Research Plan:I am currently finishing my PGY-3 year of clinical training in Radiation Oncology. During the past 8 months ...
Phase I: Intravasation - Studying intravasation in vitro or in vivo is exceptionally difficult, which means thatthis phase...
and comparing two separate mathematical models, based on the same principles but with very differentlevels of complexity a...
clinical trial studying the value of CTC measurements in the staging of Hepatocellular Carcinoma (HCC). Thistrial endeavor...
Phase III: Extravasation and Colony Formation - This phase of research will be in the form of experimentsderived from theo...
oxygen availability). Many of these factors, along with the molecular properties of the passenger mutations,may be exploit...
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My successful LRP application 2011

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LRP research plan final

  1. 1. Research Plan:I am currently finishing my PGY-3 year of clinical training in Radiation Oncology. During the past 8 months Ihave spent my research time involved with two groups at Moffitt Cancer Center: one is the NCI’s newly createdPhysical Sciences in Oncology Center (PS-OC) and the other is Moffitt’s Integrative Mathematical Oncology(IMO) group. With these two groups I have found an intellectual home and a place where my unique skill setand manner of thinking are of real value. I have had enormous success in the short time I have been involvedwith these groups: I have won a poster award, an ASCO merit award and a 1 year, $200,000 grant from theNCI. Further, I have submitted an original manuscript, have two more in preparation, and have an R21pending. This work has all been outside of my clinical training, which has continued unabated to this point. Ihave maintained an excellent standing in my clinical residency program while doing this extra work, and havecontinued to be involved in the expected load of clinical research as well - publishing two clinical manuscripts1,2last year and leading a multi-institutional, international collaboration studying glioblastoma in the elderly.Burning the candle at both ends, as it were, has allowed me to accomplish quite a bit, but what I have not beenable to do is fully devote myself to my research training.My role in the lab to date has been innovator, connector, facilitator and translator. To become the independentphysician-scientist that I envision, I must take dedicated time to further hone my thinking and research skills.To do this, I need dedicated time for research. I propose to take a three year hiatus from my clinical residencytraining to do just this. During the three years I will continue my employment at Moffitt Cancer Center, but willbe a Research Associate instead of a clinical resident. I will also be enrolled in Oxford University during thistime and officially be reading for a doctorate in mathematics. While I will physically be in the United Statesduring the majority of this time, I will be co-supervised by faculty at Moffitt and Oxford. My research during thistime, and therefore my doctoral thesis, will be focused on building and validating mathematical models of themetastatic process from primary tumor intravasation to the circulatory phase of metastasis to extravasation andcolony formation.I have chosen to study metastasis because it is currently one of the biggestoutstanding questions in cancer. While metastatic disease accounts for thelion’s share of cancer death, it is by far the least understood and mostpoorly characterized phase of cancer progression. Our efforts to date tostudy this process have been limited by our technology and, once thisprocess has begun our therapies for these patients are minimally effective.In most cases, while the primary tumor and nodal disease have richstratification by which we understand patient prognosis, metastatic diseaseis a binary event, either M0 or M1. This lack of ability to stratify metastaticdisease has not, in the past, mattered overmuch as the definitivetherapeutic options for patients with M1 disease were limited to systemicagents. Recently, however, we have made advances in our ability to treatoligometastatic disease with minimally or non-invasive local therapies, andwe have had suggestions that this actually can, in select patients, influenceoverall survival3. A greater understanding of metastasis will allow us tooptimize current therapies and develop new treatment strategies for thisenigmatic process.I plan to study this complex process in three discrete, but connected, partsand will describe the research plan as such. Phase II is the subject of arecently submitted R21 (1R21CA160127-01) and Phase III is the subject ofa funded NCI PS-OC trans-network grant (1U54CA143970-01).Jacob G. Scott •!H. Lee Moffitt Cancer Center and Research Institute • Evolution of Metastasis • 15 Nov 2010Figure 1: There are three putativeroutes of intravasation - A. leaky,cooptable endothelium4, B. chimericvasculature3 and C. motile, invasivetumor cells1.
