Computers and Chemical Engineering 29 (2005) 589–596

Gene expression measurement technologies:innovations and ethical considerations

Jessica R. Goodkinda,∗, Jeremy S. Edwardsb

a University of New Mexico Health Sciences Center, Center for Health Promotion and Disease Prevention, Albuquerque, NM 87131, USAb Department of Chemical Engineering, University of Delaware, Newark, DE 19716, USA

Received 22 September 2003; received in revised form 20 January 2004Available online 22 October 2004

Abstract

Knowledge of the overall gene expression profile within a cell can provide key insights into cellular physiology. Therefore, it is not surprisingthat a great deal of effort has been devoted to measuring the expression level of all of the genes in a cell. With the advent of DNA microarraytechnology, researchers now have the ability to collect expression data on every gene in a cell simultaneously. The vast datasets createdw of genetica e approachesc key ethicalc©

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ith this technology are providing valuable information that can be used to accurately diagnose, prevent, or cure a wide rangend infectious diseases. In this article, we describe the key technologies for measuring gene expression and discuss how thesan be used to identify the correlation between the observed expression patterns and disease. Additionally, we will highlight theonsiderations and discuss how these tremendously powerful technologies may impact society.2004 Elsevier Ltd. All rights reserved.

eywords:Gene expression; Microassay; SAGE; Polony; Ethics

. Introduction

Knowledge of the overall gene expression profile within aell can provide key insights into cellular physiology. There-ore, it is not surprising that a great deal of effort has beenevoted to measuring the expression level of all of the genes

n a cell. With the advent of DNA microarray technology,esearchers now have the ability to collect expression data onvery gene in a cell simultaneously. The vast datasets createdith this technology are providing valuable information thatan be used to accurately diagnose, prevent, or cure a wideange of genetic and infectious diseases. Thus, an area ofctive research is the development of tools to accuratelyollect, analyze, and interpret massive quantities of datan gene expression levels in the cell. There are severalxamples in the literature where computational tools have

∗ Corresponding author.E-mail addresses:jgoodkind@salud.unm.edu (J.R. Goodkind),

dwards@che.udel.edu (J.S. Edwards).

been applied to analyze gene expression data (MarcottePellegrini, Thompson, Yeates, & Eisenberg, 1999; MarcottePellegrini, Yeates, & Eisenberg, 1999; Marcotte et al., 1999;Pellegrini, Marcotte, Thompson, Eisenberg, & Yeates, 1;McGuire, Hughes, & Church, 2000; Roth, Hughes, Estep,Church, 1998; Alizadeh, Eisen, Botstein, Brown, & Stau1998; Alter, Brown, & Botstein, 2000; DeRisi et al., 2000;Eisen, Spellman, Brown, & Botstein, 1998; Spellman eal., 1998; Wilson et al., 1999; Sudarsanam, Iyer, Brown,Winston, 2000; D’Haeseleer, Wen, Fuhrman, & Somog1999; Bassett, Eisen, & Boguski, 1999; Liang, Fuhrman, &Somogyi, 1998; Chen, He, & Church, 1999; Getz, Levine& Domany, 2000; Tavazoie, Hughes, Campbell, Cho,Church, 1999; Michaels et al., 1998; Tamayo et al., 199),with the most common method being cluster analysis (Eisenet al., 1998).

The technological developments in gene expressionsurement are promising, but at the same time it is incumupon us to raise numerous ethical concerns. Scientistengineers who are involved in developing and utilizing g

098-1354/$ – see front matter © 2004 Elsevier Ltd. All rights reserved.oi:10.1016/j.compchemeng.2004.08.033

590 J.R. Goodkind, J.S. Edwards / Computers and Chemical Engineering 29 (2005) 589–596

expression technology have a responsibility to consider eth-ical issues and are in a key position to take an active role inpolicy development and other efforts to ensure that gene ex-pression measurement technologies are used ethically. Fourimportant considerations will be discussed in this manuscript:(1) insurance and employment discrimination and issues ofindividual privacy; (2) fair access to tests and treatments; (3)technological limitations such as accuracy of diagnosis, abil-ity to relate genetic information to a disease, and diagnosisof “untreatable” conditions; and (4) genetic counseling andissues about release of information to patients.

In this article, we will describe the key technologiesfor measuring gene expression and discuss how these ap-proaches can be used to identify the correlation betweenthe observed expression patterns and disease. Additionally,we will highlight the key ethical considerations and discusshow these tremendously powerful technologies may impactsociety.

