Prostate cancer (PC) is the fourth most common cancer worldwide and is recognised as one of the major men's health challenges of the twenty-first century. There has been a steady increase in PC incidence, particularly for advanced-stage PC cases, which have increased by 5% since 2014. Despite advances in diagnostic and treatment techniques, understanding of the genetic mechanisms underlying PC remains limited, especially in families with multiple cases of the disease, where genetic predispositions significantly increase the risk of developing cancer. This chapter provides a comprehensive and up-to-date review of PC, covering key aspects such as its epidemiology, aetiology and associated risk factors. From the hereditary point of view, this chapter covers the main genes associated with the susceptibility to develop PC and related pathologies. Additionally, the chapter evaluates the current paradigm of detection and treatment protocols, while also exploring future research directions.

RNA sequencing (RNA-Seq) is an advanced technique that enables the comprehensive analysis of gene expression and the transcriptome in biological samples with exceptional precision and scalability. Leveraging platforms like Illumina, PacBio, and Oxford Nanopore, RNA-Seq has revolutionized cancer research by identifying genes, isoforms, and genetic variants. When combined with bioinformatics tools, it allows the detection of gene expression signatures, alternative splicing events, and profiles of non-coding RNAs. Furthermore, single-cell analysis provides insights into tumor heterogeneity, enhancing diagnostics, prognostics, and the development of personalized therapies.Artificial intelligence (AI), particularly explainable AI (XAI), plays a pivotal role in transcriptomic data analysis. Interpretable models, such as regression analyses or decision trees, and post-hoc techniques like LIME and SHAP, improve the reliability and usability of findings by identifying key genes for clinical decision-making. These tools integrate seamlessly with high-resolution single-cell and three-dimensional analyses, exploring intratumoral heterogeneity and cellular signaling pathways.Addressing the heterogeneity of common cancers demands the integration of sequencing technologies and AI. The combination of short- and long-read RNA-Seq enables the identification of isoforms and splicing events critical to cancer biology. Together, these technologies and approaches optimize diagnostic and therapeutic strategies, paving the way for personalized treatments by detecting and characterizing genetic alterations and their impact on the tumor microenvironment.

The number of circulating tumor cells (CTC) and tumor-derived extracellular vesicles (tdEV) is an independent predictor of survival in patients with metastatic carcinomas. Being one order of magnitude more abundant than CTC, tdEV can provide complementary prognostic value. On CellSearch immunofluorescent images, operators can identify CTC and tdEV based on morphology, DNA, cytokeratin, and CD45 fluorescent intensity. This manual process is time-consuming and potentially affected by subjective interpretations. To overcome these limitations and maximize standardization among different research centers, we introduce artificial intelligence (AI) based methods for the automated analysis of CellSearch images. These methods were evaluated on fluorescent images of metastatic breast, colorectal, and prostate cancer studies. This chapter will present Contrast Maximization (CM), an AI-based software solution for automated CTC and tdEV identification, and the concept of Blood Tumor Load (BTL), which combines CTC and tdEV counts into a single interpretable biomarker ranging from 0 (favorable) to 1 (unfavorable). CM-CTC demonstrated performance comparable to or better than human operators in predicting overall survival in metastatic cancer patients, enabling rapid and reproducible enumeration of clinically relevant CTCs. Similarly, CM-tdEV outperformed tdEV classification based on human-designed gating strategies. Survival analysis further revealed that BTL outperformed CTC and tdEV counts alone, underscoring the added value of combining these two complementary biomarkers. Notably, BTL derived from CM outperformed BTL based on manual counts, highlighting the advantages of CM in automating and standardizing the analysis process, ultimately enhancing accuracy and strengthening the relationship with clinical outcomes.

Cell detection is one of the most significant tasks in circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) analysis, as these cells are emerging as potential biomarkers for cancer prognosis and diagnosis. Traditional approaches to cell detection are often manual, leading to long turnaround times and significant variability between experts. In this chapter, we first provide an overview of deep learning techniques for general cell detection, with a specific focus on CTC and CAF detection. We then discuss developments in optical imaging systems that offer high-resolution, high-quality, and all-in-focus imaging capabilities. By integrating advanced optical hardware with deep learning algorithms, we demonstrate high-accuracy and high-fidelity detection of CTCs and CAFs in microfiltered-based samples. We also emphasize the need for refined technologies and models to enhance the clinical utility of CTC characterization and deepen the understanding of metastasis. The integrated approach introduced in this chapter has the potential to establish a new automated paradigm for CTC and CAF analysis.

Shallow whole-genome sequencing (sWGS) is a cost-effective method for rapidly detecting large-scale genome alterations like copy number alterations. The sWGS workflow involves DNA extraction, library preparation, and sequencing, followed by specialized bioinformatics analyses, which we carefully review at each step. This workflow includes data preprocessing and alignment to a reference genome, with each bioinformatics stage carefully detailed as it plays a pivotal role in ensuring the accuracy and biological relevance of the results. The application of this process enables reliable data collection and analysis for monitoring cancer evolution and treatment responses by identifying critical genome changes, providing significant insights for prognosis and therapeutic decision-making across various cancer types.

