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Xbiom–AWorkbenchforBiomarker&TranslationalResearch
February2019,Version1.0
This white paper from PointCross Life Sciences Inc. (Foster City, CA) discusses theimportanceofunifyingclinicaltrialdata,molecularandotherbiomarkersfrompatientbio-samplesforsearchandaunifiedvisiontogaininsights.Wepresentthebenefitsofsuchaunifiedenvironmentasaworkbenchforbiomarkerandtranslationalresearchersfor discovering, developing, and applying biomarker for biologics based therapeuticdrugs. Thepaperpresents querymasks for stratifying cohorts and searchanalytics onclinicalandbiomarkerdataasavailableontheXbiomsolution(www.xbiom.com).Itisnot intendedtobeanoriginalpresentationofascientificdevelopment.Thispaperwillbeperiodicallyrepublishedtoreflectnewinformationanduser’s insights.Wewelcomecommentsfromourindustryclients–[email protected].
PointCrossLifeSciencesInc.1291E.HillsdaleBlvd.,Suite304
FosterCity,CA94404Tel:+1844-382-7257
www.xbiom.com
www.pointcross.com
NeedforaWorkbenchforBiomarker&TranslationalResearch...................................2ConvergenceofBioandInfoTechnologies.....................................................................................................2Traditionalclinicaltrialdata................................................................................................................................2Translationalresearchandbiomarkers..........................................................................................................3BiomarkersPurposes..............................................................................................................................................4
Growthofbiomarkerindustry....................................................................................................5Futurebiomarkers......................................................................................................................5
Unitepatient’sclinicalandbiomarkerdataforsearchanalytics.........................................................5Integratetheresearchworkbenchtobio-sampletracking....................................................................7Ingestionandloadingofclinicalandbiomarkerdata...............................................................................7
Xbiom™WorkbenchforTranslationalandBiomarkerResearch...................................8BriefdescriptionoftheXbiomplatformandsolutionsavailable...............................................8
XbiomClinicalandBiomarkerInsightsModule–WorkbenchforTranslationalandBiomarkerResearch...............................................................................................................................................10OmicsDatacurrentlysupportedinXbiom’sontologies:........................................................................11
SearchQueryMasksandStratifiedCohortSelection...............................................................12DatavisualizationandIGO.......................................................................................................16
GettingXbiombiomarkerresearchworkbenchsetupquicklyandinexpensively.....................19SoftwareasaService................................................................................................................21ContactPointCrossLifeSciencesformoreinformationanddemo...........................................21
References..................................................................................................................................................................22
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NeedforaWorkbenchforBiomarker&TranslationalResearch
ConvergenceofBioandInfoTechnologiesTwo separate revolutionary advances in BioTech and InfoTech are nowconverging to impactourhumanexperience inmanyways1. InfoTechhasbeenfollowingMoore’s Lawbydoubling thenumberof componentsandcapacityofelectronic chips every 18 months or so since 1965. Software and networktechnologyhastakenthisadvancetoevolvemachinesthatlearn,adaptandevenmimic human intelligence. Since the completion of the first human genomesequencing in the early 2000s, the BioTech industry has entered its race withadvancedhigh throughputassaysandNext-Generation sequencers thatdecodethechainsoffournucleotidesthatarepartofallDNA,RNAsandthestructureofamino-acidchainsofproteins.ThistechnologyismovingaheadatthesquareofMoore’s Law and the convergence is driving rapid change in the BioTech andBioPharma industry. Products of biological analysis today are as often in theformofdigitaldataas itmightbereceived inavial.Recentdevelopments inAIare equally applicable in both InfoTech and BioTech and the lines are gettingblurred.Someoftheseadvancesarebringingnewtargetedtherapiesanddiagnosticsfordiseasessuchascancerorrareandorphaneddiseases.Thisishappeningthroughourability topeer into theverygenomic codeandmolecularmechanisms thatdefine and direct our bodies, and then using insights from that knowledge todesign biologically derived molecules that can treat or mitigate the risk ofdiseases.There are three important disruptions that are bringing rapid changes to thepharmaceuticaldrugdevelopmentindustry–themovetorepresentclinicalstudydata instandardizeddigitalmachinereadableform(CDISC);therapidgrowth inthemolecular, protein andmetabolomic biomarkers and small molecules; andthecomingtsunamiofremotely,non-invasively,monitoredsensordatathatwillgeneratenewtypesofbiomarkersinthenearfuture.Manyofthesewillpromptnewadvancesintherapeutics.