2. Genome-scale gene expression monitoringtechnology

A number of technologies have been developed to ac-quire gene expression information on a “whole transcrip-t om-p cell.T irects es,& fc u-c

approa

2.1. Hybridization-based approaches

All hybridization-based approaches for measuring geneexpression are based on a single principle: DNA comple-mentary to all genes (or a region of the genes) is synthe-sized and placed at high density in distinct points on a ‘chip’.Fluorescently labeled cDNA from a cell or tissue sample ishybridized to the chip and the fluorescent intensity of thespots on the chip are directly related to the mRNA concen-tration in the cell or tissue sample. Currently there are pri-marily two alternative hybridization-based technologies formeasuring gene expression on a whole transcriptome level:DNA microarrays and GeneChips. DNA microarrays (Fig. 1)are essentially glass microscope slides that have pools ofDNA robotically printed on the surface. The DNA to beprinted on the DNA microarray is amplified by polymerasechain reaction (PCR). For gene expression, full-length cDNAmolecules or small fragments of the cDNA that can be usedto uniquely identify the gene can be printed on the slide.Oligonucleotide microarrays are also available from com-panies such as Agilent, who produces arrays with 60-meroligonucleotides (http://www.agilent.com/).

GeneChips are an alternate technology to DNA microar-rays. GeneChips are produced commercially by Affymetrixusing photolithography to synthesize∼25 bp oligonu-c ndert f them ten-sa e ex-p verys

ome” level, where the transcriptome is defined as the clete set of all expressed genes (transcripts) in a givenhe techniques currently in vogue are based on either dequence analysis (Weinstock, Kirkness, Lee, Earle-HughVenter, 1994; Adams, 1996) or specific hybridization o

omplex cDNA or mRNA probes to microarrays of oligonleotides or cDNAs (Ramsay, 1998; Giordano, 2003).

Fig. 1. Hybridization-based

ch for gene expression profiling.

leotides on the chip. Affymetrix GeneChips operate uhe same principle as microarrays – the concentration oRNA molecules is proportional to the image signal in

ity (Lipshutz, Fodor, Gingeras, & Lockhart, 1999). There arelso other related methods for generating arrays for genression analysis. For example, NimbleGen arrays areimilar to Affymetrix arrays (http://www.nimblegen.com/);

J.R. Goodkind, J.S. Edwards / Computers and Chemical Engineering 29 (2005) 589–596 591

Fig. 2. Sequence-based approach for gene expression profiling.

however, NimbleGen utilizes a micromirror system to syn-thesize the oligonucleotides rather than photolithography(Nuwaysir et al., 2002).

2.2. Sequencing-based approaches

In addition to hybridization-based technologies, there areseveral DNA sequencing-based approaches for measuringwhole transcriptome gene expression (Fig. 2), with themost basic being expressed sequence tag (EST) sequencing(Carulli et al., 1998). Sequencing approaches have severaladvantages over hybridization-based approaches (i.e.,sequencing approaches are more accurate and can provideabsolute quantification); however, in the past, sequencing-based approaches have been more expensive and hence lessused. Serial analysis of gene expression (SAGE) is the mostpromising whole transcriptome sequencing-based approach(Fig. 2) (Velculescu, Zhang, Vogelstein, & Kinzler, 1995).SAGE is based on the idea that only a short sequence tagis needed to uniquely identify a gene. Therefore, in SAGE,10–20 bp are sequenced to uniquely identify a gene (Ye,Usher, & Zhang, 2002; Saha et al., 2002). Other DNAsequencing-based approaches include massively parallelsignature sequencing (MPSS) (Brenner et al., 2000) andpolymerase colony approaches (discussed below) (Merritt,D

3

ax-i chesi lab-

oratory at Harvard Medical School has demonstrated that insitu polymerase chain reaction can be used to generate clonesof single nucleic acid molecules (Merritt et al., 2003; Mitra& Church, 1999; Mikkilineni et al., 2004; Mitra et al., 2003;Zhu, Shendure, Mitra, & Church, 2003; Butz, Wickstrom, &Edwards, 2003; Butz, Yan, Mikkilineni, & Edwards, 2004)in a gel matrix. This method uses acrylamide polymerized ina solution containing standard PCR reagents and a very lowconcentration of linear DNA template. This gel is poured ona glass microscope slide and the DNA amplified by PCR.As the products remain localized near their respective tem-plates, a single template molecule gives rise to a PCR colony,or “polony,” consisting of as many as 108 identical DNAmolecules. By including an acrydite modification (Kenney,Ray, & Boles, 1998) on the 5′ end of one of the PCR primers,the amplified DNA in each polony can be covalently attachedby one of its ends to the polyacrylamide matrix. Polony ra-dius decreases as template length increases and as the acry-lamide percentage increases. Using a template of 1009 bpand an acrylamide concentration of 15%,Mitra and Church(1999)were able to produce polonies with an average radiusof 6�m. At this size, the authors estimated that five milliondistinguishable polonies could be generated on a single slide.

