vol. 2 (2014), Article ID 235678, 8 pages
Using Systems Biology to Understand Cancer as an Evolutionary Process
John W. Pepper, Barbara K. Dunn, Richard M. Fagerstrom, John K. Gohagan,
and Nadarajen A. Vydelingum
Division of Cancer Prevention, National Cancer Institute, 6130 Executive Blvd. MSC 7315, Bethesda, MD 20892-7354, USA
Received 17 December 2012; Revised 17 December 2013; Accepted 18 December 2013
Unsatisfactory progress in cancer medicine and prevention calls for new research approaches. Research can
broaden its view of cancer to include not only specific molecular elements, but also the process that
explains their origin and dynamics. This process is Darwinian evolution of somatic cells. Applicable
modeling techniques are available from process-oriented systems biology. We review relevant concepts
and techniques, and their application to four key open questions in cancer prevention research. Helpful
concepts are transferable from classical evolutionary biology and ecology, while useful techniques include
computational agent-based modeling. The research questions we review include (1) why do benign neoplasms
often progress to malignancy? (2) what is the chronological sequence of molecular events in cancer
progression? (3) how can we find reliable molecular biomarkers for cancer? and (4) will evolved drug
resistance stymie efforts at a long-term cancer chemoprevention? We conclude that molecular analysis can
be usefully augmented with process-oriented systems biology to guide empirical research into the most
systems biology; cellular evolution; clonal evolution; cancer prevention
Despite the US “war on cancer” declared in 1971, cancer incidence and mortality have fallen less than
initially hoped and expected . Even within the most recent five years, cancer incidence in the
US declined less than 1%, and cancer mortality declined less than 2% . This has led some
researchers to argue that new approaches to cancer medicine are needed, including a conceptual framework
that can explicitly address the complex dynamics that arise from the cellular evolution and adaptation
that occurs in cancer as it interacts with its own microecology [18,26].
Compared to the areas in which medical research had its most dramatic successes, cancer presents fundamentally
different challenges, because it arises through a process that is endogenous to human tissues. The research
strategy of using molecular reductionism to define a fixed disease-causing entity has a brilliant track record
in the fields of infectious disease and inherited genetic disease, but has been less successful in cancer
biology. Molecular analysis quickly revealed the consistent genetic nature of nonhuman pathogen species, such
as bacteria, and of inherited genetic defects. In contrast, cancer reflects a complex process of ongoing
genetic change in human somatic cells, and it usually has no fixed molecular basis. Thus, in important ways,
a cancer is not simply a thing to be removed or destroyed, but also a process to be prevented,
controlled or managed.
Although cancer risk can be greatly increased by inherited genetic factors or infectious pathogens, neither
of those is necessary or sufficient on its own to be the cause of most cancer. Because cancer arises from a
process that is endogenous to human tissues, it is often independent of inherited genes or external insults.
Cancer biology can most naturally be organized around an understanding of this endogenous process of somatic
cellular evolution, which consists of mutations causing differential reproduction, with inheritance, among
somatic cells. Somatic evolution is a special case of the Darwinian process of evolution by natural selection.
Process-oriented approaches from systems biology can help to encompass the complexity of somatic cellular
evolution, making cancer more comprehensible and tractable.
As early as the 1970s, Cairns  and Nowell  proposed that somatic cell mutation, selection,
and resulting Darwinian evolution are the fundamental processes by which neoplasms arise, acquire malignancy,
and evade therapy. This hypothesis lay dormant for decades before being further developed and confirmed with
empirical results from modern cancer biology [14,21,29,56,57,67]. Currently though, “cancer development
at the cellular level is widely regarded as a Darwinian evolutionary process involving “natural selection”
of genetically variant cells in the context of a complex microenvironmental ecology” . Our only
amendment to the foregoing quote would be to explicitly recognize that relevant mechanisms of cellular
heredity include both genetic and epigenetic variation .
In common terminology, “cellular” selection and evolution are equivalently referred to as “clonal”
selection and evolution, because each dividing somatic cell produces a clone of progeny with a shared
genome and a shared fate.
As molecular techniques have provided an increasing wealth of data, the hypothesis of cellular evolution
in cancer has been supported in multiple cancer types. For example, cellular evolution as a basis for
neoplastic progression and changes in cancer has been documented in molecular detail in breast cancer
[47,62,61], pancreatic cancer , renal cancer , ovarian cancer ,
leukemia [6,70], colorectal cancer , and head and neck cancer .
