Systems Biology And Mathematical Oncology At The National .

Systems Biology andMathematical Oncology at theNational Cancer InstituteShannon Hughes, Ph.D.Division of Cancer Biology240-276-6180shannon.hughes@nih.govApril 28, 2017

The National Institutes of HealthOffice of theDirectorANIANIDCDNIAIDNIAMSNIDCRNIGMSFrancis Collins, M.D., Ph.D.NHGRINIEHSNEININDSNINRNINRNCMHDCCSource: http://www.nih.govCIT

NIH Budget Allocation by Institute/CenterFY2016 31BillionNCI16%NIAID15%NHLBI10%NIGMS8%Source- NIH Reporter

The NCI supports a full spectrum of cancer tPopulationScience4

Organization of the National Cancer InstituteNational Cancer Institute(Total Budget FY2014: 4.9 Billion)ResearchManagement Support( 8%)Intramural ResearchExtramural Programs( 17%)( 75%)Center for CancerResearch ( 401 M)Office of the Director( 1.1 B)Division of ExtramuralActivities ( 22 M)Division of CancerEpidemiology andGenetics ( 90 M)Division of CancerBiology ( 711 M)Division of CancerPrevention ( 320 M)Division of CancerTreatment andDiagnosis ( 1,044 M)Division of CancerControl and PopulationSciences ( 442 M)NCI-Frederick( 297 M)Source: http://fundedresearch.cancer.gov/nciportfolio/

NCI, Division of Cancer BiologyOur Mission: To ensure continuity and stability in basic cancer research whileencouraging and facilitating the emergence of new ideas, concepts,technologies and possibilities through a broad portfolio of Investigatorinitiated research and specialized NCI iologicalSystemsEnvironmentGenomicAlterations6

Cancer as a disease systemGraphics adapted from Wikipedia; Massague & Obenauf Nature 20167

Cancer systems biology and mathematical oncology providean integrative approachFigure from systemsbiology.org8

The CSBC is a community of systems biologists who aim to integrateexperimental biology and computational models across multipletemporal and spatial scales towards a better understanding of cancer.In the CSBC we define systems biology as the explicit integration ofexperimental biology and computational or mathematical modelingto build, test and/or validate hypotheses or ideas.9

CSBC Scientific Areas of Interest Dynamic, predictive models that provide a robust and actionable understanding of the effect ofmultiple biological interactions and/or incorporate multi-scale, spatial analysis over varyingresolution scales to describe cancer initiation, progression and metastasis. Models of networks and signal transduction pathways capable of predicting phenotypes in cancer,including but not limited to biochemical, statistical, graphical, logic, and relational modelingtechniques. Phenotypes might be predicted at the molecular, cellular, tissue or organ level. Predicting and validating critical genetic and epigenetic changes in the initiation and progression ofcancer. Modeling the molecular and cellular communication within and across cells of the tumor eco-system,including but not limited to the tumor micro-environment and the immune system. Integration of data obtained through new imaging modalities, such as super-resolution microscopyand cryo-electron microscopy (cryo-EM), into systems biology modeling frameworks to predict tumorphenotypes on multiple spatial scales.RFA-CA-15-01410

CSBC Scientific Areas of Interest (cont.) Prediction and validation of early disease indicators through systematic modeling of genetic factorsand other high-risk disease phenotypes. Development of modeling techniques that span the scale between basic cellular mechanism andpatient/population-level response or phenotype. In silico modeling to predict effective treatment. This includes predicting tumors most likely to benefitfrom a given treatment; converting transient responses into durable responses; and identifyingrational combinations to address the emergence of resistance in future clinical trials. Systems analysis of cancer completed in endogenous settings (in vivo or ex vivo), with considerationof the tumor microenvironment, tumor heterogeneity, and tumor plasticity.RFA-CA-15-01411

Research Themes and Systems Biology Approaches – CSBC U54sLungProstateBreastColonClinical samplecollectionEcological ModelsMachine LearningODE/PDE ty/EvolutionDrug KCCColumbiaU54U54Head and NeckMelanomaGlioblastomaALLNovel single-cellmeasurementNetwork InferenceImage AnalysisEvolutionary Theory12

