LRP research plan final

Research Plan:
I am currently finishing my PGY-3 year of clinical training in Radiation Oncology. During the past 8 months I
have spent my research time involved with two groups at Moffitt Cancer Center: one is the NCI’s newly created
Physical 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 set
and manner of thinking are of real value. I have had enormous success in the short time I have been involved
with these groups: I have won a poster award, an ASCO merit award and a 1 year, $200,000 grant from the
NCI. Further, I have submitted an original manuscript, have two more in preparation, and have an R21
pending. This work has all been outside of my clinical training, which has continued unabated to this point. I
have maintained an excellent standing in my clinical residency program while doing this extra work, and have
continued to be involved in the expected load of clinical research as well - publishing two clinical manuscripts1,2
last 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 been
able 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 independent
physician-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 residency
training to do just this. During the three years I will continue my employment at Moffitt Cancer Center, but will
be a Research Associate instead of a clinical resident. I will also be enrolled in Oxford University during this
time and officially be reading for a doctorate in mathematics. While I will physically be in the United States
during the majority of this time, I will be co-supervised by faculty at Moffitt and Oxford. My research during this
time, and therefore my doctoral thesis, will be focused on building and validating mathematical models of the
metastatic process from primary tumor intravasation to the circulatory phase of metastasis to extravasation and
colony formation.
I have chosen to study metastasis because it is currently one of the biggest
outstanding questions in cancer. While metastatic disease accounts for the
lion’s share of cancer death, it is by far the least understood and most
poorly characterized phase of cancer progression. Our efforts to date to
study this process have been limited by our technology and, once this
process has begun our therapies for these patients are minimally effective.
In most cases, while the primary tumor and nodal disease have rich
stratification by which we understand patient prognosis, metastatic disease
is a binary event, either M0 or M1. This lack of ability to stratify metastatic
disease has not, in the past, mattered overmuch as the definitive
therapeutic options for patients with M1 disease were limited to systemic
agents. Recently, however, we have made advances in our ability to treat
oligometastatic disease with minimally or non-invasive local therapies, and
we have had suggestions that this actually can, in select patients, influence
overall survival3. A greater understanding of metastasis will allow us to
optimize current therapies and develop new treatment strategies for this
enigmatic process.
I plan to study this complex process in three discrete, but connected, parts
and will describe the research plan as such. Phase II is the subject of a
recently submitted R21 (1R21CA160127-01) and Phase III is the subject of
a 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 2010
Figure 1: There are three putative
routes of intravasation - A. leaky,
cooptable endothelium4, B. chimeric
vasculature3 and C. motile, invasive
tumor cells1.

Phase I: Intravasation - Studying intravasation in vitro or in vivo is exceptionally difficult, which means that
this phase of metastasis has been largely understudied. We intend to begin attacking this phase of metastasis
beginning from first principals and building an in silico model of this enigmatic process. We hope to eventually
be able to validate our results using technologies evolved from the dorsal wound chamber system possessed
by our lab.
Overview:
To begin the process of hematogenous metastasis, a primary tumor must gain access to and begin to shed
tumor 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 mathematical
models. The proposed methods of intravasation range from a) cells moving through holes in leaky tumor
vessels 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 controlled
experimental conditions5,6,7, but the process has never been defined rigorously or mechanistically. In this
phase of my project I propose to build a hybrid discrete-continuous cellular automaton model of a primary
tumor including active local vasculature. The first iterations of this model will include vasculature as only
metastatic entrypoints and will be built on work already done in my laboratory8 but this will evolve to include the
effect of putative cancer cell chemotactic agents such as EGF and multicellular, multiphenotype, evolvable
endothelial cells in addition to the metastatic entrypoint of the vasculature. There is extensive modeling
experience available within the IMO, on angiogenesis9, cancer initiation8 and progression10 using these hybrid
cellular 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. I
will be directly involved in driving model development and in the interpretation of the results. Determining the
phenotypic characteristics of tumor cells that do and do not successfully intravasate and at what time points in
the 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 the
source 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 are
putatively 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 description
includes 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 the
entire vascular system11. When and where these CTCs create metastasis is not fully understood and is
currently the subject of intense biological study. Paget's well-known seed-soil hypothesis12 suggests that in
order 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 makes
sense from a flow standpoint, there are many others that appear not to follow this deterministic pattern - for
example, soft tissue sarcoma preferentially metastasizes to the lung which is the next stop by flow while
prostate cancer almost always skips the lung and appears first in the bone. Our central hypothesis is that
the CTC population dynamics, as influenced by both filtration fraction and flow patterns, fundamentally
influence the spread of metastases. In this study we endeavor to deconvolute the biological complexity
inherent in the circulatory phase of metastasis - physical travel constraints, cell biological changes and
chemical signaling - and to focus on vascular connections and flow dynamics to uncover novel strategies and
techniques to combat this enigmatic and fatal step in cancer progression. We will accomplish this by building