  2. 2. Phase I: Intravasation - Studying intravasation in vitro or in vivo is exceptionally difficult, which means thatthis phase of metastasis has been largely understudied. We intend to begin attacking this phase of metastasisbeginning from first principals and building an in silico model of this enigmatic process. We hope to eventuallybe able to validate our results using technologies evolved from the dorsal wound chamber system possessedby our lab.Overview:To begin the process of hematogenous metastasis, a primary tumor must gain access to and begin to shedtumor cells into the vascular system. How this process happens is the subject of much debate and uncertainty.The subject itself is exceedingly difficult to study in vivo and in vitro but does lend itself well to mathematicalmodels. The proposed methods of intravasation range from a) cells moving through holes in leaky tumorvessels to b) cancer cells evolving to mimic vessel walls an shedding their progeny into the bloodstream to c)actively migrating into the bloodstream4. Each of these processes has been observed under controlledexperimental conditions5,6,7, but the process has never been defined rigorously or mechanistically. In thisphase of my project I propose to build a hybrid discrete-continuous cellular automaton model of a primarytumor including active local vasculature. The first iterations of this model will include vasculature as onlymetastatic entrypoints and will be built on work already done in my laboratory8 but this will evolve to include theeffect of putative cancer cell chemotactic agents such as EGF and multicellular, multiphenotype, evolvableendothelial cells in addition to the metastatic entrypoint of the vasculature. There is extensive modelingexperience available within the IMO, on angiogenesis9, cancer initiation8 and progression10 using these hybridcellular automata approaches, that I plan to fully utilize for this proposal.My role in this project:I will be involved approximately 20% of my time in the next two years developing this model of intravasation. Iwill be directly involved in driving model development and in the interpretation of the results. Determining thephenotypic characteristics of tumor cells that do and do not successfully intravasate and at what time points inthe primary tumor’s life cycle will be a major step forward to understanding this initiating event in metastasis.Further, this information can inform the next step of the project, as those cells which intravasate serve as thesource for the cells entering into the circulatory phase of metastasis.Phase II: Circulatory Phase - Little is known about the life history of circulating tumor cells (CTCs) which areputatively the mechanistic agent driving hematogenous metastasis. We have recently submitted an R21(1R21CA160127-01) on this portion of the project for which I am a co-investigator. The following descriptionincludes portions of the R21 grant proposal, which I co-authored.Overview:In patients with advanced primary cancer, circulating tumor cells (CTCs) can often be found throughout theentire vascular system11. When and where these CTCs create metastasis is not fully understood and iscurrently the subject of intense biological study. Pagets well-known seed-soil hypothesis12 suggests that inorder to understand the spread of metastases, the soil (the site of a metastasis) is as important as the seed(the metastatic cells). Whilst there are some primary tumors that metastasize to organs in a pattern that makessense from a flow standpoint, there are many others that appear not to follow this deterministic pattern - forexample, soft tissue sarcoma preferentially metastasizes to the lung which is the next stop by flow whileprostate cancer almost always skips the lung and appears first in the bone. Our central hypothesis is thatthe CTC population dynamics, as influenced by both filtration fraction and flow patterns, fundamentallyinfluence the spread of metastases. In this study we endeavor to deconvolute the biological complexityinherent in the circulatory phase of metastasis - physical travel constraints, cell biological changes andchemical signaling - and to focus on vascular connections and flow dynamics to uncover novel strategies andtechniques to combat this enigmatic and fatal step in cancer progression. We will accomplish this by buildingJacob G. Scott •!H. Lee Moffitt Cancer Center and Research Institute • Evolution of Metastasis • 15 Nov 2010
