Wednesday, April 24, 2024
4/24/2024
Project summary (OVERALL) Lung cancer remains the leading cause of all cancer mortality. Improved imaging techniques enable the detection of lung cancer at earlier stages, yet a large number of patients with non-malignant lung nodules are frequently subjected to invasive diagnostic approaches. Even though surgical removal of early stage non-small cell lung cancer (NSCLC) is the most effective therapy, post-surgical recurrence of NSCLC remains a significant problem as survival. Currently there are no clinically useful biomarkers that can accurately diagnose the indeterminate nodule or identify those patients destined to have recurrence of cancer after successful surgical removal. Recently, the use of culture-independent techniques to characterize the microbiome by us and others has led to identification of microbial signatures associated with lung cancer diagnosis and prognosis among a cohort with a wide range of disease stages. Preliminary metagenomic data obtained in collaboration with Micronoma using blood samples of our NYU cohort have identified microbial signatures in systemic circulation associated with early-stage NSCLC diagnosis. Further, using a NanoString platform we have identified circulating RNA signatures predictive of early-stage NSCLC diagnosis. In addition, our preliminary data shows that lower airway signatures can be used to predict prognosis post-surgical removal of early stage cancer. These data suggest that microbial and host genomic signatures could be leveraged to develop useful biomarkers in early-stage NSCLC. The addition of metabolite measurements could further contribute to this predictive power since those are end products of microbial and host functions. Under this BCC application we will first identify top microbial/host biomarkers that predict early-stage NSCLC diagnosis and prognosis using blood and lower airway samples from a cohort of patients with lung nodules and a presumed surgical clinical Stage I (<3cm) but with a final histological diagnosis of early-stage NSCLC (TNM ¿ IIIA) or non-NSCLC nodules. We will implement cutting edge bioinformatic approaches to identify the most promising targets from these unbiased omic approach (metagenome, metabolome and transcriptome) which will guide the development of targeted approaches that to be validated under the Biomarker Reference Laboratory. These targeted approaches will include the development of targeted microbial DNA next generation sequencing, targeted metabolite measurement and custom-made NanoString panels as CLIA level assays, internally and externally validated, that will identify patients at highest risk for NSCLC diagnosis and recurrence after complete surgical resection.
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