Early Predictor Tool of Disease Using Label-Free Liquid Biopsy-Based Platforms for Patient-Centric Healthcare

Abstract

Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.

Are we ready to endorse new multiple cancer early-detection tests?

“…Circulating tumor cells (CTCs) shredded from the primary and secondary tumors can also be detected from liquid blood or urine biopsies (4-7). The cancer cells can be further utilized to generate human-derived tumor models for drug efficacy and high throughput screening assessment (8,9). In 2004, FDA approved an assay using magnetophoretic separation using ferromagnetic microparticles with antibodies targeting epithelial markers for clinical use (Cellsearch) (10).…”

“…A multiple-cancer detection blood test (Galleri) has also been commercialized (2). There are also efforts to introduce label-free technologies to identify patients at high cancer risk based on proliferative capabilities (9), biochemical cues (11), and physical attributes (4) to complement conventional screens with automated analysis for robust and high throughput screening (8). However, since its introduction, many cancer specialists warn that we might not be ready to endorse these new cancer detection tests.…”

Simplifying the complex: accessible microfluidic solutions for contemporary processes within in vitro diagnostics

“…Indeed, the information that can be gleaned from these techniques is intrinsically linked to the separation methodologythis has knock-on effects for disease diagnosis and prognosis. 167,168 Traditionally, isolation of cells and cellular products (such as extracellular vesicles) from complex tissue samples has relied on mechanical methods such as centrifugation and filtration, or fluorescence/magnetic-activated sorting performed using complex and expensive instrumentation. [169][170][171] Unsurprisingly, over the last two decades there has been much interest in applying microfluidic technologies to cell sorting, by employing passive, active or immunoaffinity-based approaches.…”

“…Indeed, the information that can be gleaned from these techniques is intrinsically linked to the separation methodology – this has knock-on effects for disease diagnosis and prognosis. 167,168…”

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

“…However, in the last decade, an increasing attention has been focused on techniques for the detection and isolation of the CTCs [12][13][14] , including label-free approaches and microfluidic inspection systems [15][16][17] . Recently, new perspectives have been foreseen in finding new strategies based on intelligent lab-on-a-chip for detecting tumor cells 18 .…”