TraditionalclinicaltrialdataClinicaltrialsaretraditionallyrunsequentiallythroughPhases1,2a,2b,3anda4thphaseaftermarket.FirststudiesinhumansinPhase1and2aassesssafety,dose-limiting toxicity and themaximum allowable dose estimations, which are
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checked with dose escalations studies. Phase 2b studies the activity and theoverallresponserates.Phase3isfocusedonefficacyasmeasuredbyend-pointssuch as reversal of disease, overall survival or progression free survival in thecases of diseases such as cancer.With traditional clinical trials the bulk of thedata collected were from observations in the clinic, lab tests, and variousdiagnostictestssuchasXrays,CTScansandsuch.Thecollecteddataarereportedinpre-designedeCRFs(CaseReportForms),whichareprocessedintostudyfiles(SDTM)thatareprocessedbyscriptstoextractanalysisdatasets(ADaM).A very positive disruption is themandate from the FDA (and PMDA) sinceDec2016todigitallyrepresenttheclinicalstudydatainastandardexchangeformatincolumnartabulationsthataredesignedforcomputerreadabilityasopposedtohumanreadablestudyreportswithtabulationsandfigures.Clinicalstudydataisnowunlockedfromproprietaryformatsinaclinicaldatamanagementsystem.ItisinanopenCDISCSDTMformatandtheanalysisdataisavailableintheADaManalysis data sets along with other descriptive files. Terminology for adverseevents, lab,observationaldata,andpathologydataarebecomingstandardized.This allows software to be easily analyze one study, multiple studies or tocomparedataacrossstudies.Italsomeansthatvirtualcohortswithpatientsfrommultiplestudiescanbetrackedandstudied.Withintheconstraintsof informedconsentandcompliancetoregulations,theclinicalstudydatacanbere-purposedforpermittedresearchforre-targeting,diagnosticsorothertherapeuticalareas.
TranslationalresearchandbiomarkersNIH2 defines biomarkers as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” Collected labtests (onblood,urine,serum,tissue)andobservationaldata(gait,response, physical examination…) in clinical trials are biomarkers, by thisdefinition.Translationalresearchbridgesthe“bench”,orlab,tothebedside(ofthepatient).Biomarkers, quantified at the bench or sequencing laboratories, are rapidlybecomingimportantindiagnostics,prognosis,predictivemarkersarebiologicallyderivedmoleculeswithsomaticorhereditarymutations.Thesemaybeinvolvedin assessing patient’s clinical outcome or developing targeted therapeutic drugmolecules.Theyincludegenes,RNAs,Proteinsandmetabolomes.Thisdisruptionthat has already changed the industry from that one that sought blockbustersmallmoleculestobiologicsthatareveryeffectiveforpreciselytargetedpatient
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populations. These biomarkers and biologic molecules are built with anunderstandingof:
• Genomes thathave insertions,deletions, translocations, SNPsandCNVs;orthatcarryepigeneticinformation,andallelicvariations.
• Transcriptomes that include various mRNAs, miRNA, signaling RNAs,ribosomalRNAs,andtRNAs
• Proteomes that carry post translationalmodifications, and contribute tochangesinregulatorymechanismsthroughactivationordegradation.
• Metabolome that play a part in regulatory functions through enzyme(aminoacids,lipids…)kinetics,transportoraccumulation
Figure1OmicsSchema-Metabolomics(ImagefromWikipedia,20096,
https://en.wikipedia.org/wiki/File:Metabolomics_schema.png)
BiomarkersPurposesAfastgrowingsegmentof industry is in identifyingmarkers inpatientsthroughassays,sequencingandtranscriptomicprofilingwithhigh–throughputlaboratorytechnologyanddiagnosticservices.Companiondiagnosticsisalsoafastgrowingindustry because increasingly precision medicines may be applied only onpatientsthatmeetveryspecificconditionsandshowspecificmarkersthatmustbediagnosed.
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Biomarkersplayanimportantroleinthemonitoringandtrackingofthediseaseprogress, or reversal with treatment. The difference between biomarkers andtherapeutic molecules can be blurred. Developing tumor specific therapeuticmoleculessuchasinimmuno-therapyandCAR-Tconnectsdiagnosticbiomarkersforspecificcohortsandtheirtherapies.