The hypotheses and observations of cellular evolution have led to an emerging scientific theory of cancer
dynamics, which elucidates origination, progression, metastasis, and response to treatment. This development
is greatly assisted by a mature literature of concepts and techniques from the classical study of Darwinian
evolution in populations of organisms. One optimistic assessment is that, despite the extreme complexity of
cancer, “most, if not all, of this complexity can be explained by classical evolutionary principles” .
Armed with an understanding of the basic process behind the complex dynamics of cancer, researchers are
increasingly adopting systems biology approaches that have proven effective for understanding complex
dynamic processes such as cellular evolution. These approaches allow researchers to reveal not only the
molecular components of cancer cells, but also the reasons why molecular components vary among cells,
and why they change over time during cancer progression. Cancer is arguably the ultimate complex biological
system . For coping with such complexity, systems biology approaches have proven effective in much
of biomedicine, including cancer biology [8,44,60], and these approaches fall into two distinct categories.
One consists of techniques for analyzing large “omics” data sets [1,33]. The other category consists of
mathematical and computational models for representing underlying processes, as opposed to a specific set of
resulting data [4,5,31]. Here, we refer to this second category as “process-oriented systems biology”.
Without using that label for it, other authors have suggested that process-oriented systems biology has the
potential to play a central role in cancer prevention .
One specific technique for conducting process-oriented systems biology is the use of “agent-based”
computer models . In these models, each cell can be explicitly represented by a computational
“agent” (a piece of software) that represents a cell as it interacts with its microenvironment, mutates,
divides, passes on acquired somatic mutations, and thereby founds a new clone. In this technique (also
called “bottom-up” simulation, or “microsimulation”), the traits and behaviors of cells are specified
in advance, but larger scale patterns and processes are not. Only as the model is used to run a simulation,
often with a large and heterogeneous population of virtual cells, do larger scale patterns and processes
emerge, often with collective outcomes that were not intuitively obvious in advance. This can generate a
process of “digital evolution” inside a computer, which can provide a detailed, transparent, and predictive
model of cellular evolution and progression under a specified set of conditions [2,52]. The results of
such simulations are not empirical data, but they can generate incisive new hypotheses that are empirically
testable, and that can guide empirical research into productive new directions. Far from substituting for
empirical research, computer models are most effective when they are closely integrated with experimentation,
and are used to refine hypotheses and better focus experiments [17,22].
Other techniques are also useful for modeling in process-oriented systems biology. For example, the authors
in , among others, have represented evolutionary dynamics using a game theoretic framework for
mathematical analysis. Their results elucidated the role of tissue interactions in directing the progression
of prostate cancer into distinct courses, ranging from indolent to highly life-threatening.
The process of somatic cellular evolution can rarely be observed directly, and most empirical data can only
represent a “snapshot” in time from this ongoing process. Explicitly defining the process thought to
underlie such observable data extends their explanatory power. Below, we consider four current questions
in cancer prevention research, and discuss a process-oriented approach to each of them.
The defining (and deadly) feature of cancer, as opposed to benign growths, is local tissue invasion,
ultimately leading to distant metastasis. Because this process emerges from interactions among multiple
cells and their microenvironments, process-oriented system models are particularly well suited to its
study. For this purpose, tools have been borrowed from both classical ecology and evolutionary biology.
Ecologists have used computational studies of spatiotemporal dynamics to compare tissue invasion by
cancer cells with the invasion of natural ecosystems by artificially introduced species .
These authors posit that their results can help explain the patterns of genetic diversity observed both
within and among tumors in a patient. They also describe a “distinct geometrical signature” of invasion
dynamics in both ecological landscapes and tumors, which they suggest may someday be used along with
advanced imaging technologies to accurately discriminate malignancies from benign growths .
Independently, biomedical researchers have also found that computer simulations of mathematical models
closely resemble the actual morphologies and spatial patterns of tumor growth and invasion, and that
predictions of the model are met by observations of real tumors in vivo . This group agreed
with the ecologists cited above that results from process-oriented models can make tumor invasion more
recognizable, and they further predicted that different invasion morphologies would correspond to
different stages of tumor progression . More recently, agent-based models have even been
calibrated to match the precancerous breast tumors of specific patients, to better track and
predict their progression, as observed by mammography .
