Navigating the Challenge of Tumor Heterogeneity in Cancer Therapy

Abstract

The future of cancer treatment lies in personalized strategies designed to specifically target tumorigenic cell populations present in an individual. Although recent advances in directed therapies have greatly improved patient outcomes in some cancers, intuitive drug design is proving more difficult than expected owing largely to the complexity of human cancers. Intratumoral heterogeneity, the presence of multiple genotypically and/or phenotypically distinct cell subpopulations within a single tumor, is a likely cause of drug resistance. Advances in systems biology are helping to unravel the mysteries of cancer progression. In this issue of Cancer Discovery, Zhao and colleagues define a path for functional validation of computational modeling in the context of heterogeneous tumor populations and their potential for drug response and resistance. Cancer Discov; 4(2); 146–8. ©2014 AACR.
See related article by Zhao et al., p. 166

Full text

2014;4:146-148. Cancer Discovery
Clare Fedele, Richard W. Tothill and Grant A. McArthur
Therapy
Navigating the Challenge of Tumor Heterogeneity in Cancer
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Authors’ Affi liations: 1 Cancer Therapeutics Program, Division of Cancer
Research, Peter MacCallum Cancer Centre, East Melbourne;
2 Sir Peter
MacCallum Department of Oncology and
3 Department of Pathology, Uni-
versity of Melbourne, Parkville; and
4 Department of Medicine, St Vincent’s
Hospital, University of Melbourne, Fitzroy, Victoria, Australia
Corresponding Author: Grant A. McArthur, Divisions of Cancer Medicine
and Research, Peter MacCallum Cancer Centre, St Andrews Place, Locked
Bag 1, A’Beckett Street, East Melbourne, VIC 8006, Australia. Phone: 61-3-
9656-1954; Fax: 61-3-9656-3717; E-mail: grant.mcarthur@petermac.org
doi: 10.1158/2159-8290.CD-13-1042
©2014 American Association for Cancer Research.
IN THE SPOTLIGHT
Navigating the Challenge of Tumor Heterogeneity
in Cancer Therapy
Clare Fedele
1 , 2 , 3 , Richard W. Tothill
1 , 3 , and Grant A. McArthur
1 , 2 , 3
,
4
Summary: Th e f ut u r e o f c a n c e r t r e a t m e n t l i e s i n p e r s on a l i z e d s tr at e g i e s d e si g n e d t o sp e c i fi c a ll y t ar g e t t u m o r-
igenic cell populations present in an individual. Although recent advances in directed therapies have greatly
improved patient outcomes in some cancers, intuitive drug design is proving more diffi cult than expected owing
largely to the complexity of human cancers. Intratumoral heterogeneity, the presence of multiple genotypically
and/or phenotypically distinct cell subpopulations within a single tumor, is a likely cause of drug resistance.
Advances in systems biology are helping to unravel the mysteries of cancer progression. In this issue of Cancer
Discovery , Z ha o a n d co l l e a g u e s d e fi n e a p a t h f o r f u n c t io n a l v a l i da t i o n o f c o m p u ta t i o n a l m od e l i n g i n t h e c o n t e x t
of heterogeneous tumor populations and their potential for drug response and resistance. Cancer Discov; 4(2);
146–8. ©2014 AACR.
See related article by Zhao et al., p. 166 (5).
As fi rst proposed by pioneers of cancer evolution the-
ory, such as Nowell and Vogelstein, cancer results from the
sequential acquisition of heritable genetic and epigenetic
events, which under selective pressures leads to propaga-
tion of dominant cancer cell populations ( 1, 2 ). Although
we usually think of cancer as having a dominant clone, it
is becoming increasingly apparent that the propensity for
cancer evolution can also lead to signifi cant genetic hetero-
geneity in an individual patient’s cancer ( 3 ). This is perhaps
the greatest challenge to the concept of personalized cancer
medicine, as one drug targeting a single genetic driver may
not be suffi cient for adequate disease control. This concept
is not new, and the use of multiagent chemotherapy may pre-
vent the emergence of drug resistance ( 4 ), just as multiagent
antibiotics prevent the emergence of resistance in tuberculo-
sis. The advent of new technologies such as next-generation
sequencing, recently approved by the U.S. Food and Drug
Administration (FDA), is now providing the tools to accu-
rately defi ne the genomic landscape of an individual patient’s
cancer and measure heterogeneity. This approach offers the
potential to rationally target an individual patient’s cancer.
