Sponsored by The Cancer Cell Map Initiative, UCSF and UC San Diego Health Sciences

Overview

The Cancer Cell Map Initiative has funds to support up to two pilot projects in cancer systems biology. Project proposals are solicited from faculty members at UCSF and nearby universities and research institutes in the Bay Area (including e.g. Stanford University) wishing to develop and apply systems biology approaches to address a compelling biomedical question in cancer research in collaboration with CCMI investigators.

Funds will be provided for one year with awardees able to re-apply for future years.

Goals of the Program

The mission of CCMI is to enable a new era of cancer discovery and treatment, based on the complete elucidation of the molecular networks underlying cancer. Medical research increasingly depends on knowledge of molecular networks of multiple types; such networks define a hierarchy of structures and processes in a cancer cell. To interrogate these networks, the CCMI leverages interactomics, imaging and computational tools at the University of California, San Francisco (UCSF), UC San Diego and Stanford University.  

This pilot project program is intended to support the short-term exploration of innovative, high-risk concepts that complement existing CCMI research (more details are available at www.ccmi.org/research and bit.ly/CCMI2pubs).

Possible topics of notable interest are as follows: 

• Technologies for large-scale gene editing in primary cells, 3D tissue models or similar
• Technology development of large-scale advanced phenotype readouts like cell migration or similar
• Mechanistic studies involving patient-derived models
• Development and implementation of data integration methods leveraging machine-learning approaches
• Studies exploring cancer and immune cells interactions

Required Application Components: 

• Using the NIH’s structure (Significance, Innovation and Approach) or a similar format the proposal should include a succinct description of the research plan along with proposed experiments, methods and analysis and detailed milestones.
  – The application will require a single Specific Aim in three pages or less (page limit includes Figures but not References). Proposals should clearly state the significance of the biomedical problem in cancer, why it will benefit from a systems biology approach and how this work will enable future competitive grant proposals.
  – Describe the available reagents and assays needed to support the research or describe what will need to be developed.
  – Applicants must provide a statement of the roles of collaborating CCMI Investigator(s) and how the resources and expertise of the CCMI will be utilized.
  
  
• NIH format biosketches for key personnel (not needed for CCMI faculty members) 
  
  
• NIH Research & Related budget and justification (no more than $50,000/year total costs)
  – Budget must follow NIH guidelines
  
  
• For applications outside of UCSF: Institutional F&A negotiated rate agreement

All documents should be submitted as one PDF to hello@ccmi.org by 11 AM PT on Monday, August 14, 2023. Please also direct any questions to hello@ccmi.org.

Review Criteria

Proposals will be reviewed and ranked according to the following criteria: potential to provide significant advances in the area of cancer systems biology; qualifications of the personnel; innovation, appropriateness and feasibility of the experimental design; complementarity to the existing research within the CCMI; and potential to be developed into R21 or R01 grants. 

Progress Reporting And Expectations 

All funded investigators will be asked to submit progress reports one month prior to the Center’s NIH progress reporting date and at the end of the project period. Progress and final reports will be provided to the Center Director and the NIH program office.

Progress reports should detail return on investment, including: 

• Presentations, abstracts, publications, etc. resulting from projects benefiting from pilot funds. 
• External, peer-reviewed funding received using pilot project data and findings. 
• Patent applications and approved patents. 
• Trials developed, with the number of patients recruited or status of trial provided.

Awardees will also be required to participate in the CCMI’s annual External Advisory Committee Meeting, every other month All Hands Meetings and other CCMI events. 

About The Cancer Cell Map Initiative (CCMI)

The CCMI is generating comprehensive maps of the key protein-protein and genetic interactions underlying cancer, and is developing computational methods using these maps to identify new drug targets and groups of patients with shared outcomes (Fig. 1). Protein-protein interaction maps – the complete set of proteins that bind to another protein – tell us about the physical structure of cancer cells. Genetic interaction maps – knowing how deleting one gene impacts how cells respond to the loss of another gene – tell us about how groups of genes function as pathways and networks. New drug targets and patient subtypes will then be identified using a variety of machine learning algorithms. Unsupervised methods such as clustering and network propagation will be used to identify patient subtypes and drug targets, respectively, and supervised methods like neural networks will be trained to predict outcomes based genetic information.

The CCMI was originally founded in 2015 by Trey Ideker (UCSD) and Nevan Krogan (UCSF) who had worked for more than a decade, often in collaboration, to establish fundamental experimental, computational and conceptual frameworks for mapping molecular networks. The first iteration of the CCMI as an NCI-funded Cancer Systems Biology Consortium culminated in comprehensive protein networks for breast and head-and-neck squamous cell carcinoma. For the second round of NCI funding through CCMI 2.0, we have elected to extend our comprehensive network mapping to central pathways important to many cancer types, beginning with the PI3K/AKT/mTOR axis and TP53, and to a third cancer type, lung squamous cell carcinoma (Fig. 1). We are expanding our network mapping pipeline, originally based on affinity purification mass spectrometry (AP-MS), by protein immunofluorescent imaging, covering physical scales in the 0.05 – 500 μm range, and cryo-EM, covering physical scales in the 1 – 50 nm range. With these additional data we aim to create spatiotemporal models of stable and transient protein interactions involved in cancer.