Overlooking Evolution: A Systematic Analysis of Cancer
Relapse and Therapeutic Resistance Research
C. Athena Aktipis1,2*, Virginia S. Y. Kwan1, Kathryn A. Johnson1, Steven L. Neuberg1, Carlo C. Maley2
1Department of Psychology, Arizona State University, Tempe, Arizona, United States of America, 2Deparment of Surger, Center for Evolution and Cancer, Helen Diller
Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
Cancer therapy selects for cancer cells resistant to treatment, a process that is fundamentally evolutionary. To what extent,
however, is the evolutionary perspective employed in research on therapeutic resistance and relapse? We analyzed 6,228
papers on therapeutic resistance and/or relapse in cancers and found that the use of evolution terms in abstracts has
remained at about 1% since the 1980s. However, detailed coding of 22 recent papers revealed a higher proportion of papers
using evolutionary methods or evolutionary theory, although this number is still less than 10%. Despite the fact that relapse
and therapeutic resistance is essentially an evolutionary process, it appears that this framework has not permeated research.
This represents an unrealized opportunity for advances in research on therapeutic resistance.
Citation: Aktipis CA, Kwan VSY, Johnson KA, Neuberg SL, Maley CC (2011) Overlooking Evolution: A Systematic Analysis of Cancer Relapse and Therapeutic
Resistance Research. PLoS ONE 6(11): e26100. doi:10.1371/journal.pone.0026100
Editor: David Steve Jacobs, University of Cape Town, South Africa
Received July 16, 2011; Accepted September 19, 2011; Published November 17, 2011
Copyright: ? 2011 Aktipis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research has been supported by the Center for Evolution and Cancer at the Helen Diller Family Comprehensive Cancer Center at University of
California San Francisco, as well as National Institutes of Health grants F32 CA144331, R01 CA140657, R03 CA137811, P01 CA91955, and R01 CA119224 as well as
the Landon American Association of Cancer Research Innovator Award for Cancer Prevention and Research Scholar Grant #117209-RSG-09-163-01-CNE from the
American Cancer Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Evolutionary theory can provide a functional framework for
understanding disease and dysfunction. One example of this is
therapeutic resistance in cancer, which is fundamentally an
evolutionary process. Neoplasms are genetically [1–9] and
epigenetically  diverse populations of billions to trillions of
cells. Therapies apply strong selective pressures to these popula-
tions, and when they do not cure the patient, they select for
resistant populations of neoplastic cells. When the tumor recurs, it
now derives from the resistant cells that survived therapy (see
Figure 1), and so application of the same therapy typically has
diminished, if any, effect [11,12]. When tested, the resistant
mutations can often be found in the gene targeted by the drug
[13–23] and are present in tumor samples taken prior to therapy
[24,25]. This shows that therapy did not create the resistance
mutations but rather selected the resistant clone from among the
standing variation in the cell population at the time of therapy.
Every known cancer drug suffers from this problem , and it is
the primary reason we have not been able to cure cancer. The
result is that virtually all cancer deaths are due to therapeutically
Given the magnitude of the problem of therapeutic resistance
and fundamentally evolutionary nature of the process, one might
expect evolutionary theory and methods to be common in
research on therapeutic resistance. However, evolutionary think-
ing has been strangely absent from research and training in
medicine in general  and evolutionary terms appear rarely in
the medical literature on antibiotic resistance  suggesting that
evolutionary approaches to therapeutic resistance in cancer might
not be very common.
An evolutionary approach to therapeutic resistance in cancer
should involve the use of evolutionary theory and the use of
methods that take into account the evolutionary nature of
therapeutic resistance. This includes (but is not limited to):
1. Using evolutionary theory
Using evolution to explain how resistance occurs.
are other popular views of resistancethat probably play some rolein
the failure of therapies including: (1) change in phenotype without
change in heritable information (such as the [epi]genotype, (2)
failure to kill cancer stem cells, (3) too low a dose (toxicity
limitations), (4) failure to deliver drug to all the cells (refugia), or (5)
between-patient differential sensitivity. However, these other
mechanisms of resistance do not lead to the diminishing
effectiveness of a drug and so are less clinically problematic than
selection for resistant subclones in the tumor. The acquisition of
therapeutic resistance is a fundamentally evolutionary process and
naturalselectionisatworkduringtreatment andcompetitive release
(the subsequent increase in population size of the resistance clone
because of the removal of competitors), even if other explanations of
resistance play some role.
Using evolution as a fundamental theoretical frame-
The theory of cancer is a theory of evolution among
somatic cells of the body . An evolutionary approach to
therapeutic resistance depends on the recognition of the popu-
lation dynamics of somatic cells and selection at that level.
2. Measuring evolution
Examining within-patient/within-tumor heterogeneity.
Because evolution is defined as changes in allele frequencies in a
PLoS ONE | www.plosone.org1November 2011 | Volume 6 | Issue 11 | e26100
population, measuring within-tumor genetic heterogeneity allows
for the study of evolutionary dynamics.
Measuring cell fitness.
Differential survival and reproduc-
tion is necessary for natural selection. Measuring cell survival and
proliferation can therefore help researchers understand the evolu-
tionary dynamics underlying therapeutic resistance.
3. Detecting resistant cells
Looking for resistant cells rather than sensitive cells.
researchers are looking only for therapeutic response or sensitivity,
they may find a drug that results in shrinkage of the tumor, but if
there are resistant cells, relapse will result. It is therefore necessary
to know whether there are cells resistant to the therapy prior to
application of that therapy in order to minimize the likelihood of
Collecting and analyzing a post-therapy sample.
therapy sample is necessary to determine how the cell population
responded to the selective pressure of therapy.
