Chasing the AIDS Virus

Transcription

1 doi: / With no HI vaccine in sight, viologists need to know how the vius will eact to a given combination dug theapy. by Thomas Lengaue, Andé Altmann, Alexande Thielen, and Rolf Kaise Chasing the AIDS ius The most challenging poblem fo physicians teating AIDS patients with anti-hi dugs is that the vius almost inevitably evolves towad esistance against any administeed dug theapy. Once esistance is manifest, the physician must change the theapy egimen, which typically consists of a combination of anti-hi dugs. Hee, we descibe bioinfomatical methods suppoting the choice of an effective follow-up theapy. Using undelying clinical-esistance databases and statistical-leaning methods, we identify as-yet-undescibed esistance mutations, pedict the level of esistance of a vial vaiant extacted fom the blood of an AIDS patient against anti-hi dugs, and estimate the expected mutational path of the vius towad esistance against specific combination dug theapies. This computational method enables us to ank possible theapies with espect to thei expected effectiveness. We also offe a computational test fo the expected effectiveness of a new dug capable of blocking vial cell enty. Ou analyses, which ae feely available on the Intenet via the seve ae used outinely fo teating about two-thids of AIDS patients in Gemany. AIDS is a majo scouge woldwide, causing millions of deaths annually. Wheeas due to education and peventive measues, the numbe of new infections in the developed wold is compaatively limited, othe pats of the wold (notably Sub-Sahaan Afica) exhibit vey high infection ates. The disease is on the ise globally. 20 The AIDS pathogen the Human Immunodeficiency ius, o HI cossed ove to humans fom apes as ecently as 100 yeas ago. The pathogen and its new host appaently have not yet adapted though co-evolution. Consequently, HI is highly pathogenic in humans, unlike chimpanzees, which exhibit vey high infection ates with the Simian Immunodeficiency ius, o SI, without pesenting debilitating symptoms. AIDS is especially lethal fo a numbe of easons. Fo the human population, one dange involves the fact that symptoms develop slowly, so hosts key insights Clinical databases can be mined to help geneate statistical models that pedict HI s vial esistance to administeed dugs. These models incopoate inteactions between dugs in combination with dug theapies, estimating futue vial escape path towad esistance to an applied dug egimen. By continually incopoating new clinical insights and dugs, the softwae tool helps suppot theapy decisions in clinical outines. 66 communications of the acm mach 2010 vol. 53 no. 3

2 Illustation by st udio M ac beth Figue 1. Replication cycle of HI, yellow dots: RT molecules, geen dots: IN molecules, ed dots: PR molecules. can be infectious fo extended peiods without thei contacts knowing. Fo infected patients one poblem involves the fact that the vius insets its genome into the genome of the infected cell. These people cannot be cleaed of the vius. As we descibe hee, the vius evolves dynamically. Thus it is difficult to poduce vaccines against HI, and no vaccine against HI is in sight. Since thee ae majo obstacles to cuing AIDS, the objectives of dug theapy ae to ease symptoms and delay pogess of the disease by suppessing vial eplication. Since the vius continually changes in a patient, physicians ae chasing a moving taget. Given a paticula dug theapy, the vius evolves towad esistance. The dug theapy then has to be changed to suppess what is now the pevalent vial vaiant in the patient. The undelying biological elationships between the vial genotype the paticula genome sequence of the vius and the vial esistance phenotype its ability to escape antivial dugs ae complex and not well undestood. Theefoe, dug theapies ae selected not so much on the basis of undestanding the undelying biology as they ae on the basis of clinical expeience. Clinical expeience in teating AIDS patients with antivial dugs has been collected fo the past 20 yeas and assembled in sizeable esistance databases. The complexity of the elationship between vial genotype and esistance phenotype suggests using statistical-leaning methods to suppot computational models fo pedicting the esistance phenotype fom the vial genotype. Fo this pupose, we have developed the Web seve geno2pheno ( offeing such analysis fo fee on the Web. Replication Cycle of HI HI is not an autonomous oganism but athe an enveloped piece of genome, oughly 10,000 lettes of genomic text (bases) in potein packaging. This tiny genomic text (compaed to thee billion lettes of the human genome) defines one of the most vicious biological killes. The stuctue of the HI vius paticle (viion) is known in detail.9 As with all viuses, to eplicate, HI uses the cells it infects, usually those of the human immune system (such as T-lymphocytes). Knowledge of the eplication cycle of HI (see Figue 1) is the basis fo all dug theapies in use today. The genome of HI does not consist of DNA (as in humans) but of the close elative RNA that in humans is used fo tanslating genomic infomation and egulating cellula pocesses. The eplication cycle of HI begins with HI using its suface ma c h vo l. 53 n o. 3 c om m u n icat i on s of t he ac m 67

