Awardee OrganizationH. LEE MOFFITT CANCER CTR & RES INST
Description
Abstract Text
PROJECT SUMMARY
Cancer Biology & Evolution (CBE) is a first-in-kind CCSG Program that emerged from systematic in-house
collaborations of mathematicians, evolutionary biologists, and basic and clinical cancer researchers. Although
these research teams investigate cancer via traditional means, they include mathematicians and theorists who
integrate multi-scalar data through quantitative models founded on evolutionary first principles. Specifically, the
CBE integrates the genocentric focus of conventional cancer research into broader Darwinian dynamics where:
(i) evolution selects for cellular adaptive phenotypes that emerge in complex ways from both mutations and
changes in the expression of normal genes; and (ii) the fitness of each cancer cell is dependent on
environmental context and will vary with temporal and spatial changes in the tumor milieu. Mathematicians play
critical roles in the CBE Program by deconvoluting the nonlinear dynamics that are manifest in complex open
systems such as cancer and by developing and applying mathematical models and computer simulations. The
unique scientific “ecosystem” of the CBE has driven the formation of innovative multidisciplinary teams that are
investigating virtually every aspect of cancer biology and therapy through a quantitative evolutionary lens. The
overall goals of CBE are to investigate and define the complex dynamics that govern the biology and
therapeutic responses of cancer, and to deliver new agents and strategies to prevent and treat refractory or
relapsed malignancies. Specifically, CBE Members: (i) generate and apply sophisticated experimental models
and methods to define and quantify spatial and temporal dynamics of molecular, cellular, and tissue properties
during cancer development, progression, metastasis, and treatment (Aim 1); (ii) develop and test theoretical
models, which are based on evolution by natural selection and are parameterized by experimental data, to
define cancer dynamics and inform new strategies for control and treatment (Aim 2); and (iii) design new
studies and clinical trials that test model predictions, to deliver effective, adaptive therapies into the clinic, and
to refine the understanding of cancer biology and therapy (Aim 3). CBE teams have implemented these goals
through: (i) combining in vivo and in silico models to understand, prevent and treat metastasis; (ii) targeting
never genes, i.e., genes where mutations are never or rarely observed, to produce a durable treatment
response; (iii) exploiting tumor dynamics to “steer” cancers toward a less invasive evolutionary trajectory; (iv)
modeling tumor evolutionary strategies that result in therapy resistance; and (v) mathematical models that
have been translated into adaptive, personalized clinical trials. The CBE Program has 24 members from nine
different academic departments. During the past funding cycle, CBE Members have published 399 cancer-
related articles, with 22% representing intra-programmatic publications and 32% being inter-programmatic
publications. Total annual grant funding for the CBE Program is robust and is currently at $9.1 million; $8.2
million is peer-reviewed, including $6.3 million from NCI.
Public Health Relevance Statement
Data not available.
NIH Spending Category
Bioengineering Cancer
Project Terms
AdoptedBasic ScienceBeliefBiologyCancer BiologyCancer CenterCancer Center Support GrantClinicClinicalClinical DataClinical ResearchClinical TrialsCollaborationsComplexComputer SimulationComputing MethodologiesDataData AnalysesDevelopmentDiseaseEcosystemEvolutionExperimental ModelsFundingGene MutationGenesGeneticGoalsGrantInvestigationMaintenanceMalignant NeoplasmsMathematicsMeasurementMethodsModelingMolecularMutationNCI Center for Cancer ResearchNatural SelectionsNeoplasm MetastasisNew AgentsNonlinear DynamicsPatientsPeer ReviewPhenotypePlayPopulationPrevention strategyPropertyPublicationsPublishingRefractoryRegulationRelapseResearchResearch PersonnelResistanceRoleScienceStrategic PlanningSystemTestingTheoretical modelTissuesTranslatinganticancer researchbasecancer cellcancer therapycomputer sciencedesigndrug discoverydynamic systemfitnessin vivoinnovationinterdisciplinary approachlensmathematical methodsmathematical modelmembermolecular dynamicsmolecular oncologymultidisciplinaryoptimal treatmentsphysical sciencepredicting responsepredictive modelingpreventprogramsrecruitresponsetreatment responsetreatment strategytumortumor progressionvirtual
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Publications
Publications are associated with projects, but cannot be identified with any particular year of the project or fiscal year of funding. This is due to the continuous and cumulative nature of knowledge generation across the life of a project and the sometimes long and variable publishing timeline. Similarly, for multi-component projects, publications are associated with the parent core project and not with individual sub-projects.
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