In this paper, we assessed the extent to which evolutionary
theory and methods have been used in research on therapeutic
resistance and relapse in cancer.
Analysis of Abstracts
To explore the extent to which evolutionary approaches have
been applied in cancer research, in Study 1 we conducted an
automated analysis identifying all papers from the PubMed
database (from 8/1/1915-10/11/2010) that contained ‘cancer’
in the title/abstract and ‘relapse’ or ‘resistance’ in the title, and
that had available English-language abstracts; this yielded 6,228
abstracts. We then employed a PERL script to count the number
of entries with evolution-related terms in the title or abstract,
regardless of the case of those words. These titles and abstracts
were then individually read by Aktipis and Maley to check that
these terms were used to refer to Darwinian evolutionary
processes in therapeutic resistance. We excluded titles/abstracts
that: 1) referred to the ‘evolution’ of a model, paradigm or
treatment practice, 2) simply used the term in the name of the
institution, 3) referred to the evolutionary conservation of a
physiological mechanism, or 4) referred to the evolutionary
history of a species. A linear regression of the frequency of
evolutionary terms over time was carried out with the R
package, weighting each data point by the inverse of the
binomial variance, 1/sqrt(p*(1-p)/n). To avoid zero variance in
years with no abstracts using evolutionary terms, a sliding
window of 3 years was used to sum the number of abstracts with
evolutionary terms as well as the (denominator of) the number of
abstracts in those years, to estimate the variance for each year
Analysis of Papers
In Study 2, we selected the 10 most recent papers (as of 10/1/
10) from each of three databases (PubMed, ISI, Medline) that
contained the terms ‘‘therapeutic resistance’’ or ‘‘relapse’’ in
TITLE and ‘‘cancer’’ in the ABSTRACT. We excluded dupli-
cates, papers not addressing relapse in cancer, and conference
abstracts, for a total of 22 unique papers (Table 1) [31–52]. Aktipis
and Maley then coded these papers for the presence or absence of
components of an evolutionary approach to therapeutic resistance.
Article coding criteria correspond to the components of an
evolutionary approach described in the introduction.
Abstract analysis results
Fewer than 1% of papers included any single evolution term.
‘Evolution’ was the most common evolution term (44 papers), with
‘evolve’ (17 papers), ‘clonal selection’ (11 papers), ‘selective
advantage’ (8 papers) and ‘clonal expansion’ (5 papers) also
appearing (Figure 2). Interestingly, the term ‘natural selection’ was
not found in any of the analyzed abstracts. Our analysis of the use
of evolution terms over time shows no use of these terms until 1983
(Figure 3). For comparison, the evolutionary theory of cancer was
published in Science in 1976 . Further, there has been little
change in the use of these evolution terms over time, though there
is high variability in early years due to few overall papers being
published on therapeutic resistance (Figure 3). Regression analysis
weighted by the variance for each year shows that the slope is not
is not significantly different from 0 (slope=6.961025, std. err.
=2.661024, p=0.79), indicating that the frequency use of
evolution terms in the therapeutic resistance literature has not
changed since 1983. Our analyses reveal that evolutionary framing
of therapeutic resistance in published articles is still rare. The
journals that included the largest numbers of abstracts with
evolution terms were Cancer Research and PNAS (Table 2), but those
journals tended to have many articles on therapeutic resistance.
The Journal of Theoretical Biology stands out as the journal with the
highest relative frequency of evolutionarily informed articles on
therapeutic resistance (50%, 2 of 4), though the small numbers
involved should caution against drawing strong inferences from
Analysis of full articles
We evaluated each of the 22 unique articles for use of
evolutionary theory and methods. We found little evidence that
evolution is used a theoretical framework for understanding
therapeutic resistance, little evidence for the use of methods for
measuring evolution, and mixed evidence for the use of methods to
detect resistant cells.
1. Using evolutionary theory.
how resistance occurs- Only two papers used evolution as an
explanation for relapse/resistance (Figure 4). One of these was a
paper about leukemia and the other about neuroblastoma. Other
explanations for resistance given in the 22 papers were insufficient
dose (2 papers) and understaging at the time of treatment (1
paper). Eleven papers ascribed resistance to between-patient
differential sensitivity, which is simply a restatement of the
results that some patients appeared to be cured while others
relapsed (rather than a true explanation). Six of the 22 papers
(27%) did not provide any explanation for resistance.
Using evolution as a fundamental theoretical framework- Two
papers used evolution as a theoretical framework for understand-
ing the results. One paper used the cancer stem cell hypothesis.
Nineteen papers did not use any theory to interpret their results.
Using evolution to explain
Figure 1. The Evolution of Resistance. An evolutionary view of
cancer reveals that therapy selects for resistant cells among an initially
heterogeneous population. When the patient relapses, the tumor is
composed of a new diverse population of resistant cells generated by
further genetic alterations.
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org2 November 2011 | Volume 6 | Issue 11 | e26100
2. Measuring evolution.
tumor heterogeneity- Variation is essential for evolution; assessing
within-tumor genetic (or epigenetic) heterogeneity thereby allows
for the study of evolutionary dynamics. Only two of the articles,
however, measured epigenetic or genetic within-tumor hetero-
geneity (Figure 5). Five papers described phenotypic heterogeneity
among cells, which can be done easily with standard immuno-
histochemical assays. However, phenotypic heterogeneity among
cells was not measured with respect to selection on those
Measuring cell fitness- Only one paper of the 22 articles
measured cell survival/proliferation differences, in this case
between an experimental model of resistant and sensitive cell
3. Detecting resistant cells.
rather than sensitive cells- The majority of papers (68%, 15 of 22)
Looking for resistant cells
either discussed or measured resistance/survival of neoplastic cells.
