Deciphering the Biology of Circulating Tumor Cells through Single-Cell RNA Sequencing: Implications for Precision Medicine in Cancer

Unveiling the impact of circulating tumor cells: Two decades of discovery and clinical advancements in solid tumors

Circulating tumor cells (CTCs) enumeration and molecular profiling hold promise in revolutionizing the management of solid tumors. Their understanding has evolved significantly over the past two decades, encompassing pivotal biological discoveries and clinical studies across various malignancies. While for some tumor types, such as breast, prostate, and colorectal cancer, CTCs are ready to enter clinical practice, for others, additional research is required. CTCs serve as versatile biomarkers, offering insights into tumor biology, metastatic progression, and treatment response. This review summarizes the latest advancements in CTC research and highlights future directions of investigation. Special attention is given to concurrent evaluations of CTCs and other circulating biomarkers, particularly circulating tumor DNA. Multi-analyte assessment holds the potential to unlock the full clinical capabilities of liquid biopsy. In conclusion, CTCs represent a transformative biomarker in precision oncology, offering extraordinary opportunities to translate scientific discoveries into tangible improvements in patient care.

Progress of single-cell RNA sequencing combined with spatial transcriptomics in tumour microenvironment and treatment of pancreatic cancer

In recent years, single-cell analyses have revealed the heterogeneity of the tumour microenvironment (TME) at the genomic, transcriptomic, and proteomic levels, further improving our understanding of the mechanisms of tumour development. Single-cell RNA sequencing (scRNA-seq) technology allow analysis of the transcriptome at the single-cell level and have unprecedented potential for exploration of the characteristics involved in tumour development and progression. These techniques allow analysis of transcript sequences at higher resolution, thereby increasing our understanding of the diversity of cells found in the tumour microenvironment and how these cells interact in complex tumour tissue. Although scRNA-seq has emerged as an important tool for studying the tumour microenvironment in recent years, it cannot be used to analyse spatial information for cells. In this regard, spatial transcriptomics (ST) approaches allow researchers to understand the functions of individual cells in complex multicellular organisms by understanding their physical location in tissue sections. In particular, in related research on tumour heterogeneity, ST is an excellent complementary approach to scRNA-seq, constituting a new method for further exploration of tumour heterogeneity, and this approach can also provide unprecedented insight into the development of treatments for pancreatic cancer (PC). In this review, based on the methods of scRNA-seq and ST analyses, research progress on the tumour microenvironment and treatment of pancreatic cancer is further explained.

Exploring the Immunological Profile in Breast Cancer: Recent Advances in Diagnosis and Prognosis through Circulating Tumor Cells

This review offers a comprehensive exploration of the intricate immunological landscape of breast cancer (BC), focusing on recent advances in diagnosis and prognosis through the analysis of circulating tumor cells (CTCs). Positioned within the broader context of BC research, it underscores the pivotal role of the immune system in shaping the disease's progression. The primary objective of this investigation is to synthesize current knowledge on the immunological aspects of BC, with a particular emphasis on the diagnostic and prognostic potential offered by CTCs. This review adopts a thorough examination of the relevant literature, incorporating recent breakthroughs in the field. The methodology section succinctly outlines the approach, with a specific focus on CTC analysis and its implications for BC diagnosis and prognosis. Through this review, insights into the dynamic interplay between the immune system and BC are highlighted, with a specific emphasis on the role of CTCs in advancing diagnostic methodologies and refining prognostic assessments. Furthermore, this review presents objective and substantiated results, contributing to a deeper understanding of the immunological complexity in BC. In conclusion, this investigation underscores the significance of exploring the immunological profile of BC patients, providing valuable insights into novel advances in diagnosis and prognosis through the utilization of CTCs. The objective presentation of findings emphasizes the crucial role of the immune system in BC dynamics, thereby opening avenues for enhanced clinical management strategies.

Circulating Nucleic Acids in Colorectal Cancer: Diagnostic and Prognostic Value

Colorectal cancer (CRC) is the third most prevalent cancer in the world and the fourth leading cause of cancer-related mortality. DNA (cfDNA/ctDNA) and RNA (cfRNA/ctRNA) in the blood are promising noninvasive biomarkers for molecular profiling, screening, diagnosis, treatment management, and prognosis of CRC. Technological advancements that enable precise detection of both genetic and epigenetic abnormalities, even in minute quantities in circulation, can overcome some of these challenges. This review focuses on testing for circulating nucleic acids in the circulation as a noninvasive method for CRC detection, monitoring, detection of minimal residual disease, and patient management. In addition, the benefits and drawbacks of various diagnostic techniques and associated bioinformatics tools have been detailed.

Circulating Tumor Cells as Predictive and Prognostic Biomarkers in Solid Tumors

Circulating tumor cells (CTCs) have emerged as pivotal biomarkers with significant predictive and prognostic implications in solid tumors. Their presence in peripheral blood offers a non-invasive window into the dynamic landscape of cancer progression and treatment response. This narrative literature review synthesizes the current state of knowledge surrounding the multifaceted role of CTCs in predicting clinical outcomes and informing prognosis across a spectrum of solid tumor malignancies. This review delves into the evolving landscape of CTC-based research, emphasizing their potential as early indicators of disease recurrence, metastatic potential, and therapeutic resistance. Moreover, we have underscored the dynamic nature of CTCs and their implications for personalized medicine. A descriptive and critical analysis of CTC detection methodologies, their clinical relevance, and their associated challenges is also presented, with a focus on recent advancements and emerging technologies. Furthermore, we examine the integration of CTC-based liquid biopsies into clinical practice, highlighting their role in guiding treatment decisions, monitoring treatment efficacy, and facilitating precision oncology. This review highlights the transformative impact of CTCs as predictive and prognostic biomarkers in the management of solid tumors by promoting a deeper understanding of the clinical relevance of CTCs and their role in advancing the field of oncology.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.