3 Statistical Leaning Methods Statistical leaning comes in two vesions: supevised and unsupevised. Unsupevised aims to elucidate pattens in unstuctued (usually high-dimensional) data sets. Hee, we exemplify unsupevised statistical leaning methods with the mutagenetic tees model. Supevised methods use data sets of (usually high-dimensional) inputs x and associated (scala o categoical) outputs y, to deive computational pocedues fo pedicting (given a new input x 0) the associated output y 0. Hee, we exemplify supevised statistical leaning though the suppot-vecto-machine model. Mixtues of mutagenetic tees. A mutagenetic tee is a tee-shaped Bayesian model; two ae included in Figue 6. The tee is ooted, and its oot epesents the vial wildtype, o the absence of mutation. Each othe tee node epesents a mutation. The edges of the tee ae diected downwad and labeled with conditional pobabilities. Given the pesence of all mutations along the path fom the oot of the tee to the souce node of an edge, the label of the edge indicates the pobability that the mutation at its taget node takes place. In pinciple, a mutagenetic tee can be used to geneate a set of vial vaiants by pefoming a andom expeiment based on the pobabilities at the edges of the tee. We ae not inteested in explicitly pefoming such an expeiment. Rathe, given a set of vial vaiants (such as the subset of vial genotypes in ou esistance database that has seen a cetain dug, like saquinavi) we ae looking fo the mutagenetic tee that geneates that set with geatest pobability (maximum likelihood model). This tee best epesents the escape of the vius towad esistance against the dug saquinavi. Despe et al. 8 pesented a method fo finding a mutagenetic tee that is optimal unde esticted cicumstances and good (only) in the geneal case; the esult is deived not in a vial context but in the context of cance eseach. We extended the method to be able to geneate seveal tees, 4 because vial escape paths do not usually submit to a single tee model, as eflected in Figue 6. Again, this method is heuistic; it does not find the best model but just a easonably good model. Rathe than labeling the edges of a mutagenetic tee with conditional pobabilities, we can also annotate them with expected times fo the elevant mutation to occu. Labeling affods a oute to analyzing the times the vius takes to escape towad esistance. 3 We use this model to assess theapy effectiveness. Classifying theapy success with suppot-vecto machines. Diffeent vesions of THEO have used diffeent multivaiate statistical-leaning methods to come up with accuate classifies. Among them ae logistic model tees 11 and suppot-vecto machines. 7 Suppot-vecto machines ae a ecent, popula method fo classifying data that egads data as points in a (usually highdimensional) Euclidean space. In ou case, each data point epesents a theapy change episode, o event whee physicians assign a new theapy based on a vial genotype seen in a patient. Some theapy selections ae successful, othes ae not. This dichotomy epesents ou binay classification poblem. The question of what is a successful theapy and what is a failue, both medically and methodically, is beyond ou scope hee. A linea suppot-vecto machine defines a hypeplane that best sepaates the set of points indicating theapy successes fom the points indicating theapy failues. The hypeplane divides the Euclidean space into two half-spaces, one fo theapy success, one fo theapy failue. What is the best hypeplane (fo minimizing isk of wong pedictions) is defined in tems of two citeia: Disciminating between theapy successes and failues. As few theapy data points as possible should be located on the wong side of the hypeplane, that is, we do not want to see theapy failues in the half-space fo the successes and vice vesa. The futhe a point is in the wong half o away fom the hypeplane on the wong side, the moe it educes the quality of the model; and Maximizing pediction eliability. The hypeplane should be as distant as possible fom the closest coectly classified points. Since the hypeplane epesents the decision bounday, points lying close to it epesent uncetain decisions, and small changes in the data o in the location of the hypeplane can evese thei classification. Quadatic pogamming techniques ae used to find the optimal hypeplane accoding to these citeia. While we have taken state-of-the-at vesions of suppot-vecto machines developed by othes, ou main objective hee is to define the Euclidean space to which we apply the suppot-vecto