GrowthofbiomarkerindustryTranslational research for Precision Medicine and demand for highly targetedtherapeuticsisdrivingrapidgrowthintheBioTechindustry.Industryanalysts3,4estimatetheglobalmarketforbiomarkersat$80Billionin2018andgrowingatabout 11%-12% annually. This growth is driven by the BioTech and the BigPharmabusinessesfocusingonbiologicsandprecision-targetedmedicines.
FuturebiomarkersAnother disruption to the drug development and healthcare industry is also aresultoftheconvergenceofthebioandinfotechworlds.Itistheavailabilityandpervasivelowcostnon-invasivetestingandmonitoringsystems.Thisbridgesthepatienttotheclinicandithasmajorimplicationsonhowmanyaspectsofclinicaltrialsmaybeconductedinthefuture.PatientsoncevisitedclinicsfortheirEKG,EEG,gait,response(tostimuli),mobilityandotherteststomonitorCV,CNSandother conditions. Wearables with sensors and radio technologies with smartsoftware will soon monitor patients all the time especially during medicalconditions-suchasAFibheartfibrillations.TheiWatchEKGisarecentexample.Thisdataisrawandoftenstreaminginreal-time.Bigdatatoolsrunalgorithmsinreal-time on such streaming data. Deep learning applications will pick outmarkersofinterestthatwillbestreamedthroughthenetworkstobematchedupwithpatientCRFs inongoingclinical trials.This isemerging technology for theBioPharmaandBioTech industrybutthesetypesof telemetryandextractionofmarkershavebeeninuseinvariousindustriesincludingoilandgasexploration,autonomous vehicles, aerospace and defense. The BioTech industry is likely tobenefit by leap frogging from thiswork. These types of biomarkerswill causedisruptions to the way eCRFs are designed, the logistics of big data, and howthesebiomarkerswillbeincorporatedintothestandardofcareofpatients.
Unitepatient’sclinicalandbiomarkerdataforsearchanalyticsUnitingpatientdatafromclinicaldata(eCRFs,healthhistoryandbaselines)andgenotypingdata alongwith themolecular andotherbiomarkers creates a veryrich research environment. Researchers can search for, and find elusive caseswhere there is a correlation between the clinical end points in patients, theirhereditary markers, and their markers from response to disease progress ortherapeuticdosing.Thisisaboutunitingdisparatedatatoprovidetheresearcher
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withasingle,unitedvisionforgaininginsights.Theintegrationoccursinthewaythedisparatedatapoolscanbesearchedfromasinglevantagepoint.Unified workbench of clinical and biomarker data offer many use-cases andbenefits:
• Discover new or potential biomarkers for companion diagnostics,predictingresponse,ortherapeuticmonitoring
• Improve benefits/risk assessment for finely stratified cohortsretrospectivelywithinandacrosstrials
• DesignAdaptivetrials• Looking for longitudinal changes in clinical and biomarker end points in
patientsorcohorts• Analyzingotherdatacollectedfrompatientsinacohort• Testinghypothesisofcompaniondiagnosticsretrospectively
The promise of biomarkers is very high, although success eluded many earlyefforts suchas genome-wideassociation studies (GWAS) that yielded fewclearmarkerssuchasagenewithaspecificinheritedmutation.Forexample,apatternof repeated CAG in the Huntingtin gene was associated with Huntington’sdisease, but continuing research is revealing additional markers5 that areinvolvedintheonset,monitoringandprogressionofthisdisease.Otherknownexamples are CEA for colon cancer and PSA as a detector for prostate cancer.Markersrelatedtoadiseaseoritscurecanofteninvolveproteinsconnectedtomultiple or fused combinations of more than one gene with specific types ofmutations. The markers related to heterogeneous tumors are also oftenheterogeneous. Breast cancer is an example where the inherited BRCA1 andBRCA2 genes are possible predictors. But today the heterogeneity of breastcancertumorsisbetterunderstoodandthetreatmentofaspecifictumor,orinthe case of heterogeneous tumors within a tumor, can mean treatmentstrategies that require assays to identify multiple proteins such as ER, PR andHER2 receptors with one approved assay6 looking at as many as 21 factors6.Understanding somatic mutations that play a role in cancer was elusive untilpromising research of implicated oncogenes showed the complexity of theinterplay of mutations in genes and the various RNA. A complex network ofmultiplegenesandmultiplemutationsareoftenimplicated.Immuno-therapies, such as modified CARs derived from patient’s T cells, arepromisingbreakthroughs7inoncologyandotherdiseases.Dealingwithdivergenttumorgenetics,tumormicroenvironments,andimmuneactivationischallengingfor immune Oncology therapies. This will raise the need to include related
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biomarkers where differentially expressed immune signatures and density ofdiverseimmunecellpopulationwithinthemicroenvironmentsoftumorcellsaremeasuredandindexedforsearch.Theseofferpromisingtherapeuticmechanismstoimprovetheclinicaloutcomefortreatmentofcancerpatients.Finding these biomarkers and their relation to the patient’s clinical findings,pathology,orthesomaticvariationoftheirmarkersrequiresearchtoolsthatcananswer specificand targetedquestionsof the researcher fromvastamountsofdatainalargenumberofdisparatedatasourcesunifiedwithintheworkbench.
