somerfield MR, et al. American Society oflinical Oncology provisional clinical opinion: testing for KRASene mutations in patients with metastatic colorectal carcinomapredict response to anti-epidermal growth factor receptor mono-

lonal antibody therapy. J Clin Oncol 2009;27:2091–6.aurent-Puig P, Taieb J. Lessons from Tarceva in pancreatic cancer:here are we now, and how should future trials be designed inancreatic cancer? Curr Opin Oncol 2008;20:454–8.vans WE, Relling MV. Pharmacogenomics: translating functionalenomics into rational therapeutics. Science 1999;286:487–91.odriguez-Antona C, Gomez A, Karlgren M, Sim SC, Ingelman-undberg M. Molecular genetics and epigenetics of the cyto-hrome P450 gene family and its relevance for cancer risk andeatment. Hum Genet 2010;127:1–17.gelman-Sundberg M. Polymorphism of cytochrome P450 and xe-obiotic toxicity. Toxicology 2002;181–182:447–52.azarou J, Pomeranz BH, Corey PN. Incidence of adverse drug re-ctions in hospitalized patients: a meta-analysis of prospectiveudies. JAMA 1998;279:1200–5.ewman WG, Hadfield KD, Latif A, et al. Impaired tamoxifen metab-lism reduces survival in familial breast cancer patients. Clin Canceres 2008;14:5913–8.amaguchi H, Hishinuma T, Endo N, et al. Genetic variation inBCB1 influences paclitaxel pharmacokinetics in Japanese pa-ents with ovarian cancer. Int J Gynecol Cancer 2006;16:979–85.ohnatty SE, Beesley J, Paul J, et al. ABCB1 (MDR 1) polymorph-ms and progression-free survival among women with ovarianancer following paclitaxel/carboplatin chemotherapy. Clin Canceres 2008;14:5594–601.rueh FW, Amur S, Mummaneni P, et al. Pharmacogenomic bio-arker information in drug labels approved by the United Statesod and drug administration: prevalence of related drug use.harmacotherapy 2008;28:992–8.acgregor PF, Squire JA. Application of microarrays to the analysisf gene expression in cancer. Clin Chem 2002;48:1170–7.ulasingam V, Diamandis EP. Strategies for discovering novelancer biomarkers through utilization of emerging technologies.at Clin Pract Oncol 2008;5:588–99.im HJ, Shen SS, Ayala AG, et al. Virtual-karyotyping with SNP mi-roarrays in morphologically challenging renal cell neoplasms: aractical and useful diagnostic modality. Am J Surg Pathol 2009;3:1276–86.onlin AK, Seidman AD. Use of the Oncotype DX 21-gene assay touide adjuvant decision making in early-stage breast cancer. Moliagn Ther 2007;11:355–60.heeler DA, Srinivasan M, Egholm M, et al. The complete genomef an individual by massively parallel DNA sequencing. Nature008;452:872–6.chuster SC. Next-generation sequencing transforms today's biol-gy. Nat Methods 2008;5:16–8.onaghi M, Karamohamed S, Pettersson B, Uhlen M, Nyren P. Real-me DNA sequencing using detection of pyrophosphate release.nal Biochem 1996;242:84–9.homasRK, Baker AC, Debiasi RM, et al. High-throughput oncogeneutation profiling in human cancer. Nat Genet 2007;39:347–51.
tephens PJ, McBride DJ, Lin ML, et al. Complex landscapes ofomatic rearrangement in human breast cancer genomes. Nature009;462:1005–10.