We developed a novel assay based on polony technologythat accurately quantifies the relative expression levels of twoaM inga ncet ered ithfle nu-c nate

iTonno, Mitra, Church, & Edwards, 2003).

. Polymerase colony approach

A new technique that minimizes the limitations and mmizes the detection power of other sequencing approas polymerase colony (polony) technology. The Church

lleles in the same cell or tissue sample (Butz et al., 2004).echanistically, this was accomplished by PCR amplifygene in a cDNA library in a thin, polyacrylamide gel. O

he polony was amplified, the two alleles of the gene wifferentially labeled by performing in situ sequencing wuorescently labeled nucleotides (Merritt et al., 2003; Mitrat al., 2003). For these sets of experiments, silent, singleleotide polymorphisms (SNPs) were used to discrimi

592 J.R. Goodkind, J.S. Edwards / Computers and Chemical Engineering 29 (2005) 589–596

the two alleles. Finally, a simple count was then performedon the differently labeled polonies in order to determine therelative expression levels of the two alleles (Fig. 3). To vali-date this technique, the relative expression levels of PKD2 ina family of heterozygous patients bearing the 4208G/A SNPwere examined and compared to a similar analysis byYan,Yuan, Velculescu, Vogelstein, and Kinzler (2002). We wereable to reproduce results in a digital version and thus demon-strate the utility of this method in human gene expressionanalysis.

3.1. Strengths and limitations of each approach

The main advantage of DNA sequencing-based methodsis that, unlike microarray-based technologies, they do notmake any assumptions about which genes are expressed in agiven tissue sample. This issue is of great importance whendealing with human samples due to the uncertainty in thegene number with estimates ranging from 30,000 to 100,000genes. Indeed, gene expression microarray technology willonly measure the level of transcripts for genes spotted orsynthesized on the glass surface. Although cDNA microar-

F gy. (A) med ich is u ars anp airing ),w ollowing polony amplification, the gel is stained with SybrGreenI and scanned with amddaaaaaii

rays can, to some extent, side-step this limitation by spottingclones of uncharacterized genes on microscope slides, there isa limit on the number of spots that can be placed on one micro-scope slide. Affymetrix GeneChips clearly suffer from thislimitation because they use DNA sequence information in thedesign on the tiling path of the GeneChip array. In additionto these innate limitations, there is the technical challengeof producing an unbiased fluorescent probe from complexnucleic acid mixtures. Therefore, whole transcriptome sur-veys are difficult to perform with DNA and oligonucleotidemicroarray-based technologies. In contrast, SAGE and MPSStechnology has been able to detect transcripts of genes thatwere not identified by genome sequence annotation or ESTsequencing, which means that it is not necessary to know theparticular gene involved in a disease a priori (Velculescu etal., 1995, 1997; Saha et al., 2002).

The main disadvantage of sequencing-based methods istheir cost. Indeed, although costs of DNA sequencing havegreatly diminished with the advent of capillary array se-quencers, the number of genes expressed in a typical eukary-otic cell is in the range of 104. SAGE technology reducesthese costs by extracting small sequence tags out of the cD-

ig. 3. Determining relative allelic expression using polony technoloesigned to amplify the region of the gene bearing a silent SNP, whairing at a particular position whereas the other allele has a G–C phich is eventually required for distinguishing these two alleles. (B) F

icroarray scanner in order to ensure that the polony amplification workedesired first-strand cDNA template. (C) Prior to hybridization of sequencingenaturation in formamide followed removal of non-acridited strand using elesingle base extension is performed with Cy5-dATP and then the gel is scand scanning is repeated, but the extension is performed with Cy5-dGTP thlleles, a composite image from the Cy5-dATP and Cy5-dGTP extensions islleles is counted. In this particular example, the “G–C” SNP represents 62.l. (2002)#1. (F) Representative polony gel for patient 50 following extension

mage generated from the independent extensions with either Cy5-dATP ornterpretation of the references to color in this figure legend, the reader is re