Figure 1: Reprinted with permission from Sprouffske et al. . The temporal order of mutations
seen in simulated cancer clones rarely matches the path order
inferred from cross-sectional data, but it does match the order inferred from genetic dependency analysis
of intratumor data. (a) Plotting the percentage of tumors with a given mutation at increasing neoplasm sizes
can be used to infer. (b) The cross-sectional path model of mutations. (c) However, the proportion of cells
within a simulated neoplasm that has a history consistent with the inferred cross-sectional path order tends
to be low (mean [SEM] = 7.3% [1.0%], n = 90 simulation runs). (d) In contrast, the proportion of cells within
a simulated neoplasm that has a history consistent with order inferred from the genetic dependency analysis is
high (mean [SEM] = 99.7% [0.1%], n = 90). Each mutation type is represented by a different color in panels
(a) and (b): loss of differentiation (LD) is green, evasion of apoptosis (EA) is light purple, limitless
replicative potential (LR) is orange, sustained angiogenesis (SA) is dark purple, genomic instability (GI)
is light green, self-sufficiency in growth signals (SG) is light blue, and insensitivity
to antigrowth signals (IA) is dark blue.
Beyond mere prediction, a central goal of cancer prevention is to block progression from benign
pre-malignancies to cancer. At a cellular level, this progression entails the acquisition of motility
by formerly sedentary neoplastic cells. In seeking ways to block this change, it will help to understand
what causes it. Building on classical evolutionary ecology, one research team  hypothesized that
the intense local competition for resources caused by abnormally rapid cell proliferation in neoplasms
selects for motile cells that can thrive by leaving the resource-depleted zones where other neoplastic
cells languish and die, and that this selection drives the evolution of somatic cell motility.
Agent-based computational models provided support for this hypothesis. Their results also suggest that
therapeutic agents designed to block cell motility, instead of killing cancer cells, will be more robust
against acquired drug resistance, and while blocking malignancy, will also select for reduced cell
proliferation and tumor growth .
A long-standing challenge in cancer biology is to understand and predict the chronological sequence of
molecular events underlying the initiation and progression of tumors . Many early genetic models
assumed that tumors were genetically homogenous, and thus could be characterized as having a single tumor
genotype. On this basis, the classic type of model was a linear sequence of events, or a “canonical path
model,” constructed from cross-sectional data on multiple patients . However, more recent evidence
revealed so much genetic heterogeneity within tumors that the assumption of one single genotype per tumor
is clearly no longer valid [49,63].
Agent-based evolutionary models of tumor progression suggest that the temporal, or evolutionary,
order of mutations acquired in the clones that survive over the lifetime of a neoplasm may not be
consistent with the path order inferred from cross-sectional data . Because cellular evolution
during tumorigenesis is a unique process in each patient, it is not valid to assume that the state of
one tumor is informative for the history of even an apparently similar tumor in a different patient.
Independent evolutionary trajectories in each patient make it difficult to reconstruct temporal order
from cross-sectional data. In contrast, observed cellular diversity can be analyzed with phylogenetic
methods from classical evolutionary biology to reconstruct cell lineages within individual tumors.
This can accurately reveal the true temporal order of events for the clones within a specific
tumor (Figure 1).
The two key results of this study  are (1) that cross-sectional data can be misleading because
of independent cellular evolution within each patient and (2) that even though a detailed study of intratumor
variation can reveal the average ordering of molecular events within that tumor, not all cells in the tumor
actually reflect that average order of events. As with any results from rigorous simulations, these should
not be treated as final conclusions, but rather as plausible hypotheses and strong candidates for empirical
Another major research direction in cancer prevention is the development of cancer biomarkers for early
and for prognosis. To date, most biomarker development has not been informed by evolutionary dynamics, but
instead has focused on specific molecules. This strategy has identified many candidate somatic genetic and
epigenetic “biomarkers” of cancer or cancer risk. However, very few specific marker molecules have proven
to be reproducibly effective for identifying cancer . Similarly, no specific molecule has yet been
validated as a robust predictor of progression to cancer, or been useful to reduce cancer mortality .
One likely reason for this lack of success is that the process of cellular evolution is fueled by stochastic
mutation, which is unpredictable and varies among individuals. Because of shared selective pressures for
survival and proliferation, different cancer cases tend to converge on the same “hallmark” cell traits
. However, because somatic mutation is stochastic, the molecular basis for these traits can differ,
and at a molecular level, each cancer can be unique in many ways , making it difficult to find
molecular biomarkers that will be consistent for different patients.
Figure 2: Reprinted with permission from Ye et al. . Comparison of the spectral karyotypes of two
cell sublines, each derived in vitro from the same cell line (MCF10A3, from a human breast carcinoma in situ).
