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Understanding comorbidities that contribute to drug response in ovarian cancer

Principal Investigator
Name
Sara Cooper
Degrees
Ph.D.
Institution
HudsonAlpha Institute for Biotechnology
Position Title
Faculty Investigator
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-362
Initial CDAS Request Approval
Apr 13, 2018
Title
Understanding comorbidities that contribute to drug response in ovarian cancer
Summary
Ovarian Cancer patients face difficult odds following a diagnosis. Platinum based chemotherapy is effective in some patients, but resistance and relapse are common. The development of novel therapeutics, including immunotherapy, offers new hope for cancer patients, however broadly effective targeted and immunotherapies are still being sought in ovarian cancer patients. Our interests in ovarian cancer have centered on using genomic information such as gene expression, along with patient data to identify therapeutic paths that are most appropriate and effective for an individual. Previous work has shown that in addition to molecular profiling including genomic information, comorbidities such as obesity, diabetes and metabolic syndrome have significant impacts on patient prognosis. Further studies are necessary to fully characterize those impacts and to understand the extent to which those comorbidities affect patient therapeutic response, potentially even in a therapy-specific way.
One area of particular interest for our group is the use of immunotherapy in ovarian cancer. Of the trials completed and underway, immunotherapy has not been especially effective in ovarian cancer, thus our group has been exploring novel therapeutics that might be used in combination with immunotherapy to illicit stronger, better and more specific immune responses. Again, we know that obesity and diabetes patients have the potential to alter the immune system and it is likely that at least to some extent this contributes to the worsened outcomes observed in patients with these comorbidities. We are interested in understanding the relationship between outcomes in ovarian cancer and markers of and diagnosis of comorbidities such as obesity and diabetes. We are particularly interested in these outcomes in the context of other markers of immune function. Immune function has been estimated by other PLCO studies using dietary information. Immune markers including cytokines and adipokines have been measured by some PLCO EEMS projects which we also hope to access.
Here we request access to PLCO data in order to test whether comorbidities such as diabetes and obesity can be used effectively as covariates to improve modeling of patient response to therapy and to what extent altered immune system function contributes to the altered prognosis.
Aims

We will complete the following Aims:
Aim 1: Assess the role for comorbidities in ovarian cancer outcome. We will calculate the statistical difference in ovarian cancer patient prognosis based on the presence of the comorbidities and determine whether significant correlations exist between the presence of comorbidities and various measures of immune function including assessing immunogenic aspects of diet and utilizing cytokine measurements collected by other PLCO researchers.
Aim 2: Develop a statistical model to describe how comorbidities contribute to patient prognosis. Using existing demographic and clinical data we will test machine learning algorithms such as random forest and LASSO regression to determine a predictive model. Each of these approaches yields both a model and a set of features that most contribute to an optimized model. Using this model we will assess the importance of immune response to patient prognosis.
These Aims will complement our existing research and help to determine whether comorbidities will play a significant role in predicting patient therapeutic response.

Collaborators

Emily Gordon, HudsonAlpha Institute