Only 18% (4 of 22) focused on response/sensitivity to the therapy.
Collecting and analyzing a post-therapy sample- Despite the
fact that a post-therapy sample is necessary to determine how the
cell population responded to the selective pressure of therapy, only
2 articles reported collecting a post-therapy sample. These two
papers were both in the journal Leukemia, and one of these two
papers used evolution as an explanation for therapeutic resistance
and as an overall framework for the paper. None of the reviewed
papers measured efficacy of the initial treatment after relapse.
Summary of data
Our literature search revealed some interesting observations
regarding therapeutic resistance and relapse research. Particularly
Table 1. Papers coded for evolutionary terms and methods in study 2.
Article titleJournal title
Predicting Post-External Beam Radiation Therapy PSA Relapse of Prostate Cancer Using
International Journal of Radiation Oncology Biology
A hypothesis and theoretical model speculating the possible role of therapy mediated
neoplastic cell loss in promoting the process of glioblastoma relapse.
Journal of Theoretical Biology
DNA repair gene expression and risk of locoregional relapse in breast cancer patients. International Journal of Radiation Oncology Biology
Involved field radiotherapy for locally advanced non-small cell lung cancer: isolated
mediastinal nodal relapse.
Minimizing early relapse and maximizing treatment outcomes in hormone-sensitive
postmenopausal breast cancer: efficacy review of AI trials.
Thoracoscopic approach in the treatment of breast cancer relapse in the internal
mammary lymph node.
Interactive CardioVascular and Thoracic Surgery
Melanoma sentinel node biopsy and prediction models for relapse and overall survival. British Journal of Cancer
HIF-1alpha is an unfavorable determinant of relapse in gastric cancer patients who
underwent curative surgery followed by adjuvant 5-FU chemotherapy.
International Journal of Cancer
Impact of Epidermal Growth Factor Receptor Expression on Disease-Free Survival and Rate of
Pelvic Relapse in Patients With Advanced Cancer of the Cervix Treated With Chemoradiotherapy.
American Journal of Clinical Oncology
Does a tertiary Gleason pattern 4 or 5 influence the risk of biochemical relapse after radical
prostatectomy for clinically localized prostate cancer?
Scandinavian Journal of Urology and Nephrology
Mantle cell lymphoma in relapse: the role of emerging new drugs. Current Opinion in Oncology
Epigenetic alterations in disseminated neuroblastoma tumour cells: influence of TMS1 gene
hypermethylation in relapse risk in NB patients.
Journal of Cancer Research and Clinical Oncology
Vascular endothelial growth factor (VEGF) and endothelial nitric oxide synthase (NOS3) polymorphisms
are associated with high relapse risk in childhood acute lymphoblastic leukemia (ALL).
Clinica Chimica Acta
Intermediate filament dynamics and breast cancer: aberrant promoter methylation of
the Synemin gene is associated with early tumor relapse.
Pattern of relapse in surgical treated patients with thoracic esophageal squamous cell
carcinoma and its possible impact on target delineation for postoperative radiotherapy.
Radiotherapy & Oncology
High dose chemotherapy as salvage treatment for unresectable late relapse germ cell tumors. Journal of Urology
Prolonged relapse-free survival in two patients with an isolated brain metastasis from
epithelial ovarian carcinoma.
Journal of Clinical Oncology
Lymphopenia assessed during routine follow-up after immunochemotherapy (R-CHOP)
is a risk factor for predicting relapse in patients with diffuse large B-cell lymphoma.
IKZF1 deletions predict relapse in uniformly treated pediatric precursor B-ALL.Leukemia
Prolonged tamoxifen treatment increases relapse-free survival for patients with primary
breast cancer expressing high levels of VEGF.
European Journal of Cancer
Donor lymphocyte infusion for leukemia relapse after hematopoietic stem cell transplantation. ScienceDirect - Transfusion and Apheresis Science
Improved survival of multiple myeloma patients with late relapse after high-dose treatment
and stem cell support, a population-based study of 348 patients in Denmark in 1994–2004.
European Journal of Haematology
These 22 recent papers [32–53] met the criteria for inclusion in study 2 and were coded for their use of evolutionary methods and theory.
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org3 November 2011 | Volume 6 | Issue 11 | e26100
striking is that studies often overlook therapeutic resistance/relapse
as a fundamentally evolutionary process. As of October 11, 2010 a
total of 6,228 articles published met the search criteria. Of those
articles, only 85 used evolution terms. The proportion of papers on
therapeutic resistance/relapse using evolution terms in these
abstract has remained essentially unchanged over time since
1983, at approximately 1% (Figure 2). In contrast, Antonovics et.
al. , found that the overall use of the word ‘‘evolution’’ in
journal articles and grant proposals has been increasing since
1991. This suggests that the infrequent use of evolutionary terms
in therapeutic resistance research may be due to barriers that are
specific to evolutionary thinking in cancer rather than general
barriers to using evolution in research.
Nevertheless, we did see slightly higher levels of use of
evolutionary approaches in the 22 articles we coded comprehen-
sively as compared to the analyzed abstracts. We found evidence
that researchers attempted to measure resistant cells, with the
majority of papers focusing on resistant cells rather than sensitive
cells, and a small number of papers (two) reporting taking post-
therapy samples. However, the focus on resistance rather than
drug sensitivity is probably due to the fact that we only selected
papers that mentioned resistance or relapse in the title. If we had
included all papers on cancer therapy, many more would focus on
initial response to the therapy. We found a few instances of
researchers using methods for measuring evolution, with only two
papers measuring within-tumor heterogeneity (Figure 4) and one
measuring cell fitness.
