One way to develop prognostic and predictive tests in oncology is through the use of genomic biomarkers. And as more diagnostics companies are recognizing the potential benefits of this method, we’re seeing an increase in the number of companion diagnostic tests relying on gene-based biomarkers. Another reason for this continued trend towards personalized medicine is the continually decreasing costs of whole-genome sequencing.Read More
Biomarkers are critical to pharmaceutical and diagnostic companies developing novel therapies and diagnostics for oncology. Being able to discover and validate the best biomarkers forms the basis of any successful cancer diagnostic test.
Types of cancer diagnostic tests
• Companion diagnostics for new therapies – this type of diagnostic test is used for patient stratification in clinical trials. Successful companion diagnostics at this stage of drug development can increase the medical value of a therapy by selecting for responders or excluding patients at risk for severe side effects. Thus, they can facilitate regulatory approval, while keeping clinical trials smaller and cost effective.
• Companion diagnostics for existing drugs – this type of diagnostic test is developed after a cancer therapy has already been on the market in order to better select the right patients for the therapy. Successful companion diagnostics for existing drugs aim to reduce overall healthcare costs and the experience of unnecessary side effects by identifying patients that will benefit from treatment.Read More
While genomics and the use of the big data it creates are seeing increasing prevalence in cancer diagnosis, the use of tissue remains the gold standard.
Tissue Phenomics™ is the big data approach to clinical oncology that enables all of the data in tissue images to be fully quantified in context. This automated quantification takes all standard pathological tissue biomarkers used to make diagnoses plus new complex tissue signatures—which often are difficult to assess with the human eye—and makes them available for bioinformatic analysis and discovery.
By combining big data from both tissue and genomics, you create a powerful diagnostic tool for better diagnosis and treatment decisions.Read More