UC San Diego Researchers Shortlisted for International Cancer Competition

Ludmil Alexandrov (L) and Trey Ideker (R) are both on teams selected for the shortlist of Cancer Grand Challenges. Alexandrov leads a team investigating the origins of cancer, while Ideker is co-investigator on a team creating AI tools for cancer research. Photo credit: UC San Diego Health Sciences

Two UC San Diego Scientists Compete for $25 Million in Global Cancer Research Challenge

International teams vie for prestigious funding to tackle cancer's most complex problems using AI and mutational signature analysis

San Diego, CA – Two University of California San Diego researchers are among 12 finalists competing for up to £20 million ($25 million) each in the Cancer Grand Challenges initiative, a global competition that brings together world-class scientists to tackle cancer's most intractable problems.

Ludmil Alexandrov, Ph.D., professor of cellular and molecular medicine and bioengineering, leads Team CAUSE, while Trey Ideker, Ph.D., professor of medicine, bioengineering, and computer science, serves as co-investigator on Team Biologia Ex Machina. Selected from a record 227 applications, the 12 finalist teams will present their proposals to a scientific committee in December 2025, with winners announced in March 2026.

The Competition: Cancer's Grand Challenges

Cancer Grand Challenges, founded in 2020 as a joint effort between Cancer Research UK and the National Cancer Institute, aims to unite world-class researchers from diverse disciplines and institutions. To date, the initiative has awarded over £315 million to 16 multidisciplinary international research teams.

In March 2025, Cancer Grand Challenges announced seven new challenges representing the most urgent, complex problems in cancer research. The substantial funding packages are designed to support ambitious, long-term projects that conventional grant mechanisms typically cannot accommodate, with teams expected to be international in nature.

The 12 shortlisted teams are competing across six challenges:

AI-Human Collaborations (2 teams): Developing AI agents to generate and test cancer research hypotheses at unprecedented scale

Cancer Avoidance (2 teams): Understanding why some people don't get cancer when their genetics or lifestyle indicate they should

The Dark Proteome (2 teams): Mapping cryptic proteins with therapeutic potential

Mutational Signatures (1 team): Identifying the causes behind DNA damage patterns

Nervous System and Cancer (2 teams): Investigating neurological connections to cancer development

Rewiring Cancer Cells (2 teams): Turning cancer's own survival mechanisms against itself to make tumors vulnerable

Professor Charles Swanton, Cancer Grand Challenges Scientific Committee Chair, noted that the latest challenges take on cancer in ways researchers haven't been able to before.

Team CAUSE: Decoding Cancer's Molecular Fingerprints

Team CAUSE is tackling mutational signatures — unique patterns of DNA damage left by environmental exposures such as pollution, tobacco smoke, or UV rays and natural cellular processes. While scientists can identify these molecular "autographs" in cancerous tissue, the sources of many signatures remain unknown.

Alexandrov developed the first comprehensive map of mutational signatures in human cancer in 2013. His lab's machine learning tool, SigProfilerExtractor, detected 20 to 50% more true positive signatures with five times fewer false positives compared to 13 existing bioinformatics tools.

The Approach: The team aims to systematically identify DNA adducts — chemical modifications that act as the "pen" writing mutational signatures — bridging the gap between observable damage and its underlying causes. Team CAUSE brings together experts from the U.S., Netherlands, and UK, integrating chemistry, genomics, AI, and patient advocacy.

Clinical Promise: Once researchers link specific signatures to their sources, public health officials could develop targeted prevention campaigns. Alexandrov's team found tobacco signatures not only in bladder cancer tissues but also in normal bladder tissues of smokers who haven't developed cancer, demonstrating how mutations accumulate with exposure.

Prostate Cancer Applications: Research has identified six mutational signatures in prostate cancer, including two clock-like signatures, two related to DNA repair defects, and two related to APOBEC mutagenesis. SBS3 — a signature associated with defective DNA repair — correlates with worse prognosis in prostate cancer. Understanding signature origins could enable personalized risk assessment and help distinguish aggressive cancers requiring immediate intervention from slow-growing tumors suitable for active surveillance.

Team Biologia Ex Machina: AI as Research Partner

Led by Dr. Marinka Zitnik at Harvard Medical School, Team Biologia Ex Machina aims to develop AI-powered "co-scientists" that work semi-independently with human researchers to generate and test thousands of cancer hypotheses at scale.

Dr. Zitnik is an Associate Professor in the Department of Biomedical Informatics at Harvard Medical School and Associate Faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence. Ideker, a UC San Diego faculty member since 2003, holds leadership positions in several federally-funded research centers and has led seminal studies establishing systems biology practices.

The Approach: The AI co-scientists will generate theories about cancer development and growth, troubleshoot to refine them, and rapidly test thousands of ideas simultaneously — a scale impossible for human teams alone. The team includes collaborators from the United States, Spain, Switzerland, and United Kingdom.

Ideker's lab recently reported a deep learning model of yeast cells that translates mutation patterns into growth predictions, with plans to develop similar models for cancer cells.

