Abstract

Simple SummaryThe circulating cancer biomarkers, known as ‘liquid biopsy’ (LB), represent a means to profile tumors non-invasively and collect information that can define therapeutic regimens for precision and personalized medicine. Various approaches have been developed for isolating and studying the individual circulating cancer biomarkers. This review focuses on LB biomarkers of circulating tumor DNA (ctDNA) and small Extracellular vesicles (sEVs). We present the most recent approaches for their isolation and characterization and elaborate on the emerging mathematical and computational models for studying the roles of these cell-released cancer biomarkers in cancer progression. We envision that the study of these new models and technologies could significantly contribute to the field of personalized medicine.During cancer progression, tumors shed different biomarkers into the bloodstream, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). The analysis of these biomarkers in the blood, known as ‘liquid biopsy’ (LB), is a promising approach for early cancer detection and treatment monitoring, and more recently, as a means for cancer therapy. Previous reviews have discussed the role of CTCs and ctDNA in cancer progression; however, ctDNA and EVs are rapidly evolving with technological advancements and computational analysis and are the subject of enormous recent studies in cancer biomarkers. In this review, first, we introduce these cell-released cancer biomarkers and briefly discuss their clinical significance in cancer diagnosis and treatment monitoring. Second, we present conventional and novel approaches for the isolation, profiling, and characterization of these markers. We then investigate the mathematical and in silico models that are developed to investigate the function of ctDNA and EVs in cancer progression. We convey our views on what is needed to pave the way to translate the emerging technologies and models into the clinic and make the case that optimized next-generation techniques and models are needed to precisely evaluate the clinical relevance of these LB markers.

Highlights

  • Simple Summary: The circulating cancer biomarkers, known as ‘liquid biopsy’ (LB), represent a means to profile tumors non-invasively and collect information that can define therapeutic regimens for precision and personalized medicine

  • The tumor microenvironment (TME) comprises a heterogeneous mixture of cellular and non-cellular components, including cancer cells, fibroblasts, immune cells, blood vessels, and intercellular signaling factors (Figure 1). It contributes to the maintenance of cancer stemness, and mounting evidence indicates that it plays a critical role in treatment resistance, uncontrolled tumor growth, promoting angiogenesis, invasion, and metastasis

  • Cancers 2022, 14, 288 sels, and intercellular signaling factors (Figure 1). It contributes to the maintenance of can2 of 25 cer stemness, and mounting evidence indicates that it plays a critical role in treatment resistance, uncontrolled tumor growth, promoting angiogenesis, invasion, and metastasis

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Summary

Clinical Significance of Cell-Released Cancer Biomarkers

The tumor microenvironment (TME) comprises a heterogeneous mixture of cellular and non-cellular components, including cancer cells, fibroblasts, immune cells, blood vessels, and intercellular signaling factors (Figure 1). It contributes to the maintenance of cancer stemness, and mounting evidence indicates that it plays a critical role in treatment resistance, uncontrolled tumor growth, promoting angiogenesis, invasion, and metastasis. LB has emerged as a viable alternative to tissue genotyping for the assessment of tumor-specific molecular alterations in cancer patients. SEVs extracted from NSCLC cells were embedded with doxorubicin (DOX), a well-known chemotherapy drug, and conjugated with gold nanoparticles These EV-based conjugates were used to deliver drugs to lung cancer cell lines and non-tumorigenic lung fibroblasts.

Methods
Conventional Methods for ctDNA Characterization
Novel Strategies for ctDNA Detection
Conventional Methods for sEV Isolation
New Strategies for EV Isolation
New Strategies for sEV Characterization
Computational Analysis of ctDNA and sEVs
Computational Models for Studying ctDNA
Computational Models for Studying sEVs
Findings
Machine Learning Approaches for Cancer Screening
Full Text
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