Integratetheresearchworkbenchtobio-sampletrackingBio-samples obtained from patients in legacy studies, with their informedconsent for theresearchpurpose,canbefurtheranalyzed,possiblywithnewertechnologies.Imaginefindingahighlystratifiedcohortwith10patientsfoundinoneormorestudies.Butthesearchalsopointsoutthatthereareadditional25patientswhomighthavejoinedthishighlystratifiedcohortbutforthefacttheirprevious screening did not look for the RNAs or proteins that the 10 patientswerescreened.Aworkbenchisaconvenientplacefromwherearesearchercanconduct a search, and know what deficiencies can be corrected within theconstraints of compliance and ICFs. It is the virtual, digital laboratory for theresearcher.
IngestionandloadingofclinicalandbiomarkerdataClinicaltrialdataiscollectedinmultiplesites,managedandanalyzedbymultipleCROsandthesponsor(theBioPharmaconductingthetrial).Datamanagersfacemanychallengesinmaintainingasinglepointoftruthforthiscollected,analyzedandstandardized(CDISC,SDTM,ADaM,TFL)data.Clinicaltrialshavetrialspecificprotocolsandstudydesigns.Thedatastructuresusedtobetrialspecificandthishasmeantthatmostofthecompletedlegacystudiesarestoredindatamodelsthatmust be transformed. Local terminologies need to be standardized beforetheycanbeusedfortranslationalorevenlongitudinalresearchandanalysis.Thisrequiresthatthedatamodelsmustbetransformedtoaconsistentdatamodelsoitissearchable.Curationandingestionforindexingimpliessmarttransformationandmappingalgorithmswithprovenanceandsupportofmachinelearning.Genomicbiomarkersare reportedwithvarying terminologyandconventions todenotethelocationandnatureofmutations,andwhethergenesareidentifiedaswildtype.Thesearebasicdefinitionsthatneedtobeharmonized.Thedisparateplatformand technologiesusedmayprovidedifferent levelsofexpressionsbutthose are important for researchers to evaluate the data. Similar to ingestingclinical data, biomarker data must be normalized for data models and
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harmonized to a common format so that they are searchable within a unifiedworkbench.Ingestion of the data needs to be supported by a dashboard for assessing thequalityofthedataanddrivingtheworkflows.
Xbiom™WorkbenchforTranslationalandBiomarkerResearch
BriefdescriptionoftheXbiomplatformandsolutionsavailableXbiom™ is a commercially available stack of solutions for BioTechs andBioPharmas. It is currently in production for both. It is available for immediateusethroughapubliccloudinstanceinAWS(Amazon),orasadedicatedinstanceinthecloudorprivatecloud.Itisalsoinproductionasanon-premiseinstallationatsomebigPharmacompanies.
