The Precision Medicine World Conference kicks off next week at the Computer History Museum in Mountain View, California. The program traverses innovative technologies, thriving initiatives, and clinical case studies that enable the translation of precision medicine into direct improvements.
Please join us for a lively panel discussion around scalable infrastructure/platforms that integrate next-generation sequencing (NGS) and other data (e.g. phenotypic) to power discovery in Pharma and the clinic.
Title: Scalable NGS Infrastructure/Platforms
Talk Details: Track 1 – Monday, January 22 at 10:30am
Moderator: Brady Davis, Chief Strategy Officer, DNAnexus
Panel Speakers:
- AstraZeneca/MedImmune – David Fenstermacher, VP BioInformatics
- Sutter Health – Greg Tranah, Director, CPMC Research Institute, Adjunct Professor Dept. of Epidemiology & Biostatistics, UCSF
- Carol Franc Buck Breast Cancer Center at UCSF– Laura Esserman, Director
- City of Hope – Sorena Nadaf, SVP & CIO
Abstract:
Health care providers increasingly require multi-omic datasets, including phenotypic data informed by genomic data. Such data needs to be obtained in an economically sustainable way and made available on an agile user-friendly platform so that these data may inform clinical care and lead to health improvements.
Pharmaceutical companies (“Pharmas”) are interested in obtaining datasets containing phenotypic/clinical and genomic information generated from patient cohorts of specific disease areas. Such datasets can help Pharma researchers identify drug targets or find biomarkers, validate hypotheses related to the interaction of genomics with disease or with specific therapies, and identify candidate populations for future clinical trials. Payers are also interested in the outcomes related to new discoveries and therapies in order to reimburse for these treatments.
This session will focus on how both healthcare provider organizations, Pharmas and Payers are working toward solving these complex and challenging problems from a technical and business model perspective.