and comparing two separate mathematical models, based on the same principles but with very different
levels of complexity and methods of parametrization. The first will be simple and analytically tenable and
understood through measurements of CTCs during a human trial. The second will be complex, requiring
evolutionary 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 as
nodes and blood vessels as directed edges. Derive an associated system of coupled ordinary
differential equations (ODEs) to describe CTC dynamics as they populate the system, originating
from a tumor source. Explore the dynamics of this system of equations and how they characterize
CTC 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 in
the 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) by
imaging (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 have
begun a collaboration with Peter Kuhn at The Scripps Research Institute. Together, we have formulated a
Figure 2. (a) Simplified
directed network of human
vasculature with nodes
representing organs and
d i r e c t e d e d g e s
r e p r e s e n t i n g a c t u a l
vascular connections -
starred edges represent
sample positions. In this
network the primary tumor
resides in the Liver. (b)
example of a coupled ODE
system representing the
CTC dynamics of the the
directed network and
a s s o c i a t e d a r r e s t e d
populations in the nodes.
* A r e p r e s e n t s t h e
p e r i p h e r a l a r t e r i a l
circulation, *B represents
the portal vein and *C
represents the inferior vena
cava

clinical trial studying the value of CTC measurements in the staging of Hepatocellular Carcinoma (HCC). This
trial endeavors to understand the relationship between CTC measurements before, during and after attempted
curative liver transplantation and clinical outcomes.
Scripps has developed a fluid phase biopsy approach that is able to identify CTCs without the need for surface
receptor-based enrichment. This method has been used for a number of morphologic studies13,14 of CTCs and
has been shown to be extremely sensitive and specific. This method will be employed in the trial of patients
with HCC during which blood draws will be assayed for the presence of CTCs. Blood will be drawn from each
of the starred edges (points *A, *B and *C) in figure 2, surrounding the most highly connected organs, before
surgery and immediately after resection of the primary tumor. Further, peripheral venous blood (*C) will be
drawn 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 of
metastasis based on a more complete
vascular network.
a. Characterize the human vascular
system as a directed network with
cancer relevant organs as nodes and
blood vessels as edges. Derive an
associated system of coupled
ordinary differential equations
(ODEs) to describe patterns of
metastatic spread.
Milestone: produce a complex network
model encompassing all relevant
organs.
b. Parametrize this more complex
network with extant observational
data on clinical metastatic spread
using a genetic algorithm
Milestone: create a genetic algorithm to
evolve parameter sets to match clinical
data on metastatic spread for each of
approximately 20 tumor initiating sites.
3. Determine how patterns of metastatic spread correlate with CTC measurements and vascular connections
in 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 in
the prediction of metastatic spread.
I intend to spend approximately 50% of my time for the next two years on this portion of the work. I will function
as project liaison to the clinical trial and prime mover on the model development for aims 1 and 2. This project
is based on a novel method of modeling metastasis of my own creation which was born of my background in
physics and systems engineering coupled with my clinical cancer training.
!
Figure 2: Schematic of project. Outlining Aim 1 - simplistic network
model of CTC dynamics parametrized by human clinical trial at
Scripps. Aim 2 - complex model of metastasis based on ﬂow inspired
rules and published medical data ﬁt with a separate genetic algorithm
for each cancer subtype. Aim 3 - Explore similarities and differences in
model parameters.