Summary of Research Themes and Systems Biology eneity/EvolutionDrug KCCU54ColumbiaU5413

Summary of Research Themes and Systems Biology eneity/EvolutionDrug KCCU54ColumbiaU5414

Physical Sciences-Oncology Network (PS-ON)Overarching GoalsGOALS: support and nurture transdisciplinary environments and research integratingthe perspectives of physical scientists (e.g., engineers, chemists, computerscientists, mathematicians, physicists) and cancer researchers to addressfundamental questions in cancer biology using approaches and theories from thephysical sciences.Research Originate and test novel, non-traditional physical sciences-based approaches tounderstanding and controlling cancer Generate orthogonal sets of physical measurements and integrate them with existingknowledge of cancer Develop and evaluate theoretical physics approaches to provide a comprehensive, dynamicpicture of cancerEducation & Outreach Coordinate education, training, career development and scientific outreach to support andpromote Physical Sciences in Oncology via patient advocates and academic educators15

PS-ON Research Crosses Multiple Length- and Time-ScalesAcross Length ScalesSuggested PS-ON Scientific Themes The Physical Dynamics of Cancer102 mPopulationsPhysical properties such as mechanical cues, transportphenomena, bioelectric signals, and thermal fluctuations canmodulate the behavior of cancer cells, the microenvironment, tumors,and the host and may regulate the initiation and progression of cancer.10-0mOrganisms Spatio-Temporal Organization in eculesAppropriate spatial and temporal organization of structures acrossmany biological and physical length-scales (e.g., subcellular, cell,tissue, organ, whole organism) and time scales is required formanaging the transfer of information that is critical for oss TimeMulti-scale computationalmodelsNovel technologies

Opportunities for Collaboration with the NCI PS-ON:U54 Centers Annual Pilot Project SolicitationsU54 Centers (PS-OC; hyperlink to Center websiteand 2017 application receipt window)Columbia (April-June)Cornell (June-July)Dana Farber (April-May)Hopkins (April-May)Methodist (April)Minnesota (Feb)MIT (July-Aug)Moffitt (March-April)Northwestern (Feb-March)Upenn (Jan-Feb)U24 CoordinatingCenterSage BionetworksU01 Projects (PS-OP)PS-ON Phase II10 U54 Centers7 U01 Projects 50 institutions 200 investigators 30 traineesand growing . . .BerkeleyGeorgia TechHarvardMichiganMITUtahVanderbiltU01 FOA: PAR-15-02117

www.synapse.org/CSBCPSON18

Association of Early Career Cancer Systems Biologists (AECCSB)Systems Approaches to Cancer BiologyCo-sponsored by the AECCSB & NCI: April 3-6 2016www.SACBmeeting.orgNEXT MEETING: NOVEMBER 7-10, 2018

Goal: Tackle challenges in cancermetastasis by employing technologiesand approaches across NCI-supportedprogramsFormat: Open call for applications25 participants chosen by scientificMentors and workshop staffVirtual pre-workshop activitiesInvestigator-initiated projectsSmall pilot project funds available at theconclusion of the workshopJune 12-14, 2017: Seattle, WA20

Goals of the Funding Opportunity Announcement (FOA)Emerging Questions in Cancer Systems Biology (U01)There are several highlighted areas of interest within the FOA. Note that the list is noninclusive and is not meant to restrict the scope of investigator-initiated research topics. Dynamics of cell-cell interactionsIntegration of information across temporal and spatial scalesTumor behaviors reflecting single cell characteristicsSystems-level analyses of the role of the microbiome in cancerThe combination of systems and synthetic biology for understanding disease mechanismsHierarchical models of cancerSystems biology aided clinical trial designPlease see Part 2, Section I Funding Opportunity Description for further details.PAR-16-13121

