PDGx Wins SBIR Contract for Assay to Inform on Checkpoint Inhibitor Therapy

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Personal Genome Diagnostics (PGDx) has been awarded a Phase I SBIR contract from the National Cancer Institute to develop a cost-effective diagnostic that can help to identify cancer patients who will most likely benefit from checkpoint inhibitor therapy. The work will focus on developing a liquid biopsy assay, based on the firm’s MutatorDETECT™ technology, for quantitatively evaluating a patient’s tumor mutation load from cell-free tumor DNA found in plasma.

PDGx says that while checkpoint inhibitors have been approved for lung, melanoma, and bladder cancers, which have relatively high mutational loads, studies indicate that there may be subsets of patients with other types of cancers who also have high mutational loads and so may respond well to immuno-oncology drugs. The firm claims that in contrast with more costly tissue biopsy-based approaches to testing patients’ mutational load at the start of treatment, its liquid biopsy assay is more cost effective and could be used to track mutational status in patients over time.

“Immuno-oncology drugs have shown great promise, but they are expensive and do not work for all patients’” stated Mark Sausen, Ph.D., GDx vp of R&D, and co-principal investigator for the NCI contract. “Affordable and accessible methods to identity patients likely to benefit are urgently needed. Over the past several years we have collaborated with pharmaceutical partners to evaluate the effects of mutational load by retrospectively applying whole-exome sequencing and our ImmunoSELECTTM analyses to clinical samples. This contract now gives us the opportunity to work with these partners to develop the MutatorDETECT assay to identify patients prospectively for clinical trial enrollment.”

Last month PDGx launched its CancerSELECT™ 125 test for pan-cancer tumor profiling. PDGx claims the assay detects key actionable and functionally important sequence mutations, including drug sensitivity and acquired drug resistance sequences, which can help to inform treatment decision making.
 








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