Seeking to improve assessment of quality of life and patient outcomes from clinical trial data.
A computer simulation model is tested for its efficiency at predicting the effect of chemotherapy on long-term quality of life measures.
This innovative research will move the field forward by demonstrating how clinical trial simulations can provide missing information in cancer treatment and outcomes.
Oncotype DX® testing is used in routine healthcare practice for profiling the gene expression in early-stage, estrogen receptor(ER)-positive, and HER2-negative breast tumors. Results from the test help to inform the potential risk of recurrence and need for chemotherapy. Scores in the low range predict low risk of recurrence. Likewise, high scores predict a high risk of recurrence and chemotherapy is generally recommended for women in the group.
There is less clarity about risk and the need for chemotherapy, however, for scores that are in the mid-range. The TAILORx study (Trial Assigning IndividuaLized Options for Treatment (Rx)) is an international trial that is beginning to answer these questions. Results from the study have already confirmed that low risk patients can forego chemotherapy and results in the other categories of risk are pending.
The trial will not, however, address an equally important question: Do women within the different categories of Oncotype Dx® score experience different long-term effects on quality of life due to chemotherapy?
Dr. Jayasekera will employ simulation modeling to extend the TAILORx trial data to several other outcomes. She will: 1) Determine the effects of chemotherapy on long-term recurrence and mortality within each Oncotype DX® score category; 2) Provide an estimate of quality-adjusted life years based on adverse events and patient-reported outcomes; and 3) Provide a ‘balance sheet’ of harms and benefits by Oncotype DX® score risk level to guide treatment discussions between clinicians and patients with early-stage breast cancer.
Overall, this innovative research will move the field forward by demonstrating how clinical trial simulations can provide missing information in cancer treatment and outcomes.
Jinani Jayasekera is a Research Instructor in the Department of Oncology at Georgetown University Medical Center. Her current research focuses on the application of mathematical simulation modeling to extend clinical trials and develop web-based decision support tools to guide treatment decisions in early-stage breast cancer. Her research aims to translate clinical trial findings to clinical practice in the context of genomic advances and personalized health care. She is actively involved in multidisciplinary collaborative research projects, ranging from disparities research, cost-effectiveness analysis, and claims-based analysis evaluating the clinical and economic burden of cancer. Under the mentorship of Drs. Jeanne Mandelblatt, Donald Berry and Clyde Schechter, she recently completed a clinical trial simulation project evaluating non-inferiority in the omission of radiation therapy among women with stage I, node negative, ER positive, HER2 negative breast cancers with a low risk of recurrence.
Dr. Jayasekera holds a Bachelor’s in Pharmacology and obtained a Master’s in Financial Economics at University of Colombo, Sri Lanka. She subsequently earned her Master’s and PhD in Pharmaceutical Health Services Research at the University of Maryland. In 2017, she was the recipient of the American Cancer Society Institutional Young Investigator Award granted by the Georgetown-Lombardi Comprehensive Cancer Center. She has presented her research at several national and international meetings. She is also a recipient of the 2013 Society for Medical Decision Making (SMDM) Lee Lusted Award in Applied Health Economics.