The Cancer Cell Map Initiative
- The Cancer Cell Map Initiative
There now exists a vast amount of sequence data from tumors associated with many different cancer types, and efforts are ongoing to extract mechanistic insight from this information. Given all of this progress, what is now needed is an integrated computational and experimental strategy that will help place these alterations into context of the higher order biological mechanisms in cancer cells. This is the goal of the Cancer Cell Map Initiative, which will create a resource that can be used for cancer genome interpretation. This will allow us to identify key complexes and pathways to be studied in greater mechanistic detail to get a deeper understanding about the biology underlying different cancer states. Genomic data derived from tumor sequencing studies identifies key genes implicated in different cancer cells. Integrated physical and genetic networks based on these factors will help put the mutations into biological context, enabling the discovery of new disease genes as interacting partners become apparent. Ultimately, all of this knowledge will translate into improved ability to stratify and treat patients based on the particular networks that are altered.
The Co-Directors of the CCMI are Drs. Nevan Krogan (UCSF) and Trey Ideker (UC San Diego). The research interests and expertise of the CCMI faculty members run the gamut from structural biologists to cancer specialists.
Dr. Nevan Krogan
Dr. Nevan Krogan is a Professor in the Department of Cellular and Molecular Pharmacology at UCSF whose lab focuses on generating, analyzing and visualizing large-scale, quantitative genetic and physical interaction maps to further understand cell physiology.
Dr. Trey Ideker
Dr. Trey Ideker is a Professor in the Departments of Medicine, Bioengineering and Computer Science at UC San Diego whose lab focuses on mapping the molecular networks underlying cancer and using these networks to guide the development of novel therapies and diagnostics.
Dr. David Agard
Dr. David Agard is a renowned structural biologist at UCSF whose research interests include the mechanism of Hsp90 chaperone function and its role in human disease, microtubule nucleation and centrosome structure, and the structure and cell biology of phage encoded tubulins.
Dr. Prashant Mali
Dr. Prashant Mali is a leader in the exploding field of genome editing. As a postdoctoral fellow in Dr. George Church’s lab at Harvard, he published one of the first papers using CRISPR/Cas9 to edit the human genome. His lab at UC San Diego is developing a range of novel applications for genome engineering including his work on screening for genetic interactions.
Dr. Jill Mesirov
Dr. Jill Mesirov, previously the Associate Director and Chief Informatics Officer at the Broad Institute, was recently appointed Associate Vice Chancellor for Computational Health Sciences at UC San Diego. Her team has developed many popular analysis and visualization software packages, such as Gene Set Enrichment Analysis, GenePattern and the Integrative Genomics Viewer.
Dr. Alan Ashworth
Dr. Alan Ashworth is the President of the Helen Diller Family Comprehensive Cancer Center at UCSF and has spearheaded the understanding of synthetic lethality in cancer, showing that these insights can form the basis of new therapeutic approaches.
Dr. Jennifer Grandis
Dr. Jennifer Grandis is the Associate Vice Chancellor of Clinical and Translational Research at UCSF and is a leading researcher in head and neck cancer. Her lab has made many seminal discoveries about the molecular mechanisms underlying the resistance to EGFR inhibitors in head and neck cancer.
Dr. Silvio Gutkind
Dr. Silvio Gutkind, previously the Chief of the Oral and Pharyngeal Cancer Branch at the National Institute of Dental and Craniofacial Research, is now the Associate Director for Basic Science at the UC San Diego Moores Cancer Center. Dr. Gutkind is a leading expert in signaling pathways and 3D cell culture models of head and neck cancer.
Dr. Laura Esserman
Dr. Laura Esserman is the Director of the Breast Care Center at the UCSF Helen Diller Family Comprehensive Cancer Center. She is also the PI for the I-SPY 2 Trial, a large clinical trial that is screening multiple drugs from multiple companies with the hope of dramatically increasing the rate of identifying safe and effective new treatments for breast cancer.
Dr. Laura van ’t Veer
Dr. Laura van ’t Veer, an expert on personalized medicine, is Leader of the Breast Oncology Program and Associate Director Applied Genomics at the UCSF Helen Diller Family Comprehensive Cancer Center. Her research aims to advance patient management by using knowledge of the genetic makeup of both the tumor and the patient to optimally assign systemic therapy.