One simple concept of heterogeneity is the existence of
subclones of resistant cells, such as BCR–ABL acute myel-
ogenous leukemia (AML) with a subpopulation of cells with
T315I mutations, or BRAF -mutant melanoma with a sub-
population of cells with NRAS mutations. In this model,
ABL or BRAF inhibition kills the sensitive cells, allowing the
outgrowth of resistant cells. The solution, then, is to inhibit
the resistant cells upfront with a T315I inhibitor or the
combination of a BRAF and MAP–ERK kinase (MEK) inhibi-
tor that kills BRAF / NRAS –mutant cells. However, this is a
simple case of oncogene addiction with the straightforward
approach of turning off the signaling of an oncogene. In real-
ity, cancer can have complex mechanisms of drug resistance,
including antiapoptotic signals and alterations to cell-cycle
checkpoints or mechanisms of DNA repair. So, how can we
deal with such biologic complexity that overlies the inher-
ently heterogeneous nature of human cancer? The answer
may come from providing the road map of an individual
patient’s cancers using sophisticated genomic and epigenetic
interrogation of a cancer coupled with functional knowledge
of how the heterogeneity can be targeted. Is it then simply a
case of adding together drugs that each alone or in synergy
kill the major subpopulations of cells? The study of Zhao and
colleagues (5) in this issue of Cancer Discovery indicates it may
not be that simple.
Zhao and colleagues (5) address the impact of intratumoral
heterogeneity on treatment response using computational
modeling and functional validation in Eμ-Myc;p19
Arf −/− mouse
lymphoma cells, a well-characterized model of human MYC-
driven lymphoma. This current study builds on previous
work by this laboratory, which determined the responses
of homogenous short hairpin RNA (shRNA)–expressing
Eμ-Myc;p19
Arf −/− cell populations to combination therapies
(6, 7 ). By using this dataset of drug–genotype interactions,
Zhao and colleagues (5) devised a mathematical algorithm
to predict the response of known mutant-cell populations to
two-drug combination therapies. To phenocopy intratumoral
heterogeneity, three-component tumor populations were gen-
erated by combining two shRNA-expressing subpopulations
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with parental cells. Interestingly, although most preclini-
cal drug development studies use sensitivity as readout of
drug effi cacy, here, prevention of subclonal resistance was
the desired outcome. Computational simulation of different
tumor compositions was undertaken and optimal combina-
tion therapy was systematically predicted. Through these
analyses, Zhao and colleagues (5) discovered a key fi nding:
that the optimal treatment for a heterogenous tumor may
not necessarily incorporate drugs that most effi ciently kill
each population separately.
In one such case, homogenous cell populations expressing
either Chk2 - or Bok -targeted shRNAs (sh Chk2 or sh Bok ) were
not predicted to have optimal sensitivity to SAHA treatment
alone or in combination with vincristine (Vin). However,
when resistance was taken into consideration, a heterogene-
ous tumor composed of both subpopulations was predicted
to optimally respond to Vin/SAHA treatment. Therefore, the
overall composition of a tumor may dictate the best treat-
ment option, and this may not necessarily follow an intuitive
rationale if considering drug sensitivity alone.
