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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Center Core Grants (P30)
Project #
5P30CA076292-22
Application #
9868926
Study Section
Subcommittee I - Transistion to Independence (NCI)
Project Start
Project End
Budget Start
2020-02-01
Budget End
2021-01-31
Support Year
22
Fiscal Year
2020
Total Cost
Indirect Cost
Name
H. Lee Moffitt Cancer Center & Research Institute
Department
Type
DUNS #
139301956
City
Tampa
State
FL
Country
United States
Zip Code
33612
Dai, Juncheng; Li, Zhihua; Amos, Christopher I et al. (2018) Systematic analyses of regulatory variants in DNase I hypersensitive sites identified two novel lung cancer susceptibility loci. Carcinogenesis :
Cherezov, Dmitry; Hawkins, Samuel H; Goldgof, Dmitry B et al. (2018) Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial. Cancer Med 7:6340-6356
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Lin, Hui-Yi; Huang, Po-Yu; Chen, Dung-Tsa et al. (2018) AA9int: SNP interaction pattern search using non-hierarchical additive model set. Bioinformatics 34:4141-4150
Neumeyer, Sonja; Banbury, Barbara L; Arndt, Volker et al. (2018) Mendelian randomisation study of age at menarche and age at menopause and the risk of colorectal cancer. Br J Cancer 118:1639-1647
Hellmann, Matthew D; Callahan, Margaret K; Awad, Mark M et al. (2018) Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer. Cancer Cell 33:853-861.e4
Trabert, Britton; Poole, Elizabeth M; White, Emily et al. (2018) Analgesic Use and Ovarian Cancer Risk: An Analysis in the Ovarian Cancer Cohort Consortium. J Natl Cancer Inst :
Palmer, Amanda M; Brandon, Thomas H (2018) How do electronic cigarettes affect cravings to smoke or vape? Parsing the influences of nicotine and expectancies using the balanced-placebo design. J Consult Clin Psychol 86:486-491
Dougoud-Chauvin, Vérène; Lee, Jae Jin; Santos, Edgardo et al. (2018) Using Big Data in oncology to prospectively impact clinical patient care: A proof of concept study. J Geriatr Oncol 9:665-672

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