The high-diversity subline CSC-3 (panel (b)) produced tumors in immunodeficient mice, while the low-diversity
subline CSC-1 (panel (a)) did not. Only subline CSC-3 with xenograft tumorigenicity (panel (b)) contained
many low-frequency chromosome aberrations not found in both sublines (indicated by the yellow shaded boxes),
in addition to the five high-frequency chromosome aberrations found in both (indicated by blue-shaded boxes).
Instead of seeking consistency across individuals in cancer-specific molecules, a different approach is
to recognize that the cellular genetic diversity of neoplasms fuels their evolution, and to measure this
diversity itself as a marker, instead of any single molecule. Classical evolutionary theory states that
the rate of evolutionary change is proportional both to trait variance and to fitness variance in the
members of the evolving population . Thus greater genetic variation among cells in a neoplasm
is expected to drive faster progression. There is substantial empirical evidence supporting this hypothesis.
In one study , five different cell lines representing various types of cancers were analyzed for
karyotypic diversity among cells. In each of these lines, the highest level of chromosomal diversity was
coupled with the strongest tumorigenicity (Figure 2). This pattern is not limited to karyotype,
or to in vitro cell lines. High cellular diversity accurately predicted risk in patients monitored for
progression from Barrett's esophagus to esophageal cancer, and simultaneously monitored by whole-genome
sequencing of repeated biopsies. Cell diversity was measured in SNPs, copy number, loss of heterozygosity,
and aneuploidy . Every tested measure of genetic diversity among cells produced highly significant
(P < .001) predictors of progression to cancer . Similar patterns have been reported in other human
tumors. In a study of human breast carcinomas, an index of cellular genetic diversity based on copy number
ratios was higher in invasive than in situ components from the same section of the same tumor .
Cellular genetic diversity was also positively correlated with tumor grade , supplemental Table 7.
(Note that these authors considered this latter trend to be only “suggestive,” needing confirmation in
a larger sample set.)
Taken together, these experimental results and observational clinical results suggest that the genetic
diversity that fuels somatic cellular evolution may be a robust general biomarker of cancer risk and
prognosis. Such a diversity marker, if proven in translational research, could simultaneously help to
address two major challenges: achieving early detection of truly dangerous neoplasms, and
avoiding overdiagnosis of nonthreatening growths.
Developing drugs for chemoprevention is a major line of research in cancer prevention that may face pitfalls
from cancer evolution. These potential pitfalls have already been demonstrated in cancer therapy. Many agents
have been developed and successfully used for selectively killing cancer cells. A very general limitation on
this approach, however, is the acquired drug resistance that results from cellular evolution .
This makes resistant relapse a routine sequel to initial tumor shrinkage during therapy [15,54], and
most patients who die of cancer are killed by a cancer that has evolved drug resistance. The problem of
acquired resistance may prove to be even more limiting for a long-term chemoprevention than it is for therapy.
While successful therapy is completed within a shorter time frame, cancer prevention might ideally continue
throughout life. Thus, any drugs that can be administered effectively only for a limited time may not be
There is clinical evidence that the time limits on effective drug administration seen during
cancer therapy can also arise in chemoprevention. This is illustrated by the selective estrogen receptor
modulators (SERMs) tamoxifen and raloxifene. Both of these have recently been tested and approved as
chemoprevention agents for breast cancer in high-risk women [24,68,69]. However, the problem of
acquired drug resistance in chemoprevention has not yet been adequately addressed. In cancer therapy, many
initially responsive tumors later develop acquired resistance to tamoxifen [45,46]. A similar acquired
resistance to raloxifene, as well as cross-resistance between these two SERMs, has been demonstrated in
vitro with breast carcinoma cells [25,37,66]. This established pattern of acquired resistance to SERMs
appears to arise in chemoprevention as well as in chemotherapy. Controlled chemoprevention trials showed a
reduction in breast cancer incidence in healthy high-risk women after using tamoxifen for five
years . However, in the adjuvant treatment setting, longer administration does not
confer further benefit in preventing new primary tumors . This result suggests
that long-term intervention to prevent a new cancer may suffer from the same limitations as long-term
chemotherapy to prevent recurrence, and for the same reason of cellular evolution of acquired drug
resistance. Achieving life-long protection through chemoprevention may require longer periods of effective
drug administration, and thus may not be possible with any drug that selects for drug-resistant variants.
