Identification of KANSARL as the First Cancer Predisposition Fusion Gene Specific to the Population of European Ancestry Origin

Gene fusion is one of the hallmarks of cancer. Recent advances in RNA-seq of cancer transcriptomes have facilitated the discovery of fusion transcripts. In this study, we report identification of a surprisingly large number of fusion transcripts, including six KANSARL (KANSL1-ARL17A) transcripts that resulted from the fusion between the KANSL1 and ARL17A genes using a RNA splicingcode model. Five of these six KANSARL fusion transcripts are novel. By systematic analysis of RNA-seq data of glioblastoma, prostate cancer, lung cancer, breast cancer, and lymphoma from different regions of the World, we have found that KANSARL fusion transcripts were rarely detected in the tumors of individuals from Asia or Africa. In contrast, they exist in 30 - 52% of the tumors from North Americans cancer patients. Analysis of CEPH/Utah Pedigree 1463 has revealed that KANSARL is a familially-inherited fusion gene. Further analysis of RNA-seq datasets of the 1000 Genome Project has indicated that KANSARL fusion gene is specific to 28.9% of the population of European ancestry origin. In summary, we demonstrated that KANSARL is the first cancer predisposition fusion gene associated with genetic backgrounds of European ancestry origin

Artificial neural network proteomic tumor classification

Here the inventors describe a tumor classifier based on protein expression. Also disclosed is the use of proteomics to construct a highly accurate artificial neural network (ANN)-based classifier for the detection of an individual tumor type, as well as distinguishing between six common tumor types in an unknown primary diagnosis setting. Discriminating sets of proteins are also identified and are used as biomarkers for six carcinomas. A leave-one-out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction