The National Science Foundation (NSF) is reporting that investigators at New York University’s Courant Institute of Mathematical Sciences have developed a novel algorithm that “makes it much easier to detect certain cancer genes, and as a test, have applied it to map a set of tumor-suppressor genes involved in lung cancer.”
The NSF gives a brief overview on how the algorithm analyses data from the gene-chips:
An algorithm is a step-by-step process for solving problems mathematically or by computer. The new one works by combining and manipulating the data generated by “gene chips,” which can scan large swaths of a genome at once to find mutations or other changes in the DNA. The algorithm estimates whether an important genomic segment is missing. The process is akin to guessing whether a new edition of a book lacks an important paragraph by checking if some of the key words in that paragraph are missing from the index.
More details from the NYU press office:
The NYU algorithm estimates the location of tumor suppressor genes by analyzing segmental deletions in the genomes from cancer patients and the spatial relation of the deleted segments to any specific genomic interval. Since the gene-chip consists of many “probes”-each one characterizing an almost unique word and its location in the already-sequenced human genome-by combining these probe-measurements, one can estimate if an important genomic segment is missing. By analogy, this process is akin to guessing if a new edition of a book is missing an important paragraph by checking if some of the important key words in that paragraph are missing from the index of the new edition. The new algorithm computes a multipoint score for all intervals of consecutive probes, and the score reflects how well the deletion of that genomic interval may explain the cancer in these patients. In other words, the computed score measures how likely it is for a particular genomic interval to be a tumor suppressor gene implicated in the disease. In order to validate their algorithm, the authors produced a high fidelity in silico model of cancer, and checked how well they can detect the right genes, as they modified various parameters of the model in an adversarial manner. Encouraged by the success of their in silico study, they applied the algorithm to currently available patient data, and discovered that they were able to detect many genes that were already known in the literature, but also, several others that are statistically equally significant, but not found by the earlier studies.
The findings also showed that the algorithm may be applied to a wider class of problems-including the detection of oncogenes, which promote the growth of cancer when they are mutated or overexpressed. As the technology and the statistical algorithms of this nature keep improving in cost and accuracy, it will prove useful in finding good biomarkers, drug discovery, disease diagnosis, and choosing correct therapeutic intervention. The members of the NYU group (the authors, Dr. Salvatore Paxia and Dr. Thomas Anantharaman) are in the process of creating a simpler user interface for their software, providing interoperability across many different chip technologies, and finally, making it publicly available in order to facilitate its free and wide-spread usage.