We also found limited evidence of researchers using evolution-
ary theory. Two of the 22 papers we coded comprehensively (9%)
used evolution as a framework and explanation (Figure 5), which
suggests that our abstract analysis may be slightly underestimating
the number of therapeutic resistance papers using evolution.
Figure 2. Use of evolution terms in relapse literature. Proportion of abstracts on therapeutic resistance/relapse using each evolution term in
6,228 PubMed abstracts going back to 1915.
Figure 3. Evolution terms in abstracts. Proportion of abstracts each year on therapeutic resistance/relapse using at least one evolution term out
of 6,228 PubMed abstracts going back to 1915 (there was no use of evolution terms before 1983).
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org4 November 2011 | Volume 6 | Issue 11 | e26100
Indeed, only one of these two papers used an evolution term
(‘‘selective advantage’’) in the abstract, and neither paper used an
evolution term in the title. Therefore, it might be the case that
papers using evolution as a framework or explanation do not
necessarily note this in the title or abstract and so would have been
missed by our analysis of abstracts.
Strikingly, only 4 of 22 (18%) of papers included an explanation
(selection for resistance or insufficient dose) for the phenomenon
under study (Figure 5). Several papers , ascribed resistance to
the fact that some patients were cured and others were not, but
this does not constitute an explanation for why this occurred.
Ascribing relapse to under-staging (as 1 paper did) also does not
explain why late stage patients were likely to relapse. This lack of
explanation is worrisome in that it is difficult to make scientific
progress if no one asks why therapeutic resistance occurs. Without
an explanation for the results, there is no theoretical framework for
generating follow-up hypotheses and study designs.
Finally, 19 of the 22 papers employed no apparent overall
theoretical framework, let alone any specific explanation for
resistance. This finding suggests that it is not the case that
evolutionary theory is unsuccessfully competing with other theories
of therapeutic resistance, but rather that there is a dismaying
absence of theory in the literature on therapeutic resistance and
relapse in cancer.
In all, these findings reveal the under-utilization of the
evolutionary perspective for the feature of cancer for which the
evolutionary approach is arguably most relevant—acquired
Table 2. Journals in which evolution terms appeared in at least two abstracts.
Journal# of evolution term in abstract
Frequency among abstracts on therapeutic
resistance in that journal
1. Cancer Research5 0.0117
2. Proceeding of the National Academy of
Sciences of the United States of America
4.International Journal of Cancer3 0.0156
5.Clinical Cancer Research3 0.0155
6.British Journal of Cancer30.0189
7.Journal of Theoretical Biology2 0.5000
8. International Journal of Oncology2 0.0323
9. Current Medicinal Chemistry2 0.1333
10. Carcinogenesis2 0.0606
11.Breast Cancer Research and Treatment2 0.0227
12. Biomedical Central Cancer2 0.0606
The rightmost column was calculated by dividing the number of cancer therapeutic resistance/relapse abstracts with evolution terms in that journal (middle column) by
the total number of abstracts on cancer therapeutic resistance/relapse in that journal (out of 6,228 across journals).
Figure 4. Explanations for resistance. Number of papers using each explanation for resistance out of 22 coded papers.
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org5 November 2011 | Volume 6 | Issue 11 | e26100
Why isn’t evolution used as a framework?
Despite the fact that relapse and therapeutic resistance is
essentially an evolutionary process, our analysis shows that this
framework has not permeated research. This is likely due to a
variety of factors including the use of methods that do not allow for
collecting evolutionary data, a lack of evolutionary training in
medical education, and psychological barriers to evolutionary
Science is limited by what we can
observe with the current tools. We can only see what is under the
proverbial lamppost. Cytological staining of chromosomes in
mitotic spreads allowed early researchers to observe sequential
accumulation of genomic lesions in leukemias back in the 1960’s,
which led directly to Peter Nowell’s formulation of the
evolutionary theory of carcinogenesis . However, much of
the last few decades of cancer research has been dominated by the
assays of molecular biology that homogenize a tissue sample in
order to measure the average protein/RNA/DNA values in the
population of cells. These methods obscure the heterogeneity
among cells in a neoplasm and make it difficult to study the
evolutionary dynamics within those neoplasms. Furthermore, most
cancer research has been based on cross-sectional study designs,
making it difficult to study changes in a neoplasm over time. This
is because most neoplasms are removed when detected, and so
cannot be followed over time. Similarly, most animal studies utilize
a serial sacrifice design, making it impossible to observe evolution
over time within the same neoplasm. Clinically, the acquisition of
post-therapy biopsies has been limited because doctors have been
reluctant to subject patients to an invasive procedure to collect a
biopsy when a tumor recurs. It is important to note however, that
both medical oncologists and the internal review boards (IRBs)
that approve of research studies, over-estimate patients’ anxiety
associated with undergoing a research-related biopsy, and under-
estimate patients’ willingness to accept risks associated with those
biopsies . This suggests that patients are more willing to
provide longitudinal biopsies than has been assumed, which would
facilitate the study and management of therapeutic resistance.
In contrast, progress in the treatment of chronic myeloid
leukemia (CML) is notable and has been due to the relative ease of
gathering longitudinal samples of blood, thereby enabling
researchers to study the dynamic, evolutionary nature of CML.
