Systems Biology of Cancer: from Cause to Therapy
Donald E. Ingber, M.D.,Ph.D.
Director, Wyss Institute for Biologically Inspired Engineering at Harvard University
Judah Folkman Professor of Vascular Biology, Harvard Medical School & Boston
Children’s Hospital, and Professor of Bioengineering, Harvard School of Engineering &
Applied Sciences
My laboratory’s work in the cancer field for the past 35 years has been based on
the realization that cancer is not a disease of uncontrolled cell growth. Instead, it results
from a breakdown of the normal mechanisms of developmental control that govern and
maintain normal tissue and organ development (1,2). We focused on the extracellular
matrix (ECM) as a potential regulator of cancer development because highly regulated
spatial variations in its turnover guide normal tissue patterning during embryogenesis,
and complete breakdown of the epithelial ECM or ‘basement membrane’ is a hallmark
of the transition from tumor to cancer (1-3). This led us to propose that local alterations
in ECM structure or mechanics can actively contribute to cancer development and
progression (1-3), and this has been confirmed experimentally by our group and others
While changes in ECM structure can actively drive cancer progression, the
underlying mechanism remains unknown. Our initial working hypothesis was that
sustained changes in ECM mechanics that increase cell tension and promote cell shape
distortion will serve as a constitutive stimulus for cell proliferation, which could lead to
emergence of gene mutations and selection of cells that grow autonomously (2,3). This
was based on the concept that cells use tensegrity architecture and spread themselves
outward into flattened forms by applying traction forces to their ECM adhesions, and
that associated changes in cytoskeletal structure transduce mechanical signals into
changes in intracellular biochemistry that promote cell cycle progression (6). We
experimentally confirmed this hypothesis in multiple studies by showing that while cell
spreading promotes growth, partial spreading suppresses growth and switches on
differentiation, and cell rounding can induce apoptosis, even though cells are stimulated
with optimal growth factors and ECM binding (7-9). Thus, this finding supported our
hypothesis that changes in physical interactions between cells and ECM can actively
drive or accelerate tumor formation by altering cell shape [2,3]. But any increase in cell
growth will result in a rise in cell packing density that should physically compress the
cells and thereby inhibit their proliferation. Thus, it remains unclear how changes in
ECM mechanics or cell shape distortion could drive cancer formation.
In separate studies, we pursued a Systems Biology approach to explor how a
signal as ‘non-specific’ as a change in cell shape could switch cells between the same
fates (growth, differentiation and apoptosis) that we normally think of being regulated by
growth factors that have evolved to bind to specific high affinity cell surface receptors.
Computer simulations based on dynamic Boolean networks and experimental results
suggest that the different cell fates that a particular cell can exhibit (e.g., growth,
differentiation, apoptosis) could represent a preprogrammed set of common end
programs or “attractors” which self-organize within the cell’s gene regulatory network
(10). To explore this we carried out gene expression profiling in human promyelocytic
HL60 cells that were induced to differentiate into neutrophils using either a specific
chemical factor (trans-retinoic acid) or a non-specific stimulus (DMSO). Our results
revealed that trajectories of neutrophil differentiation converge to a common state from
different directions of a 2773-dimensional gene expression state space; this provided
the first experimental evidence for a high-dimensional stable attractor that represents a
distinct cellular phenotype (11).
As cell states represent attractors in the geneome-wide gene regulatory network,
then control of cell behavior involves selection of preexisting behavioral modes of the
cell. If this is the case, then cell fate switching also should be able to be induced by
genetic noise (i.e., stochastic variations in gene expression profiles), and we confirmed
this experimentally as well (12). Gene expression stochasticity governs transitions
between different fates because while network dynamics driven by specific stimuli tend
to drive a cell to a local attractor in state space, transitions between attractors can occur
when noise pushes the cell out of one basin of attraction and into another.
Importantly, we recently explored if cancer formation could be driven by changes
in cell shape that lead to increases in genetic noise, given that both factors have been
independently shown to alter gene expression and induce cell fate switching.
Importantly, loss of regularity of cell shape and position are hallmarks of cancer
progression, and tumor formation is accompanied by a progressive loss of normal
shape-dependent controls over cell growth, differentiation and survival. In addition, the
fidelity of genetic control is tightly coupled to nuclear and chromatin structure, which in
turn are sensitive to cytoskeletal structure and cell shape regulation. Thus, increases in
cell shape variation that occur during early stages of tumor formation could potentially
trigger cell fate transitions that drive carcinogenesis both by harnessing mechanical
signaling pathways and enhancing genetic variability.
Using a computer simulation model, we explored the impact of physical changes
in the tissue microenvironment under conditions in which physical deformation of cells
increases gene expression variability among genetically identical cells. The model
revealed that cancerous tissue growth can be driven by physical changes in the
microenvironment: when increases in cell shape variability due to growth-dependent
increases in cell packing density enhance gene expression variation, heterogeneous
autonomous growth and further structural disorganization can result, thereby driving
cancer progression via positive feedback (13). The model parameters that led to this
prediction also were consistent with experimental measurements of mammary tissues
that spontaneously undergo breast cancer progression in transgenic C3(1)-SV40Tag
female mice. Interestingly, mammary glands in these mice also exhibit enhanced
stiffness of mammary ducts, as well as progressive increases in variability of cell-cell
relations and associated cell shape changes. These findings support the concept that
physical changes in the tissue microenvironment (e.g., altered ECM mechanics) can
promote cancer formation or accelerate cancer progression in a clonal population
through local changes in cell shape and increased phenotypic variability, even in the
absence of gene mutation.