Whenever somatic cells are dividing and subject to mutation, resistant variants are likely to arise. Any
drug that kills or suppresses individual drug-sensitive cells thereby “selects” more resistant cells to
survive with reduced competition after the removal of drug-sensitive neighbors . This leads
to a rapid out-growth, or “clonal expansion,” of a lineage of resistant cells which quickly takes the
place of the drug-sensitive cells removed by treatment .
Providing the best possible protection through chemoprevention may require ways to delay cellular evolution
of acquired resistance. Application of evolutionary theory from other contexts suggests specific strategies
. Therapeutics that change the tumor microenvironment to make it less hospitable to tumor
growth and invasion can impede all cancer cells equally, without selecting among them and thereby driving
the evolution of resistance. For example, nonsteroidal anti-inflammatory drugs meet this criterion, and
there is some evidence that they may remain effective indefinitely for long-term chemoprevention .
Other potential drug classes could also exert less selection on existing variation, and thus
slow the evolution of acquired drug resistance. Potential new drugs could exploit the fact that cancer cells
construct their own microenvironment by producing, and depending on, many shared substances that increase
the capacity of their shared microenvironment to support tumor growth and invasion. Examples include
secreted angiogenesis factors, growth and invasion factors, and immune suppression factors [53,54].
In agent-based computer simulations, drugs targeting such shared “public goods” products
do not single out the specific cells producing them, or those cells producing drug-resistant variants of
them. Thus they do not select strongly for resistant cells, and do not strongly drive the evolution of
acquired resistance  (Figure 3). Thus such drugs should retain longer-term
effectiveness both in treatment, and also in chemoprevention. Specific targets for potential drugs of
this class have been proposed, and in some cases successfully tested in animal models [53,54].
The theoretical prediction that targeting the tumor microenvironment will produce less acquired drug
resistance has also been supported clinically for some antiangiogenic drugs .
Figure 3: Simulation results reprinted from a previous work . Frequency of drug-resistant cells
after 1,000 cell generations of mutation and evolution. High transfer coefficients correspond to highly
diffusible drug targets produced by one cancer cell that provide benefits to other nearby cancer cells.
A transfer coefficient of zero corresponds to a cell-intrinsic target molecule that is not shared among
neighboring cells. Each marker represents the mean for a different drug dosage: filled circle = 100% of
full dose, open circle = 75%, triangle = 50%. Error bars show standard error across 10 simulation runs
using different seed values for the pseudorandom number generator.
Viewing cancer simply as an entity to be removed or destroyed can overlook important aspects of cancer
biology, including why cancer is dynamic and adaptive, and why it is difficult to target as a “nonself”
entity. Molecular reductionism contributes greatly to advances in cancer research, but is even more useful
within the framework of understanding the somatic cellular evolution that generates the diverse molecules
observed in cancer cells. At the heart of cancer biology is a change over time in populations of initially
normal human cells. This process is best understood as somatic cellular evolution. Process-oriented systems
biology provides tools for modeling this process to generate the most crucial hypotheses for empirical testing.
For helpful input, the authors thank Carlo C. Maley and Brian J. Reid.
- M. S. Abu-Asab, M. Chaouchi, S. Alesci, S. Galli, M. Laassri, A. K. Cheema, et al., Biomarkers in the age of omics: time for a systems biology approach, OMICS, 15 (2011), 105‒112.
- C. Adami and C. O. Wilke, Experiments in digital evolution (editors' introduction to the special issue), Artif Life, 10 (2004), 117‒122.
- C. A. Aktipis, C. C. Maley, and J. W. Pepper, Dispersal evolution in neoplasms: the role of disregulated metabolism in the evolution of cell motility, Cancer Prev Res (Phila), 5 (2012), 266‒275.
- G. An, Translational systems biology using an agent-based approach for dynamic knowledge representation: An evolutionary paradigm for biomedical research, Wound Repair Regen, 18 (2010), 8‒12.
- G. An, Q. Mi, J. Dutta-Moscato, and Y. Vodovotz, Agent-based models in translational systems biology, Wiley Interdiscip Rev Syst Biol Med, 1 (2009), 159‒171.
- K. Anderson, C. Lutz, F. W. van Delft, C. M. Bateman, Y. Guo, S. M. Colman, et al., Genetic variegation of clonal architecture and propagating cells in leukaemia, Nature, 469 (2011), 356‒361.
- J. C. Bailar 3rd and E. M. Smith, Progress against cancer?, N Engl J Med, 314 (1986), 1226‒1232.
