Skip to main content
Premium Trial:

Request an Annual Quote

Stanford to Study Pathwork Dx's Unknown Tissue Cancer Test

NEW YORK (GenomeWeb News) – The Stanford University School of Medicine has started a study evaluating a Pathwork Diagnostics tissue-of-origin diagnostic test for tumors that are hard to identify, the company said today.
 
The company said the new test uses genomics technology to help doctors determine the origin of a tumor and help them plan treatments. Stanford physicians will evaluate the test for its impact on diagnosis for cancer patients with tumors that have been hard to identify, and they will process the samples at the medical school.
 
"Our test is available as a service through our CLIA-certified laboratory so that physicians outside of Stanford University can have specimens processed and clinical results provided,” said Deborah Neff, president and CEO of Pathwork Diagnostics. Neff added that the company is “actively working to obtain FDA clearance so that we can offer a diagnostics kit directly to clinical laboratories at major medical centers.”
 
Stanford also was involved in a four-lab comparison study that used the Pathwork Tissue of Origin Test to perform diagnosis of 60 metastatic and poorly differentiated and undifferentiated tissue samples, the company said.
 
The test measures the expression of over 1,500 genes to compare a tumor’s gene expression profile to 15 known tissues, and provides a probability-based score for each potential tissue, helping doctors to rule out some tissue types.
 
The test uses the company’s Pathchip microarray and runs on the Affymetrix GeneChip System.

The Scan

Study Links Genetic Risk for ADHD With Alzheimer's Disease

A higher polygenic risk score for attention-deficit/hyperactivity disorder is also linked to cognitive decline and Alzheimer's disease, a new study in Molecular Psychiatry finds.

Study Offers Insights Into Role of Structural Variants in Cancer

A new study in Nature using cell lines shows that structural variants can enable oncogene activation.

Computer Model Uses Genetics, Health Data to Predict Mental Disorders

A new model in JAMA Psychiatry finds combining genetic and health record data can predict a mental disorder diagnosis before one is made clinically.

Study Tracks Off-Target Gene Edits Linked to Epigenetic Features

Using machine learning, researchers characterize in BMC Genomics the potential off-target effects of 19 computed or experimentally determined epigenetic features during CRISPR-Cas9 editing.