PREDICTING TUMOR MARKER OUTCOMES WITH MONTE CARLO SIMULATIONS

<body bgcolor="#ffffff" text="#000000" link="#0000ff" vlink="#0000ff" alink="#ff0000"> <p> <p><strong>PREDICTING TUMOR MARKER OUTCOMES WITH MONTE CARLO SIMULATIONS</strong></p> <p><a href="http://www.julesberman.info/randab.htm" target="_top">Perl script</a></p> <hr> <p><strong>Background: </strong>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 problem. </p> <p>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. </p> <p>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. </p> <p><strong>Technology: </strong>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.</p> <p>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. </p> <p><strong>Design: </strong>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). </p> <p><strong>Results:</strong> 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 computer. </p> <p><strong>Conclusion:</strong> 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 <a href="http://www.julesberman.info/randab.htm" target="_top">download</a>.</p>