Clinical implementation and current advancement of blood liquid biopsy in cancer

Circulating Tumor DNA in Genitourinary Cancers: Detection, Prognostics, and Therapeutic Implications

CtDNA is emerging as a non-invasive clinical detection method for several cancers, including genitourinary (GU) cancers such as prostate cancer, bladder cancer, and renal cell carcinoma (RCC). CtDNA assays have shown promise in early detection of GU cancers, providing prognostic information, assessing real-time treatment response, and detecting residual disease and relapse. The ease of obtaining a "liquid biopsy" from blood or urine in GU cancers enhances its potential to be used as a biomarker. Interrogating these "liquid biopsies" for ctDNA can then be used to detect common cancer mutations, novel genomic alterations, or epigenetic modifications. CtDNA has undergone investigation in numerous clinical trials, which could address clinical needs in GU cancers, for instance, earlier detection in RCC, therapeutic response prediction in castration-resistant prostate cancer, and monitoring for recurrence in bladder cancers. The utilization of liquid biopsy for ctDNA analysis provides a promising method of advancing precision medicine within the field of GU cancers.

Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking

BACKGROUND

High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling.

RESULTS

Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants.

CONCLUSIONS

Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

Liquid biopsy for precision diagnostics and therapeutics

Liquid biopsy (LB) has emerged as a highly promising and non-invasive diagnostic approach, particularly in the field of oncology, and has garnered interest in various medical disciplines. This technique involves the examination of biomolecules released into physiological fluids, such as urine samples, blood, and cerebrospinal fluid (CSF). The analysed biomolecules included circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), cell-free DNA (cfDNA), exosomes, and other cell-free components. In contrast to conventional tissue biopsies, LB provides minimally invasive diagnostics, offering invaluable insights into tumor characteristics, treatment response, and early disease detection. This Review explores the contemporary landscape of technologies and clinical applications in the realm of LB, with a particular emphasis on the isolation and analysis of ctDNA and/or cfDNA. Various methodologies have been employed, including droplet digital polymerase chain reaction (DDP), BEAMing (beads, emulsion, amplification, and magnetics), TAm-Seq (tagged-amplicon deep sequencing), CAPP-Seq (cancer personalized profiling by deep sequencing), WGBS-Seq (whole genome bisulfite sequencing), WES (whole exome sequencing), and WGS (whole-genome sequencing). Additionally, CTCs have been successfully isolated through biomarker-based cell capture, employing both positive and negative enrichment strategies based on diverse biophysical and other inherent properties. This approach also addresses challenges and limitations associated with liquid biopsy techniques, such as sensitivity, specificity, standardization and interpretability of findings. This review seeks to identify the current technologies used in liquid biopsy samples, emphasizing their significance in identifying tumor markers for cancer detection, prognosis, and treatment outcome monitoring.

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.

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.

Tumor genomic profiling and personalized tracking of circulating tumor DNA in Vietnamese colorectal cancer patients

Background

Colorectal cancer (CRC) is the fifth most common cancer with rising prevalence in Vietnam. However, there is no data about the mutational landscape and actionable alterations in the Vietnamese patients. During post-operative surveillance, clinical tools are limited to stratify risk of recurrence and detect residual disease.

Method

In this prospective multi-center study, 103 CRC patients eligible for curative-intent surgery were recruited. Genomic DNA from tumor tissue and paired white blood cells were sequenced to profile all tumor-derived somatic mutations in 95 cancer-associated genes. Our bioinformatic algorithm identified top mutations unique for individual patient, which were then used to monitor the presence of circulating tumor DNA (ctDNA) in serial plasma samples.

Results

The top mutated genes in our cohort were APC, TP53 and KRAS. 41.7% of the patients harbored KRAS and NRAS mutations predictive of resistance to Cetuximab and Panitumumab respectively; 41.7% had mutations targeted by either approved or experimental drugs. Using a personalized subset of top ranked mutations, we detected ctDNA in 90.5% of the pre-operative plasma samples, whereas carcinoembryonic antigen (CEA) was elevated in only 41.3% of them. Interim analysis after 16-month follow-up revealed post-operative detection of ctDNA in two patients that had recurrence, with the lead time of 4-10.5 months ahead of clinical diagnosis. CEA failed to predict recurrence in both cases.

Conclusion

Our assay showed promising dual clinical utilities in residual cancer surveillance and actionable mutation profiling for targeted therapies in CRC patients. This could lay foundation to empower precision cancer medicine in Vietnam and other developing countries.

