Mission:
Developing implementations of Systems Biology in cancer research that
are most effective.
Advancing knowledge on system properties of cancer relating to signal
transduction, angiogenesis, drug resistance and searching new biomarkers.
Both to the benefit of a better treatment of the disease.
Mottos:
cancer is a systems biology (network) disease
to cure the network one should target the
network
more "sensors" are needed for
interrogation of cancer cells
systems biology footprints of early tumors
Traditional medical biological research into disease focuses on the identification
of the single component, perhaps molecule, of the patient that has gone
awry. Cancer research has not been different; it searched for the 'onco'gene. The altered activity of such a single gene was expected to
be responsible for the disease. However, rather than one such gene, more
than 70 oncogenes were found, and in no case a tumor has been shown to be the
result of the altered activity of a single such oncogene. This group has proposed that in reality
cancer is a systems biology disease, or a network disease. This means that it is the regulatory
network that is dysfunctioning such that the cell escapes normal growth control
by its multicellular environment.
The implications for cancer research and ultimately for cancer therapy could
be strong. Diagnosis should not be
targeted to single specific molecules, but to altered states of the
network. If the network is ill, one should cure the
network. Hence, drugs and
therapies should not target specific molecules but rather the altered state of
the network. Moreover, the
response of the tumor to any therapy is determined by many processes at the
same time, such as pharmacokinetics, drug metabolism, drug transport to the
target, interaction with the cellular biochemical network, including cellular
defense. Advanced knowledge on the
integration of the dynamic responses of these (sub-) processes may lead to
better treatment strategies. For a
heterogeneous, multifactorial disease such as cancer, the networks of different
individuals with different polymorphisms will have been affected differently by
similar tumors and they will respond differently to the same drugs: Therefore. personalized medicine makes
much sense.
Monitoring and diagnosis
The progress of a disease that is caused by the alteration of a single
gene in a homogeneous genetic environment, could be assessed by monitoring a
single variable, such as the concentration of a growth factor. For a Systems Biology disease, one should
measure many more variables in vivo at key points in the whole process, in a sense comparable to
intermediary concentrations in a crude oil refinery plant. Sensors that measure locally a specific
concentration are still rare, although imaging techniques are being improved
and the search for new biomarkers is an active research field.
Pharmacokinetic data and images of the tumor mass can often be obtained, but
they are near the beginning and near the end point of the whole process.
Measurements at intermediary points, e.g. in the network of cellular
biochemical reactions, could provide information of the efficiency of the
therapy. It would provide a more
rapid feedback than presently available, which should be useful to continue or
moderate therapies that would otherwise just produce toxicity. For example, if one aims to inhibit the
EGF receptor, a measurement on the phosphorylation status of proteins of
signaling pathways just downstream the receptor could give information on the
efficiency of inhibition. To-day such measurements are still rare. Biomarker proteins or peptides or
metabolites, directly secreted by cancer cells or generated ÒdownstreamÓ (e.g.
activated metalloproteases or immune-derived lipid peroxidation), as Óimages of
the diseaseÓ, could also provide an informative readout. The technology for
this is being developed.
New drug-treatment strategies
Tumor cells differ from normal cells in a number of mutations in a
limited number of genes or in the DNA that controls their expression level. Therewith tumor cells are rather
similar to the other cells of the patient. Because of the many different cell types in a patient, the few
properties in which the tumor cells differ from the other cells in their tissue
of origin, often correspond to properties of cells in different tissues in the
same patient. Accordingly,
inhibition of individual molecules that are slightly different in the tumor
cells, is rarely effective and often toxic for other tissues in the patient.
Using Systems Biology, this group investigates the principles behind
possibly novel strategies underpinning tumor treatment. One such principle is differential
network-targeting drug design. The
underlying concept is that one wishes to target the altered network rather than
altered molecules in tumor cells and that this should be done in ways that
differentiate best between the network in the tumor and the corresponding
networks in all other host cells. One
working hypothesis on the mechanism of action of the classical DNA-damaging anti-cancer
agents is the relative weakness to cope with damage (e.g. by p53 mediated
halting of the cell cycle). A
working hypothesis that is used to explain the action of kinase targeting
agents is that the cancer cell has become addicted to signalling for survival
pathways. Although the kinase activity may be higher than in normal cells the
addiction makes the kinase a fragile point, suitable to attack. Here Metabolic and Hierarchical Control
Analysis are implemented and there is inspiration from differential network-based
drug design against Trypanosoma brucei, also developed in this department.
