Predictive Modeling of Drug Sensitivity

340 Pages·2016·13.834 MB·English

by  Ranadip Pal (Auth.)

Predictive Modeling of
Drug Sensitivity
Predictive Modeling of
Drug Sensitivity
Ranadip Pal
TexasTechUniversity,Lubbock,TX,UnitedStates
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Preface
Inrecentyears,thestudyofthepredictivemodelingofdrugsensitivitieshasreceived
aboostduetotheever-growinginterestinprecisionmedicineandtheavailabilityof
large-scale pharmacogenomics datasets. However, predictive modeling in this area
is confronted with significant challenges, due to the high dimensionality of feature
setsandthepresenceoflimitedsamplescombinedwiththecomplexitiesofgenetic
diseases that includes nonlinearities and variability among individual patients. Re-
searchershavetriedtotackletheseissuesbyrefiningmodelinferenceforcommonly
used predictive models and designing new models specific to drug sensitivity
prediction,alongwithincorporatingadditionaldatasources,suchashigh-throughput
datafromDNA,epigenomic,transcriptomic,proteomic,andmetabolomiclevels.It
has been observed that the accuracy and precision of a drug sensitivity predictive
model is often exceedingly dependent on the approaches considered for data
processing,featureselection,parameterinference,andmodelerrorestimationrather
thanthespecifictypeofmodelused.Thus,aproperunderstandingoftheunderlying
biologyalongsidetheissuesrelatedtogenomicdatameasurementtechniques,feature
extraction,accuracyestimation,andmodelselectionishighlyrelevantfordesigning
drug sensitivity predictive models that can have an actual impact on personalized
medicine.
This book is an attempt to cover the basic principles underlying the predictive
modeling of drug efficacy for genetic diseases, especially cancer. A significant
portion of the book is based on collaborative research carried out in my laboratory
for last 6 years, including a top performance in an NCI-DREAM drug sensitivity
predictionchallenge,andthedesignofanovelframeworktointegratefunctionaland
genomicinformationinpredictivemodeling.Thebookisstructuredasfollows:the
introductory chapter discusses the current personalized medicine landscape, along
withfuturetrends.Areviewofmolecularbiologyandpharmacologyconcepts,along
with data characterization methodologies, are discussed in Chapter 2. Chapter 3
discusses techniques and issues related to extracting relevant information from
availabledatasources.ModelvalidationtechniquesareconsideredinChapter4,and
are followed by a description of tumor growth models in Chapter 5. The overview
of predictive modeling techniques based on genomic characterizations is presented
in Chapter 6, and is followed by the specific model types of Random Forests and
Multivariate Random Forests in Chapters 7 and 8, respectively. Modeling based on
integrated functional and genomic characterizations are considered in Chapters 9
and 10. Both the model-based and model-free designs of combination therapy are
considered in Chapter 11. Chapter 12 provides a compendium of available online
resources that are relevant to drug sensitivity prediction. The book concludes with
a chapter on the challenges that need to be addressed before the full potential of
genome-basedpersonalizedcancertherapyisachieved.
I hope that the book provides an integrated resource to help understand the
current state-of-the-art in drug sensitivity predictive modeling. This book will
xi
xii Preface
likelybenefit studentsandresearchers whoareinterestedinapplying mathematical
and computational tools to analyze genomic and functional data for personalized
therapeutics.
I would like to acknowledge the various individuals and organizations whose
support and collaborations were instrumental in the development of the multiple
ideas presented in this book. First of all, I would like to thank my collaborator of
many years Dr. Charles Keller who has provided excellent biological insights on
multiple aspects of personalized medicine, and is the person who motivated me to
workinthisarea.Iwouldalsoliketothankmembersofmyresearchgroup,especially
Dr.NoahBerlow,Dr.SaadHaider,Mr.QianWan,Mr.RaziurRahman,Dr.Mehmet
UmutCaglar,Mr.KevinMatlock,andMr.CarlosDe-Nizwhohavediligentlyworked
inthisresearchtopicandhavebeenco-authorsonmultiplepublicationsreferredto
inthisbook.Finally,IwouldliketoacknowledgetheNationalScienceFoundation
andtheNationalCancerInstitutefortheirsupportofthisresearch.