Initially, the gene of interest is polony amplified from cDNA. The prirs aresed to discriminate the two alleles. In this illustration, one allele beA–Tat the same position. Note: one of primers has a 5′ acridite modification (star

properly. Each black dot represents the polony amplification of one copy of theprimer, the polony is made single stranded by denaturing the double stranded

ctrophoresis. (D) A sequencing primer is hybridized to the single stranded polony,nned. To label the other allele, the process of denaturation, hybridation, extension,e second time through. (E) To determine the relative expression levels of thetwofirst generated and then the number of polonies (transcripts) for each of the two

5% of the population (i.e., five out of eight polonies). Figure adapted from Yan ets with fluorescently labeled dATP (green) and dGTP (red). This is a composite

Cy5-dGTP as described inFig. 1. The colors used in the image are artificial. (Forferred to the web version of the article.)

J.R. Goodkind, J.S. Edwards / Computers and Chemical Engineering 29 (2005) 589–596 593

NAs (Velculescu et al., 1995), however, the cost of reachingwhole-transcriptome coverage with DNA sequencing-basedsurveys is in the range of several thousand dollars. SAGEsurveys restricted to∼10,000 tags may not detect gene ex-pression for as many as 30% of mammalian mRNAs. Indeed,it is estimated that 30% of the genes in the genome makeup far less than a few tenths of a percent of the total tran-scriptome, and produce an average of less than 10 copiesper cell (Ostrow, Woods, Vosika, & Faras, 1979). Assum-ing a transcriptome is composed of transcripts from∼15,000genes and that a given cell contains 350,000 mRNA molecule(numbers taken from a typical textbook example of the tran-scriptome of a mammalian cell), we calculate that 322,000tags would be needed to get a 99% probability of detectinga tag from one of these weakly transcribed genes. Consider-ing that SAGE tags present at less than 10 copies are usuallynot included in a census of the transcriptome, the number oftags that would be required to accurately measure the levelof expression of weakly transcribed genes is in the range of106, which is possible with MPSS.

4. Gene expression analysis and cancer

One potential application of the gene expression technolo-g f can-c it hasb tumo( se-q rem ,e e, ev-e sifyt dedf res-s andd essiop nd iti ,1 n-tC

iag-n tt dif-f intod Theirw map iffer-e Theyf asd rmo ble tod

ors,B oar-

rays. They included on the DNA microarrays genes thathave been shown or suspected to be important in immunol-ogy or cancer, or are known to be expressed in lymphoidcells (Alizadeh et al., 1998). They included 17,856 cDNAclones on the microarray. In the supplementary informationto the paper, the authors provide the datasets from the exper-iments. The data sets contain∼4000 genes; therefore, manyof the 17,856 cDNA clones are from the same gene. It shouldbe noted that the sequencing-based approaches could haveachieved a larger coverage of the genome because a prioriknowledge of the genes is not required for sequencing-basedapproaches. Hence, the experiments by Brown, Staudt, andco-workers were unable to measure gene expression levelsfor many genes. Sequencing-based approaches will be ableto simultaneously monitor the expression of every gene in thesystem, without biases due to our a priori knowledge of thetumor.

Brown, Staudt, and co-workers used their DNA microar-rays to study the gene expression patterns in three adultlymphoid malignancies, DLBCL, follicular lymphoma (FL),and chronic lymphocytic leukemia (CLL). In total, the au-thors analyzed 96 different normal and malignant samples,and used a hierarchical clustering algorithm to identifygroups of genes based on similarity of expression profilesacross the samples. Additionally, they clustered the samplesb thorsf ciesb twom ofg enest ells.T -likeD perw n oft entifyp s ofc ssifyc tc ata

5

g int aree opef d forp t dif-f nts.H ul at-t thep tech-n uesa imaryc tions

ies discussed above is in detection and classification oer. There are hundreds of different types of cancer, andeen estimated that at least five genes are mutated in aHilgers & Kern, 1999). Based on these data, it was subuently estimated that there are∼200 different genes that autated in different cancers (Wooster, 2000). Furthermoreach gene can be mutated at different locations; thereforry tumor will be unique. Using clinical symptoms to clas

umors therefore cannot provide all of the information neeor successful treatment. With high-throughput gene expion technologies, it is now possible to screen a sampleetermine the genes that are expressed. Therefore, exprrofiling has attracted great interest in cancer biology a

s likely to revolutionize cancer diagnosis (Alizadeh et al.998; Perou et al., 1999), by using cluster analysis to ide

ify genes that characterize the cancer (Bittner et al., 2000;lark, Golub, Lander, & Hynes, 2000).The importance of expression profiling and cancer d

osis is illustrated byAlizadeh et al. (1998)who showed thahe most common subtype of non-Hodgkin’s lymphoma,use large B-cell lymphoma (DLBCL), can be groupedistinct subgroups based on the gene expression profile.ork was motivated by the observation that B-cell lymphoatients receiving the same diagnosis often had quite dnt responses to therapy and long-term survival rates.

