Extracellular Vesicles as a Diagnostic

Extracellular Vesicles in the Early Diagnosis of Small Cell Lung Cancer

Lung cancer is the primary cause of cancer related deaths in the U.S and carries a 5-year survival rate of 17%. There are several reasons for these staggering facts including tumor heterogeneity, limited targeted therapeutics and late clinical presentation of disease. In the last several years, the development of therapeutics targeted to Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoid Kinase (ALK) has led to improved outcomes among selected patients. Early detection represents an ideal approach to offer curative intent therapy to a large number of patients. Recently, the National Lung Screening Trial demonstrated that low radiation dose computerized tomography (LDCT) among selected high-risk smokers could result in a 20% reduction in lung cancer mortality. These results have revolutionized how clinicians approach lung cancer early detection but appear to only be applicable to cases of non-small cell carcinoma of the lung (NSCLC). Small cell lung cancer (SCLC) is a tumor type with neuroendocrine differentiation carrying a high rate of molecular aberrations that comprises 13% of lung cancer cases. To date, there have been very few effective therapies for SCLC beyond standard chemotherapeutic agents, however, our understanding for the molecular underpinnings and heterogeneity of this disease is increasing and new therapeutic targets as well as diagnostic/prognostic markers should derived from knowledge accumulated. SCLC carries a high mortality (less than 5% 5-year survival) and only a minority of new cases of small cell lung cancer is diagnosed at early stage (30%). Recent studies demonstrated benefit from surgery and potentially adjuvant chemotherapy for early stage (pT1-2N0M0) SCLC. Any potential benefits of LDCT for the early detection of SCLC remain largely unknown, although recent studies suggested no survival benefit. The lack of reliable modalities for SCLC early detection may be addressed by both improving risk stratification to identify those at the highest risk for SCLC and through the development of complementary biomarkers that can inform clinical decision-making. An unmet need in current SCLC detection and treatment has been that reliable biomarkers the lack of can detect the disease early and provide informative models for risk stratification.

Extracellular vesicles (EVs) are small membrane-bound particles comprised of exosomes, microvesicles, Retrovirus-like particles and apoptotic bodies (ABs or apoptosomes) that are released from a wide variety of cell types by different mechanisms. Over the last decade, a number of studies have found that EVs can influence major tumor-related pathways, such as hypoxia-driven epithelial-to-mesenchymal transition (EMT), cancer stemness, angiogenesis, metastasis, and cachexia. EVs contain RNAs although at different composition. The project is to characterize the molecular content of EVs from patients in order to identify informative early diagnostic markers for SCLC. In addition, we will adapt a single EV characterization platform, tethered lipoplex nanoparticle (TLN) biochip to capture EVs and identify EV-encapsulated RNA targets at individual EV level to test the viability of EV-based diagnostic markers for ‘early SCLC diagnosis.

Tissue and exosomal miRNA editing in Non-Small Cell Lung Cancer. Nigita, G., Distefano, R., Veneziano, D., Romano, G., Rahman, M., Wang, K., Pass, H., Croce, C. M., Acunzo, M., Nana-Sinkam, P. (2018) Sci Rep. 8 (1). PMID: 29976955

Extracellular vesicle biology in the pathogenesis of lung disease. Nana-Sinkam P. Croce C. Wang K. (2017) Am J Respir Crit Care Med. 196 (12), 1510-1518. PMID: 28678586

Extracellular mRNA Detected by Tethered Lipoplex Nanoparticle Biochip for Lung Adenocarcinoma Detection. Lee, J., Yang, Z., Rahman, M., Ma, J., Kwak, K. J., McElory, J., Shilo, K., Goparaju, C., Yu, L., Rom, W., Kim, T. K., Wu, X., He, Y., Wang, K., Pass, H. I., Nana-Sinkam, S. P. (2016) Am J Respir Crit Care Med.193 (12) 1431. PMID: 27304243

Key Project Personnel:

Kai Wang