machine. We must theefoe addess the following issues: Repesenting vial genotypes. Should we use binay indicato vaiables? Which mutations should we conside? Consideing all possible mutations leads to a highdimensional space and is thus infeasible; and Additional infomation fo the method. The theapy we want to apply is a necessay input. Additional input includes pedictions of esistance factos against single dugs, the pobability that the vius will achieve esistance against a dug in a cetain time inteval (estimated via the mutagenetic tees), and pevious antietovial dugs to which the patient was exposed. Addessing them is difficult, as we must balance the amount of infomation we pesent to the method against the available data. The moe infomation we pesent, the moe complex ae the esulting models. Howeve, we must find the best model on the basis of limited data. If models ae too complex we incu the isk of ovetaining the model. An ovetained model incopoates not only pattens petaining to the phenomenon o pocess we want to analyze and whose esults we want to pedict (hee vial esistance) but also idiosyncasies of the paticula data set on which we deived the model. Such idiosyncasies do not genealize to futue data. Thus an ovetained model suffes fom educed pedictive powe. We have pefomed seveal studies and epoted ou choices. 1,2,18v potein gp120 to bind to suface poteins of the host cell. This binding event tigges a cascade of stuctual changes of the paticipating poteins that esult in HI enteing the host cell. Once inside, HI sheds its molecula envelope and uses a special vial potein the evese tansciptase (RT) to copy its RNA genome to DNA. The DNA is then tanspoted into the cell nucleus whee it is spliced into the genome of the host cell with the help of a second vial potein the integase (IN). At this stage, the vial DNA is called a povius. Once the cell begins to divide, as it does within an immune esponse, it manufactues all components of the vius. These components assemble nea the cell suface, and a new still-immatue viion buds fom the cell. In a final matuation step, stings of vial poteins in the immatue viion (the so-called polypoteins) ae cleaved to yield the functional vial poteins. This endes the viion infectious. The potein pefoming the cleavage is the vial potease (PR). Each host cell is able to poduce thousands of viions fo a long peiod befoe inevitably dying. Dug Theapies Against HI Moe than two dozen dugs against HI ae in clinical use; see gov/oashi/aids/vials.html fo the cuent list of U.S. Food and Dug Agencyappoved anti-hi dugs. All ae small molecules that block (inhibit) the function of a specific potein involved in the vial eplication cycle, the so-called taget potein. One way to block a potein is to bind to it in a place that deacti- 68 communications of the acm mach 2010 vol. 53 no. 3

4 vates the potein, eithe by eplacing its natual binding patne o by intefeing with essential potein movements. Taget poteins can be vial o human. The classical taget poteins ae vial, namely RT and PR. Oiginally, vial poteins wee pefeed because one does not want to intefee with unknown functions of human taget poteins. Howeve, vial taget poteins have the disadvantage that the vius can quickly change them though mutation and thus evolve towad dug esistance. Moe ecently, human poteins have also been tageted by antivial dugs. Towad Resistance If the vius wee not so vaiable, one o two AIDS dugs would suffice. But the vius changes its genome with pactically evey copy. The eason fo such flexibility is that RT lacks a poofeading mechanism and does not epai copy eos. Mutations in the HI genome can esult in changes in the composition of its poteins. Most of these changes ae detimental o even lethal to the vius, but with many millions to even billions of vius copies poduced daily in the same patient, chances ae high that a vial vaiant will aise quickly whose taget potein emains functional even in the pesence of a dug. Such a vius is esistant to the dug. Suppessing vial eplication means educing the numbe of expeiments the vius can pefom to poduce a esistant vaiant. In ode to incease the baie of the vius to escape towad esistance, seveal dugs tageting diffeent vial poteins ae given simultaneously. This scheme, called highly active antietovial theapy, o HAART, endes theapies effective fo much longe peiods of time. The vius always wins. Most cuent theapies emain effective fo only months to a few yeas. Antivial Theapies Once the vius is esistant, the teating physician must select a new dug theapy that effectively suppesses the pesent vial vaiant. The standad of cae today is to use diagnostic tools fo selecting a new theapy egimen. Thee ae two fundamental appoaches towad this goal: Phenotypic esistance testing. Phenotypic esistance testing basically povides a lab test, essentially exposing the vius taken fom a patient s blood seum in cell