ocused on a form of non-Hodgkin’s lymphoma knowniffuse large B-cell lymphoma. They focused on this fof lymphoma because in the past it has not been possiefine subgroups of DLBCL based on morphology.

To analyze the gene expression in the human tumrown, Staudt, and co-workers constructed DNA micr

r

n

ased on the similarity of the expression profiles. The auound that hierarchical clustering identified the malignanased on the gene expression profiles. They identifiedolecularly distinct forms of DLBCL; one characteristicerminal centre B cells, and another that expressed g

ypically expressed in activated peripheral blood B che data indicated that patients with germinal centre BLBCL had a significantly better overall survival. This paas significant because it indicated that the classificatio

umors based on the gene expression patterns can idreviously undetected and clinically significant subtypeancer. Expression profiling has also been used to clautaneous malignant melanoma (Bittner et al., 2000), breasancer tumors (Perou et al., 2000), and to identify genes thre important for metastasis (Clark et al., 2000).

. Ethical considerations

The innovative technologies that are rapidly emerginhe field of high-throughput gene expression analysisxciting and promising. These technologies provide hor earlier and more accurate diagnosis of diseases anersonal genetic profiling that can determine and predic

erences in individuals’ responses to different treatmeowever, our enthusiasm should be tempered by caref

ention to numerous ethical considerations. Typically,redominant emphasis of scientists is on scientific andological developments, with the belief that ethical issbout how these technologies are used are not our proncern. We believe that some of the ethical considera

594 J.R. Goodkind, J.S. Edwards / Computers and Chemical Engineering 29 (2005) 589–596

are so critical that it is the responsibility of all of us to con-sider these issues before and in conjunction with any researchin the field.

There are numerous ethical issues raised by using high-throughput gene expression profiling technologies. We focuson four that are particularly compelling and which we believedeserve immediate attention: (1) insurance and employmentdiscrimination and issues of individual privacy; (2) fair accessto tests and treatments; (3) technological limitations such asaccuracy of diagnosis, ability to relate genetic information toa disease, and diagnosis of “untreatable” conditions; and (4)genetic counseling and issues about release of information topatients.

Issues of individual privacy and insurance and employ-ment discrimination are one of the most common ethical con-cerns cited by researchers, doctors, and consumers (Friend& Stoughton, 2002; Zupke & Stephanopoulos, 1995). Forexample, one of the applications of Affymetrix GeneChiptechnology is to create personal gene expression profiles forindividuals. Once these data are available, who has the right tothis information? Will employers and health insurance com-panies be able to demand access to such information andrefuse coverage or employment to individuals whose geneexpression profiles reveal high risk for certain conditions? Ifindividuals wish to protect their privacy by not revealing thisi ge oro e of-t ces ing olv-i mlyt or-t ationa rsalh d, andm theU rtaind

ent.I lablet ivelye ntlyc ares alsh offerd cesst havei thata com-p enee t notr pled nce tot g top rdi thatp files

or predispositions to diseases or treatment. But in some cases,knowledge may actually hurt rather than help individuals(or might help certain people while hurting others).

In the rush to develop new diagnostic tools, it is also im-portant to keep in mind the technological limitations that weface. Three major issues are: accuracy of diagnosis, our abil-ity to relate genetic information to a disease, and diagnosisof “untreatable” conditions. Our capability to accurately di-agnose diseases and/or treatment responses is limited by ourtechnology, and therefore we need to be cautious in how thetest results are reported to the individual. For example, cDNAmicroarray technology typically is only able to identify genesthat are up-regulated or down-regulated two-fold. For in-stance, a related example from a protein profiling technique todetect ovarian cancer reports a false positive rate of 5%. Sinceovarian cancer is a rare disease that only occurs in 20 out of100,000 women, if this test were used to screen the generalpopulation of women, it would falsely alarm 250 women forevery real case of ovarian cancer it detected (Service, 2003).On the other hand, other genetic diagnostic techniques (al-though not gene expression profiling techniques), even well-established ones that routinely test for muscular dystrophyand cystic fibrosis, fail to identify mutations in 10–40% ofpatients tested (Sinclair, 2002). False negative rates are evenworse for less well-characterized genes. These false negativesa ctorso ase.T e se-r falsen netict eci-s cy.

