The ARPA-H Advanced Analysis for Precision Cancer Therapy (ADAPT) program aims to use predictive models to match treatments with changing tumor biology and prevent disease progression. 

Funding for awardees varies in amount and is contingent upon the recipient meeting aggressive milestones specific to their project.

The ADAPT performers are:

Technical Area 1: Therapy Recommendation Techniques 

  • Arizona State University: The team will perform tumor ecology-based and evolution-based modeling to identify key resistance traits that change over time, including gene activity, oncogenic state, cancer cell evolvability, immune evasion, and signaling network activity.
  • Stanford University: The team will use interpretable artificial intelligence models to identify complex relationships between tumor features, drug response, and survival times using longitudinal pathological, radiological and molecular data.
  • University of California, San Diego: The team will build dynamic biomarkers that anticipate tumor evolution with predictions of drug response that can be updated frequently with new data to enable adaptive treatment for patients.
  • Massachusetts Institute of Technology: The team will develop machine learning algorithms that can utilize genetic, molecular, imaging, and electronic health record data to recommend optimal therapy strategies.
  • Brigham & Women’s Hospital: The team will develop tools for fusing clinical and genomic data into foundational models that the therapy recommendation teams will use to identify cancer resistance traits and create treatment response biomarkers.   

Technical Area 2: Evolutionary Clinical Trial Design 

  • University of North Carolina at Chapel Hill (breast cancer): The team will develop an evolutionary trial for breast cancer patients using new statistical approaches that maximize the likelihood of treatment success for each individual patient using biomarker-driven therapies.
  • Beckman Research Institute of the City of Hope (lung cancer): The team will create a biomap of immunotherapy resistance mechanisms in non-small cell lung cancer and validate new biomarker-guided therapy in near real-time to improve the success of patient treatments.
  • UT MD Anderson Cancer Center (colon cancer): The team will undertake a novel two-phase umbrella clinical trial for patients with colon cancer that quickly identifies and targets emergent resistant traits through integration of drug response biomarkers and resistance-targeting drugs. 

Technical Area 3: Treatment and Analysis Platform 

  • DNAnexus: The team will develop a Treatment and Analysis Platform (TAP), a secure cloud-based data ecosystem that enables collaboration among clinicians and multidisciplinary researchers.
  • Washington University: The team will augment and extend the ADAPT TAP by integrating their Multi-modal Analysis with XNAT (eXtensible Neuroimaging Archive Toolkit) (MAX) system, providing additional state-of-the-art tools to manage, analyze, and explore clinical and imaging data.