Xbiom™isbuiltontopofthePointCrossOntologyplatform,Orchestra™.Xbiom™usesamicro-servicesstrategytokeepanevergreenarchitecturethatisinformedby biomarker and study data considerations, search, analytics, and immersivedatavisualizationwithabackdropofsecurity,workflowautomationandartificialintelligence engines for machine learning. Xbiom has workflows and tools to
©2011PointCross.AllRightsReserved.PuttingKnowledgetoWork4PointCrossLifeSciences
Xbiom©SuiteforResearch&RegulatoryinClinical&Nonclinical
Insights:Nonclinical
Insights:ClinicalAndBiomarker
WorkbenchforTranslational&BiomarkerResearch
Xbiom™builton
Orchestra™Platform
MySEND–FreeDownloadableDesktoptool
Regulatory:Nonclinical
Regulatory:Clinical
SmartTransformation
DataStandardization
SEND-ASSUREServices
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supportregulatoryprocessesinClinicalandNonclinical;andithasworkbenchesfor research in clinical translational and biomarkers and nonclinical. A briefdescriptionofmodulessupportingregulatoryandnonclinicalarelistedbelow.Amore detailed description of Xbiom™ Insights – Clinical & Biomarkers, theclinical and biomarker workbench for translational research is provided in thenextsection.Xbiom™ Regulatory NonClinical automates the conversion and preparation ofnonclinical studies to the requiredSEND IGversionand theselectedControlledTerminology(CDISC).SENDandnon-GLPstudiescanbeingestedintotheXbiomInsights Nonclinical workbench for cross-study toxicology and drug safetyresearch.Xbiom™ Regulatory Clinical streamlines study workflows between CROs,Sponsorsandthestatistical teamspreparingclinical trialdata forsubmissiontoregulatory agencies. Clinical study data that needs to be available fortranslational research can be read by the Xbiom™ Insights – Clinical Biomarkerworkbench.Additionalservicesanda freelydownloadable tool forvalidationofCDISCstandardizedclinicalandnonclinicaldataisavailable.Xbiom™Insights–Nonclinical isaworkbenchfordiscoveryoftoxicologysignalsandpatternsacross studiesand specieswithinNonclinical studies. Thismodulepermitseasycross-studysearchthroughtheuseofaNonclinicaldatarepository,whichcontainsnormalizeddatathroughsemanticintegration.Xbiom™ Smart Transformation module allows data managers to curate andtransform clinical, nonclinical and biomarker data from data-lakes to a targetmodel.Thesolutionallowsuserstorapidlyhandledisparatedatafrommultiplesources,harmonizeandnormalizeintoasingleDevelopmentandResearch,FAIRrepository. This process is supported and controlled by Xbiom MetadataGovernancetoensureconsistencyofthesponsorproprietaryontologies.
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XbiomClinicalandBiomarkerInsightsModule–WorkbenchforTranslationalandBiomarkerResearchXbiom™ Clinical Insights is the module for the translational and biomarkerresearch workbench. It is a semantically unified and searchable resource ofavailableclinical trials,molecular biomarkers, bio-sample availability and bio-sampletrackingdataandbioinformaticsregistries.Theplatformprovidessecureaccess to users and other applications to search for highlystratifiedcohorts,retrospectivelyorprospectively,usingsimplequerymasksandeasyBooleanlogicacrossalldatasetscollectedduringthedrugdevelopmentlifecycle.Xbiom’sontologyiscuratedandcarriesallthegeneticvariantsasandwhentheyare encountered. As biomarker data is ingested a smart transformation enginewithamachine-learningalgorithmmapsandingestthedatabeforeitisindexed.Clinicaldataisnormalizedandharmonizedtothe“global”uniqueidentitysothatthe indexed search engine will respond with the right result even when asynonym is used in the search. A comprehensive dashboard to manage theingestionisprovidedandclientdatamanagerscanbetrainedonitsuse.Xbiomisbuiltontopofdiversifieddatatypesfromhigh-throughputtechnologiesincluding NGS, Mass Spectrometry, Array or Seq based Expression, FlowCytometry,andFISHtechnologies.Thisprovidesresearcherswitheasierwaystoassimilateand incorporatefinerclassificationofbiologicalsub-stratato lookforpreciseuseofatargetedtherapies.Industry continues to discover, or re-evaluate, complex genome involvedphenomena such as epigenetics and cell signaling, and vast stretches ofnoncodingsectionsofthegenome,oncethoughtofas“junkDNA”,inregulatorymechanisms.RapidlyreducingcostandincreasingtechnologyofNext-GenerationsequencingofDNAandRNAwithWGSandwholeexomesequencingalongwithadvances in immunohistochemistry and image processing is making theconsumable biomarker metadata easier to obtain and use in search analyticsacross all the data regimes. The data is still huge and considerable technologygoesintomakingiteasyforthebenchscientiststointeractwithitwithoutabevyof data wrangling programmers and statisticians. Biomarker or translationalscientistsneedasingledigitalwindowprovidedbyXbiom’sworkbench intothebenchandthebedsideofclinicalpatients.