RanadipPal
September,2016
CHAPTER
1
Introduction
CHAPTER OUTLINE
1.1 CancerStatistics............................................................................... 3
1.2 PromiseofTargetedTherapies.............................................................. 4
1.3 MarketTrends.................................................................................. 6
1.3.1 BiomarkerTesting............................................................... 6
1.3.2 PharmaceuticalSolutions...................................................... 7
1.3.3 Value-DrivenOutcomes ........................................................ 7
1.4 RoadblockstoSuccess....................................................................... 8
1.4.1 LinkingPatient-SpecificTraitstoEfficaciousTherapy.................... 8
1.4.2 HighCostsofTargetedTherapies............................................. 8
1.4.3 ResistancetoTherapies........................................................ 9
1.4.4 PersonalizedCombinationTherapyClinicalTrials ......................... 9
1.5 OverviewofResearchDirections............................................................ 10
References............................................................................................ 12
Personalizedmedicinereferstotherapytailoredtoanindividualpatientratherthana
one-size-fits-all approach designed for an average patient. The idea of personalized
medicine has been in existence for more than 2400 years with the notable example
of 5th century BC Greek physician Hippocrates who treated patients based on
their humor imbalances [1]. The balancing of humors or bodily fluids as the
basis of medical practice remained as the guiding principle in medicine for about
two millennia [1]. The personalized treatments were based on the convictions of
the era, that is, humors which later on was discovered to be incorrect based on
advanced understanding of human anatomy and physiology. The personalization
therapyoptionsforthe20thcenturywereprimarilybasedontheresultsofdifferent
testsonbodilyfluids,alongwithadvancedvisualizationapproachessuchasX-rays
ormagneticresonanceimagingscans.
Theadvent ofgenomic characterization ofindividual patientsinthelastdecade
of the 20th century opened up numerous possibilities for personalized therapy.
PredictiveModelingofDrugSensitivity.http://dx.doi.org/10.1016/B978-0-12-805274-7.00001-4 1
Copyright©2017ElsevierInc.Allrightsreserved.
2 CHAPTER 1 Introduction
Thegenomiccharacterizations deliver considerably moredetailedinformationona
patientwithageneticdiseaseascomparedtophenotypicobservationsornonmolec-
ulartests.AccordingtotheUSFoodandDrugAdministration(FDA),Personalized
Medicine (or Precision Medicine) in the current era entails “disease prevention
and treatment that takes into account differences in people’s genes, environments
and lifestyles.” The definition of personalized medicine provided by the National
Cancer Institute (NCI) involves “A form of medicine that uses information about a
person’s genes, proteins, and environment to prevent, diagnose, and treat disease.”
The Precision Medicine initiative launched by President Obama in 2015 considers
theuseofpatients’uniquecharacteristics,includinggenomesequence,microbiome
composition, health history, lifestyle, and diet to tailor treatment and prevention
strategiesbyhealthcareproviders.TheUSNationalAcademyofScienceshasdefined
personalized medicine as “the use of genomic, epigenomic, exposure and other
data to define individual patterns of disease, potentially leading to better individual
treatment.”
Anunderlyingthemeofpersonalizedmedicinecurrentlyistheincorporationofan
individual’sgenomicinformationwithothercharacteristicsintherapeuticdecisions.
Thecurrentbroaderchallengesinthisareainvolve[1]:
(C1) Detectingmeaningfuldifferencesinthegeneticcharacterizationsofindividual
patientsanddecipheringhowthataffectsresponsestodifferenttherapies.
(C2) Incorporationofthisknowledgeandmethodologiesinclinicalpractice.
(C3) Incorporationofthisrefinedpatient-specificapproachintohealthcareand
regulatorysystemsthatarebuiltaroundoldermedicalparadigms.
This book primarily considers advances in the broader challenge area C1 with
characterizationsoftumorculturesorcelllinesoftenbeingusedinplaceofpatient
tumorcharacterizations.Weconsiderapproachesforpredictingtumorsensitivityto
a drug or drug combination based on genomic characterizations and available drug
information.Thebookisstructuredasfollows.Chapter2providesabriefoverview
of molecular biology followed by various genomic and functional characterization
approachesalongwithareviewofpharmacologyconcepts.Chapter3discussesvar-
iousfeatureselectionandextractiontechniquesforunearthingrelevantinformation
from the available genomic and functional characterizations. Chapter 4 examines
various methodologies for validating a designed drug sensitivity prediction model.