Another principle is the dual drug therapy and the polymorphism enhanced
drug therapy. Here the idea is to
look for cases in which two drugs may promote each otherÕs effectiveness,
without promoting each others toxicity.
The cooperativity sought here is one that should emerge from the
network. Where one drug can reinforce
a second drug, a polymorphism is likely to do the same, suggesting that then
the second drug should be more effective in a subset of patients.
Yet another principle underpins the just-in-time strategy. This is based on the hypothesis that
actions of biological signals depend on the time at which they are received. Hence changing the temporal dynamic of
signal transduction may strongly affect the tumorÕs signal transduction.
In most cases the tumor cells have an enhanced rate of mutagenesis
leading to a further increase in the number of mutations. A tumor only becomes a malignant tumor
when the malignant mutant cells are selected by the conditions around the
tumor. Treatment with anti-tumor
drugs will lead to selection of drug-resistant cells. The principles resulting from these selection processes, and
the systems mechanism of drug resistance are being looked for.
How we work:
We focus on the development of quantitative measurements, which give extra information
on drug responses to make targeted therapies more effective. In daily practice,
this involves improving knowledge on biochemical networks (network-based drug
design), both inside the cancer cell and just outside the cancer cell
(tumor-stroma interactions), analytical chemistry, bioinformatics and
biomathematical modeling. The latter can give insight into system properties,
emerging from non-linear and complex intracellular interactions, which cannot
be understood intuitively. The group works both in the Faculty of Earth and
Life Sciences and the Medical Center.
In the laboratory we focus on measuring drug transport and measuring aspects of
the signaling through the MAP-kinase pathways and pathway leading to production
of vascular endothelial growth factor (VEGF). In vivo
we have investigated the response to doxorubicin in patients with breast
cancer, using micro-arrays. Body
fluids of cancer patients are collected before and after the start of
chemotherapy and analyzed. Then differences
will be interpreted with a systems biology approach.
Members:
á Prof.dr. Jan Lankelma (group leader)
á Ing. Henk Dekker (research technician)
á
Dr Wim de Boer
(voluntary part-time senior research associate)
á
Dr Rafael
fernandez Luque (voluntary part-time senior research associate)
á
Dr Frank
Bruggeman (NISB)
á
Prof. dr. Hans V.
Westerhoff (differential network based drug design)
Former members:
á
Dennis Mans,
Ph.D.
á
Ellen Spoelstra,
Ph.D.
á
Sipko Műlder,
Ph.D.
á
Peter Wielinga,
Ph.D.
á
Jorrit Hornberg,
Ph.D.
á
Rebecca Hegeman (undergraduate
student)
á
Mirjam Eppink (undergraduate student)
á
Marloes
Tijssen (undergraduate student)
á
Jurgen
Haanstra (undergraduate student)
á
Marjolein
Bij de Vaate
(undergraduate student)
á
Christian R. Geest (undergraduate student)
á
Petra
Langerak (undergraduate student)
Information for students:
For undergraduate students we organize yearly the course Integrative Tumor Cell Biology (start next course May, 2008) and for master students in Oncology the course on Systems Biology of Cancer (start next course May, 2008). For students interimships on research topics can be followed (preferably at least for 5 months). For more information on these courses and interimships please contact the group leader.
Order the video " Cells and Cytostatics ; Basics of cellular pharmacokinetics"
The Department of Tumor Cell Biology is situated in the Department of Molecular Cell Physiology at the Faculty of Earth and Life Sciences of the Vrije Universiteit Amsterdam
Our
address is:
Prof. dr J.Lankelma
Tumor
Cell Biology
Vrije Universiteit Amsterdam
Faculty of Earth and Life Sciences
Department of Molecular Cell Physiology
De Boelelaan 1085
NL-1081 HV AMSTERDAM
The Netherlands