On the basis of these fi ndings, functional validation was
performed in vitro . As predicted computationally, treatment
of heterogeneous sh Chk2 /sh Bok cell populations with Vin/
SAHA resulted in effi cient killing of sh Bok cells while pre-
venting sh Chk2 population outgrowth. In contrast, treat-
ment with an alternate strategy systematically predicted
to be least effective [irinotecan/chlorambucil (IRT/CBL)]
resulted in strong selection and outgrowth of a resistant
sh Chk2 population. The effi cacy of Vin/SAHA treatment was
maintained even when an additional level of genetic com-
plexity was introduced into the system through inclusion
of a cell population expressing both sh Chk2 and sh Bok . A s
expected, drug combinations predicted to optimally target
each cell subpopulation individually were not as effi cient in
the context of population heterogeneity.
The authors further validated their model in vivo . s h Chk2
a n d s h Bok Eμ-Myc;p19
Arf −/− lymphoma cells were cotransplanted
into immunocompetent mice, followed by systemic treatment
with different drug combinations. As predicted, the combina-
tion of Vin/SAHA successfully prevented selective outgrowth
of any subpopulation. Signifi cantly, tumor-free survival was
also improved compared with IRT/CBL treatment. By experi-
mentally determining the responses of homogeneous tumors
to drug combinations in vivo , t h e a u t h o r s t h e n e x p a n d e d
their analyses to computationally predict the in vivo r e s p o n s e
of three-component heterogeneous tumors with all possible
subpopulation proportions (i.e., 0%–100%). Interestingly, sys-
tematic modeling predicted that even the presence of a low-
abundance cell variant (0.1% of the overall population) is
suffi cient to affect the overall tumor response to treatments.
These studies highlight the potential power of compu-
tational approaches in modeling and predicting biologic
behavior. Over the last decade, advances in systems biology
have greatly enhanced our understanding of complex bio-
logic systems, in particular human cancers. Intratumoral
heterogeneity presents a signifi cant challenge to the devel-
opment of effective anticancer therapies. In the endeavor to
develop better treatments, systems approaches are likely to
play a fundamental role. Toward this end, large-scale molecu-
lar analysis and drug screening of human cancer cell lines
have been undertaken, such as the human Cancer Cell Line
Encyclopedia and Genomics of Drug Sensitivity studies ( 8, 9 ).
Zhao and colleagues (5) provide functional validation of
such systematic approaches, providing a strong foundation
for future predictive computing in preclinical drug develop-
ment and clinical decision-making. In particular, this study
highlights the importance of considering resistance as a sig-
nifi cant contributing factor in overall drug effi cacy, especially
in the context of intratumoral heterogeneity. Currently, very
few drugs that show promise in preclinical testing, mostly on
homogenous populations of cancer cell lines, actually trans-
late to clinical effi cacy ( 10 ).
H o w e v e r , t h e a p p l i c a t i o n o f s y s t e m s b i o l o g y t o c a n c e r t r e a t -
ment remains in its relative infancy, and there are major hurdles
still to be overcome. Computational modeling requires intimate
knowledge of subpopulations present within a heterogeneous
tumor. We are only beginning to understand the complexity
of some human tumors and the level of genetic and epigenetic
heterogeneity present therein, which are extremely diffi cult to
model experimentally. One particularly challenging concept
is that cancer is not a static entity and that many tumors can
potentially undergo continual genetic evolution, allowing adap-
tation to new selective pressures such as anticancer treatment.
It is unclear how systematic modeling will be able to cope with
genomic instability and seemingly random genetic alterations
occurring within cancer cells, not to mention the added com-
plication contributed by nongenetic factors affecting reversible
tumor cell behavior, including microenvironmental infl uences
such as stromal components and even exosomes.
N o t w i t h s t a n d i n g t h e b i o l o g i c c o m p l e x i t i e s o f m o d e l i n g
cancer overall, an additional technical challenge in clinical
translation is considering to what extent a single biopsy will be
sensitive enough to identify all subpopulations present within
a tumor. This may be particularly diffi cult in solid cancers.
Liquid biopsies, including circulating tumor cells and cell-free
tumor DNA, could be part of the answer as they will allow
continued monitoring of cancer progression without the need
for more-invasive tissue biopsy. As we build large compendia
and knowledge on drug-to-genotype relationships, focus must
therefore also be given to better detection strategies.