cultue to inceasing dug concentations and obseving quantitatively how quickly the eplication ate of the vius declines. The decline is compaed with the decline of the eplication ate of a nonesistant efeence vius. The compaison yields a quantitative measue of vial esistance against individual dugs, the esistance facto. This measue is the dug concentation that cuts the eplication ate of the patient s vius in half divided by the dug concentation that cuts the eplication ate of the efeence vius in half. Lage esistance factos mean geate esistance. Phenotypic esistance testing meets with majo obstacles when used in clinical pactice, mainly because such testing is esticted to labs with high secuity levels and is thus difficult to standadize and not sufficiently accessible. Cost is anothe issue. Genotypic esistance testing. In contast, genotypic esistance testing detemines the genomic sequences of the elevant pats of the vial genome taken fom a patient s blood seum. The elevant genome sequence can be obtained cheaply, quickly, and with standadized pocedues by many laboatoies. Howeve, it is not easy to infe the esistance phenotype fom the vial genotype. iologists used to pefom this intepetation by hand with the help of a so-called mutation table; mutation tables ae offeed and continually updated by such authoities as the Intenational AIDS Society, 10 collecting the global knowledge on mutations obseved to cause esistance against specific dugs. Figue 2 is an except fom a mutation table coveing thee potease inhibitos. The blue ba epesents the potein sequence, Figue 2. Except fom a mutation table. 10 hee the potease with 99 amino-acid positions. Numbes inside the blue ba indicate potein-sequence positions. The amino acid of the efeence vius at that position is given above the numbe. Resistance mutations at that position ae indicated below the numbe. Each ow petains to a single dug named to the left of the ow. Mutations ente the table as a esult of committee consensus. Moe ecently, the tables have been tuned into expet systems that povide moe complex ules. These systems can also expess inteactions between diffeent mutations that esult in esistance o susceptibility of the vius to a given dug. 16 Computational Biology One poblem with mutation tables and expet systems is they ae the esult of a consensus among human expets, athe than being systematically deived fom the undelying clinical data. This is whee the contibution of computational biology comes in. If we can ende the clinical esistance databases compute-eadable, we can apply statistical-leaning methods to systematically deive estimates of the esistance phenotype fom the vial genotype. We can also assess not only the level of esistance of the vius pesent in the patient but also estimate the path the vius will take towad esistance in the futue if pesented with a specific dug theapy, along with the time the vius will take to get thee. Since 1988, we have been patnes in a numbe of consotia collecting HIesistance data compising vial genotypes, associated clinical makes (such as counts of vius and immune cells in the blood), and phenotypic-esistance data whee available. We did this nationally in Gemany though the Aevi database. 17 In 2004, we co-founded mach 2010 vol. 53 no. 3 communications of the acm 69

5 Figue 3. Decision tee fo esistance against the AIDS dug saquinavi. S I S S R I L G F R M S S S L T I the EuResist consotium, whose database is the esult of integating seveal lage esistance databases fo all of Euope. 18 To ou knowledge, the EuResist database is the lagest HI-esistance database woldwide, haboing data on just unde 100,000 theapies fo almost 34,000 patients. Paied data on vial-mutations and clinical esponse to teatment is available fo moe than 5,000 theapies. Identifying new esistance mutations. Given an HI-esistance database, we use statistical methods to systematically find esistance mutations. A esistance mutation is one, such that viuses esistant (against a given dug) ae highly eniched among the vial vaiants with the mutation, unlike the ones without the mutation. The infomation content a mutation habos on vial esistance against a given dug can be quantified in vaious ways, including mutual infomation and distance fom the decision bounday in a disciminatoy classifie. Using such methods, we have uncoveed new, that is, as-yet-undescibed esistance mutations. 19 That study won a Best Pesentation awad at the Thid Euopean HI Resistance Wokshop, Athens, Geece, in This pee ecognition eflects how much viologists and clinicians ae inteested in appoaches to identifying esistance mutations beyond the classical mutation tables. Resistance pediction based on complete vial genomes. The second class of models incopoates multivaiate analysis to systematically deduce the kind of infomation offeed less systematically by ule-based expet systems. We have poduced many such models, including classifies (into the esistance classes esistant and susceptible) and egession models that estimate the numeical value of the esistance facto. All models ae tained on the data available in ou esistance databases, notably genotype-phenotype pai data, that is, vial vaiants fo which we have both the vial genotype and the esistance facto. We employed decision tees 6 and andom foests to detemine the classifications. Fo egession we found suppot-vecto machines ae most effective. 