Potentiality of Exosomal Proteins as Novel Cancer Biomarkers for Liquid Biopsy

Liquid biopsy has been rapidly developed in recent years due to its advantages of non-invasiveness and real-time sampling in cancer prognosis and diagnosis. Exosomes are nanosized extracellular vesicles secreted by all types of cells and abundantly distributed in all types of body fluid, carrying diverse cargos including proteins, DNA, and RNA, which transmit regulatory signals to recipient cells. Among the cargos, exosomal proteins have always been used as immunoaffinity binding targets for exosome isolation. Increasing evidence about the function of tumor-derived exosomes and their proteins is found to be massively associated with tumor initiation, progression, and metastasis in recent years. Therefore, exosomal proteins and some nucleic acids, such as miRNA, can be used not only as targets for exosome isolation but also as potential diagnostic markers in cancer research, especially for liquid biopsy. This review will discuss the existing protein-based methods for exosome isolation and characterization that are more appropriate for clinical use based on current knowledge of the exosomal biogenesis and function. Additionally, the recent studies for the use of exosomal proteins as cancer biomarkers are also discussed and summarized, which might contribute to the development of exosomal proteins as novel diagnostic tools for liquid biopsy.

Cell-Free DNA in Dermatology Research

In various diseases, particularly cancer, cell-free DNA (cfDNA) has been widely studied as a marker of disease prognosis or to facilitate the detection of therapeutic targets. In dermatology, most studies have focused on melanoma; other skin diseases such as vascular malformations and psoriasis have also been examined. Genetic alterations unique to the tissue of origin such as sequence variations, copy number alterations, chromosomal rearrangements, differential DNA methylation patterns, and fragmentation patterns can be identified in circulation providing information on patient disease status. These alterations can be detected either by PCR-based methods or next-generation sequencing depending on the target of interest. In this article, we discuss the origins of cfDNA, the most common methods of detection, current studies assessing cfDNA as a biomarker, and cfDNA's potential clinical applications in melanoma and other skin diseases. In addition, we provide important factors to consider during blood processing and DNA extraction as well as limitations for each assay.

KRAS: A Druggable Target in Colon Cancer Patients

Mutations in KRAS are among the most frequent aberrations in cancer, including colon cancer. KRAS direct targeting is daunting due to KRAS protein resistance to small molecule inhibition. Moreover, its elevated affinity to cellular guanosine triphosphate (GTP) has made the design of specific drugs challenging. Indeed, KRAS was considered ‘undruggable’. KRASG12C is the most commonly mutated variant of KRAS in non-small cell lung cancer. Currently, the achievements obtained with covalent inhibitors of this variant have given the possibility to assess the best therapeutic approach to KRAS-driven tumors. Mutation-related biochemical assets and the tissue of origin are expected to influence responses to treatment. Further attempts to obtain mutant-specific KRAS (KRASG12C) switch-II covalent inhibitors are ongoing and the results are promising. Drugs targeted to block KRAS effector pathways could be combined with direct KRAS inhibitors, immunotherapy or T cell-targeting approaches in KRAS-mutant tumors. The development of valuable combination regimens will be essential against potential mechanisms of resistance that may arise during treatment.

Single-Tube qPCR Detection and Quantitation of Hotspot Mutations Down to 0.01% Variant Allele Fraction

Clinically and biologically, rare DNA sequence variants are significant and informative. However, existing common detection technologies are either complex and time-consuming in workflow, or restricted in the limit of detection (LoD), or do not allow for multiplexing. Blocker displacement amplification (BDA) method can stably and effectively detect and enrich multiple rare variants with LoD around 0.1% variant allele fraction (VAF). Nonetheless, the detailed mutation information has to be identified by additional sequencing technologies. Here, we present allele-specific BDA (As-BDA), a method combining BDA with allele-specific TaqMan (As-TaqMan) probes for effective variant enrichment and simultaneous single nucleotide variant or small insertions and deletions (INDELs) profiling. We demonstrated that As-BDA could detect mutations down to 0.01% VAF. Further, As-BDA could detect up to four mutations with low to 0.1% VAF per reaction using only 15 ng DNA input. The median error of As-BDA in VAF determination is approximately 9.1%. Comparison experiments using As-BDA and droplet digital PCR on peripheral blood mononuclear cell clinical samples showed 100% concordance for samples with mutations at ≥ 0.1% VAF. Hence, we have shown that As-BDA can achieve simultaneous enrichment and identification of multiple targeted mutations within the same reaction with high clinical sensitivity and specificity, thus helpful for clinical diagnosis.

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.