Project 1: Systematic Identification of Driver Networks in Cancer
Project Leaders: Krogan and van ‘t Veer
Co-Investigators: Agard, Ashworth, Ideker, Grandis and Gutkind
A vast number of mutations contribute to cancer, but the observed non-random combinations of those leading to transformation highlight the importance of hallmark pathways and networks in cancer progression. While many pathways have been implicated in cancer, attributes such as tumor heterogeneity, tissue of origin, and degree of progression lead to each case exhibiting a unique subset of altered pathways. Taken together, this diversity among cancer types and their origins has complicated the development of targeted cancer treatments. We propose to systematically identify the protein networks driving cancer, across a range of tumor types starting with head and neck squamous cell carcinoma and breast cancer. Coupled with functional validation and high-resolution structural analysis of the key protein interactions and complexes, we anticipate major insights into the underlying tumor biology as well as the potential to unravel genetic vulnerabilities of therapeutic relevance.
Project 2: Mapping the Pharmacogenetic Landscape for Precision Medicine
Project Leaders: Ashworth and Mali
Co-Investigators: Esserman, Grandis, Gutkind, Ideker, Krogan, Mesirov and van ‘t Veer
It is well known that cancer is tremendously heterogeneous with few tumors having the same set of mutated, amplified, or deleted genes. Clearly these molecular differences alter a tumor’s responsiveness to chemotherapy, but current knowledge of how the tumor genotype influences drug sensitivity is poor. We will seek to vastly increase our understanding of pharmacogenetic interactions in cancer (gene-gene and gene-drug interactions). Recognizing that oncogenic transformation requires alteration of the function of many genes, we will use state-of-the-art high-throughput epistasis mapping and data analysis pipelines to systematically interrogate the function and pairwise interactions of a panel cancer driver genes and therapeutic targets in both head and neck squamous cell carcinoma and breast cancer, expecting to identify many new synthetic lethal relationships. Anticipating the discovery of multiple therapeutically relevant synthetic lethal interactions, we have already formulated a plan for rapid clinical testing of the most promising hits as new treatment arms on the I-SPY 2 trial in breast cancer. Through this work, we expect to develop fundamental new insights into the genetic logic and functional synergies underlying cancer pathways as well as to greatly expand the ability of clinicians to practice precision oncology.
Project 3: Using Networks to Seed Hierarchical Whole-Cell Models of Cancer
Project Leaders: Ideker and Mesirov
Co-Investigators: Esserman, Grandis, Gutkind and van ‘t Veer
Knowledge of cell biology is often modeled in the form of molecular networks and interaction maps, consisting of sets of genes and gene-gene (or protein-protein) pairwise interactions. In reality, however, biological systems are not simply one large protein network, but consist of a deep and dynamic hierarchy of functional subsystems ranging across many orders of magnitude in scale. Here, we move beyond basic interaction maps to instead use molecular interaction data to develop hierarchical structure/function models of the cancer cell. This hierarchical structure will be developed using the protein-protein interaction data generated here and backstopped by public networks; it will provide an objective definition of a cancer cell by systematically identifying the hierarchical relations among its associated systems of genes and proteins. We will next use this descriptive hierarchy to seed a predictive whole-cell model of cancer. This hierarchical model will be validated and revised by applying it to predict therapeutic responses in PDXs of head and neck and breast tumors as well as inform an ongoing I-SPY 2 breast cancer clinical trial.
Core 1: Functional Genomics
Core Leaders: Krogan and Mali
The Functional Genomics Core provides cutting-edge innovative technologies for the functional characterization of the genome in a reliable, reproducible and cost-efficient manner. We provide combinatorial genetic knockout by CRISPR, CRISPRi and CRISPRa, mass spectrometry characterization, and expertise in data processing for these experimental platforms. This core is partnered with two well established facilities: the Thermo Fisher Scientific Proteomics Facility for Disease Target Discovery located at the J. David Gladstone Institutes and the UCSD Institute for Genomic Medicine Genomics Center sequencing facility. We are also creating a new CRISPR screening core that leverages our foundational expertise in genome engineering.
Core 2: Bioinformatics Infrastructure and Services
Core Leaders: Ideker and Mesirov
The Bioinformatics Infrastructure and Services Core provides support to all three CCMI projects at all stages of research and publication. The Core is made up of three major components: Cytoscape and the Cytoscape Cyberinfrastructure (CI); the Network Data Exchange (NDEx); and the CCMI Data and Analysis Portal. Cytoscape provides a range of tools for the analysis and visualization of biological networks. NDEx provides the database infrastructure to support the sharing, review and dissemination of network data and models. It also enables a consolidated access point to public biological network resources for use by CCMI investigators. Finally, the CCMI Data and Analysis Portal provides a common access point for software tools and pipelines and for their associated data. This component will be supported by GenePattern Notebooks, which will facilitate the development of workflow pipelines and the sharing and reproducibility of analyses.