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Mol C1186


mmert-Buck MR, Bonner RF, Smith PD, et al. Laser captureicrodissection. Science 1996;274:998–1001.trausberg RL, Simpson AJ, Wooster R. Sequence-based cancerenomics: progress, lessons and opportunities. Nat Rev Genet03;4:409–18.aher CA, Kumar-Sinha C, Cao X, et al. Transcriptome sequencingdetect gene fusions in cancer. Nature 2009;458:97–101.orozova O, Marra MA. Applications of next-generation se-uencing technologies in functional genomics. Genomics 2008;2:255–64.y TJ, Minx PJ, Walter MJ, et al. A pilot study of high-throughput,quence-based mutational profiling of primary human acuteyeloid leukemia cell genomes. Proc Natl Acad Sci U S A 2003;0:14275–80.yner JW, Erickson H, Deininger MW, et al. High-throughputequencing screen reveals novel, transforming RAS mutations inyeloid leukemia patients. Blood 2009;113:1749–55.hao Q, Caballero OL, Levy S, et al. Transcriptome-guided charac-rization of genomic rearrangements in a breast cancer cell line.roc Natl Acad Sci U S A 2009;106:1886–91.eroukhim R, Mermel CH, Porter D, et al. The landscape of somaticpy-number alteration across human cancers. Nature 2010;463:9–905.eck J, Urnovitz HB, Mitchell WM, Schutz E. Next generationequencing of serum circulating nucleic acids from patients withvasive ductal breast cancer reveals differences to healthy andonmalignant controls. Mol Cancer Res 2010;8:335–42.ickerson ML, Jaeger E, Shi Y, et al. Improved identification of vonippel-Lindau gene alterations in clear cell renal tumors. Clin Can-r Res 2008;14:4726–34.rmanac R, Sparks AB, Callow MJ, et al. Human genome sequenc-g using unchained base reads on self-assembling DNA nano-rays. Science 2010;327:78–81.ervice RF. Gene sequencing. The race for the $1000 genome.cience 2006;311:1544–6.argill M, Altshuler D, Ireland J, et al. Characterization of single-ucleotide polymorphisms in coding regions of human genes. Natenet 1999;22:231–8.eich DE, Gabriel SB, Altshuler D. Quality and completeness ofNP databases. Nat Genet 2003;33:457–8.herry ST, Ward MH, Kholodov M, et al. dbSNP: the NCBI databasef genetic variation. Nucleic Acids Res 2001;29:308–11.rr N, Chanock S. Common genetic variation and human disease.dv Genet 2008;62:1–32.aresso K, Broeckel U. Genotyping platforms for mass-throughputenotyping with SNPs, including human genome-wide scans. Advenet 2008;60:107–39.cCarthy MI, Abecasis GR, Cardon LR, et al. Genome-widessociation studies for complex traits: consensus, uncertaintynd challenges. Nat Rev Genet 2008;9:356–69.astinen T, Raitio M, Lindroos K, Tainola P, Peltonen L, SyvanenC. A system for specific, high-throughput genotyping by allele-ecific primer extension on microarrays. Genome Res 2000;10:31–42.yvanen AC. Toward genome-wide SNP genotyping. Nat Genet005;37 Suppl:S5–10.emp Z, Carvajal-Carmona L, Spain S, et al. Evidence for a colo-ctal cancer susceptibility locus on chromosome 3q21-24 from aigh-density SNP genome-wide linkage scan. Hum Mol Genet06;15:2903–10.am RK, Zhang WW, Loblaw DA, et al. A genome-wide associationreen identifies regions on chromosomes 1q25 and 7p21 as riskci for sporadic prostate cancer. Prostate Cancer Prostatic Dis08;11:241–6.emignani F, Landi S, Moreno V, et al. Polymorphisms of theopamine receptor gene DRD2 and colorectal cancer risk. Cancerpidemiol Biomarkers Prev 2005;14:1633–8.amm F, Heuser M, Morgan M, et al. Single nucleotide polymor-