Because cytology reveals tumor heterogeneity at the single cell
level, researchers were able to recognize the driving mutation in
CML (the BCR-ABL gene fusion) [55,57], develop a successful
drug (imatanib) to target that lesion , observe the selective
effects of imatanib treatment [59,60] and, with that knowledge in
hand, develop second-line drugs (e.g., dasatinib) that work on
imatanib- resistant CML [22,61]. Rapid progress in CML
illustrates how studying the evolutionary process accelerates
research and leads to treatments for even therapeutically resistant
cancers. It is perhaps not surprising that the 2 (of 22) papers that
collected post-therapy samples were published in the journal
Fortunately, improved study designs and technologies are
making it easier to study the evolutionary dynamics of other
cancers as well. Taking multiple biopsies, or assaying single cells,
from a solid tumor enables one to detect cellular diversity within
the neoplasm [7,9,62], and to generate phylogenetic inferences of
the genetic events in the history of that neoplasm [63–65]. Deep
sequencing is becoming common and is revealing the presence of
genetic diversity within neoplasms [66,67]. This trend should
continue as more single cell assays are developed. Finally, animal
studies may be improved by taking longitudinal biopsies, rather
than sacrificing animals at different time points, though the
wounding from the biopsy removal may perturb the system.
amplify the effectiveness of research and treatment with better
penetration of evolutionary approaches to cancer. Although a
theoretical understanding of cancer as an evolutionary process has
been generally accepted in cancer biology, our literature review
shows that research on therapeutic resistance grounded in evolu-
tionary theory has been largely neglected to date. Furthermore,
evolutionary thinking has not yet been incorporated into medical
education, although this can be overcome by developing a clear set
of training goals  and incorporating them into medical school
Because evolutionary medicine is not currently a core
component of medical education, a great deal of attention has
recently been paid to the question of how to more effectively
increase exposure to evolutionary approaches in medical training.
There is a great opportunity to
Figure 5. Measurement of heterogeneity in recent articles. Numbers of papers measuring each type of heterogeneity out of 22 coded articles.
Only 2 of 22 papers measured epigenetic or genetic within-tumor heterogeneity.
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org6November 2011 | Volume 6 | Issue 11 | e26100
This includes a recent Sackler Colloquium on the topic and a
paper, co-authored by a large number of evolutionary medicine
experts, entitled, ‘‘Making evolutionary biology a basic science for
medicine’’ . In this paper, the authors provide a set of general
recommendations and specific learning objectives for effectively
incorporating evolutionary theory into medical education. These
include pre-med competencies such as understanding natural
selection, the role of mutation and drift, the use of the comparative
method and the role of tradeoffs. They also lay out a number of
medical competencies, which include understanding the use of
phylogenetic methods, co-evolution, somatic evolution and the
evolutionary origins of senescence.
Despite the current lack of evolutionary training in medical
schools, efforts to incorporate evolution biology into medical
curricula are being developed at Harvard, Yale and John Hopkins
. Also, the National Evolutionary Synthesis Center (NESCent)
is supporting a working group on the topic ‘‘Infusing Medical
Education with Evolutionary Thinking,’’ with a number of goals
including evaluating present evolutionary education in medical
schools, developing evolutionary medicine curricula and evaluat-
ing the effectiveness of novel educational interventions on student
learning and clinical problem solving. Given the fundamental role
of evolutionary theory in cancer biology, and the lack of its use in
contemporary research, we strongly support these efforts.
Understanding the tendencies and
biases in human cognition may help us to identify psychological
barriers to evolutionary thinking in cancer . Some of these
psychological barriers may apply to evolutionary thinking in
general. Thinking in evolutionary terms is not intuitive, even for
the well informed [69,70]. Also, many lay people and healthcare
professional may react negatively to interventions framed in terms
of evolution. General barriers such as these may be addressed by
using familiar analogies to explain evolutionary processes, such as
the evolution of antibiotic resistance or pesticide resistance.
Other barriers may be more specific to evolutionary thinking in
the domain of cancer. To address these specific psychological
barriers, our research team is currently investigating misconcep-
tions about cancer (held by medical students and medical
professionals) that reflect a lack of evolutionary thinking. One of
these misconceptions is the tendency to essentialize tumors,
whereby one views a cancerous tumor as an entity with some
internal property or essence that gives rise to its outward
appearance [68,71,70]. However, cancerous tumors are neither
unitary nor static, but collections of mutable cells with differential
capacities for proliferation. There are over 200 different kinds of
cancer currently recognized and it is important to understand that
clinically advanced tumors are nearly always heterogeneous
populations of differentially mutated cells. Just as essentialist
thinkers have difficulty conceptualizing a species as being a
collection of unique individuals rather than a homogenous group
, it may also be counterintuitive for some to think of tumors as
being a collection of heterogeneous and mutable cells. An
essentialist bias may make it difficult for researchers to study
how neoplastic cell populations change in response to the selective
pressures of therapy, and thus interfere with the development of
strategies to prevent or manage therapeutic resistance.
Future research should address the barriers that impede the
progress of applying the evolutionary approach to cancer research
and treatment. The problems may lie in the unfamiliarity of
evolutionary principles (e.g., created by inadequate training and
education), the dominance of non-evolutionary approaches used
by grant and manuscript reviewers, or in psychological barriers to
thinking about cancer in evolutionary terms. Our analyses show
that most cancer research on therapeutic resistance has not
utilized an evolutionary approach. Of course, not all research on
acquired therapeutic resistance has to focus on the change in the
cell population in response to therapy. For example, the molecular
mechanism of resistance could be studied without reference to the
evolutionary dynamics that produced it. However, the compo-
nents of an evolutionary approach that we identified are almost
entirely absent from the literature on therapeutic resistance and
relapse. This is surprising given that acquired therapeutic
resistance is one of the clearest cases of the relevance of
evolutionary theory in cancer. Grounding cancer research and
treatment in the principles of evolutionary theory may elicit new
and more successful interventions, as illustrated by progress in the
treatment of CML.
We thank Liz Dong and Aresh Vasefi for their assistance in this project.
Conceived and designed the experiments: CAA VK KJ SN CM.
