The spread of cancer throughout the body is driven by circulating tumour cells (CTCs)1. These cells detach from the primary tumour and move from the blood stream to a new site of subsequent tumour growth. They also carry information about the primary tumour and have the potential to be valuable biomarkers for disease diagnosis and progression, and for the molecular characterization of certain biological properties of the tumour. However, the limited sensitivity and specificity of current methods to measure and study these cells in patient blood samples prevent the realization of their full clinical potential. The use of microfluidic devices is a promising method for isolating CTCs2, 3; however, the devices are reliant on three-dimensional structures, which limit further characterization and expansion of cells on the chip. Here we demonstrate an effective approach to isolate CTCs from blood samples of pancreatic, breast and lung cancer patients, by using functionalised graphene oxide nanosheets on a patterned gold surface. CTCs were captured with high sensitivity at low concentration of target cells (73% ± 32.4 at 3–5 cells/mL blood).

The potential utility of circulating tumor cells (CTCs) to guide clinical care in oncology patients has gained momentum with emerging micro- and nanotechnologies. Establishing the role of CTCs in tumor progression and metastasis depends both on enumeration and on obtaining sufficient numbers of CTCs for downstream assays. The numbers of CTCs are few in early stages of cancer, limiting detailed molecular characterization. Recent attempts in the literature to culture CTCs isolated from metastatic patients using monoculture have had limited success rates of less than 20%. Herein, we have developed a novel in-situ capture and culture methodology for ex-vivo expansion of CTCs using a three dimensional co-culture model, simulating a tumor microenvironment to support tumor development. We have successfully expanded CTCs isolated from 14 of 19 early stage lung cancer patients. Expanded lung CTCs carried mutations of the TP53 gene identical to those observed in the matched primary tumors. Next-generation sequencing further revealed additional matched mutations between primary tumor and CTCs of cancer-related genes. This strategy sets the stage to further characterize the biology of CTCs derived from patients with early lung cancers, thereby leading to a better understanding of these putative drivers of metastasis.

Early detection of metastasis can be aided by circulating tumor cells (CTCs), which also show potential to predict early relapse. Due to the limited CTC numbers in peripheral blood in early stages, we investigated CTCs in pulmonary vein blood accessed during surgical resection of tumors. Pulmonary vein (PV) and peripheral vein (Pe) blood specimens from patients with lung cancer were drawn during the perioperative period and assessed for CTC burden using a microfluidic device. From 108 blood samples analyzed from 36 patients, PV had significantly higher number of CTCs compared to pre-operative Pe (p<0.0001) and intra-operative Pe (p<0.001) blood. CTC clusters with large number of CTCs were observed in 50% of patients, with PV often revealing larger clusters. Long term surveillance indicated that presence of clusters in pre-operative Pe blood predicted a trend toward poor prognosis. Gene expression analysis by RT-qPCR revealed enrichment of p53 signaling and extracellular matrix involvement in PV and Pe samples. Ki67 expression was detected in 62.5% of PV samples and 59.2% of Pe samples, with the majority (72.7%) of patients positive for Ki67 expression in PV having single CTCs as opposed to clusters. Gene ontology analysis revealed enrichment of cell migration and immune-related pathways in CTC clusters, suggesting survival advantage of clusters in circulation. Clusters display characteristics of therapeutic resistance, indicating the aggressive nature of these cells. Thus, CTCs isolated from early stages of lung cancer are predictive of poor prognosis and can be interrogated to determine biomarkers predictive of recurrence.