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.
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.
| Hosny, Ahmed; Parmar, Chintan; Quackenbush, John et al. (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500-510 |
| Dou, Tai H; Coroller, Thibaud P; van Griethuysen, Joost J M et al. (2018) Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLoS One 13:e0206108 |
| Barry, Joseph D; Fagny, Maud; Paulson, Joseph N et al. (2018) Histopathological Image QTL Discovery of Immune Infiltration Variants. iScience 5:80-89 |
| Parmar, Chintan; Barry, Joseph D; Hosny, Ahmed et al. (2018) Data Analysis Strategies in Medical Imaging. Clin Cancer Res 24:3492-3499 |
| Aerts, Hugo J W L (2018) Data Science in Radiology: A Path Forward. Clin Cancer Res 24:532-534 |
| Napel, Sandy; Mu, Wei; Jardim-Perassi, Bruna V et al. (2018) Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer 124:4633-4649 |
| 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 |
| Schlauch, Daniel; Paulson, Joseph N; Young, Albert et al. (2017) Estimating gene regulatory networks with pandaR. Bioinformatics 33:2232-2234 |
| Sonawane, Abhijeet Rajendra; Platig, John; Fagny, Maud et al. (2017) Understanding Tissue-Specific Gene Regulation. Cell Rep 21:1077-1088 |
| 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 |
Showing the most recent 10 out of 39 publications