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hism in the mutational hotspot of WT1 predicts a favorableutcome in patients with cytogenetically normal acute myeloidukemia. J Clin Oncol 2010;28:578–85.
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uang YT, Heist RS, Chirieac LR, et al. Genome-wide analysis ofurvival in early-stage non-small-cell lung cancer. J Clin Oncol009;27:2660–7.un X, Li F, Sun N, et al. Polymorphisms in XRCC1 and XPG andsponse to platinum-based chemotherapy in advanced non-smallell lung cancer patients. Lung Cancer 2009;65:230–6.ujawski L, Ouillette P, Erba H, et al. Genomic complexity identifiesatients with aggressive chronic lymphocytic leukemia. Blood008;112:1993–2003.ing L, Ellis MJ, Li S, et al. Genome remodelling in a basal-e breast cancer metastasis and xenograft. Nature 2010;464:99–1005.ada M, Kanai F, Tanaka Y, et al. Prognostic significance of geneticlterations detected by high-density single nucleotide polymor-hism array in gastric cancer. Cancer Sci 2010;101:1261–9.imon R. Microarray-based expression profiling and informatics.urr Opin Biotechnol 2008;19:26–9.im MJ, Ro JY, Ahn SH, Kim HH, Kim SB, Gong G. Clinicopatho-gic significance of the basal-like subtype of breast cancer: a com-arison with hormone receptor and Her2/neu-overexpressinghenotypes. Hum Pathol 2006;37:1217–26.ybkaer K, Iqbal J, Zhou G, Chan WC. Molecular diagnosis andutcome prediction in diffuse large B-cell lymphoma and otherubtypes of lymphoma. Clin Lymphoma 2004;5:19–28.olub TR, Slonim DK, Tamayo P, et al. Molecular classification ofancer: class discovery and class prediction by gene expressiononitoring. Science 1999;286:531–7.lizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuserge B-cell lymphoma identified by gene expression profiling.ature 2000;403:503–11.ittner M, Meltzer P, Chen Y, et al. Molecular classification of cu-neous malignant melanoma by gene expression profiling. Nature000;406:536–40.eer DG, Kardia SL, Huang CC, et al. Gene-expression profiles pre-ict survival of patients with lung adenocarcinoma. Nat Med 2002;:816–24.otti A, Mukherjee S, Petersen R, et al. A genomic strategy to refinerognosis in early-stage non-small-cell lung cancer. N Engl J Med006;355:570–80.hanasekaran SM, Barrette TR, Ghosh D, et al. Delineation of prog-ostic biomarkers in prostate cancer. Nature 2001;412:822–6.onnefoi H, Potti A, Delorenzi M, et al. Validation of gene signaturesat predict the response of breast cancer to neoadjuvant chemo-erapy: a substudy of the EORTC 10994/BIG 00-01 clinical trial.ancet Oncol 2007;8:1071–8.pentzos D, Levine DA, Kolia S, et al. Unique gene expressionrofile based on pathologic response in epithelial ovarian cancer.Clin Oncol 2005;23:7911–8.rank O, Brors B, Fabarius A, et al. Gene expression signature ofrimary imatinib-resistant chronic myeloid leukemia patients.eukemia 2006;20:1400–7.otti A, Dressman HK, Bild A, et al. Genomic signatures to guidee use of chemotherapeutics. Nat Med 2006;12:1294–300.artmann LC, Lu KH, Linette GP, et al. Gene expression profilesredict early relapse in ovarian cancer after platinum-paclitaxelhemotherapy. Clin Cancer Res 2005;11:2149–55.alin GA, Croce CM. MicroRNA signatures in human cancers. Natev Cancer 2006;6:857–66.how TF, Youssef YM, Lianidou E, et al. Differential expressionrofiling of microRNAs and their potential involvement in renal cellarcinoma pathogenesis. Clin Biochem 2010;43:150–8.iu CG, Calin GA, Volinia S, Croce CM. MicroRNA expressionrofiling using microarrays. Nat Protoc 2008;3:563–78.etias SM, Lianidou E, Yousef GM. MicroRNAs in clinical oncology:t the crossroads between promises and problems. J Clin Pathol009;62:771–6.chaefer A, Jung M, Mollenkopf HJ, et al. Diagnostic and prognos-
c implications of microRNA profiling in prostate carcinoma. Int Jancer 2010;126:1166–76.otiropoulou G, Pampalakis G, Lianidou E, Mourelatos Z. Emerging



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les of microRNAs as molecular switches in the integrated circuitf the cancer cell. RNA 2009;15:1443–61.steller M. Epigenetics in cancer. N Engl J Med 2008;358:1148–59.ulfkuhle JD, Liotta LA, Petricoin EF. Proteomic applications fore early detection of cancer. Nat Rev Cancer 2003;3:267–75.ogers S, Girolami M, Kolch W, et al. Investigating the correspon-ence between transcriptomic and proteomic expression profilesing coupled cluster models. Bioinformatics 2008;24:2894–900.n der Merwe DE, Oikonomopoulou K, Marshall J, Diamandis EP.ass spectrometry: uncovering the cancer proteome for diagnos-s. Adv Cancer Res 2007;96:23–50.eissenstein U, Schneider MJ, Pawlak M, et al. Protein chip basediniaturized assay for the simultaneous quantitative monitoring ofncer biomarkers in tissue extracts. Proteomics 2006;6:1427–36.hang H, Kong B, Qu X, Jia L, Deng B, Yang Q. Biomarker discov-y for ovarian cancer using SELDI-TOF-MS. Gynecol Oncol 2006;2:61–6.iller JC, Zhou H, Kwekel J, et al. Antibody microarray profiling ofman prostate cancer sera: antibody screening and identification

f potential biomarkers. Proteomics 2003;3:56–63.omaschin AD, Youssef Y, Chow TF, et al. Exploring the patho-enesis of renal cell carcinoma: pathway and bioinformaticsalysis of dysregulated genes and proteins. Biol Chem 2009;0:125–35.iu KW, DeSouza LV, Scorilas A, et al. Differential protein expres-ons in renal cell carcinoma: new biomarker discovery by massectrometry. J Proteome Res 2009;8:3797–807.ohnke PL, Mulligan SP, Christopherson RI. Membrane proteomicsr leukemia classification and drug target identification. Curr Opinol Ther 2009;11:603–10.angar RC, Varnum SM, Bollinger N. Studying cellular processesd detecting disease with protein microarrays. Drug Metab Rev