- S. G. Baker and B. S. Kramer, Systems biology and cancer: Promises and perils, Prog Biophys Mol Biol, 106 (2011), 410‒413.
- D. Basanta, J. G. Scott, M. N. Fishman, G. Ayala, S. W. Hayward, and A. R. Anderson, Investigating prostate cancer tumour-stroma interactions: clinical and biological insights from an evolutionary game, Br J Cancer, 106 (2012), 174‒181.
- E. L. Bearer, J. S. Lowengrub, H. B. Frieboes, Y. L. Chuang, F. Jin, S. M. Wise, et al., Multiparameter computational modeling of tumor invasion, Cancer Res, 69 (2009), 4493‒4501.
- T. Boehm, J. Folkman, T. Browder, and M. S. O'Reilly, Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance, Nature, 390 (1997), 404‒407.
- J. Cairns, Mutation selection and the natural history of cancer, Nature, 255 (1975), 197‒200.
- P. J. Campbell, S. Yachida, L. J. Mudie, P. J. Stephens, E. D. Pleasance, L. A. Stebbings, et al., The patterns and dynamics of genomic instability in metastatic pancreatic cancer, Nature, 467 (2010), 1109‒1113.
- B. Crespi and K. Summers, Evolutionary biology of cancer, Trends Ecol Evol, 20 (2005), 545‒552.
- J. J. Cunningham, R. A. Gatenby, and J. S. Brown, Evolutionary dynamics in cancer therapy, Mol Pharm, 8 (2011), 2094‒2100.
- C. Curiel-Lewandrowski, T. Nijsten, M. L. Gomez, L. M. Hollestein, M. B. Atkins, and R. S. Stern, Long-term use of nonsteroidal anti-inflammatory drugs decreases the risk of cutaneous melanoma: results of a United States case-control study, J Invest Dermatol, 131 (2011), 1460‒1468.
- T. S. Deisboeck, Z. Wang, P. Macklin, and V. Cristini, Multiscale cancer modeling, Annu Rev Biomed Eng, 13 (2011), 127‒155.
- N. Drake, Forty years on from Nixon's war, cancer research `evolves', Nat Med, 17 (2011), 757.
- E. R. Fearon and B. Vogelstein, A genetic model for colorectal tumorigenesis, Cell, 61 (1990), 759‒767.
- B. Fisher, J. Dignam, J. Bryant, and N. Wolmark, Five versus more than five years of tamoxifen for lymph node-negative breast cancer: Updated findings from the National Surgical Adjuvant Breast and Bowel Project B-14 randomized trial, J Natl Cancer Inst, 93 (2001), 684‒690.
- S. A. Frank and M. A. Nowak, Problems of somatic mutation and cancer, Bioessays, 26 (2004), 291‒299.
- H. B. Frieboes, B. R. Smith, Y. L. Chuang, K. Ito, A. M. Roettgers, S. S. Gambhir, et al., An integrated computational/ experimental model of lymphoma growth, PLoS Comput Biol, 9 (2013), e1003008.
- H. B. Frieboes, X. Zheng, C. H. Sun, B. Tromberg, R. Gatenby, and V. Cristini, An integrated computational/experimental model of tumor invasion, Cancer Res, 66 (2006), 1597‒1604.
- E. M. Gabriel and I. Jatoi, Breast cancer chemoprevention, Expert Rev Anticancer Ther, 12 (2012), 223‒228.
- Z. O. Gao, Z. P. Gao, J. Z. Fields, and B. M. Boman, Development of cross-resistance to tamoxifen in raloxifene-treated breast carcinoma cells, Anticancer Res, 22 (2002), 1379‒1383.
- R. A. Gatenby, A change of strategy in the war on cancer, Nature, 459 (2009), 508‒509.
- R. A. Gatenby, R. J. Gillies, and J. S. Brown, Evolutionary dynamics of cancer prevention, Nat Rev Cancer, 10 (2010), 526‒527.
- M. Gerlinger, A. J. Rowan, S. Horswell, J. Larkin, D. Endesfelder, E. Gronroos, et al., Intratumor heterogeneity and branched evolution revealed by multiregion sequencing, N Engl J Med, 366 (2012), 883‒892.
- M. Greaves and C. C. Maley, Clonal evolution in cancer, Nature, 481 (2012), 306‒313.
- D. Hanahan and R. A. Weinberg, Hallmarks of cancer: the next generation, Cell, 144 (2011), 646‒674.