  3. 3. and comparing two separate mathematical models, based on the same principles but with very differentlevels of complexity and methods of parametrization. The first will be simple and analytically tenable andunderstood through measurements of CTCs during a human trial. The second will be complex, requiringevolutionary computing methods to parameterize with published data on metastatic spread.Specific Aims:1. Develop a mechanistic, physics inspired model of CTC dynamics based on a simplified vascular network.a. Characterize the human vascular system as a simple directed network, representing organs asnodes and blood vessels as directed edges. Derive an associated system of coupled ordinarydifferential equations (ODEs) to describe CTC dynamics as they populate the system, originatingfrom a tumor source. Explore the dynamics of this system of equations and how they characterizeCTC population dynamics.Milestone: characterize theoretical dynamics of the network and system of equations.b. Experimentally parametrize the model using actual CTC measurements from specific points inthe vascular network at specific points in time from a human clinical trial run at Scripps Clinic.Milestones: define a filtration coefficient (η) for the lung, liver and gut, define a CTC circulation half life.c. Experimentally parametrize primary tumor characteristics as they relate to CTC shedding (β) byimaging (volumetric), pathologic and phenotypic characteristics.Milestone: define correlations between physical tumor characteristics and CTC shedding rate (β).To obtain accurate measurements of CTCs at the different points in the vascular network and in time, we havebegun a collaboration with Peter Kuhn at The Scripps Research Institute. Together, we have formulated aJacob G. Scott •!H. Lee Moffitt Cancer Center and Research Institute • Evolution of Metastasis • 15 Nov 2010Figure 2. (a) Simplifieddirected network of humanvasculature with nodesrepresenting organs andd i r e c t e d e d g e sr e p r e s e n t i n g a c t u a lvascular connections -starred edges representsample positions. In thisnetwork the primary tumorresides in the Liver. (b)example of a coupled ODEsystem representing theCTC dynamics of the thedirected network anda s s o c i a t e d a r r e s t e dpopulations in the nodes.* A r e p r e s e n t s t h ep e r i p h e r a l a r t e r i a lcirculation, *B representsthe portal vein and *Crepresents the inferior venacava
  4. 4. clinical trial studying the value of CTC measurements in the staging of Hepatocellular Carcinoma (HCC). Thistrial endeavors to understand the relationship between CTC measurements before, during and after attemptedcurative liver transplantation and clinical outcomes.Scripps has developed a fluid phase biopsy approach that is able to identify CTCs without the need for surfacereceptor-based enrichment. This method has been used for a number of morphologic studies13,14 of CTCs andhas been shown to be extremely sensitive and specific. This method will be employed in the trial of patientswith HCC during which blood draws will be assayed for the presence of CTCs. Blood will be drawn from eachof the starred edges (points *A, *B and *C) in figure 2, surrounding the most highly connected organs, beforesurgery and immediately after resection of the primary tumor. Further, peripheral venous blood (*C) will bedrawn at several points during the recovery and follow up period. These data, as well as other clinical,histopathologic and radiologic data will be built into the model in Aim 1.2. Develop a complex network model ofmetastasis based on a more completevascular network.a. Characterize the human vascularsystem as a directed network withcancer relevant organs as nodes andblood vessels as edges. Derive anassociated system of coupledordinary differential equations(ODEs) to describe patterns ofmetastatic spread.Milestone: produce a complex networkmodel encompassing all relevantorgans.b. Parametrize this more complexnetwork with extant observationaldata on clinical metastatic spreadusing a genetic algorithmMilestone: create a genetic algorithm toevolve parameter sets to match clinicaldata on metastatic spread for each ofapproximately 20 tumor initiating sites.3. Determine how patterns of metastatic spread correlate with CTC measurements and vascular connectionsin the prediction of clinical cancer metastasis.a. Compare experimentally parametrized model with clinically parametrized model.Milestone: determine the importance of CTC dynamics and organ filtration parameter measurement inthe prediction of metastatic spread.My role in this project:I intend to spend approximately 50% of my time for the next two years on this portion of the work. I will functionas project liaison to the clinical trial and prime mover on the model development for aims 1 and 2. This projectis based on a novel method of modeling metastasis of my own creation which was born of my background inphysics and systems engineering coupled with my clinical cancer training.Jacob G. Scott •!H. Lee Moffitt Cancer Center and Research Institute • Evolution of Metastasis • 15 Nov 2010!Figure 2: Schematic of project. Outlining Aim 1 - simplistic networkmodel of CTC dynamics parametrized by human clinical trial atScripps. Aim 2 - complex model of metastasis based on flow inspiredrules and published medical data fit with a separate genetic algorithmfor each cancer subtype. Aim 3 - Explore similarities and differences inmodel parameters.
