Oct 6
Thrilled to visit the Department of Quantitative Sciences at
Johns Hopkins Sidney Kimmel Cancer Center this week to share our
work on a next-generation ARPA-H ADAPT clinical trial in
metastatic breast cancer (MBC). MBC remains one of oncology’s
toughest challenges — driven by evolving treatment res...Read more
Thrilled to visit the Department of Quantitative Sciences at
Johns Hopkins Sidney Kimmel Cancer Center this week to share our
work on a next-generation ARPA-H ADAPT clinical trial in
metastatic breast cancer (MBC). MBC remains one of oncology’s
toughest challenges — driven by evolving treatment resistance
and limited long-term control. Through the ARPA-H ADvanced
Analysis for Precision cancer Therapy (ADAPT) program, we aim to
change this paradigm by building evolutionary trials that learn
and adapt as the cancer evolves. Our recently funded 500-patient
trial (with @UNC_Lineberger and the Translational Breast Cancer
Research Consortium) integrates: 🔹 Serial tumor profiling
(ctDNA, imaging) to detect resistance in real time 🔹 Dynamic
treatment adaptation across multiple lines of therapy 🔹
Embedded biomarker discovery and N-of-1 subtrials for precision
decision-making Statistically, the design leverages a Bayesian
adaptive framework using a piecewise exponential model for PFS.
This enables: • Posterior-driven decisions and predictive
probabilities • Interim stopping for efficacy or futility •
Seamless learning between biomarker discovery and validation
phases We’ve also formalized three trial phases — warm-up,
active learning, and test — to accelerate biomarker refinement
and clinical translation. Thank you to my Hopkins colleagues for
an outstanding discussion on the future of adaptive oncology
trials and quantitative modeling. Exciting collaborations ahead
as we work toward truly real-time precision medicine. #ARPAH
#ClinicalTrials #Bayesian #Oncology #CancerDataScience
#AdaptiveDesign #PrecisionOncology
