A Microfluidic Liquid Biopsy Platform to Monitor Protein Biomarker Heterogeneity in Single Circulating Therapy‐Resistance Cancer Cell

Abstract

Tumor cells display heterogenous molecular signatures during the course of cancer and create distinct tumor cell subpopulations which challenge effective therapeutic decisions. Detection and monitoring of these heterogenous molecular events at single cell level are imperative to identify tumor cell subpopulations and to engage the best therapeutic options for the individual patient. Herein, a microfluidic liquid biopsy platform to analyze circulating tumor cells (CTCs) at single cell level is reported. The individual CTCs are captured in an alternating current‐induced microfluidic platform and analyzed by using surface‐enhanced Raman scattering spectroscopy. This platform selectively captures single CTCs from the patient's peripheral blood mononuclear cells. Using cell line models and patient samples, it is shown that the assay can simultaneously detect multiple protein biomarkers on a single CTC. The platform can stratify the CTCs into different subpopulations based on their cancer‐associated protein signature changes in response to drug treatment. This enables the identification of CTC subpopulations that are probably not responding to treatment and may assist clinicians in specifically monitoring and eliminating therapy‐resistant cancer cells within a lesion. This single CTC monitoring chip will likely have high clinical importance in disease diagnosis and treatment monitoring, and advance the knowledge of cancer heterogeneity.

Multiplexed Surface Protein Detection and Cancer Classification Using Gap-Enhanced Magnetic–Plasmonic Core–Shell Raman Nanotags and Machine Learning Algorithm

“…A key result of this study is to determine the ability of multiplexed SERS detection for classification of different cancer cell lines in a mixture using the power of machine learning technique, which has not been achieved with previously SERS nanotag-based approaches (Table S3). ,,,− For machine learning-based cancer cell identification, a two-dimensional plane map and the labeled deconvoluted data were utilized to generate the t-SNE mappings. Random Forest Classifier was used to train a multiclass random forest model for cell classification.…”

The integrated on-chip isolation and detection of circulating tumour cells

“…The configuration used on-chip under alternating current-induced mixing of the cells close to chip-laden electrodes, improving CTC capture efficiency, while minimizing non-specific adsorption of loosely bound species within an Ab-functionalized microfluidic chip. 160 This configuration was capable of stratifying CTCs and identifying drug-resistant forms by mapping cancer-specific cell-surface biomarkers. Another approach for mapping CTC surface biomarkers through after a semi-automated single step isolation of CTCs on a micropore membrane filter coupled to single cell Raman mapping was proposed.…”