Circulating tumor cells (CTCs) shed by solid tumors can travel to distant sites to initiate metastatic foci. These cells can be detected in the blood and bone marrow. In addition, CTCs can be found in fluids that accumulate in the pleural or peritoneal cavity, a complication of metastatic cancer called malignant effusion. Efforts to characterize CTCs in malignant effusions can face limitations because of the low proportion of CTCs relative to other cells (primarily immune cells) in these fluids. Therefore, expanding CTCs through in vitro culture could address this limitation and enable functional studies. Here, we describe a method for culturing breast cancer CTCs as organoids (clusters of tumor cells) amenable to downstream profiling and functional studies.

Circulating tumor cells (CTCs) are a group of cells that travel through the circulatory system of a cancer patient, sowing distant sites and forming the seeds for metastatic nodules. Studying the genetic mechanisms involved in the CTC phenotype could enable significant strides in understanding metastasis. Initially, CTC studies were limited in scope to the enumeration of this rare population of cells. However, recent advancements in expanding viable CTCs ex vivo made in the last two decades have enabled researchers to properly interrogate this new frontier of metastatic research, resulting in groundbreaking findings expanding upon the simplistic models of metastasis previously used. In this chapter, we will focus on the technological advancements that have enabled the isolation and propagation of CTCs. First, we will highlight two eras of CTC culture: one focused on growing viable epithelial cells and the other focused on modeling CTC heterogeneity, such as through the capture of CTC clusters. Next, we will explore methods for generating in vivo CTC-derived xenograft (CDX) models. We will further compare and contrast these methods, discussing where these models fit within the spectrum of metastasis models. Finally, we will preview how CTC expansion may play a crucial role in the era of personalized medicine.

Circulating tumor cells (CTCs) are emerging biomarkers for cancer prognosis, yet existing methods to isolate CTCs from liquid biopsies rely on cell surface markers, which restrict the heterogeneity of the sampled cells. Here, we describe a method for the detection of disease-specific CTCs from peripheral blood using both positive and negative enrichment strategies. We also implement the newly designed Smart-seq3 library preparation protocol, allowing for the sequencing of full-length CTC transcripts at the single-cell level. We demonstrate the utility of this isolation method by isolating melanoma cells spiked into healthy blood, followed by single-cell RNA-seq.

Pancreatic cancer is one of the most aggressive malignancies, characterized by poor prognosis and high metastatic potential. Circulating tumor cells (CTCs) are critical mediators of metastasis and hold promise as biomarkers for early detection and therapy monitoring. However, their extreme rarity and heterogeneity pose significant challenges for their enrichment and analysis. Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology to address these challenges by providing high-resolution insights into individual CTC biology.This chapter presents a comprehensive workflow for isolating and analyzing pancreatic cancer-derived CTCs using scRNA-seq. The protocol integrates size-based microfluidic enrichment, followed by negative selection via fluorescence-activated cell sorting (FACS), and concludes with single-cell transcriptome sequencing combined with target mutation detection. This method addresses critical purity and cell recovery limitations, allowing for the robust characterization of rare CTC populations. The insights gained from this workflow deepen our understanding of pancreatic cancer progression and facilitate the identification of novel therapeutic targets. By providing a practical example, this chapter serves as a valuable guide for researchers seeking to optimize single-cell sequencing techniques in their CTC studies.

MicroRNAs (miRNAs) play a crucial role in the analysis of circulating tumor cells (CTCs) due to their unique properties and involvement in cancer biology. miRNAs in CTCs can help to identify early changes associated with metastasis, allowing for early intervention. Specific miRNAs are associated with drug resistance mechanisms, and their presence or absence in CTCs can help predict the effectiveness of treatment options, enabling personalized therapy adjustments. Changes in miRNA expression levels in CTCs can also be used to monitor disease progression or detect early signs of recurrence after treatment. This is especially important in managing cancers that are prone to relapse. Therefore, the in-depth characterization of physiological miRNA functions is an important basic research question, and miRNAs even have a high potential as biomarkers both for prognosis and diagnosis. To exploit this potential, it is mandatory to accurately quantify the miRNA expression not only in tumor bulk, but also on the single CTC level. Here, we describe a protocol that facilitates miRNA sequencing library preparation in CTCs. The protocol can be combined with different single-cell isolation methods (e.g., micromanipulation and FACS sorting). After cell lysis, sequencing adapters are ligated to the miRNAs, other ncRNA species and adapter dimers are reduced by exonuclease digest, the miRNA library is reverse transcribed, amplified and purified. Furthermore, quality controls are described to select only high-quality samples for sequencing.

