GenomeDx Biosciences Implements Clarity LIMS for use in Clinical Lab
News Jan 28, 2015
Genologics has announced that GenomeDx Biosciences has implemented GenoLogics’ laboratory information management system, Clarity LIMS, to support whole transcriptome analysis workflows as part of their mission to address unmet clinical needs in the management of prostate cancer. Headquartered in Vancouver BC, GenomeDx will use Clarity LIMS in its CLIA-certified lab in San Diego, CA.
In the U.S. alone, there are an estimated 2.5 million men who are living with prostate cancer. An estimated 230,000 are diagnosed each year with over 100,000 undergoing surgery to treat their cancer. According to clinical risk assessment, nearly half of men should be considered for post-surgery radiation even though their cancer may not recur.
Using their genomic test, the Decipher® Prostate Cancer Classifier, GenomeDx is able to predict the probability of cancer spread for men after surgery. Clinicians can then use this information, along with other clinical factors, to better guide postoperative treatment decisions and in certain men, avoid the side effects and high costs associated with treatment.
GenomeDx will use the cloud-based version of Clarity LIMS to support samples processed using the Decipher Test. Built specifically for genomics labs, Clarity LIMS provides end-to-end sample tracking, automation, preconfigured workflows, and superior usability.
According to Andy Katz, PhD and Chief Operating Officer at GenomeDx, “We chose Clarity LIMS because it supports our laboratory quality assurance policies and it fits into our existing environment through use of the robust application programming interface (API).”
“We are happy to support GenomeDx and their transformational work to personalize treatment for cancer patients,” says Michael Ball, CEO of GenoLogics. “We look forward to working with them to optimize Clarity LIMS so they can continue to provide patients with actionable treatment information.”
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.