Goals of the Funding Opportunity Announcement (FOA)Emerging Questions in Cancer Systems BiologyIn addition to addressing specific biological hypotheses, the continued success of cancersystems biology depends on the development of new methodologies to address complexand multivariate questions, including new theoretical, mathematical and computationaltechniques, multi-scale modeling approaches capable of integrating across scales fromthe molecular to the population level, and new biological tools and systems for informingand testing cancer systems biology generated hypotheses.PAR-16-13122

Key Dates for PAR-16-131PreApplicationWebinarLetter of IntentDue DatesApplicationDue DatesReview DatesEarliestAnticipatedStart DatesRound1Apr 27, 2016May 24, 2016June 24,2016Oct/Nov 2016Apr 2017Round2Sep, 2016Oct 18, 2016Nov 18, 2016Mar/Apr 2017Aug 2017Round3May 8, 2017May 23, 2017June 23,2017Oct/Nov 2017Apr 2018Round4TBD, est Aug 2017Oct 24, 2017Nov 24, 2017Mar/Apr 2018Aug 2018Round5TBD, est Feb 2017May 22, 2018June 22,2018Oct/Nov 2018Apr 2019Round6TBD, est Aug 2017Oct 23, 2018Nov 23, 2018Mar/Apr 2019Aug 2019PAR-16-13123

F30 and F31: NRSA for Predoctoral FellowsObjective:To provide support for trainees’ research training componentin a MD/PhD program (F30) or PhD program (F31)Candidate:Applicants must be enrolled in a MD/PhD program within thefirst 48 months of enrollment (F30), or a PhD program (F31).US citizenship or green cardMentor:Principal investigator(s) with R01 or R01-like funding who canprovide mentorship in both research and career development.Award:For F30: 22,920 /yr; Tuition and fees: up to 21,000 /yr:Training Related Expenses: 4,200/yr; Up to 6 years.For F31: 22,920 /yr; Tuition and fees: up to 16,000 /yr;Training Related Expenses: 4,200/yr; Up to 5 years24

F30 Applications, Awards and Success RatesAwardsSuccess 54010205002013-F302014-F302015-F30Success RateApplications / AwardsApplications2016-F30Fiscal Year - Mechanism25

F31 Applications, Awards and Success RatesAwardsSuccess 0252013-F312014-F312015-F31Success RateApplications / AwardsApplications2016-F31Fiscal Year - Mechanism26

A Pilot Program:the F99/K00 Predoc-to-Postdoc Transition Award There is a need to attract the very best graduate students to commit to aresearch career as independent researchers Highlighting a pathway to success may be an effective approach intrying to address this need The experiment: developing a new funding mechanism that would:(1) empower the selected students in securing the most desirablepostdoc positions(2) place them at strong positions to compete for the K99/R00 awardfor transitioning to independent les/PA-16-193.html27

Unique Features of the F99/K00 Award Dual phase funding: support for up to 2 years for 3rd/4th-year studentsto complete graduate study, and 4 years for postdoctoral training incancer research preferably at a different institution3rd/4th yr gradstudentsF99 PredocK00 PostdocK99 Postdoc Institutional Nomination of Applicants: one nomination per institutioneach year to strengthen institutional input in the selection process Open to the best international students, removing one NRSArestriction considered outdated by the research community The K00 fellows, from the first day as postdocs, will be employees withfamily and retirement benefits A-16-193.html28

F99/K00 Year 1 Portfolio AnalysisApplicationsAwardsSuccessRate 3155Top 150 NCI Institutions62335329

F32: NRSA for Postdoctoral FellowsObjective:To support research training for postdoctoral applicantswho have the potential to become productiveindependent research investigatorsCandidate: Postdoctoral Fellows (Finishing PhD/Early post-doc,yrs1-3). US citizenship or green cardMentor:Principal investigator(s) with R01 or R01-like fundingwho can provide mentorship in both research and careerdevelopment.Award:Up to three years’ stipend at 42,840 - 56,375/yr;Tuition and fees: up to 16,000 /yr; Training RelatedExpenses: up to 8,850/yr; Full-time effort30