Single-cell sequencing (SCS) technology has become an important tool for researchers to study human diseases, including cancer, providing insights into phenotypic and genetic tumor heterogeneity, targeted therapy resistance, and metastasis. SCS technology has proven to be a remarkable tool for shedding light on the impressive molecular heterogeneity of circulating tumor cells (CTCs). However, the extreme rarity and biological heterogeneity of CTCs have imposed technical challenges for their analyses at the single-cell level, and affected the success of robust processing of complex and costly downstream methodologies. Here, we present the entire workflow we developed to enrich, detect and isolate single non-small-cell lung cancer (NSCLC) CTCs to characterize tumor cell subpopulation resistant to targeted therapy. Once isolated, individual CTCs underwent a molecular workflow including whole-genome amplification (WGA) and qualification, followed by two SCS techniques, including targeted next-generation sequencing (NGS) for somatic variant identification and the low-pass whole genome sequencing (LP-WGS) for the analysis of copy number alterations (CNA).

The presence and frequency of circulating tumor cells (CTCs) in the peripheral blood of many cancer types, including breast cancer, can be an indicator of later metastasis. Assessing their molecular features may be useful in understanding the mechanisms behind metastasis. However, CTCs are very rare and require quick processing in order to preserve viability. Here, we describe a method to enrich and isolate single CTCs from the cryopreserved blood fractions of patients with breast cancer, to allow for delayed isolation. This protocol includes immunomagnetic depletion to enrich for CTCs followed by single-cell isolation using the DEPArray NxT™. Whole-genome amplification of isolated CTCs allows for many downstream applications to be performed, such as shallow whole genome sequencing for copy number analysis or quantitative polymerase chain reaction (qPCR) for genomic analyses.

The purpose of this chapter is to provide a comprehensive, step-by-step methodology for the analysis of circulating tumor cell (CTC) interactions within the blood microenvironment, from the acquisition of CTCs to single-cell transcriptomic sequencing and subsequent data analysis. Our methodological approach involved the following steps: (1) Data Preprocessing: The raw sequencing data were subjected to stringent quality control and preprocessing to remove artifacts and outliers that could potentially skew the analysis. (2) Seurat analysis: Utilizing the Seurat package, we performed dimensionality reduction techniques such as principal component analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to visualize and cluster CTCs based on their gene expression profiles. (3) CellphoneDB integration: We employed CellphoneDB to analyze the interactions between CTCs and other cell types within the blood, elucidating potential interaction pairs that could be targeted for therapeutic intervention. (4) Statistical graphics: The ggplot2 package was used to create informative and visually appealing graphs that summarized the results of our analysis, facilitating the interpretation of complex data for both oncologists and biologists. By following this methodological framework, we were able to provide a comprehensive analysis of CTC interactions, offering valuable insights that could inform the development of targeted therapies in oncology.

Small cell lung cancer (SCLC) exhibits extreme numbers of circulating tumor cells (CTCs) that are operative in tumor dissemination and possibly drug resistance, ultimately resulting in a dismal prognosis. Tumor cells depend on kinases, which are triggered by a range of growth factors for their malignant proliferation. Insulin-like growth factor-I receptor (IGF-1) and vascular endothelial growth factor receptor (VEGFR), c-Kit, epidermal growth factor receptor (EGFR), as well as downstream signaling transmitters such as phosphoinositide 3-kinase (PI3K), Akt and the mammalian target of rapamycin (mTOR) and a range of other kinases were investigated as potential anti-tumor targets in SCLC. Despite the expression of rational targets, the studies with tyrosine kinase inhibitors (TKIs) in SCLC have been very disappointing in the clinic. The preclinical activity of kinase inhibitors against native and resistant SCLC cell lines failed to predict efficacy in patients. Screening of a kinase inhibitor library showed activity of most compounds against single SCLC CTC cells but high chemoresistance of BHGc10 spheroids (termed tumorospheres). Only a few hydrophobic multitarget inhibitors proved active against the spheroids, and drugs that failed in clinical trials targeting IGF-1R, AURORA kinase and Polo kinases showed low activity against CTC tumorospheres. These results demonstrate that for SCLC, kinase inhibitors should be tested against spheroids to obtain a better prediction of their potential clinical activity.

In the last years, several body fluid-based tests, able to detect molecular alterations derived from tumors, have been proposed, and numerous others are in continuous development. These tests, commonly referred to as "liquid biopsies," encompass a wide spectrum of diagnostic procedures, such as circulating tumor cells (CTCs), cell-free circulating tumor DNA (ctDNA), circulating tumor-derived exosome, and other biomarkers, whose detection can offer precious information about tumor volume and disease status. Nevertheless, these novel biomarkers have limited sensitivity and specificity when used alone. In this scenario, molecular imaging with positron emission tomography (PET), due to its wide availability and the consolidated role in cancer patients' management, could be coupled with liquid biopsy for cancer characterization, patient stratification, and the early detection of recurrence. In this chapter, we will focus on studies that combine liquid biopsy and PET-based parameters across different cancer types and therapeutic settings.