Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

Magnetic lanthanide sensor with self-ratiometric time-resolved luminescence for accurate detection of epithelial cancerous exosomes

Fluorescence-based LB (liquid biopsy) offers a rapid means of detecting cancer non-invasively. However, the widespread issue of sample loss during purification steps will diminish the accuracy of detection results. Therefore, in this study, we introduce a magnetic lanthanide sensor (MLS) designed for sensitive detection of the characteristic protein, epithelial cell adhesion molecule (EpCAM), on epithelial tumor exosomes. By leveraging the inherent multi-peak emission and time-resolved properties of the sole-component lanthanide element, combined with the self-ratiometric strategy, MLS can overcome limitations imposed by manual operation and/or sample complexity, thereby providing more stable and reliable output results. Specifically, terbium-doped NaYF4 nanoparticles (NaYF4:Tb) and deformable aptamers terminated with BHQ1 were sequentially introduced onto superparamagnetic silica-decorated Fe3O4 nanoparticles. Prior to target binding, emission from NaYF4:Tb at 543 nm was partially quenched due to the fluorescence resonance energy transfer (FRET) from NaYF4:Tb to BHQ1. Upon target binding, changes in the secondary structure of aptamers led to the fluorescence intensity increasing since the deconfinement of distance-dependent FRET effect. The characteristic emission of NaYF4:Tb at 543 nm was then utilized as the detection signal (I1), while the less changed emission at 583 nm served as the reference signal (I2), further reporting the self-ratiometric values of I1 and I2 (I1/I2) to illustrate the epithelial cancerous features of exosomes while ignoring possible sample loss. Consequently, over a wide range of exosome concentrations (2.28 × 102-2.28 × 108 particles per mL), the I1/I2 ratio exhibited a linear increase with exosome concentration [Y(I1/I2) = 0.166 lg (Nexosomes) + 3.0269, R2 = 0.9915], achieving a theoretical detection limit as low as 24 particles per mL. Additionally, MLS effectively distinguished epithelial cancer samples from healthy samples, showcasing significant potential for clinical diagnosis.

Modeling the Effect of Spatial Structure on Solid Tumor Evolution and Circulating Tumor DNA Composition

Circulating tumor DNA (ctDNA) monitoring, while sufficiently advanced to reflect tumor evolution in real time and inform cancer diagnosis, treatment, and prognosis, mainly relies on DNA that originates from cell death via apoptosis or necrosis. In solid tumors, chemotherapy and immune infiltration can induce spatially variable rates of cell death, with the potential to bias and distort the clonal composition of ctDNA. Using a stochastic evolutionary model of boundary-driven growth, we study how elevated cell death on the edge of a tumor can simultaneously impact driver mutation accumulation and the representation of tumor clones and mutation detectability in ctDNA. We describe conditions in which invasive clones are over-represented in ctDNA, clonal diversity can appear elevated in the blood, and spatial bias in shedding can inflate subclonal variant allele frequencies (VAFs). Additionally, we find that tumors that are mostly quiescent can display similar biases but are far less detectable, and the extent of perceptible spatial bias strongly depends on sequence detection limits. Overall, we show that spatially structured shedding might cause liquid biopsies to provide highly biased profiles of tumor state. While this may enable more sensitive detection of expanding clones, it could also increase the risk of targeting a subclonal variant for treatment. Our results indicate that the effects and clinical consequences of spatially variable cell death on ctDNA composition present an important area for future work.

A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

Modeling the effect of spatial structure on solid tumor evolution and ctDNA composition

Circulating tumor DNA (ctDNA) monitoring, while sufficiently advanced to reflect tumor evolution in real time and inform on cancer diagnosis, treatment, and prognosis, mainly relies on DNA that originates from cell death via apoptosis or necrosis. In solid tumors, chemotherapy and immune infiltration can induce spatially variable rates of cell death, with the potential to bias and distort the clonal composition of ctDNA. Using a stochastic evolutionary model of boundary-driven growth, we study how elevated cell death on the edge of a tumor can simultaneously impact driver mutation accumulation and the representation of tumor clones and mutation detectability in ctDNA. We describe conditions in which invasive clones end up over-represented in ctDNA, clonal diversity can appear elevated in the blood, and spatial bias in shedding can inflate subclonal variant allele frequencies (VAFs). Additionally, we find that tumors that are mostly quiescent can display similar biases, but are far less detectable, and the extent of perceptible spatial bias strongly depends on sequence detection limits. Overall, we show that spatially structured shedding might cause liquid biopsies to provide highly biased profiles of tumor state. While this may enable more sensitive detection of expanding clones, it could also increase the risk of targeting a subclonal variant for treatment. Our results indicate that the effects and clinical consequences of spatially variable cell death on ctDNA composition present an important area for future work.

Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA

Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.

Numerical investigation of ternary particle separation in a microchannel with a wall-mounted obstacle using dielectrophoresis

In recent years, microfluidic-based particle/cell manipulation techniques have catalyzed significant advances in several fields of science. As an efficient, precise, and label-free particle/cell manipulation technique, dielectrophoresis (DEP) has recently attracted widespread attention. This paper presents the design and investigation of a straight sheathless 3D microchannel with a wall-mounted trapezoidal obstacle for continuous-flow separation of three different populations of polystyrene (PS) particles (5, 10 and 20 µm) using DEP. An OpenFOAM code is developed to simulate and investigate the movement of particles in the microchannel. Then, the code is validated by performing various experimental tests using a microdevice previously fabricated in our lab. By comparing the numerical simulation results with the experimental tests, it can be claimed that the newly developed solver is highly accurate, and its results agree well with experimental tests. Next, the effect of various operational and geometrical parameters such as obstacle height, applied voltage, electrode pairs angle, and flow rate on the efficient focusing and separation of particles are numerically investigated. The results showed that efficient particle separation could only be achieved for obstacle heights of more than 350 µm. Furthermore, the appropriate voltage range for efficient particle separation is increased by decreasing the electrode angle as well as increasing the flow rate. Moreover, the results showed that by employing the appropriate channel design and operational conditions, at a maximum applied voltage of 10V, a sample flow rate of 2.5μL/min could be processed. The proposed design can be beneficial for integrating with lab-on-a-chip and clinical diagnosis applications due to advantages, such as simple design, no need for sheath flow, the simultaneous ternary separation of particles, and providing precise particle separation.

Diagnostic value of liquid biopsy in the era of precision medicine: 10 years of clinical evidence in cancer

Liquid biopsy is a diagnostic repeatable test, which in last years has emerged as a powerful tool for profiling cancer genomes in real-time with minimal invasiveness and tailoring oncological decision-making. It analyzes different blood-circulating biomarkers and circulating tumor DNA (ctDNA) is the preferred one. Nevertheless, tissue biopsy remains the gold standard for molecular evaluation of solid tumors whereas liquid biopsy is a complementary tool in many different clinical settings, such as treatment selection, monitoring treatment response, cancer clonal evolution, prognostic evaluation, as well as the detection of early disease and minimal residual disease (MRD). A wide number of technologies have been developed with the aim of increasing their sensitivity and specificity with acceptable costs. Moreover, several preclinical and clinical studies have been conducted to better understand liquid biopsy clinical utility. Anyway, several issues are still a limitation of its use such as false positive and negative results, results interpretation, and standardization of the panel tests. Although there has been rapid development of the research in these fields and recent advances in the clinical setting, many clinical trials and studies are still needed to make liquid biopsy an instrument of clinical routine. This review provides an overview of the current and future clinical applications and opening questions of liquid biopsy in different oncological settings, with particular attention to ctDNA liquid biopsy.