5 Ou statistical-leaning methods ae state-of-the-at and adapted to the espective poblem; the sideba Statistical Leaning Methods outlines two such methods: mutagenetic tees and suppot-vecto machines. Modeling and featue selection ae the focus of the effot. Appopiate statistical validation of the esulting models epesents anothe majo aspect of ou eseach. Figue 3 is a decision tee fo the esistance of HI against the PR inhibito saquinavi. The banching nodes ae labeled with amino-acid positions in the taget potein PR. Teminal nodes ae labeled with the classes esistant and susceptible, espectively. Edges leaving a node ae labeled Figue 4. Sample output of geno2pheno esistance analysis. Revese tansciptase inhibitos Potease inhibitos with amino acids found at these positions. The amino acid of the efeence vius (no mutation) is in ed. The path leading fom the oot of the tee (top) to the blue aow indicates a single mutation at position 54 fom the efeence Isoleucine (I) to aline (). (All othe edges along the path epesent the efeence vius.) The esulting vius is esistant accoding to the model (ed teminal node). Howeve, if in position 72, thee is also a mutation fom the efeence Isoleucine (I) to aline () (ed aow), then the vius is susceptible (geen teminal node) to teatment with the dug. Such esensitization events pesent inteactions between diffeent mutations and ae deived systematically fom the pocedue of leaning decision tees fo dug esistance. Coss-validation helps us show that ou decision tees make accuate pedictions in appoximately 90% of all cases. Ou esistance models ae the basic sevice of the geno2pheno seve. Pacticing physicians and laboatoy viologists paste in the nucleic acid sequence of the elevant genes of the vial vaiant extacted fom a patient s blood. The analysis esponds with the kind of output listed in Figue 4, whee each ow epesents a dug. Column 1 names the dug. Column 2 gives the estimated esistance facto. Column 3 Dug RF Z-scoe Scoed Mutations ZD k 123E 179D 103N 115F 74 ddl A 177E d4t T 122K 123E 190A 3TC FTC K 115F ABC E 214F 115F TDF E 214F 115F 200A 219T NP N 179D 102N 211K EF N 179D 200A 214F E SQ P 71T 37N ID P 71T RT P 71T 3I NF P 3I AP P LP P 71T 35D AT T 63P TP I DR D 70 communications of the acm mach 2010 vol. 53 no. 3

6 figue 5. two favoed mutational escape paths of hi fom the theapy with the Rt inhibito zidovudine. tam1 Path M D K l T K l n W F/Y E/Q gives a nomalized value eflecting the significance of the esistance value. Column 4 lists mutations found in the input sequence, ed if they stengthen the esistance of the vius and geen if they weaken it. The data in the figue points to stong esistance against many inhibitos of RT and theapy options tageting PR. The Geno2pheno seve is the basis fo suppoting teatment decisions in about two-thids of HI-infected patients teated in Gemany. 13 This means at least 12,000 decisions fo teatment selection pe yea in Gemany involve geno2pheno o its findings. Chasing the vius. This analysis teats each dug sepaately. Given the output in Figue 4, the physician assesses the esistance level of the vius against each individual dug and manually composes the combination dug theapy that is (hopefully) effective against the pesent vius. We also look into the futue of the vius. Pesented with a given combination dug theapy, how will it eact? What ae its mutational escape paths and how long will the theapy stay effective? The vius does not just andomly intoduce mutations. Rathe, it follows moeo-less established mutational escape paths; Figue 5 outlines two favoed paths fom a theapy with the single AIDS dug zidovudine (ZD, AZT). (The notation is analogous to that of Figue 3.) We denote with K70R the mutation of K to R in position 70 (of RT). Hence, one escape path is K70R followed by K219E/Q. The biological easons fo the vius following these paths ae not well undestood. But the paths show up in a clinical HI-esistance database. Finding them is simple if we have longitudinal data. The data compises sequences of vial genotypes and clinical tam2 Path paametes fom the same patient ove long peiods of time. Howeve, such data is difficult to come by. Ou databases ae dominated by coss-sectional data involving only a few o single data points fo each patient. Nevetheless, we ae still able to identify favoed escape paths fom coss-sectional data, as in Figue 5. A database of coss-sectional data on theapies