PREDICTING TUMOR MARKER OUTCOMES WITH MONTE CARLO SIMULATIONS
Background: Genome and proteome research have
promised a revolution in tumor diagnosis. The revolution has not
arrived. In fact, only a handful of new markers have appeared in the
past several years. A simple thought experiment demonstrates the
In a retrospective study, Dr. X demonstrated a "perfect" tumor
marker that never failed to distinguish between two tumor variants
(aggressive and indolent) with identical morphology. In this example,
an aggressive variant grows 10 times as fast and metastasizes at ten
times the rate of the indolent variant with the same morphology.
In a prospective trial of the same marker, 200 tumors are excised at
the time of clinical detection (tumor size 2 cm). Dr. X finds that 100
of the tumors stain as "indolent variants" and 100 tumors stain as
"aggressive variants". The trials follows all 200 patients, determining
survival at five years. At the end of the trial, there is no survival
difference between patients with "indolent variants" and patients with
"aggressive variants". The marker is considered a total failure, with
millions of dollars wasted on the prospective trial.
Technology: How is this possible? In the
prospective study, all tumors were excised at 2 cm. Survival after
excision was determined entirely by the presence of metastases, as
patients with aggressive or indolent tumors without metastasis [prior
to excision] were cured by the procedure. Since the aggressive tumors
have a growth rate 10 times that of the indolent tumors, they reached 2
cm size in 1/10th the time required for the indolent tumors. The rate
of metastasis in the aggressive tumor is 10 times that of the indolent
tumor, but since aggressive tumors had 1/10th the growth history in
which to metastasize, both the aggressive and indolent tumors had the
same number of metastastic cases when the tumors were excised. Hence,
there was no difference in the survival outcome between the tumor
variants. Dr. X may have benefited from a simulation model designed to
predict outcomes from a set of biological conditions and restraints.
The purpose of this project is to provide general scripts for
predicting tumor marker outcomes using calculation-intensive Monte
Carlo algorithms that model tumor growth and metastasis.
Design: Perl scripts written by the author made use
of a random number generator to create Monte Carlo simulations of tumor
growth and metastasis. Scripts were written with two isomorphic
simulations, probabilistic prediction (fast) and brute-force per/cell
random number generation (slow).
Results: Simulations predicted differences in
growth and metastatic occurrences from preset potential probabilities.
Monte Carlo algorithms using per cell calculations required seconds to
minutes for each tumor growth simulation, on a 2.79 GHz desk-top
Conclusion: Computer simulations may be helpful
when they model plausible outcomes unanticipated by human thought. Perl
is a free, open source, cross-platform language. All Perl scripts,
along with explanatory text, are placed in the public domain and are
available for download.