Performed the experiments: CAA KJ CM. Analyzed the data: CAA VK
CM. Wrote the paper: CAA VK KJ SN CM.
1. Gonzalez-Garcia I, Sole RV, Costa J (2002) Metapopulation dynamics and
spatial heterogeneity in cancer. Proc Natl Acad Sci U S A 99: 13085–13089.
2. Castro MA, Dalmolin RJ, Moreira JC, Mombach JC, de Almeida RM (2008)
Evolutionary origins of human apoptosis and genome-stability gene networks.
Nucleic Acids Res 36: 6269–6283.
3. Heng HH, Stevens JB, Liu G, Bremer SW, Ye KJ, et al. (2006) Stochastic cancer
progression driven by non-clonal chromosome aberrations. J Cell Physiol 208:
4. Murphy DS, Hoare SF, Going JJ, Mallon EE, George WD, et al. (1995)
Characterization of extensive genetic alterations in ductal carcinoma in situ by
fluorescence in situ hybridization and molecular analysis. J Natl Cancer Inst 87:
5. Harada T, Okita K, Shiraishi K, Kusano N, Kondoh S, et al. (2002)
Interglandular cytogenetic heterogeneity detected by comparative genomic
hybridization in pancreatic cancer. Cancer Res 62: 835–839.
6. Yachida S, Jones S, Bozic I, Antal T, Leary R, et al. (2010) Distant metastasis
occurs late during the genetic evolution of pancreatic cancer. Nature 467:
7. Merlo LM, Shah NA, Li X, Blount PL, Vaughan TL, et al. (2010) A
Comprehensive Survey of Clonal Diversity Measures in Barrett’s Esophagus as
Biomarkers of Progression to Esophageal Adenocarcinoma. Cancer Prev Res
(Phila) 3: 1388–1397.
8. Tsao JL, Yatabe Y, Salovaara R, Jarvinen HJ, Mecklin JP, et al. (2000) Genetic
reconstruction of individual colorectal tumor histories. Proc Natl Acad Sci U S A
9. Park SY, Gonen M, Kim HJ, Michor F, Polyak K (2010) Cellular and genetic
diversity in the progression of in situ human breast carcinomas to an invasive
phenotype. J Clin Invest 120: 636–644.
10. Varley KE, Mutch DG, Edmonston TB, Goodfellow PJ, Mitra RD (2009)
Intra-tumor heterogeneity of MLH1 promoter methylation revealed by
deep single molecule bisulfite sequencing. Nucleic Acids Res 37: 4603–
11. Coley HM (2008) Mechanisms and strategies to overcome chemotherapy
resistance in metastatic breast cancer. Cancer Treat Rev 34: 378–390.
12. Engelman JA, Janne PA (2008) Mechanisms of acquired resistance to epidermal
growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer.
Clin Cancer Res 14: 2895–2899.
13. Schimke RT (1984) Gene amplification, drug resistance, and cancer. Cancer Res
14. Carman MD, Schornagel JH, Rivest RS, Srimatkandada S, Portlock CS, et al.
(1984) Resistance to methotrexate due to gene amplification in a patient with
acute leukemia. J Clin Oncol 2: 16–20.
15. Horns RC, Jr., Dower WJ, Schimke RT (1984) Gene amplification in a leukemic
patient treated with methotrexate. J Clin Oncol 2: 2–7.
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org7November 2011 | Volume 6 | Issue 11 | e26100
16. Trent JM, Buick RN, Olson S, Horns RC, Jr., Schimke RT (1984) Cytologic
evidence for gene amplification in methotrexate-resistant cells obtained from a
patient with ovarian adenocarcinoma. J Clin Oncol 2: 8–15.
17. Kobayashi S, Boggon TJ, Dayaram T, Janne PA, Kocher O, et al. (2005) EGFR
mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med
18. Wang TL, Diaz LA, Jr., Romans K, Bardelli A, Saha S, et al. (2004) Digital
karyotyping identifies thymidylate synthase amplification as a mechanism of
resistance to 5-fluorouracil in metastatic colorectal cancer patients. Proc Natl
Acad Sci U S A 101: 3089–3094.
19. Taplin ME, Bubley GJ, Ko YJ, Small EJ, Upton M, et al. (1999) Selection for
androgen receptor mutations in prostate cancers treated with androgen
antagonist. Cancer Res 59: 2511–2515.
20. Visakorpi T, Hyytinen E, Koivisto P, Tanner M, Keinanen R, et al. (1995) In
vivo amplification of the androgen receptor gene and progression of human
prostate cancer. Nat Genet 9: 401–406.
21. Chen LL, Trent JC, Wu EF, Fuller GN, Ramdas L, et al. (2004) A missense
mutation in KIT kinase domain 1 correlates with imatinib resistance in
gastrointestinal stromal tumors. Cancer Res 64: 5913–5919.
22. Shah NP, Skaggs BJ, Branford S, Hughes TP, Nicoll JM, et al. (2007) Sequential
ABL kinase inhibitor therapy selects for compound drug-resistant BCR-ABL
mutations with altered oncogenic potency. J Clin Invest 117: 2562–2569.
23. Tamborini E, Bonadiman L, Greco A, Albertini V, Negri T, et al. (2004) A new
mutation in the KIT ATP pocket causes acquired resistance to imatinib in a
gastrointestinal stromal tumor patient. Gastroenterology 127: 294–299.
24. Kreuzer KA, Le Coutre P, Landt O, Na IK, Schwarz M, et al. (2003)
Preexistence and evolution of imatinib mesylate-resistant clones in chronic
myelogenous leukemia detected by a PNA-based PCR clamping technique. Ann
Hematol 82: 284–289.
