This Career Development Award will support Lori Sakoda, PhD, in her transition to independence as a translational researcher in lung cancer. Her long-term career goal is to inform and improve real-world strategies for lung cancer detection and control by leading transdisciplinary research that integrates analysis of complex, large-scale biomedical data. Risk prediction models could be valuably employed to optimize the benefit-to-harm ratio of screening strategies for lung cancer in smokers. To support their use in clinical practice, however, there must be convincing evidence of their predictive ability to identify smokers at highest risk for lung cancer and/or to differentiate those presenting with malignant versus benign lung nodules. Her mentored research will evaluate whether a newly developed, clinically-oriented risk prediction model for lung cancer, as proposed or modified, could aid decision-making in the context of lung cancer screening.
The specific aims are to 1) validate the predictive performance of the model; 2) determine the incremental value of adding genetic and other clinical predictors to the model; and 3) examine the predictive performance of the baseline model and the best predictive extended model in persons who meet the U.S. Preventative Services Task Force eligibility criteria for lung cancer screening with low-dose computed tomography. As an exploratory aim, the predictive performance of these same two models will be assessed in the subgroup of screening- eligible persons diagnosed incidentally with lung nodules.
These aims will be addressed by integrating survey, whole genome genotyping, and electronic health record (EHR) data on a large, contemporary cohort of smokers in the Kaiser Permanente Northern California (KPNC) Research Program on Genes, Environment, and Health. The proposal builds on the candidate's prior training in cancer epidemiology to fill knowledge gaps in clinical domains (lung pathophysiology, lung cancer detection and management practices, and medical decision-making) and scientific domains pertinent to integrated analysis of EHR and other complex, large-scale data (biostatistics, genetic epidemiology, and biomedical informatics), which will allow her to more effectively generate and translate scientific evidence into clinical practice. Training will be acquired from coursework, seminars, professional society meetings, and experiential learning, under the guidance of a highly qualified team of mentors and scientific advisors. She will also build clinical and scientifc partnerships essential to succeed in her current setting. The KPNC Division of Research is an ideal training environment, given its long history of important contributions to cancer screening guidelines, due to both its scientific leadership and its access to an ethnically diverse and stabl membership (currently over three million adults) for whom EHR data are kept indefinitely. The proposed plan will provide the candidate with preliminary data to develop a competitive R01 proposal, along with specialized knowledge and skills to successfully establish a transdisciplinary research program focused on optimizing strategies for lung cancer detection and control.

Public Health Relevance

Lung cancer remains the leading cause of cancer mortality, due in part to limitations in identifying high risk individuals and detecting tumors at earlier and curable stages. The study will inform whether a newly developed risk prediction model for lung cancer, as proposed or modified, could be used to identify high risk smokers who would benefit most from lung cancer screening and follow-up of abnormal imaging results. This work will also provide essential training and mentorship for the candidate to establish independence as a translational researcher focused on improving lung cancer detection and control.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Academic/Teacher Award (ATA) (K07)
Project #
5K07CA188142-05
Application #
9684592
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Perkins, Susan N
Project Start
2015-05-15
Project End
2020-10-31
Budget Start
2019-05-01
Budget End
2020-10-31
Support Year
5
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Kaiser Foundation Research Institute
Department
Type
DUNS #
150829349
City
Oakland
State
CA
Country
United States
Zip Code
94612
Check, Devon K; Albers, Kathleen B; Uppal, Kanti M et al. (2018) Examining the role of access to care: Racial/ethnic differences in receipt of resection for early-stage non-small cell lung cancer among integrated system members and non-members. Lung Cancer 125:51-56
Sakoda, Lori C; Henderson, Louise M; Caverly, Tanner J et al. (2017) Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions. Curr Epidemiol Rep 4:307-320
Hoffmann, Thomas J; Passarelli, Michael N; Graff, Rebecca E et al. (2017) Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer. Nat Commun 8:14248
Lohavanichbutr, Pawadee; Sakoda, Lori C; Amos, Christopher I et al. (2017) Common TDP1 Polymorphisms in Relation to Survival among Small Cell Lung Cancer Patients: A Multicenter Study from the International Lung Cancer Consortium. Clin Cancer Res 23:7550-7557
Menter, Alex R; Carroll, Nikki M; Sakoda, Lori C et al. (2017) Effect of Angiotensin System Inhibitors on Survival in Patients Receiving Chemotherapy for Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer 18:189-197.e3
Gould, Michael K; Sakoda, Lori C; Ritzwoller, Debra P et al. (2017) Monitoring Lung Cancer Screening Use and Outcomes at Four Cancer Research Network Sites. Ann Am Thorac Soc 14:1827-1835