Liquid biopsy in lung cancer

Lung cancer is a highly prevalent malignancy worldwide and the primary cause of mortality. The absence of systematic and standardized diagnostic approaches for identifying potential pulmonary nodules, early-stage cancers, and indeterminate tumors has led clinicians to consider tissue biopsy and pathological sections as the preferred method for clinical diagnosis, often regarded as the gold standard. The conventional tissue biopsy is an invasive procedure that does not adequately capture the diverse characteristics and evolving nature of tumors. Recently, the concept of 'liquid biopsy' has gained considerable attention as a promising solution. Liquid biopsy is a non-invasive approach that facilitates repeated analysis, enabling real-time monitoring of tumor recurrence, metastasis, and response to treatment. Currently, liquid biopsy includes circulating tumor cells, circulating cell-free DNA, circulating tumor DNA, circulating cell-free RNA, extracellular vesicles, and other proteins and metabolites. With rapid progress in molecular technology, liquid biopsy has emerged as a highly promising and intriguing approach, yielding compelling results. This article critically examines the significant role and potential clinical implications of liquid biopsy in the diagnosis, treatment, and prognosis of lung cancer.

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.

Can we define breast cancer HER2 status by liquid biopsy?

Human Epidermal growth factor Receptor 2 (HER2) assessment is crucial for breast cancer treatment. Therapeutic decisions for recurrent cases often rely on primary tumor status. However, mounting evidence suggests that tumors show dynamic changes and up to 10% of breast cancer modify their initial status during progression. It is still debated whether these changes reflect a biological evolution of the disease or are secondary to primary tumor heterogeneity. Certainly, repeating HER2 assessment during breast cancer trajectory is important for the increasing availability of effective anti-HER2 drugs. In response to this need, circulating biomarkers such as circulating tumor cells (CTCs) and cell-free circulating tumor DNA (ctDNA) offer the potential to safely and repeatedly assess HER2 status over time. This chapter outlines current methods for testing HER2 in CTCs and ctDNA, and reviews clinical trials evaluating its prognostic and predictive value in patients with breast cancer, as well as recent advances in the field.

Automatic trajectory control of single cells using dielectrophoresis based on visual feedback

This paper deals with the automatic control of the trajectory of T-lymphocytes using dielectrophoretic (DEP) actuation. Dielectrophoresis is a physical phenomenon induced by a non-uniform electric field enabling application of a force on a dielectric object. In most of the cases, it is used in a passive way. The electric field is in a steady state and the force applied on the cells depends on the cell's characteristics and position inside the channel. These systems are limited as cells with similar characteristics will undergo the same forces. To overcome this issue, active devices where the electric field changes over time were developed. However, the voltages that should be applied to generate the desired electric field are mostly computed offline using finite element methods. Thus, there is a low number of devices using automatic approaches with dielectrophoretic actuation where the electric field is computed and updated in real time based on the current position of the cell. We propose here an experimental bench used to study the automatic trajectory control of cells by dielectrophoresis. The computation of the dielectrophoretic force is done online with a model based on the Fourier series depending on the cell's characteristics, position and electric field. This model allows the use of a controller based on visual feedback running at 120 Hz to control the position of cells inside a microfluidic chip. As cells are sensitive to the electric field, the controller limits the norm of the electric field while maximizing the gradient to maximize the DEP force. Experiments have been performed and T-lymphocytes were successfully steered along several types of trajectories at a speed of five times their size per second. The mean error along those trajectories is below 2 μm. The viability of the cells has been checked after the experiments and confirms that this active DEP actuation does not harm the cells.

Advances in Osteosarcoma

PURPOSE OF REVIEW

This article gives a brief overview of the most recent developments in osteosarcoma treatment, including targeting of signaling pathways, immune checkpoint inhibitors, drug delivery strategies as single or combined approaches, and the identification of new therapeutic targets to face this highly heterogeneous disease.

RECENT FINDINGS

Osteosarcoma is one of the most common primary malignant bone tumors in children and young adults, with a high risk of bone and lung metastases and a 5-year survival rate around 70% in the absence of metastases and 30% if metastases are detected at the time of diagnosis. Despite the novel advances in neoadjuvant chemotherapy, the effective treatment for osteosarcoma has not improved in the last 4 decades. The emergence of immunotherapy has transformed the paradigm of treatment, focusing therapeutic strategies on the potential of immune checkpoint inhibitors. However, the most recent clinical trials show a slight improvement over the conventional polychemotherapy scheme. The tumor microenvironment plays a crucial role in the pathogenesis of osteosarcoma by controlling the tumor growth, the metastatic process and the drug resistance and paved the way of new therapeutic options that must be validated by accurate pre-clinical studies and clinical trials.

Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells

Circulating tumor cells (CTCs) are precursors to cancer metastasis. In blood circulation, they take various forms such as single CTCs, CTC clusters, and CTC-leukocyte clusters, all of which have unique characteristics in terms of physiological function and have been a subject of extensive research in the last several years. Unfortunately, conventional methods are limited in accurately analysing the highly heterogeneous nature of CTCs. Here we present an effective strategy for simultaneously analysing all forms of CTCs in blood by virtual-freezing fluorescence imaging (VIFFI) flow cytometry with 5-aminolevulinic acid (5-ALA) stimulation and antibody labeling. VIFFI is an optomechanical imaging method that virtually freezes the motion of fast-flowing cells on an image sensor to enable high-throughput yet sensitive imaging of every single event. 5-ALA stimulates cancer cells to induce the accumulation of protoporphyrin (PpIX), a red fluorescent substance, making it possible to detect all cancer cells even if they show no expression of the epithelial cell adhesion molecule, a typical CTC biomarker. Although PpIX signals are generally weak, VIFFI flow cytometry can detect them by virtue of its high sensitivity. As a proof-of-principle demonstration of the strategy, we applied cancer cells spiked in blood to the strategy to demonstrate image-based detection and accurate classification of single cancer cells, clusters of cancer cells, and clusters of a cancer cell(s) and a leukocyte(s). To show the clinical utility of our method, we used it to evaluate blood samples of four breast cancer patients and four healthy donors and identified EpCAM-positive PpIX-positive cells in one of the patient samples. Our work paves the way toward the determination of cancer prognosis, the guidance and monitoring of treatment, and the design of antitumor strategies for cancer patients.

Circulating Tumor Cells in Cancer Diagnostics and Prognostics by Single-Molecule and Single-Cell Characterization

Circulating tumor cells (CTCs) represent an interesting source of biomarkers for diagnosis, prognosis, and the prediction of cancer recurrence, yet while they are extensively studied in oncobiology research, their diagnostic utility has not yet been demonstrated and validated. Their scarcity in human biological fluids impedes the identification of dangerous CTC subpopulations that may promote metastatic dissemination. In this Perspective, we discuss promising techniques that could be used for the identification of these metastatic cells. We first describe methods for isolating patient-derived CTCs and then the use of 3D biomimetic matrixes in their amplification and analysis, followed by methods for further CTC analyses at the single-cell and single-molecule levels. Finally, we discuss how the elucidation of mechanical and morphological properties using techniques such as atomic force microscopy and molecular biomarker identification using nanopore-based detection could be combined in the future to provide patients and their healthcare providers with a more accurate diagnosis.

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

Background

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80–85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

Methods

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan–Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.

Results

In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.

Conclusion

Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.