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. Th...
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.
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 here 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.
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,...
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 (...
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.
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.
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...