DescriptionsoftumorgrowthmodelsareconsideredinChapter5.Theoverviewof
drugsensitivitypredictivemodelingtechniquesbasedongenomiccharacterizations
are discussed in Chapter 6, and details on the specific approach of random forest
modeling is presented in Chapter 7. Chapter 8 considers the extension of the
random forest framework to multivariate random forests (MRF) that incorporates
the correlations among various drug responses in modeling. Chapter 9 considers
analternativestrategyfordrugsensitivityprediction,termedtargetinhibitionmaps,
basedonfunctionalandgeneticcharacterizations.Chapter10considerstheproblem
ofthedesigningdynamicnetworksbasedonfunctional(drugperturbation)data.The
problemofdesignofcombinationtherapybasedonpredictivemodelsisconsidered
in Chapter 11. Chapter 12 presents a compilation of various online resources
1.1 Cancer statistics 3
relevanttodrugsensitivitymodeling.Thefinalchapterofthebookconcludeswitha
discussiononthevariouschallengesthatneedtobeaddressedbeforeachievingthe
fullpotentialofgenome-basedpersonalizedcancertherapy.
1.1 CANCER STATISTICS
The application of the predictive modeling techniques discussed in this book are
primarilyforthegeneticdiseaseofcancerandthusitishighlyrelevanttodiscussthe
extentofthediseaseandwherewecurrentlystandintermsoftacklingthedisorder.
Inasimplifiedsense,cancerconsistsofagroupofdiseasesaffectingvariousorgans
inthebodythatinvolvesabnormalcellgrowthandiscausedbygeneticalterations.In
morebiologicalterms,cancercanbeexplainedbythehallmarksofcancercomprised
of biological capabilities acquired during the multistep development of human
tumors [2]. The hallmark capabilities include (a) sustaining proliferative signaling,
(b) evading growth suppressors, (c) resisting cell death, (d) enabling replicative
immortality, (e) inducing angiogenesis, (f) activating invasion and metastasis with
potentialadditionalhallmarksof(g)reprogrammingofenergymetabolism,and(h)
evadingimmunedestruction[3].Accordingtocancer.gov,anestimated1.68million
people in the United States will develop some form of cancer in the year 2016 and
thisnumberispredictedtoincreaseincomingyears.Themortalityratefromcancer
has decreased relative to earlier decades, but it is nevertheless high with a rate of
171.2 per 100,000 men and women per year. There are more than a hundred types
of cancer and the most common forms of cancer in 2016 in terms of organ sites
areprojectedtobebreastcancer,lungandbronchuscancer,prostatecancer,colon
andrectumcancer,bladdercancer,melanomaoftheskin,non-Hodgkinlymphoma,
thyroid cancer, kidney and renal pelvis cancer, leukemia, endometrial cancer, and
pancreatic cancer. Cancers can vary widely in their aggressiveness and expected
clinicaloutcomes.Somecancerssuchasglioblastoma[4]areuniformlyfatal,while
somesuchasprostrate[5]oftendonotkillthepatientbeforetheydieofsomeother
cause. It is estimated that around 39.6% of men and women in the United States
will be diagnosed with cancer at some point during their lifetime. Expenditures
on cancer care in the United States reached around $125 billion in 2010 and are
expected to reach $156 billion in 2020. Cancer is also a leading cause of death
worldwide, with 8.2 million deaths attributed to this disease in 2012. The number
ofnewcancercasesworldwideisexpectedtoriseto22millionwithinthenexttwo
decades.
The ubiquitous nature of cancer intensifies our efforts to understand the causes,
prevention,diagnosis,andeffectivetreatmentforthisdisease.Researcheffortsinthe
lastfewdecadeshavebeenabletoprovideanimprovedunderstandingoftheorigins
of cancer and development of new approaches for cancer therapy, but we have still
torealizethefullpotentialofpersonalizedtherapyintheclinic.Multiplechallenges
stillexistbothintheresearchandimplementationdomainsthatneedtobeaddressed
tosignificantlylowerthecancermortalityrateandprovideagoodqualityoflifeto
cancersurvivors.
4 CHAPTER 1 Introduction
1.2 PROMISE OF TARGETED THERAPIES
Treatment of cancer has been approached in various ways and the most commonly
applied techniques are chemotherapy and radiation therapy. A persistent issue
with chemotherapy and radiation therapy is the undesirable side effects that can
significantly reduce the quality of life for the survivor. Furthermore, even with the
success of new radiation therapy and chemotherapy methods in extending the life
spanofcancerpatients,thedeathrateattributedtosolidtumorcancersisstaggeringly
highatover450,000peopleintheUnitedStatesalone[6].Thisstaggeringnumber
indicatesasignificantcohortofcancerpatientsessentiallyfailingfirst-andsecond-
linecancertherapies.Manyexpertssuggestthatpersonalizedmedicineistheanswer
forpatientswhohavefailedfirst-andsecond-linetherapies.Themonikerofperson-
alized medicine is largely being defined by the rapidly emerging market of subtler
therapies being led by new diagnostic biomarkers that lead to actionable therapies.
