chemotherapy - do we know why it works?
Western General Hospital, UK
theoretical modelling using the Gompertz function by Larry Norton and others,
there has been increasing interest in the Clinical Oncology community for
giving dose-dense chemotherapy. One of the main obstacles to curing cancer
is the limitation in dose imposed by the tolerance of the normal tissues,
such as the bone marrow. Attempts had been made to overcome this with high
dose chemotherapy and bone marrow or stem cell transplantation. This approach
has not proved very succesful in solid cancers. Further work has suggested
that an alternative strategy is to give the chemotherapy more frequently with
GCSF to assist in a ore rapid recovery of the bone marrow. Theoretical modelling
has predicted that it would be better in the adjuvant therapy of breast cancer,
and recently early data from the CALGB co-operative group in the USA has confirmed
this. However, in my hands, modelling based on Gompertz does not necessarily
show an advantage, particularly when one considers that the benefit seen with
adjuvant therapy for many patients is not due to cure, but delayed recurrence.
This will be discussed, and a challenge issued to modellers to get involved
in this field!
Knowledge Engineering with Cancer Modelling
University of Pittsburgh, USA
William E. Shirey, Michele Morris
cancer research, clinical testing is a permanent bottleneck. Due to sample
size limitations, few of the promising ideas can be tested clinically, so
clinical questions must be chosen carefully. A principal investigator designing
a new cancer clinical study for the chosen question must exercise ``due diligence''.
This comes in two parts: gathering relevant information from prior studies,
and using that information judiciously to make study design choices. The bridge
from the first part to the second is often the formation of mental model of
the disease from the literature review. This is not so easy; only a small
subset of the ballooning treasury of human knowledge about cancer can be grasped
by one person, resulting in the neglect of relevant facets, while mental modelling
may suffer from poor intuition about the behavior of complex systems.
To augment the usual informal thought experiments, the OncoTCap cancer
software workbench is designed to support, integrate and document the information-gathering
and modelling tasks. An application to lung cancer is demonstrated.
mathematical models reliably yet indiscriminately
reproduce the outcome of breast cancer chemotherapy trials
Weizmann Institute of Science, Israel
Coauthor(s): David Cameron
and Sallo Stemmer
clinical practice the derivation of a new chemotherapy protocol is a very
tedious, time consuming and often-futile process. It is thus often questioned
whether mathematical simulations coupled with experimentally derived parameters
of the physical phenomena may assist to planning and predicting the course
of cancer treatment. We apply mathematical modeling to simulate the course
and outcome of some of the recent breast cancer chemotherapy trials. Several
simple phenomenological modes of cancer growth were simulated in the study.
In addition, a detailed model that explicitly incorporates the biology of
the vasculature and growth factors into the tumour dynamics was derived. Each
model represents an unperturbed tumour growth, superimposed by periods of
treatment applications. To estimate the treatment effect we have simulated
the survival data of cohorts of patients using plausible distributions of
model parameters (a Monte-Carlo procedure). The simulated survival (Kaplan-Mayer)
curves derived by ALL of the models are similar to what may be clinically
observed. These results caution against a naive application of any particular
model in clinical trials and reiterate the need of specific parameter estimation
in individual patients. Some basic notions of mathematical modelling in medicine
will be discussed.