Rationale: With the advent of the genome project, as well as sophisticated proteomics technologies, it has become increasingly clear that although a disease is named as such, the name is an umbrella for many variations of that disease. Further, yet another level of complexity is introduced given the diverse genetic backgrounds of individual patients in which a disease is manifested. Thus, developing effective methods to enable the practice of personalized medicine is a priority for translational science. In this proposal, we utilize a paradigm-shifting technology, called mass cytometry, in which protein expression, protein function, and molecular signatures in many dimensions are measured simultaneously on a cell-by-cell basis in primary ovarian cancer samples. These single cells can be considered as the basic unit of the tumor, akin to individual bricks that build a house.
Objective: Epithelial ovarian cancer is treated clinically as a single disease with most patients getting the same chemotherapeutic regimen, although molecular and genetic analyses have shown the designation encompasses a multitude of pathologies. Mass cytometry has the potential to measure ~150 parameters per single cell, and with current and proposed bioinformatics tools will provide the critical level of detail needed for a further increase in understanding and classification of ovarian cancer.
Applicability: It is anticipated that leveraging the data from this multidimensional single cell analysis combined with modern genotyping technology and health information technologies, prescribing therapies most effective at the individual patient level will become an achievable goal.
What types of patients will the research help and how will it help them? The application of mass cytometry to analyze ovarian cancer samples is anticipated to help all ovarian cancer patients regardless of the stage or grade of their disease. We hope that our work can overcome one or both of the major barriers (deeper classification and early detection) to improving therapeutic outcome for ovarian cancer patients. We expect that deep proteomic profiling of single cells enabled by mass cytometry will have the greatest impact in providing a far more detailed characterization of the disease with potentially major implications for choosing the optimal therapeutic outcome of those existing or providing a lead for development of new targeted agents. Additionally, it is quite likely that a by-product of our research could lead to new approaches for early detection.
What are the potential clinical applications, benefits, and risks? A deeper understanding of the disease picked apart into the single cell units (like the bricks of a building, see above) will provide important information about drugs that will kill the tumor versus those that will have no effect. Clear benefits are that (i) patients will be able to get prescriptions for drugs most likely to be beneficial; and (ii) patients won't get prescriptions for drugs that won't work against their tumor. They can therefore be spared the anguish of drugs with very unpleasant side effects such as the chemotherapeutic regimens that are given to most ovarian cancer patients. Risks involve the validity of the classification and making sure a tumor is not "mis-classified." It is not acceptable for a tumor to be classified as unresponsive to a drug when there would have been a benefit. Our experiments will be designed to dramatically reduce this risk.
What is the projected time it may take to achieve a patient-related outcome? It is hard to say at this early stage in the project and will be dependent on the number of samples we need to run to have confidence in our classification. Optimistically, we could be looking at a time frame within the 5-year duration of this award.
We have designed our research to be very "translational." In other words, all the endpoints and final goals have in mind conquering ovarian cancer, and the research can be applied directly to some clinical aspect of ovarian cancer.
What are the likely contributions of this study to advancing the field of research? We anticipate that studying ovarian cancer at the level of its basic building blocks will give us a renewed understanding of the disease. We envision this information providing new criteria for classifying ovarian cancer disease subtypes. These criteria will necessarily change treatment strategies and importantly identify new ones not previously considered.