The goal of this study is to demonstrate the feasibility of using a novel, validated panel of Non-Small Cell Lung Cancer (NSCLC) histology-predictive genes (the "A/S signature) as a diagnostic tool for use with small-volume Fine Needle Aspirate (FNA) biopsies. Objectives: 1. To establish FNA biopsy requirements for FNA-based subtype classification of NSCLC. 2. To define a "fixed statistical model" of histologic subtype prediction in NSCLC. Study methods: To establish FNA biopsy requirements for gene expression-based subtype classification of NSCLC, patients with presumed newly diagnosed NSCLC, where radiographic studies and clinical description favor a probable diagnosis of NSCLC, will undergo FNA biopsy according to current standard techniques . For this part of the study, approximately 40 biopsies of confirmed NSCLC will be collected for analysis. To define a fixed statistical model of histologic subtype prediction in NSCLC, we will prospectively collect 50 FNAs. These FNAs will represent Adenocarcinoma (AC) and Squamous Cell Carcinoma (SCC) cases at a ratio of approximately 1:1. Additional cases of not otherwise specified (NOS), should they be encountered, may also be collected for later analysis. FNA samples qualified based on cell number or ribonucleic acid (RNA) yield (depending on the findings of our primary objective)will be assayed on the QGS platform.
Study Type
OBSERVATIONAL
Enrollment
103
Wake Forest University Health Sciences
Winston-Salem, North Carolina, United States
Use of Ribonucleic acid-based molecular signature of tumor samples obtained by fine needle aspirate to discriminate subtypes of tumors relevant to treatment and outcomes.
To test a an 8-gene panel's accuracy in discriminating Non-Small Cell Lung Cancer tumors subtypes that are relevant to treatment and expected outcomes.
Time frame: Day 1
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