- H. Hatzikirou, A. Chauviere, A. L. Bauer, A. Leier, M. T. Lewis, P. Macklin, et al., Integrative physical oncology, Wiley Interdiscip Rev Syst Biol Med, 4 (2012), 1‒14.
- D. Hirsch, J. Camps, S. Varma, R. Kemmerling, M. Stapleton, T. Ried, et al., A new whole genome amplification method for studying clonal evolution patterns in malignant colorectal polyps, Genes Chromosomes Cancer, 51 (2012), 490‒500.
- E. Kolker, E. Stewart, and V. Ozdemir, Opportunities and challenges for the life sciences community, OMICS, 16 (2012), 138‒147.
- D. C. Krakauer, J. P. Collins, D. Erwin, J. C. Flack, W. Fontana, M. D. Laubichler, et al., The challenges and scope of theoretical biology, J Theor Biol, 276 (2011), 269‒276.
- X. Li, P. L. Blount, T. L. Vaughan, and B. J. Reid, Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view, PLoS Comput Biol, 7 (2011), e1001087.
- X. Li, P. C. Galipeau, C. A. Sanchez, P. L. Blount, C. C. Maley, J. Arnaudo, et al., Single nucleotide polymorphism-based genome-wide chromosome copy change, loss of heterozygosity, and aneuploidy in Barrett's esophagus neoplastic progression, Cancer Prev Res (Phila), 1 (2008), 413‒423.
- H. Liu, E. S. Lee, B. Chen, K. P. Mckian, A. De Los Reyes, and V. C. Jordan, Cross-resistance of tamoxifen and raloxifene on the growth of raloxifene-resistant breast cancer cells in vivo and in vitro, Breast Cancer Res Treat, 69 (2001), 255.
- P. Macklin, M. E. Edgerton, A. M. Thompson, and V. Cristini, Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): from microscopic measurements to macroscopic predictions of clinical progression, J Theor Biol, 301 (2012), 122‒140.
- P. P. Maglio and P. L. Mabry, Agent-based models and systems science approaches to public health, Am J Prev Med, 40 (2011), 392‒394.
- D. E. Marco, S. A. Cannas, M. A. Montemurro, B. Hu, and S. Y. Cheng, Comparable ecological dynamics underlie early cancer invasion and species dispersal, involving self-organizing processes, J Theor Biol, 256 (2009), 65‒75.
- M. Martin, Comparing invasive species to metastatic cancers inspires new insights for modelers, J Natl Cancer Inst, 100 (2008), 88‒89.
- M. Masuda, S. Toh, T. Wakasaki, M. Suzui, and A. K. Joe, Somatic evolution of head and neck cancer -- biological robustness and latent vulnerability, Mol Oncol, 7 (2013), 14‒28.
- L. Merlo, N. Shah, X. Li, P. Blount, T. Vaughan, B. Reid, et al., A comprehensive survey of clonal diversity measures in Barrett's esophagus as biomarkers of progression to esophageal adenocarcinoma, Cancer Prev Res (Phila), 3 (2010), 1388‒1397.
- M. Mijuškovic, S. M. Brown, Z. Tang, C. R. Lindsay, E. Efstathiadis, L. Deriano, et al., A streamlined method for detecting structural variants in cancer genomes by short read paired-end sequencing, PLoS One, 7 (2012), e48314.
- E. A. Musgrove and R. L. Sutherland, Biological determinants of endocrine resistance in breast cancer, Nat Rev Cancer, 9 (2009), 631‒643.
- A. Nagalingam, M. Tighiouart, L. Ryden, L. Joseph, G. Landberg, N. K. Saxena, et al., Med1 plays a critical role in the development of tamoxifen resistance, Carcinogenesis, 33 (2012), 918‒930.
- N. Navin, J. Kendall, J. Troge, P. Andrews, L. Rodgers, J. McIndoo, et al., Tumour evolution inferred by single-cell sequencing, Nature, 472 (2011), 90‒94.
- C. K. Ng, S. L. Cooke, K. Howe, S. Newman, J. Xian, J. Temple, et al., The role of tandem duplicator phenotype in tumour evolution in high-grade serous ovarian cancer, J Pathol, 226 (2012), 703‒712.
- D. G. Nord, ed., Intratumor Diversity and Clonal Evolution in
Cancer, vol. 112 of Advances in Cancer Research, Academic
Press, Oxford, 2011.