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BiomarkerdatasupportedcurrentlyinXbiom
OmicsDatacurrentlysupportedinXbiom’sontologies:
OMICS Measurement MeasurementTypes
Assays,Technologies,Methods
DataVendors
ReferenceDictionaries
CurrentBiomarkerCounts
Genomics
GeneticVariation
ShortVariant DNATargetedSequencing,DigitalPCR
FoundationMedicines;SysmexInostics;AppliedBiosystems
NCBIGeneInfo,COSMIC
59670**
(Genes)
CopyNumberAlterationRearrangements
Transcriptomics
Sequencing TargetedRNASequencing
TargetedRNASequencing
Illumina NCBIGeneInfo
Expression GeneExpression;TargetedmRNAexpression
Microarray,Nanostring
AltheaDx;NanoStringTechnologies
NCBIGeneInfo
miRNAexpression qRT-PCR Covance miRBase 2656(miRNA)
*TargetedmRNAdetection
InsituHybridization
Proteomics
Expression ExpressionofProteins
Immunohistochemistry
MosaicLaboratories
UniProt(SwissProt)
20214(Proteins)
Concentration ConcentrationofProteins
Immunoassay,ELISA,MultiplexAssay
MyriadRBM
ImmunecellsPopulation
*CellSorting(ClusterofDifferentiation)Cellsurfaceproteins
Flowcytometry
Metabolomics Concentration ConcentrationofMetabolites
LiquidChromatography-MassSpectrometry
BIOCRATESLifeSciencesAG
HumanMetabolomeDatabase(HMDB)
114100(Metabolit
es)
Note:*indicates‘WorkInProgress’Note:**Genecountof59,670includes:Types ofGene(CurrentTotal
59,670,NCBI) CountsProteincoding 20684NoncodingRNA 18079
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Types ofGene(CurrentTotal59,670,NCBI) Counts
Pseudogenes 16392Unknown 2467Other 866TransferRNA(tRNA) 642SmallnucleolarRNA(snoRNA) 431SmallNuclear(snRNA) 63RibosomalRNA(rRNA) 43SmallConditionalRNA(scRNA) 3
SearchQueryMasksandStratifiedCohortSelectionAkeyfeatureoftheXbiomworkbenchisthequeryscreenswheretheresearcherisprovidedacomprehensivelistofparametertosearchbasedonacompilationofallthedataavailablefromallpatientsandbiomarkerdataavailable.Thequerymasks are separated into convenient segments within which the range ofavailabledata for any selectedparameter is provided to theuser as a prompt.The selections in the query masks can be combined with Boolean logic. Thesearchresultscanbesavedandthesearchcriteriacanbesavedsothatitcanbesharedwithcolleaguesorre-appliedasnewdatabecomesavailabletomonitorongoingstudies.
Figure2
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StudySummaryThis isa simplequerymask to limit the search to specific studies,or candidatedrugmolecule, or study type amongmany attributes associatedwith the TrialSummary.
SubjectHistoryBaselineThisquerymaskallowstheusertosetvariousparametersthatdefinethecohortofinterestincludingtheirage(aslimitedbytheanonymizationrules),sex,heightraceetc.andtheirgenotypeaswellasspecificconditionsintheirmedicalhistory.Allavailablebaselinelabworkpriortotheclinicaltrialisalsocatalogedhereandmaybesetinthesearchcriteria.
ClinicalParametersThis query set up the specific ranges of clinical findings or end-points that theresearcherislookingfor–e.g.ALTbetweentwosetvalues,oracombinationofALT,Bilirubinandotherlabbiomarkersthatdefineacertaintypeofresponseonthepatient.Percentchangesfrompatient’sbaselinewillalsobeavailableinthenextrelease.