Given the importance of network-wide non-linear dynamics for cell fate switching
and cancer development, we recently teamed with Jim Collin’s group at the Wyss
Institute and Boston University to use another computational Systems Biology approach
to evaluate gene expression changes in context of the entire gene regulatory network
during mammary cancer formation in the transgenic C3(1)-SV40Tag mouse model. We
constructed models of gene regulatory network connectivity with the Mode-of-action by
Network Identification tool (14). This tool was first trained on 3,000 gene microarray
datasets from various mouse tissues and organs under diverse conditions to develop a
basal gene regulatory network connectivity model. This is a directed graph that relates
the concentration of each gene transcript to that of every other transcript across the
genome. Genes are connected in this network if the activity of one gene influences the
transcriptional state of the other, regardless of whether the effect occurs at
transcriptional or post-transcriptional levels and independently of the type or
directionality of the interaction.
The trained gene network model was then tested with transgenic and wild type
mammary gland transcriptome data to identify the earliest changes that rewire the gene
regulatory network during tumor formation. In female transgenic mice, breast tumor
formation progresses in a highly reproducible manner, with hyperplasia first appearing
at ~ 12 weeks of age, DCIS-like lesions at ~ 16 weeks, and invasive carcinomas at 20
weeks (15). To focus on early events in tumorigenesis, whole genome transcriptome
profiles were obtained for mammary glands isolated from 8 week-old transgenic mice
when the transgene is highly expressed, but the glands remain histologically normal.
Candidate transcription factors were ranked and the HoxA1 gene emerged as a highly
significant potential key mediator of early cancer progression. Importantly, silencing this
gene in cultured mouse or human mammary tumor spheroids using siRNA, resulted in
increased acinar lumen formation, reduced tumor cell proliferation, and restoration of
normal epithelial polarization. When the HoxA1 gene was silenced in vivo via
intraductal delivery of nanoparticle-formulated siRNA through the nipple of transgenic
mice with early stage disease, tumor incidence was significantly reduced (15). These
are just some of the examples of how systems biology can be applied to attack the
cancer problem from analysis of causal mechanisms to development of novel
Ingber DE, Jamieson JD. Cells as tensegrity structures: architectural regulation of
histodifferentiation by physical forces tranduced over basement membrane. In:
Andersson LC,Gahmberg CG, Ekblom P, eds. Gene Expression During Normal
and Malignant Differentiation. Orlando: Academic Press; 1985; 13-32.
Ingber DE. Cancer as a disease of epithelia-mesenchymal interactions and
extracellular matrix regulation. Differentiation 2002; 70: 547-560.
Ingber DE, Madri JA, Jamieson JD. Role of basal lamina in the neoplastic
disorganization of tissue architecture. Proc. Natl. Acad. Sci. U.S.A. 1981; 78:39013905.
Sternlicht MD, Bissell MJ, Werb Z. The matrix metalloproteinase stromelysin-1 acts
as a natural mammary tumor promoter. Oncogene 2000; 19: 1102-1113.
Paszek MJ, Zahir N, Johnson KR, Lakins JN, Rozenberg GI, Gefen A, ReinhartKing CA, Margulies SS, Dembo M, Boettiger D, Hammer DA, Weaver VM.
Tensional homeostasis and the malignant phenotype. Cancer Cell 2005; 8:241-54.
Ingber, DE. Cellular Tensegrity: defining new rules of biological design that govern
the cytoskeleton. J. Cell Sci. 1993; 104:613-627.
Singhvi R, Kumar A, Lopez G, Stephanopoulos GN, Wang DIC, Whitesides GM,
Ingber DE. Engineering cell shape and function. Science 1994; 264:696-698.
Chen CS, Mrksich M, Huang S, Whitesides G, Ingber DE. Geometric control of cell
life and death. Science 1997; 276:1425-1428.
Mammoto A, Huang S, Moore K, Oh P and Ingber DE. Role of RhoA, mDia, and
ROCK in cell shape-dependent control of the Skp2-p27kip1 pathway and the G1/S
transition. J. Biol. Chem. 2004; 279: 26323-25330.
Huang S, Ingber DE. Shape-dependent control of cell growth, differentiation, and
apoptosis: switching between attractors in cell regulatory networks. Exp Cell Res
2000; 261: 91-103.
Huang S, Eichler G, Bar-Yam Y, Ingber DE. Cell fates as attractors in gene
expression state space. Phys. Rev. Lett. 2005; 94: 128701-12802.
Chang H, Hemberg M, Barahona M, Ingber DE and Huang S. Transcriptome-wide
noise controls differentiation potential of mammalian progenitor cells. Nature 2008
Werfel J, Krause S, Bischof AG, Mannix RJ, Tobin H, Bar-Yam Y, Bellin RM and
Ingber DE. How Changes in Extracellular Matrix Mechanics and Genetic Noise
Might Combine to Drive Cancer Progression. PLoS ONE 2013; 8: e76122.
D. di Bernardo et al., Chemogenomic profiling on a genome-wide scale using
reverse-engineered gene networks. Nat Biotechnol 2005; 23, 377.
Brock A, Krause S, Li H, Kowalski M, Goldberg MS, Collins JJ, Ingber DE.Silencing
HoxA1 by Intraductal Injection of siRNA Lipidoid Nanoparticles Prevents Mammary
Tumor Progression in Mice. Sci. Trans. Med. 2014; 6:217ra2.

Systems Biology of Cancer: From Cause to Therapy