25. Roche-Lestienne C, Preudhomme C (2003) Mutations in the ABL kinase
domain pre-exist the onset of imatinib treatment. Semin Hematol 40: 80–82.
26. Chabner BA, Roberts TG, Jr. (2005) Timeline: Chemotherapy and the war on
cancer. Nat Rev Cancer 5: 65–72.
27. Nesse RM, Bergstrom CT, Ellison PT, Filer JS, Gluckman P, et al. (2010)
Making evolutionary biology a basic science for medicine. Proc Natl Acad Sci
USA 107(Suppl.): 1800–1807.
28. Antonovics J, Abbate JL, Baker CH, Daley D, Hood ME, et al. (2007) Evolution
by Any Other Name: Antibiotic Resistance and Avoidance of the E-Word. PLoS
Biol 5(2): e30. doi:10.1371/journal.pbio.0050030.
29. Merlo LM, Pepper JW, Reid BJ, Maley CC (2006) Cancer as an evolutionary
and ecological process. Nat Rev Cancer 6: 924–935.
30. Agresti A, Caffo B (2000) Simple and effective confidence intervals for
proportions and differences of proportions result from adding two successes
and two failures. Am Stat 54: 280–288.
31. Fuchsjager MH, Pucar D, Zelefsky MJ, Zhang Z, Mo Q, et al. (2010) Predicting
Post-External Beam Radiation Therapy PSA Relapse of Prostate Cancer Using
Pretreatment MRI. Int J Radiat Oncol Biol Phys 78(3): 743–750.
32. Kumar Mallik M (2010) A hypothesis and theoretical model speculating the
possible role of therapy mediated neoplastic cell loss in promoting the process of
glioblastoma relapse. J Theor Biol 266(4): 496–503.
33. Le Scodan R, Cizeron-Clairac G, Fourme E, Meseure D, Vacher S, et al. (2010)
DNA repair gene expression and risk of locoregional relapse in breast cancer
patients. Int J Radiat Oncol Biol Phys 78(2): 328–336.
34. Vanneste BG, Haas RL, Bard MP, Rijna H, Va ´ldes Olmos RA, et al. (2010)
Involved field radiotherapy for locally advanced non-small cell lung cancer:
isolated mediastinal nodal relapse. Lung Cancer 70(2): 218–220.
35. Markopoulos CJ (2010) Minimizing early relapse and maximizing treatment
outcomes in hormone-sensitive postmenopausal breast cancer: efficacy review of
AI trials. Cancer Metastasis Rev 29(4): 581–594.
36. Conrado-Abrao F, Das-Neves-Pereira JC, Fernandes A, Jatene FB (2010)
Thoracoscopic approach in the treatment of breast cancer relapse in the internal
mammary lymph node. Interact Cardiovasc Thorac Surg 11(3): 328–330.
37. Mitra A, Conway C, Walker C, Cook M, Powell B (2010) Melanoma sentinel
node biopsy and prediction models for relapse and overall survival. Br J Cancer
38. Nakamura J, Kitajima Y, Kai K, Hashiguchi K, Hiraki M (2010) HIF-1alpha is
an unfavorable determinant of relapse in gastric cancer patients who underwent
curative surgery followed by adjuvant 5-FU chemotherapy. Int J Cancer 127(5):
39. Perez-Regadera J, Sanchez-Mun ˜oz A, De-la-Cruz J, Ballestin C, Lora D, et al.
(2010) Impact of Epidermal Growth Factor Receptor Expression on Disease-
Free Survival and Rate of Pelvic Relapse in Patients With Advanced Cancer of
the Cervix Treated With Chemoradiotherapy. Am J Clin Oncol.
40. Servoll E, Saeter T, Vlatkovic L, Nesland J, Waaler G, et al. (2010) Does a
tertiary Gleason pattern 4 or 5 influence the risk of biochemical relapse after
radical prostatectomy for clinically localized prostate cancer? Scand J Urol
Nephrol 44(4): 217–222.
41. Diefenbach CS, O’Connor OA (2010) Mantle cell lymphoma in relapse: the role
of emerging new drugs. Curr Opin Oncol 22(5): 419–423.
42. Grau E, Martinez F, Orellana C, Canete A, Yanez Y, et al. (2010) Epigenetic
alterations in disseminated neuroblastoma tumour cells: influence of TMS1 gene
hypermethylation in relapse risk in NB patients. J Cancer Res Clin Oncol 136(9):
43. Demacq C, Vasconcellos VB, Izidoro-Toledo TC, da Silva Silveira V,
Canalle R, et al. (2010) Vascular endothelial growth factor (VEGF) and
endothelial nitric oxide synthase (NOS3) polymorphisms are associated with
high relapse risk in childhood acute lymphoblastic leukemia (ALL). Clin Chim
44. Noetzel E, Rose M, Sevinc E, Hilgers RD, Hartmann A, et al. (2010)
Intermediate filament dynamics and breast cancer: aberrant promoter
methylation of the Synemin gene is associated with early tumor relapse.
45. Cai WJ, Xin PL (2010) Pattern of relapse in surgical treated patients with
thoracic esophageal squamous cell carcinoma and its possible impact on target
delineation for postoperative radiotherapy. Radiother Oncol 96(1): 104–107.
46. Lorch A, Rick O, Wu ¨ndisch T, Hartmann JT, Bokemeyer C, et al. (2010) High
dose chemotherapy as salvage treatment for unresectable late relapse germ cell
tumors. J Urol 184(1): 168–173.
47. Choo BA, Walji N, Spooner D, Barber P, Fernando IN (2010) Prolonged
relapse-free survival in two patients with an isolated brain metastasis from
epithelial ovarian carcinoma. J Clin Oncol 28(17): 271–272.