MolecularBiomarkersTheuserspecifiesDNA,RNAorProteinsandthelistofavailablemutationsinthedatabase are made available for selection. When I-O (immuno-oncology)biomarkersareaddedthesewillincludethequantitativemagnitudeoramountofexpression.Anumberofscreensshowinghowthesebiomarkersareselectedforsearchareshownbelow.
Figure3
Figure4
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Figure10
Figure11
Figure12
Bio-samplesMostBioTechsorbigPharmaengagedintranslationalresearchordevelopmentofbiologicswilllikelyhaveastrategyformanagingtheclinicaltrialpatient’sbio-samplesthroughabio-bankpartner.Thesesamplescanoftenbere-analyzedtoobtaindatathatwasnotcollectedearlier.Ifthecohortsearchshowsthatsomepotential patients meet all the criteria except that their samples were notscreenedforaspecificproteomic,transcriptomicormetabolomicassaythentheavailability of viable bio-samples is important in discovering the potentialavailabilityofadditionaldata.Thisiswhereretrospectivetranslationalanalysisofclinicaltrialdatacanbepowerful.
SDTMVariablesAlthough Xbiom is a workbench that can be used directly by clinical andbiomarker researchersmostbigPharmacompanieshavea legacy teamofdatamanagers, programmers, and bio-statisticians analyze their clinical data to the
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statisticalanalysisplan.RecentlythatdataisbecomingcodeddigitallyintoCDISCSDTM standards for submission to regulatory agencies. Thismeans that Xbiomusers couldbe the same team thatnormallyworkswith the clinical operationsteams.XbiomprovidesaquerymaskwhereSDTMvariablescanbeusedsothatthese users can make the same queries in terminology with which they arefamiliar.
DatavisualizationandIGOXbiom has two powerful ways for biomarker researchers to access, view andanalyzetheresultsoftheirsearchandfindings.
1. Xbiomincludesabuilt-inIGO(InteractiveGraphicsObject)thatallowstheuser to interactwith the data as inmostmodern data visualization andanalyticsplatformssuchasSpotFire,JMP,Qlik,Tableau,andothers.Thesearepointandclickapplicationswithconsiderableflexibility.
2. Xbiom provides an OData open interface that allows external 3rd PartyApplications (SAS,SpotFire,R, Jreview,andmanyothers) to interactandlettheusertodrivequeriesfromscriptsasiftheywereloggedintoXbiomandstillgetaccesstothesearchandanalyticsresults.Userswill interactfrom applications such as R, SAS, Python using interactive interpretivetoolslikeJupyter.
3. GeneOntology(GO)EnrichmentTheuser specifiesgenesof their interestobtained fromhigh throughput“x”RNAexpressionexperiments(ex:Microarray,Nanostring,RNA-Seq).ByInternallymappinggenetoGOtermsinXbiom,thefunctionalannotationand gene enrichment analysis is performed by Fischer’s Exact Test, todetermine collective biological function which are statistically over-orunder-representedwithinthelistofinterestinggenes.
Figure13
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Figure14
SomeexamplesoftheIGOareincludedhere.
Figure15
Thefollowingchartshowsasanexample:• HowmanyDNAcopiesareamplifiedforeachgene-withinoracross
studies?
• CompareCopyNumberAlterationsacrossgenesandacrosscohorts
©2018PointCross.AllRightsReservedPuttingKnowledgetoWork10PointCross
InteractiveCharts–AdverseEvents
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Figure16
Figure17
©2018PointCross.AllRightsReservedPuttingKnowledgetoWork6PointCross
GroupedBar–CNAAmplification
©2018PointCross.AllRightsReservedPuttingKnowledgetoWork4PointCross
InteractiveCharts–DNAVariants
InteractivechartforDNAvariantsdisplaysincidences/proportionsofdifferenttypesofDNAvariantsinthegivenpopulationofwithinoracrossstudies.Theproportionsofsubjectsarefurtherbrokendownatadifferentlevelofentities-likegene,variantstype,cohort&sex Inthefirstgo,userwillbeabletosurfaceoutwhichgenesarethemostaffectedonesintheheapofreported/examinedgenes
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Figure18
Figure19
GettingXbiombiomarkerresearchworkbenchsetupquicklyandinexpensivelyXbiomisacomprehensiveandpowerfulworkbenchbutitcanbesetupandreadytobeusedwithsecureddataoftheBioTech’s.PointCrosssetsupanXbiominstanceontheAWScloudunderasubscriptionagreement(AWS,Amazonis
©2018PointCross.AllRightsReservedPuttingKnowledgetoWork9PointCross
BoxandWhisker-mRNAExpression
ThisgraphshowsmRNAexpressionlevelsanditschangesacrosscohorts
©2018PointCross.AllRightsReservedPuttingKnowledgetoWork14PointCross
Z-Transform-ByVisitandParameter
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partneredwithPointCross)inamatterofdays.PointCrossalsoprovidesdataservicestocurateandloadclinicalstudyandbiomarkerdataforafeebasedonthenumberofstudiesanddatavolume.SomeofthestepstogettingtheXbiomworkbenchsetup:
1. [email protected]. Ifthereisinterest,enterintoanNDAorconfidentialityagreement.You
willalsobeprovidedastandardpricesheetwiththesubscriptionpricesandthesetupfeesandservicesforloadingyourdata.
