Early Detection of NSCLC Using Stromal Markers in Peripheral Blood

Abstract

A recent screening trial showed that the use of low dose computed tomography (LDCT) resulted in a 20 reduction in lung cancermortality, however there was a 96 false positive rate associated with LDCT. Thus, there is an immediate clinical need to develop diagnostic biomarker that would select patients with CT detected nodules for further testing. The ease with which blood can be sampled makes it a logical choice in which to discover diagnostic biomarkers, however the clinical utility of tumor derived proteins, miRNA or circulating tumor cells as blood-based biomarkers has been limited. In this proposal, instead of tumor-derived biomarkers, we will focus on host response to tumor growth. It has been well documented that tumor growth systemically stimulates and mobilizes BM-derived hematopoietic cells to the tumor bed to establish a permissive microenvironment. Preliminary studies in our lab have shown that in lung cancer patients, the circulating myeloid cells are transcriptionally altered and the alteration is tumor dependent. The specific transcriptomic signature of circulating myeloid cells may provide us unique resources for lung cancer biomarker discovery. Therefore, we hypothesized that the circulating BM-derived myeloid cells carry specific transcriptomic signature, which may be useful for early lung cancer diagnosis. The specific aims are: Aim 1. To identify a NSCLC-dependent transcriptomic signature in circulating myeloid cells. Aim 2. To validate the diagnostic value of the specific gene signatures of circulating myeloid cells in NSCLC patients with lung nodules.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2017
Accession Number
AD1048117

Entities

People

  • Dingcheng Gao

Organizations

  • Weill Cornell Medicine

Tags

DTIC Thesaurus Topics

  • Blood
  • Blood Cells
  • Cancer
  • Cardiovascular System
  • Cells
  • Data Analysis
  • Detection
  • Diseases And Disorders
  • Gene Expression
  • Granulocytes
  • Hematopoietic Cells
  • Lung Cancer
  • Medical Personnel
  • Myeloid Cells
  • Neoplasms
  • Supervised Machine Learning
  • X-Ray Computed Tomography

Fields of Study

  • Biology
  • Medicine

Readers

  • Immunology
  • Oncology and Biomarker-Based Cancer Detection.