with zidovudine will not contain many viuses having mutation M41L but not the mutation T215F/Y. This mutational patten indicates the diection of the escape path. We have developed statistical models that pinpoint the paths, so-called mixtues of mutagenetic tees, fom the database 4 ; Figue 6 outlines the tees deived fom the database concening zidovudine theapy. Figue 6 outlines a mixtue model of two mutagenetic tees, the bottom one expessing the two thymidine analogue mutations (TAM) escape paths and the top one (noise tee) expessing an unstuctued escape to esistance. The mixtue model indicates that 78% of the data is explained by the escape via the TAM paths; 22% can be viewed as noise. The sideba exploes mixtues of the mutagenetic tees model. The analysis of vial escape is available on the geno2pheno seve via the applet known as THEO (theapy optimization), which anks all easonable theapies by the pobability of thei staying effective fo six months o longe fo the Web-seve vesion of the softwae. The statistical method fo doing this is discussed in the sideba section on suppot-vecto machines. Figue 7 outlines the esults of THEO on the same data as in Figue 5. Taining the model equies data encompassing the vial genotype, the dugs involved in the theapy, and clinical follow-up data on the effectiveness of the theapy. How to chaacteize a successful theapy is complex. We do not, fo example, need the esistance facto to be input fo each quey. We can supply it though ou computational-esistance pediction method discussed ealie. Also, the expected futue vial evolution can be estimated though mutagenetic tees. THEO, which has been validated extensively, impoves the accuacy figue 6. two mutagenetic tees indicating the escape paths in figue 5; wild type indicates the efeence vius. 41l n tam EQ 67n 69Dn 69Dn wild type 70 wild type 210W 215FY 41l 210W 215FY tam1 219EQ MAch 2010 vol. 53 no. 3 CommunICAtIonS of the ACm 71

7 contibuted aticles of theapy selection substantially1,2; fo example, appoximately 24% of the theapy selections epoted in the 2006 vesion of ou Aevi database tuned out to be ineffective. Using THEO could have helped educe the eo ate in selecting effective theapies below 15%. The EuResist poject ( adds two qualities to the eseach we discuss hee: Data collection includes data fom seveal Euopean counties; and, on the EuResist pediction seve, thee independently developed pediction engines ae executed and etun individual esults and a consensus pediction.18 New Dugs Using sophisticated methods to administe antivial combination dug theapies does not obviate demand fo Figue 7. THEO applet. gp41 HI gp120 Coecepto Host Cell Figue 8. Poteins paticipating in HI cell enty (coutesy Pfize). 72 comm unicatio ns o f th e acm ma c h vo l. 5 3 no. 3 CD4 continually developing new dugs. Fo an individual patient, administeing a dug povokes esistance mutations that accumulate within the vius genome. Eventually, only new dugs with new modes of action o even new taget poteins will delive additional effective dug theapies. Moeove, AIDS dugs age as esistance mutations accumulate in the global vial population, necessitating continuous development of new dugs. And clinical side effects enfoce the development of new dugs with the same mode of action as existing old dugs. Such new dugs might eplace the old dugs but might also povoke slightly diffeent esistance mutations. Dugs tageting RT and PR wee the basis of AIDS theapy until the ealy 2000s. Since 2003, dugs tageting othe poteins have come onto the maket. Especially attactive tagets fo anti-hi dugs ae poteins facilitating cell enty of HI. Such tagets ae chosen because blocking vial cell enty helps pevent integation of the vial DNA into the cellula genome. To undestand how we block vial cell enty we must look at the pocess of HI enteing the cell in moe detail (see Figue 8). Fist, the vial suface potein gp120 binds to the cellula ecepto potein CD4. This leads to a confomational change in gp120 so it can then bind to an additional cellula potein, the so-called co-ecepto. The binding of gp120 to the cellula coecepto tigges the actual vial cell enty, duing which the helical (cokscew-like) vial suface potein gp41 penetates the cellula membane, and the hull of HI fuses with the cellula membane. HI can use one of two cellula suface poteins CCR5 o CXCR4 as a co-ecepto; some vial stains use eithe. The co-ecepto specificity of HI is also called vial topism. A vius using CCR5 is called R5-vius. Analogously, a vius using CXCR4 is called X4-vius. A vius using eithe co-ecepto is called dual-topic, o R5/X4-vius. ial topism has impotant clinical consequences. Fo example, the initial infection esults almost exclusively in an R5-vius population; we assume that X4-viuses may infect the patient but can be contolled initially by the immune system. Appoximately 1% of the