005;37:473–87.ulfkuhle JD, Edmiston KH, Liotta LA, Petricoin EF III. Technologysight: pharmacoproteomics for cancer-promises of patient-ilored medicine using protein microarrays. Nat Clin Pract Oncol06;3:256–68.odovitz S, Joos T, Bachmann J. Protein biochips: the calm beforee storm. Drug Discov Today 2005;10:283–7.arber K. Human Cancer Genome Project moving forward despiteme doubts in community. J Natl Cancer Inst 2005;97:1322–4.heng W, Long J, Gao YT, et al. Genome-wide association studyentifies a new breast cancer susceptibility locus at 6q25.1. Natenet 2009;41:324–8.eager M, Orr N, Hayes RB, et al. Genome-wide association studyf prostate cancer identifies a second risk locus at 8q24. Nat Genet07;39:645–9.eager M, Chatterjee N, Ciampa J, et al. Identification of a newrostate cancer susceptibility locus on chromosome 8q24. Natenet 2009;41:1055–7.andi MT, Chatterjee N, Yu K, et al. A genome-wide association

1

1

1

1

11

11

1

1

1

1

1
udy of lung cancer identifies a region of chromosome 5p15 asso-iated with risk for adenocarcinoma. Am J Hum Genet 2009;85:79–91.
9151. P

2

acrjournals.org


ong H, Ramus SJ, Tyrer J, et al. A genome-wide association studyentifies a new ovarian cancer susceptibility locus on 9p22.2. Natenet 2009;41:996–1000.u C, Xu B, Yuan P, et al. Genome-wide examination of geneticariants associated with response to platinum-based chemothera-y in patients with small-cell lung cancer. Pharmacogenet Geno-ics 2010;20:389–95.eshchenko VV, Kuo PY, Shaknovich R, et al. Genome wide DNAethylation analysis reveals novel targets for drug development inantle cell lymphoma. Blood. Epub ahead of print 2010.chadt EE. Molecular networks as sensors and drivers of commonuman diseases. Nature 2009;461:218–23.trausberg RL, Buetow KH, Emmert-Buck MR, Klausner RD. Theancer genome anatomy project: building an annotated gene index.rends Genet 2000;16:103–6.nutsen T, Gobu V, Knaus R, et al. The interactive online SKY/M-ISH and CGH database and the Entrez cancer chromosomesarch database: linkage of chromosomal aberrations with the ge-ome sequence. Genes Chromosomes Cancer 2005;44:52–64.ubble J, Demeter J, Jin H, et al. Implementation of GenePatternithin the Stanford Microarray Database. Nucleic Acids Res 2009;7:D898–901.untzer J, Eggle D, Lenhof HP, Burtscher H, Klostermann S. Theoche Cancer Genome Database (RCGDB). Hum Mutat 2010;31:07–13.u CH, Yeh LS, Huang H, et al. The Protein Information Resource.ucleic Acids Res 2003;31:345–7.eshava Prasad TS, Goel R, Kandasamy K, et al. Human Proteineference Database—2009 update. Nucleic Acids Res 2009;37:767–72.ollins F. Has the revolution arrived? Nature 2010;464:674–5.umppanen M, Gruvberger-Saal S, Kauraniemi P, et al. Basal-e phenotype is not associated with patient survival in estro-en-receptor-negative breast cancers. Breast Cancer Res 2007;:R16.enter JC. Multiple personal genomes await. Nature 2010;464:676–7.oukos DH. Novel clinico-genome network modeling for revolution-ing genotype-phenotype-based personalized cancer care. Expertev Mol Diagn 2010;10:33–48.oukos DH. Systems medicine: a real approach for future person-lized oncology? Pharmacogenomics 2010;11:283–7.oukos DH. Bionetworks-based personalized medicine versusomparative-effectiveness research or harmonization of both inancer management? Expert Rev Mol Diagn 2010;10:247–50.unter DJ, Kraft P, Jacobs KB, et al. A genome-wide associationudy identifies alleles in FGFR2 associated with risk of sporadicostmenopausal breast cancer. Nat Genet 2007;39:870–4.joblom T, Jones S, Wood LD, et al. The consensus codingequences of human breast and colorectal cancers. Science006;314:268–74.ternational network of cancer genome projects. Nature 2010;464:93–8.earson H. Biologists initiate plan to map human proteome. Nature
008;452:920–1.



2010;8:1175-1187. Published OnlineFirst August 6, 2010.Mol Cancer Res Maria Diamandis, Nicole M.A. White and George M. Yousef Patient ManagementPersonalized Medicine: Marking a New Epoch in Cancer

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