Suspicious screening-detected lung nodules present a formidable challenge to patients and their providers. The standard of care lacks accuracy in predicting a) malignancy from benign disease and b) indolent vs aggressive behavior. The answer to these questions justifies diametrically opposed strategies (biopsy vs follow up) each of which carries huge consequences including cure of the cancer, risk of death during a procedure or risk of dying from not intervening early in the disease. This application will focus o the behavior of early stage adenocarcinoma of the lung and not on the distinction between benign from malignant nodules. We assembled a unique multidisciplinary group of experts to tackle this problem in an original way. We will develop a retrospective and a prospective repository for both tissue (ADC fresh frozen tissues, blood) and images from which we will derive detailed quantitative structural imaging analysis, targeted genomic analysis and single cell analysis to interrogate the functional genomics of these tumors. The integration of this multidimensional data imaging/molecular/cellular/epidemiology will allow us to identify and validate cellular and molecular determinants of tumor behavior in the context of their inter- and intra-tumor heterogeneity. With these results, we will be able to build integrated models of ADC behavior, validate a new genomic molecular test on circulating DNA and propose prospective studies that would eventually offer a different intervention based on these predictions i.e. surgery, vs no surgery, adjuvant immuno- or chemotherapy vs no adjuvant therapy and therefore reduce overtreatment and ultimately increase the rate of cure and reduce healthcare cost.

Public Health Relevance

With implementation of screening programs for lung cancer, we are facing challenges related to overdiagnosis and overtreatment of indolent lung adenocarcinomas (ADC). Our project will improve prediction models by integrating quantitative imaging, molecular and cellular determinants to be paradigm-shifting in the clinical management of patients with early ADC.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
6U01CA196405-02
Application #
9248530
Study Section
Special Emphasis Panel (ZCA1)
Project Start
2015-09-24
Project End
2020-08-31
Budget Start
2016-04-30
Budget End
2016-08-31
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Type
DUNS #
079917897
City
Nashville
State
TN
Country
United States
Zip Code
37232
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Nakajima, Erica C; Frankland, Michael P; Johnson, Tucker F et al. (2018) Assessing the inter-observer variability of Computer-Aided Nodule Assessment and Risk Yield (CANARY) to characterize lung adenocarcinomas. PLoS One 13:e0198118
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