  5. 5. Phase III: Extravasation and Colony Formation - This phase of research will be in the form of experimentsderived from theoretical work that I have already done with collaborators from MIT15. In this project, awarded agrant from the NCI (Trans-network supplement to 1U54CA143970-01), we endeavor to understand the role ofpassenger mutations in the determination of metastatic fitness.Overview:It is well known that many patients can have subclinical metastases if their bone marrow is analyzed, yet thesepatients do not necessarily progress to clinically evident metastatic disease. We currently have no test todetermine which patients harbor clinically important metastases - which would become widely metastatic.Work done by Klein et al. analyzing insertion and deletion patterns in individual metastatic cells, has beenshown that 1) there exists a large amount of genomic diversity in these colonizing cells, and 2) the colonieshave a genomic history suggestive of a population bottleneck and selective sweeps common in evolvingpopulations16. Population genetics have been used to study primary tumor growth by mathematically relatingthe balance between driver mutations, or mutations in cancer causing pathways that bring about thetumorogenic phenotype, and passenger mutations, or mutations across the genome in pathways unrelated tocancer that arise by hitchiking to driver mutations. This balance between drivers and passengers is critical indetermining whether the early tumor progresses to invasive cancer or diminishes to extinction.We have begun theoretical work in which we used a similar approach that contained significant geneticheterogeneity in the primary tumors and metastasizing cells to gain new insights into the metastatic processand to better understand why some tumors are more able to succeed after arresting in a foreign stroma thanothers15. Our initial model showed that the probability of a tumor producing successful metastases iscorrelated with both primary tumor factors (e.g.: tumor size and age as well as the mutational spectrum of thecancer) and also factors relating to the new microenvironment in which the metastases grow (e.g. pH andJacob G. Scott •!H. Lee Moffitt Cancer Center and Research Institute • Evolution of Metastasis • 15 Nov 2010Figure 3. Model overviewshowing primary tumor modelof fitness function modified bystochastically accrued driverand passenger mutations withmodification to includeevolution in foreign stroma.Model prediction that greatermutational load leads tolesser metastatic fitness. Invitro and in vivo biologicalresults showing cells grownin mutagenic and clastogenicc o n d i t i o n s f o r m a n ygenerations exhibit lessmetastatic and migrationalfitness
  6. 6. oxygen availability). Many of these factors, along with the molecular properties of the passenger mutations,may be exploited by therapies. Our underlying hypothesis is that more mutations leads to morepassengers which, in turn, leads to less metastatic fitness.Our experimental plan involves subjecting luciferase transfected human cancer cells (both MDA-mb-231_lucand MCF-10a_luc_Her2) to differential levels of mutagenic exposure. The resultant cells will then be subjectedto in vitro migration assays and in vivo metastasis assays to ascertain their phenotypic metastatic fitness. Thecells found to have the widest variation in metastatic fitness will then have their genotypes analyzed todetermine mutation load. Molecular methods will be used to detect mutations in the coding regions (full exomesequencing) and copy-number alterations (SNP-array) allowing us to correlate metastatic phenotype withnumber and severity of passenger and driver mutations using previously developed bioinformatic approaches.My role in this project:I conceived of the theoretical work which led to the hypothesis for this project and I am a co-investigator on thealready awarded NCI trans-network PS-OC grant. I will spend approximately 30% of my time over the next twoyears on this portion of the project. I will be instrumental in the modeling as well as the experimental aspectsof this project. I will be involved in the in vitro and in vivo biological experiments and will be a prime mover inthe model building as well as the data analysis (in silico). We have two manuscripts in preparation at this time(one theoretical and one biological) and I share the first author position on both.Summary/Big Picture:Metastasis is complex, but I hope to come to a deeper understanding of it by reducing it to a series of simpler,more tenable steps. Each of these steps will be studied initially as stand alone processes, but with the endgoal being to integrate them into a cohesive suite of models of metastasis. I hope to be able to view this suiteof models as a single entity to better understand the process and to elucidate weak points in the cascade thatwould be amenable to intervention. While working on these projects I will be walking in the clinical, basicscientific and theoretical worlds equally. I will be serving to sharpen my skills as a researcher and workingtoward a research based doctoral degree. The combination of these experiences and endeavors will give methe skills that I need to be reach my goal of being an effective physician-scientist.References:1. Scott JG, et al. Int J Radiat Oncol Biol Phys. Epub 2010 Jul 312. Scott JG, et al. in press, NeuroOncology3. Milano MT, et al. Cancer 2008, Feb 1; 112(3):650-8. PMID: 180722604. Bockhorn M, et al. Lancet Oncology 2007, 8(5):444. PMID: 174669023. Wyckoff JB, et al. Cancer Research 2007,67(6):2649. PMID: 173635854. Chang YS, et al. Proc Natl Acad Sci USA; 2009. 97(26):14608-14613. PMID: 111210635. Mazzone M, et al. Cell 2009, 136(5):839 PMID: 192171506. Basanta D, et al. Cancer Res. Sep 1;69 (17):7111-20. PMID: 197067777. Chaplain MA, et al. Annu Rev Biomed Eng. 2006;8:233-57. PMID: 168345568. Anderson A. R. A., et al. Cell 2006; 127, 905- 915. PMID: 171297789. Jiao LR, et al. J Clin Oncol; Dec 20; 27(36):6160-5. PMID: 1988452910.Paget S. Lancet 1889; 1:571-57311.Marrinucci D, et al. J Oncol 2010; 861341. PMID: 2011174312.Marrinucci D, et al. Arch Pathol Lab Med; 2009;133(9):1468-1471 PMID: 1972275713.McFarland C, Scott JG. et al. Koch Symposium, Boston MA, 2010 Jun 1014.Klein CA, et al. Lancet 2002, 360(9334):683. PMID: 12241875Jacob G. Scott •!H. Lee Moffitt Cancer Center and Research Institute • Evolution of Metastasis • 15 Nov 2010