8 Caucasian population woldwide lacks a functional gene fo CCR5, has no appaent symptoms, and is esistant to being infected by HI. As the disease pogesses, a vius using CXCR4 can become dominant. Tagets fo dugs that block cell enty ae the vial suface potein gp41 and the cellula co-ecepto CCR5. The latte is tageted by the dug Selzenty/ Celsenti, which contains the active substance maavioc (developed by phamaceutical manufactue Pfize). Regulatoy agencies in Euope and the U.S. equie vial topism testing befoe administation of this dug. As with esistance analysis, thee ae again two options fo a vial topism test: One is a lab-based phenotypic test, the othe a genotypic test with computebased intepetation. The advantages and disadvantages of each ae simila to those in esistance testing; fo example, phenotypic tests ae accuate but take a long time and ae expensive and not always easily accessible. Moeove, and in contast to phenotypic esistance tests, phenotypic topism tests povide only a classification into X4-capable o not-x4-capable and no quantification of the isk of using the wong co-ecepto. The main poblem with genotypic tests is the elucidation of the genotypephenotype elationship. The geno2pheno seve offes a pediction fo vial topism fom genotype. As with esistance analysis it is based on caeful modeling of the input and on the development of a multivaiate statistical model tained on genotype-phenotype pai data. 12 In this instance, the phenotype is the vial topism, not the esistance against a dug, though the co-ecepto switch can be viewed as a way fo HI to evade dugs blocking CCR5. Thee notable advantages of this genotypic appoach ae lowe costs, wide availability, and a quantification of the isk of using the CXCR4 co-ecepto. Since thee ae majo obstacles to cuing AIDS, the objectives of dug theapy ae to ease symptoms, suppess vial eplication, and delay pogess of the disease. Measuing the ial Quasi-Species A poblem with genotypic data that seems moe elevant fo pedicting vial topism than fo pedicting dug esistance is that the patient habos not a single vial vaiant but athe a divese vial population, o so-called quasi-species. Classical genotypic measuements educe the quasispecies to a single vial vaiant (the dominant one) o to a sequence consensus of a few fequent vial vaiants. Howeve, minoities of X4-vius pesent in the vial quasi-species (but not detected by the genotypic test) can accumulate in the patient unde theapy with CCR5-blockes. Detecting such minoities may be clinically impotant, and phenotypic tests ae able to detect them. To enable genotypic tests to also detect them, we use new deep sequencing technology called pyosequencing 14 to geneate data fom which appopiate computational pocedues econstuct (with geat accuacy) the pofile of the whole quasispecies. One of ou cuent eseach activities tagets pedicting vial topism and its clinical consequences based on such data. Outlook The wok descibed hee can now be extended in seveal diections. Fo example, a multitude of questions petain to the statistical-modeling pocedue, including those involving the epesentativeness of the clinical databases, how to impove pediction accuacy when sufficient taining data is unavailable, and how to follow diffeent notions of theapy success. Moe fundamental, the technology applies to othe vial infections, the pathogens of which exhibit dynamic evolutionay development, a popety shaed by Hepatitis C (caused by HC) and Hepatitis B (caused by HB). In both cases, dug development and the collection of esistance data has not advanced as fa as it has fo HI. We ae involved in pojects that collect such data, intending to tansfe ou technology to these diseases. We have gone beyond infectious diseases and applied the mutagenetic-tees technology to assessing the status of tumo pogession in cances fom data on the evolutionay degeneation of the genomes of the elated tumo cells. 15 Thus fa, ou analysis is based mostly on patten matching with limited concete biology in the fom of mechanistic models of the ceation of the vial phenotype. Methods fom expeimental viology and systems biology can be used to geneate data that facilitates development of such models. Incopoating them into the pediction of vial mach 2010 vol. 53 no. 3 communications of the acm 73