Phase III: Extravasation and Colony Formation - This phase of research will be in the form of experiments
derived from theoretical work that I have already done with collaborators from MIT15. In this project, awarded a
grant from the NCI (Trans-network supplement to 1U54CA143970-01), we endeavor to understand the role of
passenger 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 these
patients do not necessarily progress to clinically evident metastatic disease. We currently have no test to
determine 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 been
shown that 1) there exists a large amount of genomic diversity in these colonizing cells, and 2) the colonies
have a genomic history suggestive of a population bottleneck and selective sweeps common in evolving
populations16. Population genetics have been used to study primary tumor growth by mathematically relating
the balance between driver mutations, or mutations in cancer causing pathways that bring about the
tumorogenic phenotype, and passenger mutations, or mutations across the genome in pathways unrelated to
cancer that arise by hitchiking to driver mutations. This balance between drivers and passengers is critical in
determining 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 genetic
heterogeneity in the primary tumors and metastasizing cells to gain new insights into the metastatic process
and to better understand why some tumors are more able to succeed after arresting in a foreign stroma than
others15. Our initial model showed that the probability of a tumor producing successful metastases is
correlated with both primary tumor factors (e.g.: tumor size and age as well as the mutational spectrum of the
cancer) and also factors relating to the new microenvironment in which the metastases grow (e.g. pH and
Figure 3. Model overview
showing primary tumor model
of fitness function modified by
stochastically accrued driver
and passenger mutations with
modification to include
evolution in foreign stroma.
Model prediction that greater
mutational load leads to
lesser metastatic fitness. In
vitro and in vivo biological
results showing cells grown
in mutagenic and clastogenic
c o n d i t i o n s f o r m a n y
generations exhibit less
metastatic and migrational
fitness

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 more
passengers which, in turn, leads to less metastatic fitness.
Our experimental plan involves subjecting luciferase transfected human cancer cells (both MDA-mb-231_luc
and MCF-10a_luc_Her2) to differential levels of mutagenic exposure. The resultant cells will then be subjected
to in vitro migration assays and in vivo metastasis assays to ascertain their phenotypic metastatic fitness. The
cells found to have the widest variation in metastatic fitness will then have their genotypes analyzed to
determine mutation load. Molecular methods will be used to detect mutations in the coding regions (full exome
sequencing) and copy-number alterations (SNP-array) allowing us to correlate metastatic phenotype with
number and severity of passenger and driver mutations using previously developed bioinformatic approaches.
I conceived of the theoretical work which led to the hypothesis for this project and I am a co-investigator on the
already awarded NCI trans-network PS-OC grant. I will spend approximately 30% of my time over the next two
years on this portion of the project. I will be instrumental in the modeling as well as the experimental aspects
of this project. I will be involved in the in vitro and in vivo biological experiments and will be a prime mover in
the 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 end
goal being to integrate them into a cohesive suite of models of metastasis. I hope to be able to view this suite
of models as a single entity to better understand the process and to elucidate weak points in the cascade that
would be amenable to intervention. While working on these projects I will be walking in the clinical, basic
scientific and theoretical worlds equally. I will be serving to sharpen my skills as a researcher and working
toward a research based doctoral degree. The combination of these experiences and endeavors will give me
the 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 31
2. Scott JG, et al. in press, NeuroOncology
3. Milano MT, et al. Cancer 2008, Feb 1; 112(3):650-8. PMID: 18072260
4. Bockhorn M, et al. Lancet Oncology 2007, 8(5):444. PMID: 17466902
3. Wyckoff JB, et al. Cancer Research 2007,67(6):2649. PMID: 17363585
4. Chang YS, et al. Proc Natl Acad Sci USA; 2009. 97(26):14608-14613. PMID: 11121063
5. Mazzone M, et al. Cell 2009, 136(5):839 PMID: 19217150
6. Basanta D, et al. Cancer Res. Sep 1;69 (17):7111-20. PMID: 19706777
7. Chaplain MA, et al. Annu Rev Biomed Eng. 2006;8:233-57. PMID: 16834556
8. Anderson A. R. A., et al. Cell 2006; 127, 905- 915. PMID: 17129778
9. Jiao LR, et al. J Clin Oncol; Dec 20; 27(36):6160-5. PMID: 19884529
10.Paget S. Lancet 1889; 1:571-573
11.Marrinucci D, et al. J Oncol 2010; 861341. PMID: 20111743
12.Marrinucci D, et al. Arch Pathol Lab Med; 2009;133(9):1468-1471 PMID: 19722757
13.McFarland C, Scott JG. et al. Koch Symposium, Boston MA, 2010 Jun 10
14.Klein CA, et al. Lancet 2002, 360(9334):683. PMID: 12241875

LRP research plan final

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LRP research plan final