The International Alliance for Cancer Early Detection is an ambitious project to speed research, catch cancer early and improve patients’ lives.

ACED is an international network of institutions leading research into early detection. We work together to tackle complex questions and amplify the impact of our research. The alliance offers:

• Awards, training and education to invest in a new generation of scientists
• Multi-institutional awards to strengthen collaboration
• Rapid funding for researchers to respond to evolving questions

The OHSU Knight Cancer Institute is a founding partner.

Awards

ACED offers awards to researchers in early detection, including:

• Ph.D. Training Awards for graduate students
• Pathway Awards for early career researchers
• Skills Exchange and Development Travel Awards
• Pilot and Project Awards to support new and established research

CEDAR members can learn how to apply for ACED awards on the CEDAR Wiki.

ACED supports projects in these early-detection areas and more:

• Enabling interception
• Hereditary cancers and risk
• Inequalities in cancer early detection
• Biomarker discovery and validation
• Human-based discovery research
• Risk stratification
• Preclinical early detection model systems
• Translational and clinical research

Award recipients

ACED has given awards to several CEDAR researchers. Meet two of them.

Q: What led you to the field of early detection?
At 17, witnessing my father's battle with late-stage kidney disease ignited my passion for medicine. Simultaneously, my late grandmother battled stage III breast cancer. Her hope never wavered, which truly inspired me. Her story underscores the importance of early detection. If her cancer was caught earlier, she may have had more time.

Q: What are you working on now?
I joined Dr. Ngo’s lab at CEDAR, where I am focused on developing a cell-free platform to detect epigenetic modifications. My goal is to enhance current liquid biopsy sequencing technologies. My aspiration is to lead scientific advancements in early cancer detection and make diagnoses more accurate.

Q: How has ACED affected your career?
The ACED lecture series helped me build a comprehensive understanding of early cancer detection from the lab to the clinic which has reshaped how I view my own research. Beyond this, the access to a network of renowned leaders at all partnering ACED centers has opened opportunities that will shape my career.

Q: What drew you to the field of early detection?
I’m an anatomist by training and have focused on tissue engineering throughout my career. I was drawn to early detection after I developed a method to spatially pattern single cells to engineer tissues with subcellular resolution. At the time, there wasn’t a way to precisely control the microenvironment at this scale. Now, with bioprinting, we can fabricate the tumor microenvironment cell by cell to create living copies of patient biopsies, opening many new possibilities.

Q: What are you working on now?
My work is focused on manipulating the composition and spatial organization of cells within the ductal carcinoma in situ tumor microenvironment. Our goal is to pick apart these tumors to uncover the fundamental biology for biomarker discovery and predictive tumor models to better stratify lethal vs nonlethal disease.

Q: How has ACED affected your career?
Being selected as an ACED recipient was such an honor. ACED has boosted my career by providing excellent training in all aspects of early detection research through the summer school and weekly lecture series. ACED and the annual Early Detection Conference have provided an invaluable platform to connect with experts around the globe. I’m especially excited for the opportunity to spend some time at the University of Cambridge through the ACED Skills Exchange program.

ACED partners

Founded in 2019, ACED was launched by these leaders in early detection:

• Cancer Research UK
• Canary Center at Stanford University
• University of Cambridge
• OHSU Knight Cancer Institute
• University College London
• University of Manchester