Clinical Promise: AI systems could analyze molecular patterns across thousands of tumors to identify overlooked therapeutic targets, predict effective drug combinations, and compress the typical decade-long timeline from discovery to clinical application. The team's work aims to accelerate translation of discoveries into clinics, ultimately identifying new treatment targets across many tumor types.

Prostate Cancer Applications: Prostate cancer exhibits marked clinical heterogeneity with variable patient outcomes, and molecular characterization has revealed striking mutational heterogeneity. AI-powered research could help determine which patients benefit from active surveillance versus aggressive treatment, discover approaches for castration-resistant disease, and predict effective combination therapies.

What's at Stake

Rather than siloed laboratories making incremental progress, Cancer Grand Challenges has shown that integrated effort creates momentum greater than the sum of its parts, shifting the timeline toward near-future clinical impact.

Professor Judy E. Garber, Vice Chair of the Scientific Committee, stated that Cancer Grand Challenges funding allows diverse teams to push boundaries, be creative and bold, and uncover novel biology with new ways to exploit it for cancer treatment.

Both UC San Diego teams exemplify the initiative's international, interdisciplinary approach, bringing together expertise in genomics, chemistry, computer science, clinical medicine, and patient advocacy to address challenges no single researcher, laboratory, or country could solve alone.

The December interviews will determine which teams receive transformative funding to pursue research that could reshape cancer prevention, diagnosis, and treatment for years to come.

Sources

  1. UC San Diego Today. "UC San Diego Researchers Shortlisted for International Cancer Competition." September 2025. https://today.ucsd.edu/story/uc-san-diego-researchers-shortlisted-for-international-cancer-competition 

  2. Jacobs School of Engineering, UC San Diego. "Ludmil Alexandrov - Professor, Bioengineering." https://jacobsschool.ucsd.edu/node/3617

  3. Wikipedia. "Ludmil Alexandrov." Last modified April 27, 2024. https://en.wikipedia.org/wiki/Ludmil_Alexandrov

  4. UC San Diego Today. "Mutational Signature Linking Bladder Cancer and Tobacco Smoking Found With New AI Tool." https://today.ucsd.edu/story/mutational-signature-linking-bladder-cancer-and-tobacco-smoking-found-with-new-ai-tool

  5. Alexandrov, L. B., et al. "The repertoire of mutational signatures in human cancer." Nature, 578(7793), 94-101 (2020). https://pubmed.ncbi.nlm.nih.gov/32025018/

  6. National Library of Medicine. "Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment." PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC10369904/

  7. IDEKER LAB, UC San Diego. "Trey Ideker." https://idekerlab.ucsd.edu/trey-ideker/

  8. National Cancer Institute. "Dr. Trey Ideker: Mapping the Circuitry of a Cancer Cell." https://www.cancer.gov/about-nci/organization/dcb/research-programs/csbc/trey-ideker

  9. Cancer Grand Challenges. "Meet the finalists looking to take on cancer's toughest challenges." September 24, 2025. https://www.cancergrandchallenges.org/meet-the-finalists-looking-to-take-on-cancers-toughest-challenges

  10. Cancer Research UK. "The latest teams bidding for up to £20m to tackle cancer's greatest challenges." September 24, 2025. https://news.cancerresearchuk.org/2025/09/24/the-latest-teams-bidding-for-up-to-20-million-to-tackle-cancer-grand-challenges/

  11. Harvard DBMI. "Marinka Zitnik, PhD." https://dbmi.hms.harvard.edu/people/marinka-zitnik

  12. Zitnik Lab. "Artificial Intelligence for Medicine and Science." https://zitniklab.hms.harvard.edu/

  13. Cancer Grand Challenges. "New challenges." https://www.cancergrandchallenges.org/new-challenges

  14. ResearchConnect. "2025 Cancer Grand Challenges Programme Open for Applications." March 12, 2025. https://myresearchconnect.com/2025-cancer-grand-challenges-programme-open-for-applications/

  15. University College London. "International research team led by UCL shortlisted for Cancer Grand Challenges." September 25, 2025. https://www.ucl.ac.uk/medical-sciences/news/2025/sep/international-research-team-led-ucl-shortlisted-cancer-grand-challenges

  16. Cancer Grand Challenges. "The world's brightest minds going head-to-head to take on cancer's toughest challenges." https://www.cancergrandchallenges.org/news/the-worlds-brightest-minds-going-head-to-head-to-take-on-cancers-toughest-challenges

  17. National Library of Medicine. "Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges." PMC. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865812/

  18. National Library of Medicine. "Molecular evolution of early onset prostate cancer identifies molecular risk markers and clinical trajectories." PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC7444093/

  19. Villa, M., et al. "Comprehensive analysis of mutational processes across 20,000 adult and pediatric tumors." Nucleic Acids Research, 53(13), gkaf648 (July 2025). https://academic.oup.com/nar/article/53/13/gkaf648/8196081

  20. National Library of Medicine. "The Mutational Landscape of Prostate Cancer." PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC4342117/

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