- P. C. Nowell, The clonal evolution of tumor cell populations, Science, 194 (1976), 23‒28.
- S. Y. Park, M. Gönen, H. J. Kim, F. Michor, and K. Polyak, Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype, J Clin Invest, 120 (2010), 636‒644.
- R. T. Pennock, Models, simulations, instantiations, and evidence: the case of digital evolution, Journal of Experimental & Theoretical Artificial Intelligence, 19 (2007), 29‒42.
- J. W. Pepper, Defeating pathogen drug resistance: guidance from evolutionary theory, Evolution, 62 (2008), 3185‒3191.
- J. W. Pepper, Somatic evolution of acquired drug resistance in cancer, in Targeted Therapies: Mechanisms of Resistance, D. Gioeli, ed., Springer-Verlag, New York, 2011, 127‒134.
- J. W. Pepper, Drugs that target pathogen public goods are robust against evolved drug resistance, Evol Appl, 5 (2012), 757‒761.
- J. W. Pepper, C. S. Findlay, R. Kassen, S. L. Spencer, and C. C. Maley, Cancer research meets evolutionary biology, Evol Appl, 2 (2009), 62‒70.
- O. Podlaha, M. Riester, S. De, and F. Michor, Evolution of the cancer genome, Trends in Genetics, 28 (2012), 155‒163.
- G. Price, Selection and covariance, Nature, 227 (1970), 520‒521.
- D. F. Ransohoff, Promises and limitations of biomarkers, in Cancer Prevention II, vol. 181 of Recent Results in Cancer Research, Springer-Verlag, Berlin, 2009, 55‒59.
- S. Rosenfeld and I. Kapetanovic, Systems biology and cancer prevention: all options on the table, Gene Regul Syst Bio, 2 (2008), 307‒319.
- S. Shah, R. Morin, J. Khattra, L. Prentice, T. Pugh, A. Burleigh, et al., Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution, Nature, 461 (2009), 809‒813.
- S. Shah, A. Roth, R. Goya, A. Oloumi, G. Ha, Y. Zhao, et al., The clonal and mutational evolution spectrum of primary triple-negative breast cancers, Nature, 486 (2012), 395‒399.
- D. Shibata, Heterogeneity and tumor history, Science, 336 (2012), 304‒305.
- R. Siegel, D. Naishadham, and A. Jemal, Cancer statistics, 2012, CA Cancer J Clin, 62 (2012), 10‒29.
- K. Sprouffske, J. W. Pepper, and C. C. Maley, Accurate reconstruction of the temporal order of mutations in neoplastic progression, Cancer Prev Res (Phila), 4 (2011), 1135‒1144.
- R. F. Swaby and V. C. Jordan, Low-dose estrogen therapy to reverse acquired antihormonal resistance in the treatment of breast cancer, Clin Breast Cancer, 8 (2008), 124‒133.
- F. Thomas, D. Fisher, P. Fort, J. P. Marie, S. Daoust, B. Roche, et al., Applying ecological and evolutionary theory to cancer: a long and winding road, Evol Appl, 6 (2013), 1‒10.
- V. G. Vogel, Selective estrogen receptor modulators and aromatase inhibitors for breast cancer chemoprevention, Curr Drug Targets, 12 (2011), 1874‒1887.
- V. G. Vogel, J. P. Costantino, D. L. Wickerham, W. M. Cronin, R. S. Cecchini, J. N. Atkins, et al., Effects of tamoxifen vs raloxifene on the risk of developing invasive breast cancer and other disease outcomes: The NSABP Study of Tamoxifen and Raloxifene (STAR) P-2 trial, JAMA, 295 (2006), 2727–2741.
- M. J. Walter, D. Shen, L. Ding, J. Shao, D. C. Koboldt, K. Chen, et al., Clonal architecture of secondary acute myeloid leukemia, N Engl J Med, 366 (2012), 1090‒1098.
- C. J. Ye, J. B. Stevens, G. Liu, S. W. Bremer, A. S. Jaiswal, K. J. Ye, et al., Genome based cell population heterogeneity promotes tumorigenicity: the evolutionary mechanism of cancer, J Cell Physiol, 219 (2009), 288‒300.
John W. Pepper, Barbara K. Dunn, Richard M. Fagerstrom, John K. Gohagan, and Nadarajen A. Vydelingum,
“Using Systems Biology to Understand Cancer as an Evolutionary Process,”
Journal of Evolutionary Medicine, vol. 2,
Article ID 235678, 8 pages,