But even in the face of such challenges, the potential power
of computational modeling cannot be underestimated when
considering the future of personalized cancer treatment.
Knowledge of a patient’s tumor composition may allow for
systematic prediction of therapy response, based on pre-
established drug–genotype matrices, that permits clinicians
to make rational decisions on treatment strategies ( Fig. 1 ).
This may even extend to multiple rounds of treatment to
sequentially target individual subpopulations that may arise
de novo . Currently, next-generation sequencing provides the
tool to form a detailed picture of the genetic composition of
a patient’s cancer. Although it does not directly give infor-
mation of the host microenvironment or subpopulations
of cancer-initiating cells or cancer stem cells, it does allow
description of heterogeneity and the frequency of genetic
subpopulations. If these data are integrated with a broad
and deep knowledge base of the ability of drugs to synergize
to target heterogeneous cell populations, then the goal of
a personalized cancer medicine in a signifi cant proportion
of cancers will be within our grasp. To reach this goal, we
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Figure 1. The proposed future of personalized cancer medicine. Bioinformatic analysis of a biopsy taken from a patient tumor will reveal the presence
of distinct cell subpopulations. Consultation of an established drug–genotype matrix will allow treating clinicians to computationally determine optimal
combination therapies specifi c for that patient.
Patient tumor
Drug 1
Drug 2
Drug 3
Drug 4
Possible tumor cell subpopulations
Drug response
Sensitive Resistant
must continue to invest in preclinical and clinical functional
datasets of combination drug therapies powered by compu-
tational biologic models to provide the data systems needed
for personalized cancer medicine.
Disclosure of Potential Confl icts of Interest
No potential confl icts of interest were disclosed.
Published online February 5, 2014.
REFERENCES
1. Nowell PC . The clonal evolution of tumor cell populations . Science
1976 ; 194 : 23 – 8 .
2. Vogelstein B , Fearon ER , Hamilton SR , Kern SE , Preisinger AC , Lep-
pert M , etal. Genetic alterations during colorectal-tumor develop-
ment . N Engl J Med 1988 ; 319 : 525 – 32 .
3. Burrell RA , McGranahan N , Bartek J , Swanton C . The causes and
consequences of genetic heterogeneity in cancer evolution . Nature
2013 ; 501 : 338 – 45 .
4. DeVita VT Jr, Young RC , Canellos GP . Combination versus single
agent chemotherapy: a review of the basis for selection of drug treat-
ment of cancer . Cancer 1975 ; 35 : 98 – 110 .
5 . Z h a o B , P r i t c h a r d J R , L a u f f e n b u r g e r D A , H e m a n n M T . A d d r e s s -
ing genetic tumor heterogeneity through computationally predictive
combination therapy. Cancer Discov 2014;4:166–74 .
6 . J i a n g H , P r i t c h a r d J R , W i l l i a m s R T , L a u f f e n b u r g e r D A , H e m a n n M T .
A mammalian functional-genetic approach to characterizing cancer
therapeutics. Nat Chem Biol 2011;7:92–100.
7. Pritchard JR, Bruno PM, Gilbert LA, Capron KL, Luaffenburger DA,
Hemann MT. Defi ning principles of combination drug mechanisms
of action. Proc Natl Acad Sci U S A 2013;110:E170–9.
8. Barretina J , Caponigro G , Stransky N , Venkatesan K , Margolin
A A , K i m S , e t al. The Cancer Cell Line Encyclopedia enables pre-
dictive modelling of anticancer drug sensitivity . Nature 2012 ; 483 :
603 – 7 .
9. Yang W , Soares J , Greninger P , Edelman EJ , Lightfoot H , Forbes S ,
etal. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for
therapeutic biomarker discovery in cancer cells . Nucleic Acids Res
2013 ; 41 : D955 – 61 .
10. Hutchinson L , Kirk R . High drug attrition rates—where are we going
wrong? Nat Rev Clin Oncol 2011 ; 8 : 189 – 90 .
Research.
on February 6, 2014. © 2014 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from