ityt on-s e dis-e eatesi ctuald rdingD s int ngesi s aren xist-i willn t tog e theb , andt d to1t anyi enialo mosti thes

ndi-t olo-g rent

nformation, insurance companies might refuse coveraffer it only at higher rates. Although these questions ar

en raised and discussed, current technological advanene expression profiling highlight the importance of res

ng these issues more immediately, explicitly, and uniforhrough legislation and policy. It is interesting and impant to note that issues about health insurance discriminre likely not of great concern in countries with univeealth care, such as Australia, Canada, Japan, Englanany other parts of Europe. Adopting a similar system innited States could undoubtedly alleviate or lessen ceiscrimination concerns.

A related issue involves access to testing and treatmt is clear that these new technologies are only avaio certain people, mostly because they are prohibitxpensive. For example, an Affymetrix GeneChip curreosts approximately $800, and SAGE experimentsignificantly more expensive. However, even if individuad equal access to testing, would the same diagnosisifferent options to different people, based upon their ac

o treatments? For instance, some individuals mightnsurance or their own private means to treat a conditionnother person’s insurance might refuse to cover. Tolicate this issue further, an individual with a personal gxpression profile which suggests that he or she mighespond to a particular treatment (i.e., the DLBCL examescribed above) might be denied access by their insura

he treatment. This could result in individuals either havinay for their own treatment or forgo it if they could not affo

t. Many geneticists argue that “knowledge is power,”atients have the right to know their gene expression pro

re misleading and misinterpretations may result in dor patients ignoring other important indications of disehus, both false positives and false negatives can havious consequences. Furthermore, the problems withegatives and false positives are amplified in prenatal ge

esting, in which parents may be making irreversible dions about whether to continue or terminate a pregnan

A second technological limitation involves our abilo relate gene expression profiling information to theet of disease. Most diseases are not simple single-genases. Therefore, diagnostic screening typically delin

ncreased risk for a disease rather than providing an aiagnosis. For example, the work described above regaLBCL, hierarchical clustering was used to find change

he overall pattern gene expression and not simply chan expression of a single gene. Improved statistical tooleeded to define the probability of an expression profile e

ng as part of a cluster. Additionally, inclusion in a clusterot likely be 100% predictive. For instance, with respecenotyping data, physicians are still unable to determinreast cancer risks due to mutations in the BRCA genes

he lifetime risk estimates are about 40–80% (compare0% risk in the general population) (Couzin, 2003). As with

he case of false positives, this may needlessly alarm mndividuals and could result in other problems such as df health coverage or increased premiums, when in fact

ndividuals with the genetic mutation will never developpecified disease.

Our ability (or lack thereof) to treat diseases or coions we diagnose with gene expression profiling technies highlights a third technological limitation and concur

J.R. Goodkind, J.S. Edwards / Computers and Chemical Engineering 29 (2005) 589–596 595

ethical concern. If we utilize one of the new gene expressionprofiling technologies to characterize a diseases for which notreatment exists, what purpose does this information serve?Who should have access to the information? Related to thisissue is the current screening that assesses differential re-sponses to a particular treatment. What hope can we provideto individuals whose profiles suggest they will not respondto existing treatments?

A final important ethical consideration involves geneticcounseling. In some ways, this issue relates to all of the otherethical issues discussed. For instance, in terms of individualprivacy, who can or should genetic counselors share informa-tion with? How should information be imparted to individualswho do not have access to treatment or who have conditionsfor which no treatment exists? The precision and accuracy ofgene expression profiling technologies affects how a certainresult is explained to patients and what information should orshould not be shared. If information is not released or sharedappropriately, this can cause undue stress in patients. This isparticularly problematic given the negative impact that stresshas on immune system functioning.

As “insiders,” we have the greatest ability to bring theseissues to the forefront of discussion and ensure that ethicalguidelines are created and observed. Engineers and scientistshave a responsibility to society and since we have the powera at wea mbiaS fail tov

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nd influence in these decisions, we need to ensure thre part of the decision process. As we saw in the Colupace Shuttle disaster, scientists, and engineers whooice their concerns may regret their silence later.

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