Typically, biomarker specified drugs are used as a complementary treatment with
radiation therapy and chemotherapy. Although, the general idea of personalized
therapies as adjuvants is occasionally challenged. An example of this new view
of cancer diagnostics/treatment methodology is Gleevec. While the biomarker for
chronicmyelogeneousleukemia(CML),theso-calledPhiladelphiachromosomeand
its resulting aberrant tyrosine kinase activity indicates the need to block a singular
pathway is seen as an exceptional case, nonetheless Gleevec treatment has entirely
replaced morestandard treatmentssuchasbonemorrow transplantsandaggressive
chemotherapy.ThisuniquenessoftheCMLbiomarker/treatmentcombinationisdue
tothefactthatunlikemostothercancers,whicharecausedbyamultitudeofcomplex
interactinggeneticandenvironmentalfactorsandthereforehavemanytargets,CML
is caused by a single aberrant protein related to a consistent chromosomal translo-
cation [7]. The Gleevec success does point to the potential success of biomarker-
basedtreatment.SomeothercancertargetedtherapiesthathavebeenFDAapproved
forpatientswithspecificgeneticcharacteristicsinclude(a)Trastuzumab:Approved
for breast cancer that is HER2+; (b) Crizotinib: Approved for nonsmall cell lung
carcinoma patients with a chromosomal rearrangement causing a fusion of EML4
and ALK genes; (c) Vemurafenib: Approved for late-stage melanoma patients with
V600EBRAFmutation.Fig.1.1showstheeffectofVemurafenibtherapyintheform
offluorodeoxy-glucose(FDG)uptakeonamelanomapatientwithBRAFmutation.
FDGisoftenusedtostudytumormetabolismandreductioninitsuptakeisusually
followedbytumorregression.(d)Dabrafenib:Approvedformelanomapatientswith
V600EBRAFmutation;(e)Trametinib:ApprovedforpatientswithV600Emutated
metastaticmelanoma.
Note that the previous list includes few examples, is in no way exhaustive,
and there are numerous other targeted therapies that have been approved or in the
pipelineforapproval[8].Someofthetargetedtherapieshavebeenapprovedbasedon
cancerstageandprevioustreatmentresponseratherthanspecificgeneticmutations
in the patient, such as the use of kinase inhibitor Sorafenib for treatment of locally
recurrentormetastatic,progressivedifferentiatedthyroidcarcinoma(DTC)resistant
1.2 Promise of targeted therapies 5
(A) (B)
FIG.1.1
PETscanimagesofaBRAF-mutantmelanomapatientshowingFDGuptakebeforeand
aftertherapywithVemurafenib.(A)Beforetherapy.(B)Aftertherapy.
(ReprintedwithpermissionfromMacmillanPublishersLtd:Nature467(7315):596–599.
doi:10.1038/nature09454,copyright2010.)
toradioactiveiodinetreatment[9].Certaintargetedtherapieshavebeenapprovedas
part of a combination therapy, for instance Lapatinib has been approved for breast
cancerpatientsincombinationwithCapecitabine.
Theuseoftargetedtherapieshasalsobeenconsideredforothergeneticdisorders
besides cancer. For instance, the drug Kalydeco (also known as ivacaftor) is used
to treat cystic fibrosis patients who are shown to have a specific genetic mutation
knownasG551Dmutation[10].TheG551DmutationaltersCFTRproteinactivity
byreplacingtheaminoacidglycine(G)inposition551withasparticacid(D).The
targeteddrughelpstorestoretheproteinactivitybybindingtothetransportchannels
andallowingproperflowofsaltandwateronthesurfaceofthelungs.
The previous examples provide cases of approved targeted therapies that have
achieveddecentmeasuresofsuccessintreatingspecificdiseaseconditions.However,
significantly more can be achieved by understanding the drivers for each patient’s
tumor and catering therapies to meet their individual needs. Since each patient
might require targeting a distinct set of proteins, the personalized approach will