Header: In a press release March 7, 2024, the Biden administration announces its "ADAPT" program, via ARPA-H, which is new programs in precision oncology and adaptive oncology. The total budget is $400M.
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See the press release here.
See the ADAPT home page at ARPA, here.
Here's a solicitation for a "Proposer's Day," here. And a solicitation website here. See a 25 page PDF solicitation details document here.
See coverage at Genomeweb here. Adjacent news, Zephyr AI raises $111M for AI-guided precision medicine.
The project results should be open license, open source. To me, this might be problematic. Let's say you get $20M to develop something, and are successful. You get your $20M repaying your demonstrated costs. Now it takes $10M to replicate and confirm the project and go to FDA. How do you get the $10M to finish the investment if there is no ROI? (I don't know).
Program Should Include Rebooting Outdated NCD 90.2, Medicare and NGS
My Medicare Takeaway: This massive evolution toward newer molecular tests and tje assessment of tumor evolution should be the nail-in-the-coffin of the now-ridiculous NCD 90.2, which forbids the tumor genome to be assessed more than once in a Medicare patient.
The only MAC system that has allowed serial monitoring, the MolDx MAC, has had to use well-meaning but truly goofy and bizarre workarounds stacked on each other, like calling each test for minimal residual disease or tumor evolution, "four assays over one year are one test," to fit the one test rule.
Making Medicare coverage look like the high school game "Twister." Let's stop it now. Fix the outdated and warped "one test rule" in NCD 90.2.
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AI CORNER
The deepest document, clicking through the various press releases and websites, is the 25 page PDF solicitation discription. Here's an independent analysis by Chat GPT4:
Advanced Research Projects Agency for Health (ARPA-H) Solicitation for ADvanced Analysis for Precision Cancer Therapy (ADAPT)
The Advanced Research Projects Agency for Health (ARPA-H) has announced a solicitation for proposals under its ADvanced Analysis for Precision cancer Therapy (ADAPT) program. The initiative aims to revolutionize cancer care by developing adaptive strategies for treating cancer as it evolves. The program focuses on leveraging novel clinical trial designs, advanced tumor biology measurement, and analysis technologies to match each patient's evolving cancer with the most effective therapy. The goal is to improve survival for patients with metastatic cancer by facilitating a continuous and interconnected learning process that rapidly informs clinical practice. Proposals may address individual Technical Areas (TAs) or a combination thereof, spanning therapy recommendation techniques, evolutionary clinical trials, and a treatment & analysis platform.
Key Takeaways:
- Program Objective: Accelerate better health outcomes through innovative research on adaptive cancer treatment strategies.
- ADAPT Program: Aims to create an adaptive treatment platform to match treatments with tumor changes, improving survival rates for metastatic cancer patients.
- Eligibility: Open to various organizations capable of tackling the program's challenges across its three interconnected TAs.
- Technical Areas (TAs): Includes
- Therapy Recommendation Techniques (TA1),
- Evolutionary Clinical Trial (TA2), and
- Treatment & Analysis Platform (TA3).
- Proposal Submission: Proposals should address the program's structure, integration, and metrics, adhering to specific content and format requirements.
- Equity and Accessibility: Proposals must include a plan to ensure equitable health care access, reflecting the diversity of the U.S. population.
- Funding and Awards: Multiple awards anticipated, with the scope and budget aligned with the complexity of proposed solutions.
- Data and Software Standards: Emphasis on open-source technology, interoperability, and adherence to existing health IT standards.
- Intellectual Property: Encourages alignment with open-source regimes to promote a sustainable ecosystem for translational oncology.
- Collaboration: Expected among performers to share information freely, work on common standards, and ensure the program's goals are met.
This summary encapsulates the main aspects of the ADAPT program solicitation, targeting innovations in cancer treatment through adaptive strategies and collaborative research efforts.
User
Can you provide a summary and 5 takeaways for each of the programs, being tA1, TA2, and TA3.
ChatGPT
TA1: Therapy Recommendation Techniques Summary
TA1 focuses on developing data-driven methods to identify resistant cancer traits, discover new predictive biomarkers of drug response, and guide optimized treatments based on a tumor's genetic and phenotypic traits. It encompasses three sub-areas: Multi-Modal Data Fusion (TA1.1), Resistant Trait Modeling (TA1.2), and Biomarkers that Predict Drug Response (TA1.3). The aim is to leverage comprehensive assessments of tumor biology, including genetic, protein-level data, and clinical data, to build predictive biomarkers for personalized cancer treatment strategies.
Key Takeaways for TA1:
Objective: To enhance the precision of cancer treatment by identifying tumor resistance traits and developing predictive biomarkers for drug response.
Multi-Modal Data Fusion (TA1.1): Focuses on integrating various data types to provide a more accurate and comprehensive understanding of tumor data.
Resistant Trait Modeling (TA1.2): Aims to increase the precision and number of identified resistant traits through advanced tumor evolution models.
Biomarkers for Drug Response (TA1.3): Seeks to develop and test multi-modal biomarkers that will be implemented within an evolutionary clinical trial.
Collaborative Approach: Requires collaboration among systems biologists, data scientists, and clinicians to analyze and interpret tumor biology and clinical data for therapy optimization.
TA2: Evolutionary Clinical Trial Summary
TA2 is designed to revolutionize cancer clinical trials by introducing an evolutionary clinical trial framework that adjusts drug treatments based on how the tumor evolves during therapy. It includes three sub-areas: New Tumor Measurement Technologies (TA2.1), Evolutionary Trial Protocol (TA2.2), and Evaluation of TA1 Biomarkers (TA2.3). The goal is to improve cancer treatment by integrating advanced tumor analysis methods to identify emerging resistant traits, enabling clinicians to adapt treatments effectively.
Key Takeaways for TA2:
Innovative Design: Proposes a new type of clinical trial that dynamically adjusts treatments in response to changes in tumor biology.
Tumor Measurement Technologies (TA2.1): Emphasizes the collection of diverse temporal tumor measurements for resistance trait identification and biomarker development.
Evolutionary Trial Protocol (TA2.2): Develops a modular, open-source protocol design capable of responding to tumor changes in near-real-time.
Biomarker Evaluation (TA2.3): Focuses on testing and integrating predictive biomarkers into the evolutionary trial to guide therapy selection as the tumor evolves.
Patient-Centric Approach: Aims to significantly improve patient care by personalizing treatment strategies based on individual tumor characteristics.
TA3: Treatment & Analysis Platform Summary
TA3 aims to create an open-source collaboration space for developing, analyzing, and sharing data models and trial protocols between researchers and clinicians. This platform will facilitate the real-time availability of harmonized multi-modal and patient data, supporting adaptive therapy recommendation techniques. Key focus areas include building a collaboration ecosystem, creating a comprehensive data lake, facilitating data processing, promoting data standardization and linkage, and developing open APIs and toolsets.
Key Takeaways for TA3:
Collaborative Ecosystem: Establishes a platform to unite clinicians and scientists, enhancing access to data and therapy regimen analysis.
Data Lake Creation: Maintains a centralized repository for various data types, improving data accessibility and management.
Data Processing Facilitation: Develops pipelines for rapid tumor biology measurement data processing, including quality control and data curation.
Standardization and Open APIs: Implements data standardization and linkage processes, alongside developing open APIs for broad data access and sharing.
User-Friendly Tools: Aims to create intuitive, AI-powered dashboards and data exploration tools, making biomedical data easily accessible and usable for the scientific and clinical communities.