3. Getaninventoryofallthestudiesandbiomarkerdataandgetafirmfixedpriceforthedataservices.
4. BeginthesubscriptionserviceforXbiom.5. UsetheVirtualDataRoomtosharethedata.PointCrosswillloadthedata
andcontacttheassignedpointofcontacttogetanyclarificationsneededonthedata.
6. Usertrainingororientationwillbeprovidedtothekeyuserspriortohandover.
7. PointCrosswillperiodicallyreachoutorinvitekeyuserstoitsroadmapspecialinterestgroupsothatyourtechnicalneedscanbeincludedintheproductroadmap.
Adedicatedprivatecloudhostingoron-premiseoptionsareavailableonrequest.WhyshouldBioTechsandBiologicsfocusedcompaniesconsiderXbiom?
1. The last BioTech that signed up with PointCross went live within a dayaftertheysubscribedtotheXbiomSaaSservice
2. Theirclinicaldatawascuratedandingestedandloadedin2weeks3. Itisasingleworkbenchforbiomarkerresearch–one-stoptodata,analysis
andinsights4. Itrespectsconfidentialclinicaldata5. Itintegratestheknowledgeavailableintheglobalregistries6. Discover new or potential biomarkers for companion diagnostics,
predictors,ortherapies7. Improve benefits/risk assessment for finely stratified cohorts
retrospectivelywithinandacrosstrials8. Adaptivetrials
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9. Looking for longitudinal changes in clinical and biomarker end points inpatientsorcohorts
10. Analyzingotherdatacollectedfrompatientsinacohort11. Testinghypothesisofcompaniondiagnosticsretrospectively
SoftwareasaServiceOurXbiomsoftwareasaserviceensuresthatnewcapabilitiesthathelpresearchersareconstantlymadeavailable.Wearealsoconstantlyupdatingthevariousontologiesforthebiomarkerlistsbyreferencingauthoritativeregistries.Alloftheseareavailableallowingtheresearcherstoaccessthelatest.
ContactPointCrossLifeSciencesformoreinformationanddemoAvarietyofresourcesstandreadytohelpgetmoreinformationonXbiom,andhowtogetstarted.VisitourXbiomwebsiteatwww.Xbiom.comVisitthecompanyatwww.pointcross.comEmailusatinsights@pointcross.comtosetupanonlinedemooraon-sitemeeting
PointCrossLifeSciencesInc.1291E.HillsdaleBlvd.,Suite304
FosterCity,CA94404Tel:+1844-382-7257
ChrisMillerforUSbasedcalls+1310-528-4712
HubertGermainforEUbasedcalls+133607694061
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References
1. 21Lessonsforthe21stCentury,YuvalNoahHarari,2018,Spiegel&Grau,ISBN9780525512172
2. WhatareBiomarkers?,KyleStrimbuandJorgeA.Tavel,M.D.,
PMCID:PMC3078627,NIHMSID:NIHMS259967,PMID:20978388,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078627/
3. Biomarkers:TechnologiesandGlobalMarkets,September2018,BIO061E,
BCCResearchStaff
4. BiomarkersMarketbyProduct,Type,DiseaseIndication,Application-GlobalForecastto2021,February2017,BT2120,https://www.marketsandmarkets.com/Market-Reports/biomarkers-advanced-technologies-and-global-market-43.html
5. MakingMeaningfulClinicalUseofBiomarkers,MatthewJ
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