Cancers are generally composed of diverse cells that vary in genetics—these variations often make a particular cancer more susceptible or resistant to a certain treatment. Pointing out these variations can help health care providers determine a suitable treatment that will likely succeed. However, conventional testing methods for these variations are prone to mishaps and so recent computer programs have made processes less challenging.
Watch this video below to learn more about understanding genetic testing and cancer:
Now, a study published in Nature Biotechnology aims to improve these cancer predictions by judging the accuracy of these computer methods.
"Our new framework provides a foundation which, over time as it is run against more tumours, will hopefully become a much-needed, unbiased, gold-standard benchmarking tool for assessing models that aim to characterize a tumor’s genetic diversity," says joint-lead author Maxime Tarabichi, postdoc in the Cancer Genomics Laboratory at the Crick.
The study specifically describes the development of an open-source software program that uses an algorithm to predict various measure of gentic diversity including proportion of cancerous cells in a tumor sample, number of genetically different groups of cancer cells within a sample, proportion of cells within each of these groups, genetic mutations in each group and the genetic relationship between groups.
Scientists analyzed 580 predictions and added new features to the tumour-simulation software program that can create more realistic tumours.
"Computer simulations in cancer genomics are helping us develop more accurate tools, as we understand where these tools perform well, and where they need improvement," says author Peter Van Loo, group leader in the Cancer Genomics Laboratory at the Crick. "Further developing these tools, so they more closely match real-life tumours, should ultimately help clinicians better match patients with personalised medicines."
Source: Science Daily
