Speaker Profile

M.D., Ph.D., CEO, SEngine Precision Medicine

Biography
Dr. Carla Grandori is an internationally recognized physician-scientist who pioneered new technologies to develop the next generation of cancer therapeutics tailored to each individual’s tumor. Dr. Grandori’s entrepreneurial experience and network drove the growth of SEngine Precision Medicine. She has recruited a world-class team including a Nobel Prize Laureate and a Pulitzer Prize-winning oncologist. Dr. Grandori raised seed and Series A funding for SEngine Precision Medicine to enable CLIA certification for SEngine’s high-throughput test, the PARIS® assay, guiding cancer treatment options. This funding enables the development of therapies targeting previously undruggable oncogenes including MYC, KRAS and TP53. The oncology startup is accelerating drug discovery and functional precision medicine to transform cancer care. Dr. Grandori received her MD from the University of Rome and Ph.D. from the Rockefeller University in New York. The majority of her scientific career was at the Fred Hutchinson Cancer Research Center in Seattle.


AI Showcase Showcase:
SEngine Precision Medicine

Founded in 2015, SEngine Precision Medicine is a privately-held biotech startup, a spin-off the Fred Hutchinson Cancer Research Center, based in Seattle, WA.

Leveraging Functional Data For Personalized Cancer Treatments
SEngine is generating functional drug response data to guide personalized treatments for patients employing tumor-derived organoids, thus simulating cancer growth in 3D. These data, based on hundreds of drugs for each molecularly profiled cancer, will feed AI predictive algorithms.

 Session Abstract – PMWC 2020 Silicon Valley


The PMWC 2020 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.