Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

• Published in: View (Beijing, China) Vol. 1; no. 3
• Main Authors: Miccio, Lisa, Cimmino, Flora, Kurelac, Ivana, Villone, Massimiliano M., Bianco, Vittorio, Memmolo, Pasquale, Merola, Francesco, Mugnano, Martina, Capasso, Mario, Iolascon, Achille, Maffettone, Pier Luca, Ferraro, Pietro
• Format: Journal Article
• Language: English
• Published: Wiley 01.09.2020
• Subjects: cancer; CTCs detection; digital holography artificial intelligence; label‐free; liquid biopsy; microfluidics; Quantitative Phase‐Contrast Imaging
• ISSN: 2688-3988; 2688-268X; 2688-268X
• DOI: 10.1002/VIW.20200034

Circulating tumor cells (CTCs) are rare tumor cells released from primary, metastatic, or recurrent tumors in the peripheral blood of cancer patients. CTCs isolation from peripheral blood and their molecular characterization represent a new marker in cancer screening, a diagnostic tool called “liquid biopsy” (LB). Compared to traditional tissue biopsy that is invasive and does not reveal tumor heterogeneity, LB is noninvasive and reflects in “real‐time” tumor dynamism and drug sensitivity. In the frame of LB, a new paradigm based on single‐cell and label‐free analysis based on morphological analysis is emerging. Here, we review the latest research developments in this emerging vision of LB. In particular, we survey and discuss recent improvements in microfluidics, imaging label‐free diagnosis and cell classification by artificial intelligence and how to combine them to realize an intelligent platform based on lab‐on‐chip technology. This prospect appears to open up promising and intriguing new scenarios for cancer management through single‐cell analysis that will revolutionize the future of early cancer diagnosis and therapeutic choice with disruptive impact on the society. The most promising approach to liquid biopsy has its roots in the smart integration between label‐free quantitative phase microscopy, accurate manipulation of microfluidic streams, and artificial intelligence. Lab‐on‐a‐chip devices with embedded imaging functions can now rely on robust deep learning architectures to generate accurate classification results from single‐cell analysis of blood flows. Flow engineering allows sorting and controlling the rotation of blood components, thus permitting added‐value high‐throughput tomographic inspection of circulating tumor cells.