Investigating Field Cancerization in Non-Small Cell Lung Cancer Subtype Using Emerging Genomic Approaches

  • De, Subhajyoti S (PI)

Project Details

Description

Lung cancer remains a major cause of cancer-related deaths in the USA, and it has high prevalence among the armed forces personnel and Veterans. Lung cancer screening has saved lives, but nearly 95% of the pulmonary nodules of indeterminate potential detected in lung CT do not progress to advanced disease, leading to over-diagnosis, while increasing the risk of missing or undermanaging the truly bad ones. Unfortunately, identifying actionable molecular, pathological, or radiomic features that are present in cancerous growths, even at a very early stage, but are rare in normal or noncancerous lung tissues, has proved difficult. This is because many of such features may not always be present in all lung cancers and, on the other hand, could also be present in noncancerous tissues. Here, we propose to examine an important, but understudied signature of cancerous growth - genomic instability marked by complex structural variations and rearrangements (CSVs), which are hallmarks of nearly all cancers but rarely occur in somatic cells from normal tissues or benign tumors. We will use a newly developed molecular technique, called optical mapping, to scan for CSVs at a genome-wide scale and at single-DNA molecule-level resolution in premalignant tissues from clinical specimen and especially focus on the CSV signatures that are associated with malignant tumors. Our study will help answer whether some lung lesions are indeed born to be bad. The frameworks will also be more generally applicable for studying the governing principles of somatic evolution in other cancers and other disease contexts. It will also be timely, in light of new clinical trials to assess utility of optical mapping techniques for cancer diagnosis in Canada and Europe.

StatusActive
Effective start/end date8/1/22 → …

Funding

  • U.S. Army: $549,500.00

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.