48. Porrata LF, Rsitow K, Inwards DJ, Ansell SM, Micallef IN, et al. (2010)
Lymphopenia assessed during routine follow-up after immunochemotherapy (R-
CHOP) is a risk factor for predicting relapse in patients with diffuse large B-cell
lymphoma. Leukemia;7): 1343–1349.
49. Kuiper RP, Waanders E, van der Velden VH, van Reijmersdal SV,
Venkatachalam R, et al. (2010) IKZF1 deletions predict relapse in uniformly
treated pediatric precursor B-ALL. Leukemia 24(7): 1258–1264.
50. Sanchez BC, Sundqvist M, Fohlin H, Spyratos F, Nordenskjold B, et al. (2010)
Prolonged tamoxifen treatment increases relapse-free survival for patients with
primary breast cancer expressing high levels of VEGF. Eur J Cancer 46(9):
51. Yegin ZA, Ozkurt ZN, Aki SZ, Sucak GT (2010) Donor lymphocyte infusion for
leukemia relapse after hematopoietic stem cell transplantation. Transfus Apher
Sci 42(3): 239–245.
52. Vangsted AJ, Klausen TW, Andersen NF, Abildgaard N, Gang AO, et al. (2010)
Improved survival of multiple myeloma patients with late relapse after high-dose
treatment and stem cell support, a population-based study of 348 patients in
Denmark in 1994–2004. Eur J Haematol 85(3): 209–216.
53. Nowell PC (1976) The clonal evolution of tumor cell populations. Science 194:
54. Nakamura J, Kitajima Y, Kai K, Hashiguchi K, Hiraki M, et al. (2010) HIF-
1alpha is an unfavorable determinant of relapse in gastric cancer patients who
underwent curative surgery followed by adjuvant 5-FU chemotherapy.
Int J Cancer 127(5): 1158–71.
55. Nowell PC (1962) The minute chromosome (Phl) in chronic granulocytic
leukemia. Blut 8: 65–66.
56. Agulnik M, Oza AM, Pond GR, Siu LL (2006) Impact and perceptions of
mandatory tumor biopsies for correlative studies in clinical trials of novel
anticancer agents. J Clin Oncol: 24(30): 4801–4807. doi:10.1200/
57. Rowley JD (1973) Letter: A new consistent chromosomal abnormality in chronic
myelogenous leukaemia identified by quinacrine fluorescence and Giemsa
staining. Nature 243: 290–293.
58. Kantarjian H, Sawyers C, Hochhaus A, Guilhot F, Schiffer C, et al. (2002)
Hematologic and cytogenetic responses to imatinib mesylate in chronic
myelogenous leukemia. The New Engl J Med 346: 645–652.
59. Shah NP, Nicoll JM, Nagar B, Gorre ME, Paquette RL, et al. (2002) Multiple
BCR-ABL kinase domain mutations confer polyclonal resistance to the tyrosine
kinase inhibitor imatinib (STI571) in chronic phase and blast crisis chronic
myeloid leukemia. Cancer Cell 2: 117–125.
60. Branford S, Rudzki Z, Walsh S, Grigg A, Arthur C, et al. (2002) High frequency
of point mutations clustered within the adenosine triphosphate-binding region of
BCR/ABL in patients with chronic myeloid leukemia or Ph-positive acute
lymphoblastic leukemia who develop imatinib (STI571) resistance. Blood 99:
61. Shah NP, Tran C, Lee FY, Chen P, Norris D, et al. (2004) Overriding imatinib
resistance with a novel ABL kinase inhibitor. Science 305: 399–401.
62. Maley CC, Galipeau PC, Finley JC, Wongsurawat VJ, Li X, et al. (2006)
Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat
Genet 38: 468–473.
63. Reid BJ, Barrett MT, Galipeau PC, Sanchez CA, Neshat K, et al. (1996)
Barrett’s esophagus: ordering the events that lead to cancer. Eur J Cancer Prev 5
Suppl 2: 57–65.
64. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, et al. (2011) Tumour
evolution inferred by single-cell sequencing. Nature 472: 90–94.
65. Thirlwell C, Will OC, Domingo E, Graham TA, McDonald SA, et al. (2010)
Clonality assessment and clonal ordering of individual neoplastic crypts shows
polyclonality of colorectal adenomas. Gastroenterology 138: 1441–1454, 1454
66. Shah SP, Morin RD, Khattra J, Prentice L, Pugh T, et al. (2009) Mutational
evolution in a lobular breast tumour profiled at single nucleotide resolution.
Nature 461: 809–813.
67. Campbell PJ, Pleasance ED, Stephens PJ, Dicks E, Rance R, et al. (2008)
Subclonal phylogenetic structures in cancer revealed by ultra-deep sequencing.
P Natl Acad Sci U S A 105: 13081–13086.
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org8November 2011 | Volume 6 | Issue 11 | e26100
68. Aktipis CA, Maley CC, Neuberg SL (2010) Psychological barriers to Download full-text
evolutionary thinking in medicine. Evol Med Rev. http://evmedreview.com/
69. Shtulman A (2006) Qualitative differences between naive and scientific theories
of evolution. Cognitive Psychol 52: 170–194.
70. Shtulman A, Schulz L (2008) The relation between essentialist beliefs and
evolutionary reasoning. Cognitive Sci 32: 1049–1062.
71. Gelman SA (2003) The Essential Child: Origins of Essentialism in Everyday
Thought. Oxford: Oxford University Press.
Overlooking Evolution in Cancer Relapse
PLoS ONE | www.plosone.org9 November 2011 | Volume 6 | Issue 11 | e26100