High Diagnostic Accuracy Demonstrated for New Kidney Cancer Test

A clinical study finds a new genomic test for kidney cancer highly valuable for diagnosis, subtyping, and outcome prediction for renal cancers, an important complement to tissue staining based pathology.

Accurately diagnosing kidney cancer using pathology alone is challenging and can delay time to first treatment, particularly if surgical resections are required. Cancer Genetics, Inc. (CGI; Rutherford, NJ, USA), a leader in DNA-based cancer diagnostics, presents results of a collaborative study conducted with the Cleveland Clinic that validate CGI's novel kidney cancer microarray test. This microarray test demonstrated 93% diagnostic sensitivity with 99% specificity. It facilitates the diagnosis, subtyping, and outcome prediction for kidney cancer patients. The validation study was performed using samples from 188 kidney cancer patients treated at the Cleveland Clinic. The data and analysis were presented by Dr. Magi-Galluzzi of the Cleveland Clinic at the 2014 United States & Canadian Academy of Pathology (USCAP) 103rd annual meeting in San Diego (CA, USA).

"This compelling study with Cleveland Clinic, along with initial work with Memorial Sloan Kettering, provides significant evidence that our technology has unparalleled value as a breakthrough diagnostic for kidney cancer," said Panna Sharma, CEO of Cancer Genetics; "These new data come at an opportune time, since March is Kidney Cancer Awareness month, and our test has the ability to both subtype and help predict outcome from a small amount of DNA taken from either paraffin-embedded or fine needle aspirate. This is very unique and will be a tremendous aid for both pathologists and oncologists helping patients in the battle against kidney cancer."

The study, "Evaluation of a decision tree in the diagnosis of renal neoplasms based on genomic aberrations detected by array-CGH," was completed in conjunction with a translational team at the Cleveland Clinic. The study reviewed 15 targeted genomic regions for copy number data across the 188 patient samples. Samples were included from all major subtypes of renal cancer, and were initially selected for the study based on pathologic diagnosis: 62 clear cell carcinomas, 56 papillary carcinomas, 34 chromophobe carcinomas, and 36 oncocytomas.

Additional highlights include the following. Only 2 µg of DNA material was needed. Overall tumor subtyping ranged from 97% diagnostic accuracy for clear cell, 93% for chromophobe, 91% of papillary, and 86% in oncocytomas. Of the 188 samples, 173 were correctly assigned to a tumor subtype. Specificity of the molecular diagnosis using the array ranged from 100% for chromophobe; 99% for oncocytoma, and 97% for both clear cell and papillary kidney cancer subtypes.

The results support implementation of the CGI kidney cancer algorithm and test in a clinical setting to provide highly accurate subtyping and outcome prediction for renal cancers. CGI is currently making the test available as a laboratory-developed-test (LDT). The panel is also being transferred to a Next-Generation Sequencing (NGS) platform for additional assessment of genomic aberrations.

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