9 ACM Jounal on Computing and Cultual Heitage JOCCH publishes papes of significant and lasting value in all aeas elating to the use of ICT in suppot of Cultual Heitage, seeking to combine the best of computing science with eal attention to any aspect of the cultual heitage secto. esistance and theapy effectiveness should incease the accuacy of the elevant pediction pocedues and help futhe ou undestanding of how the vial phenotype develops. Finally, though not included in ou pesent analysis, host factos, including a patient s immunotype, also play a ole in disease development and the effectiveness of dug theapy. Fo instance, it is unde debate whethe the immune system initially suppesses the enichment of peexisting X4-viuses in the vial quasi-species. If this is the case, solely detecting X4 minoities need not be clinically significant; such detection does not necessaily pedict the beakthough of the vial vaiants, as long as the immune system is intact. Indeed, we and othes have obseved that the isk of X4-vius emeging ises with deceasing immune-cell count, eflecting the deceased intensity of the patient s immune esponse. Such obsevations stongly encouage constuction of a compehensive model that includes infomation on all thee playes pathogen, dug, and host. Acknowledgments The wok epoted hee is the esult of extensive intedisciplinay collaboation. We thank all involved scientists, past and pesent, especially the computational biologists Niko Beeenwinkel and Tobias Sing, the Aevi consotium, especially Eugen Schülte, Matin Däume, and Hauke Walte, and the Euesist consotium, especially, Fancesca Incadona, Mauizio Zazzi, and Andes Sönnebog. The wok has been patially funded by Deutsche Foschungsgemeinschaft, gant Ho 1582/1-3, KA 1569/1-3 (Aevi) and EU gants LSHG-CT (BioSapiens) and IST (EuResist) and is being patially funded by BMBF gant C (HI Cell Enty), BMG gant 310/4476 (RESINA), and EU gant HEALTH-F (CHAIN). Refeences 1. Altmann, A. et al. Pedicting the esponse to combination antietovial theapy: Retospective validation of geno2pheno-theo on a lage clinical database. Jounal of Infectious Diseases 199, 7 (Ap. 2009), Altmann, A. et al. Impoved pediction of esponse to antietovial combination theapy using the genetic baie to dug esistance. Antivial Theapy 12, 2 (2007), Beeenwinkel, N. et al. Leaning multiple evolutionay pathways fom coss-sectional data. Jounal of Computational Biology 12, 6 (July/Aug. 2005), Beeenwinkel, N. et al. Estimating HI evolutionay pathways and the genetic baie to dug esistance. Jounal of Infectious Diseases 191, 11 (June 2005), Beeenwinkel, N. et al. Geno2pheno: Estimating phenotypic dug esistance fom HI-1 genotypes. Nucleic Acids Reseach 31, 13 (July 2003), Beeenwinkel, N. et al. Divesity and complexity of HI-1 dug esistance: A bioinfomatics appoach to pedicting phenotype fom genotype. Poceedings of the National Academy of Science USA 99, 12 (June 2002), Chistianini, N. and Shawe-Taylo, J. An Intoduction to Suppot ecto Machines. Cambidge Univesity Pess, Cambidge, U.K., Despe, R. et al. Infeing tee models fo oncogenesis fom compaative genome hybidization data. Jounal of Computational Biology 6, 1 (Sping 1999), Fields, B.N., Knipe, D.M., and Howley, P.M. Fields iology, Fifth Edition. Woltes Kluwe Health/ Lippincott Williams & Wilkins, Philadelphia, PA, Johnson,.A. et al. Update of the dug-esistance mutations in HI-1. Topics in HI Medicine 16, 5 (Dec. 2008), Landweh, N., Hall, M., and Fank, E. Logistic model tees. Machine Leaning 59, 1 2 (May 2005), Lengaue, T. et al. Bioinfomatics pediction of HI co-ecepto usage. Natue Biotechnology 25, 12 (Dec. 2007), Lengaue, T. and Sing, T. Bioinfomatics-assisted anti- HI theapy. Natue Reviews Micobiology 4, 10 (Oct. 2006), Magulies, M. et al. Genome sequencing in micofabicated high-density picolite eactos. Natue 437, 7057 (Sept. 15, 2005), Rahnenfühe, J. et al. Estimating cance suvival and clinical outcome based on genetic tumo pogession scoes. Bioinfomatics 21, 10 (May 2005), Rhee, S.Y. et al. Human immunodeficiency vius evese tansciptase and potease sequence database. Nucleic Acids Reseach 31, 1 (Jan. 2003), Roomp, K. et al. Aevi: A secue platfom fo designing pesonalized antietovial theapies against HI. In Poceedings of the Thid Intenational Wokshop on Data Integation in the Life Sciences (Hinxton, U.K. July 20 22). Spinge elag, Belin, Heidelbeg, 2006, Rosen-Zvi, M. et al. Selecting anti-hi theapies based on a vaiety of genomic and clinical factos. Bioinfomatics 24, 13 (July 2008), Sviche,. et al. Involvement of novel human immunodeficiency vius type 1 evese tansciptase mutations in the egulation of esistance to nucleoside inhibitos. Jounal of iology 80, 14 (July 2006), UNAIDS Repot on the Global AIDS Epidemic. UNAIDS, Geneva, Switzeland, 2008; GlobalRepot/2008/2008_Global_epot.asp Thomas Lengaue (lengaue@mpi-inf.mpg.de) is Diecto of the Depatment of Computational Biology and Applied Algoithmics at the Max Planck Institute fo Infomatics, Saabücken, Gemany. Andé Altmann (altmann@mpi-inf.mpg.de) is a staff scientist in the Depatment of Computational Biology and Applied Algoithmics at the Max Planck Institute fo Infomatics, Saabücken, Gemany. Alexande Thielen (athielen@mpi-inf.mpg.de) is a staff scientist in the Depatment of Computational Biology and Applied Algoithmics at the Max Planck Institute fo Infomatics, Saabücken, Gemany. Rolf Kaise (olf.kaise@uk-koeln.de) is a staff scientist at the iological Institute, Köln, Gemany ACM /10/0300 $ communications of the acm mach 2010 vol. 53 no. 3