Diagnostics Startup Droplet Biosciences Partners With Nvidia To Speed Cancer Testing
Diagnostics firm Droplet Biosciences said on Tuesday it is collaborating with Nvidia (NVDA.O), opens new tab to use the chipmaker’s AI infrastructure to speed up post-surgery cancer test results.
The company has been using Nvidia Parabricks, a GPU-accelerated software suite, to speed up genomic data analysis for DNA sequencing.
Droplet said its method can detect residual disease in 24 hours by analyzing lymphatic fluid collected post surgery, compared to the four to six weeks it typically takes for tumor remnants to appear in blood-based tests.
“By leveraging NVIDIA Parabricks’ acceleration, we’ve been able to compress some of our most computationally intensive steps from more than a day down to just a few hours,” said Droplet’s chief scientific officer, Wendy Winckler in a statement.
Droplet said it also realized operational benefits despite higher hourly costs for GPU compute, adding, “The dramatically reduced runtime results in a lower overall cost per sample.”
“It’s primarily allowing us to parallelize and run more of these (tests) at the same time and then, importantly, much faster,” Wendy Winckler told Reuters.
Faster turnaround allows patients to get the results while still in the hospital, while avoiding extra visits or long waits for traditional blood tests.
The diagnostic startup’s first clinical test is for HPV-negative head and neck cancer, validated under the Clinical Laboratory Improvement Amendments.
The test is available as part of an early access program with select partners, Winckler said. Its customers include the University of Pittsburgh Medical Center, Cleveland Clinic and Mayo Clinic.
Droplet is a member of NVIDIA Inception, an AI startup accelerator program, an
d an NVIDIA AI Enterprise customer.
By combining Nvidia’s technology, Droplet Biosciences is transforming cancer detection, said Rory Kelleher, global head of business development for life sciences at NVIDIA, adding “the computational burden of deep-sequencing analysis has long been a bottleneck in cancer diagnostics”.
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