Liquid Biopsy: Blood-Based Analyses of ctDNA and CTCs

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

Critical Factors in the Analytical Work Flow of Circulating Tumor DNA-Based Molecular Profiling

BACKGROUND

Liquid biopsy testing, especially molecular tumor profiling of circulating tumor DNA (ctDNA) in cell-free plasma, has received increasing interest in recent years as it serves as a reliable alternative for the detection of tumor-specific aberrations to guide treatment decision-making in oncology. Many (commercially available) applications have been developed, however, broad divergences in (pre)analytical work flows and lack of universally applied guidelines impede routine clinical implementation. In this review, critical factors in the blood-based ctDNA liquid biopsy work flow are evaluated.

CONTENT

In the preanalytical phase, several aspects (e.g., blood collection tubes [BCTs], plasma processing, and extraction method) affect the quantity and quality of the circulating cell-free DNA (ccfDNA) applicable for subsequent molecular analyses and should meet certain standards to be applied in diagnostic work flows. Analytical considerations, such as analytical input and choice of assay, might vary based on the clinical application (i.e., screening, primary diagnosis, minimal residual disease [MRD], response monitoring, and resistance identification). In addition to practical procedures, variant interpretation and reporting ctDNA results should be harmonized. Collaborative efforts in (inter)national consortia and societies are essential for the establishment of standard operating procedures (SOPs) in attempts to standardize the plasma-based ctDNA analysis work flow.

SUMMARY

Development of universally applicable guidelines regarding the critical factors in liquid biopsy testing are necessary to pave the way to clinical implementation for routine diagnostics.

Droplet microfluidics for CTC-based liquid biopsy: a review

Circulating tumor cells (CTCs) are important biomarkers of liquid biopsy. The number and heterogeneity of CTCs play an important role in cancer diagnosis and personalized medicine. However, owing to the low-abundance biomarkers of CTCs, conventional assays are only able to detect CTCs at the population level. Therefore, there is a pressing need for a highly sensitive method to analyze CTCs at the single-cell level. As an important branch of microfluidics, droplet microfluidics is a high-throughput and sensitive single-cell analysis platform for the quantitative detection and heterogeneity analysis of CTCs. In this review, we focus on the quantitative detection and heterogeneity analysis of CTCs using droplet microfluidics. Technologies that enable droplet microfluidics, particularly high-throughput droplet generation and high-efficiency droplet manipulation, are first discussed. Then, recent advances in detecting and analyzing CTCs using droplet microfluidics from the different aspects of nucleic acids, proteins, and metabolites are introduced. The purpose of this review is to provide guidance for the continued study of droplet microfluidics for CTC-based liquid biopsy.

Nucleic Acid Biomarkers in Waldenström Macroglobulinemia and IgM-MGUS: Current Insights and Clinical Relevance

Waldenström Macroglobulinemia (WM) is an indolent lymphoplasmacytic lymphoma, characterized by the production of excess immunoglobulin M monoclonal protein. WM belongs to the spectrum of IgM gammopathies, ranging from asymptomatic IgM monoclonal gammopathy of undetermined significance (IgM-MGUS), through IgM-related disorders and asymptomatic WM to symptomatic WM. In recent years, its complex genomic and transcriptomic landscape has been extensively explored, hereby elucidating the biological mechanisms underlying disease onset, progression and therapy response. An increasing number of mutations, cytogenetic abnormalities, and molecular signatures have been described that have diagnostic, phenotype defining or prognostic implications. Moreover, cell-free nucleic acid biomarkers are increasingly being investigated, benefiting the patient in a minimally invasive way. This review aims to provide an extensive overview of molecular biomarkers in WM and IgM-MGUS, considering current shortcomings, as well as potential future applications in a precision medicine approach.