Advances in genomics have led us to recognize that tumors are characterized by distinct molecular events that drive development and progression of disease. But the need for repeated sampling of heterogeneous tumors and the relatively high cost of the assays provides limited opportunities to monitor the disease and its response to treatment. New quantitative imaging techniques and the emerging field of radiomics provides opportunities to search for predictive biomarkers using non-invasive imaging assays that can be used throughout the course of treatment. Indeed, we have recently demonstrated that radiomic biomarkers have strong prognostic performance in large cohorts of lung and head and neck cancer patients, and are associated with the underlying gene-expression and somatic mutation patterns. Our transformative hypothesis is that radiomic analysis, either alone or in combination with genomic mutational profile data obtained from pre- treatment biopsies, can provide a detailed characterization of the tumor phenotype. In this proposal, we will develop a radiomics system that will be shared with the public, develop a rigorous statistics platform specific for analyzing radiomic and genomics data, and apply our developments on a large cohort of non-small cell lung cancer (NSCLC) using tumor samples for which we have both non-invasive CT(PET) imaging data and mutational profiling data. We will also explore whether the radiomic image features quantifying the tumor phenotype are related to genomic mutational profiles, providing a means to monitor non-invasively the molecular state of the disease throughout therapy. This proposal takes advantage of the Profile study at our institute, a comprehensive personalized cancer medicine initiative generating mutational data on the majority of patients undergoing therapy. Profile launched using an assay testing for 471 somatic mutations and expanded in 2013 to exome sequencing. Approximately 12,000 patients are currently enrolled in Profile each year. Therefore, within the time period of this project, we will have access to >4000 NSCLC patients with imaging and genomic mutation data. We will also leverage existing public and private databases to validate the most relevant biomarkers we discover. To achieve our goals we have assembled an interdisciplinary team including experts in imaging, computational biology, molecular biology, oncology, and bioinformatics.

Public Health Relevance

One of the most difficult yet important tasks in providing cancer care is predicting early in treatment whether a patient's tumor is likely to respond to treatment. We now recognize that outcome in cancer depends on molecular changes in the tumor cell that activate particular genetic programs. In this proposal, we will build a system to extract information from non-invasive imaging technologies for the development of imaging based biomarkers that can predict outcome in cancer and to search for correlations with molecular alterations in the tumor.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA190234-04
Application #
9395890
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Tata, Darayash B
Project Start
2015-01-09
Project End
2019-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
076580745
City
Boston
State
MA
Country
United States
Zip Code
02215
Yip, Stephen S F; Liu, Ying; Parmar, Chintan et al. (2017) Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep 7:3519
Coroller, Thibaud P; Agrawal, Vishesh; Huynh, Elizabeth et al. (2017) Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC. J Thorac Oncol 12:467-476
Domenyuk, Valeriy; Zhong, Zhenyu; Stark, Adam et al. (2017) Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach. Sci Rep 7:42741
Agrawal, Vishesh; Coroller, Thibaud P; Hou, Ying et al. (2017) Lymph node volume predicts survival but not nodal clearance in Stage IIIA-IIIB NSCLC. PLoS One 12:e0174268
Trebeschi, Stefano; van Griethuysen, Joost J M; Lambregts, Doenja M J et al. (2017) Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR. Sci Rep 7:5301
Rios Velazquez, Emmanuel; Parmar, Chintan; Liu, Ying et al. (2017) Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer. Cancer Res 77:3922-3930
Niu, Ben; Paulson, Joseph Nathaniel; Zheng, Xiaoqi et al. (2017) Simplified and representative bacterial community of maize roots. Proc Natl Acad Sci U S A 114:E2450-E2459
Lopes-Ramos, Camila M; Paulson, Joseph N; Chen, Cho-Yi et al. (2017) Regulatory network changes between cell lines and their tissues of origin. BMC Genomics 18:723
Paulson, Joseph N; Chen, Cho-Yi; Lopes-Ramos, Camila M et al. (2017) Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data. BMC Bioinformatics 18:437
Yip, Stephen S F; Parmar, Chintan; Blezek, Daniel et al. (2017) Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation. PLoS One 12:e0178944

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