United States Machine Tender

To address the problem a JAX team developed CUP-AI-Dx a machine learning tool that uses RNA sequence data for analysis. In a paper recognized as one of the best of 2020 by EBioMedicine the researchers show that CUP-AI-Dx has high accuracy when applied to real-world data sets and provides an important clinical tool to help guide therapies for CUP patients.What is CUP and what does it have to do with metastatic cancersBased on their molecular attributes most metastatic cancers can be traced back to their site of origin e.g. breast colorectal skin etc. Its an important piece of information that helps guide therapeutic strategy. Unfortunately up to five percent of the time the site of origin cannot be determined making these cancers of unknown primary CUP even more difficult to treat. Sadly patients with CUP have a one-year survival rate of only 25 percent making improved diagnostic methods essential for better patient prognoses. To help address the problem a team co-led by Jackson Laboratory Assistant Professor Sheng Li Senior Director of Computational Sciences Krishna Karuturi and Associate Director of Computational Sciences Joshy George developed a machine learning framework to help predict the primary site and molecular subtype of cancer samples. The tool called CUP-AI-Dx uses RNA sequencing data for analysis incorporating the expression of 817 genes as input. CUP-AI-Dx incorporates a 1D Inception convolutional neural network model to infer metastatic cancers primary tissue of origin. It simultaneously allows for robust identification of a tumors molecular subtype further enhancing clinical insight.Using genetic tools to target cancerAs presented in CUP-AI-Dx A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence a paper published in EBioMedicine the research team used the transcriptional profiles of more than 18000 primary tumors representing 32 cancer types from The Cancer Genome Atlas TCGA to train the model. Once optimized CUP-AI-Dx was tested on nearly 400 metastatic samples correctly identifying the tissue of origin 96.7 percent of the time in a test dataset. When applied to clinical-grade RNA-seq dataset generated from two different institutes in the U.S. and Australia the model predicted the primary site as the top option with an accuracy of 87 percent and 72.5 respectively.