CONEXIC algorithmic software: tracking the genetic footprints of cancer
Lead Inventors: Dana Pe'er, PhDProblem or Unmet Need:Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. Multiple new genes have been implicated in cancer through several existing techniques, but the most significant lesson from these studies is that each tumor is unique and typically harbors a large number of genetic lesions, of which only a few drive proliferation and metastasis. Thus, identifying driver mutations (genetic changes that promote cancer progression) and distinguishing them from passengers (those with no selective advantage) has emerged as a major challenge in the genomic characterization of cancer.The copy number and expression in cancer (CONEXIC) is a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. The software includes three components: BioLearn (to run CONEXIC), Genatomy (interactive visualization of results) and LitVAN (interpretation of results). Compared to existing techniques, CONEXIC includes a number of key components that allow for a score-guided search to identify the combination of modulators that best explains the behavior of a gene expression. The algorithm can extract this information from across tumor samples by searching for those combinations with the highest score within the amplified or deleted regions. CONEXIC demonstrates the ability of integrative Bayesian approaches to identify candidate drivers with biological and therapeutic importance to cancer.
CONEXIC combines copy number and gene expression to provide greater sensitivity in identifying significantly aberrant regions that would not be selected based on DNA alone CONEXIC includes a number of key improvements over existing technologies that allow for a score-guided search to identify the combination of modulators that best explains the behavior of a gene expression CONEXIC paves the way toward personalized therapy for cancer by revealing the genetic drivers of this highly complex and heterogeneous disease
Determining cancer drivers unique to individual tumors Identify genes that influence a phenotype of interest (cancer malignancy or drug sensitivity) Potential to predict genetic determinants of drug response Potential to adapt to have impact on other forms of personalized medicine
The copy number and expression in cancer (CONEXIC) is a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. The software includes three components: BioLea...
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