J Liq Biopsy. 2024 Feb 11;4:100146. doi: 10.1016/j.jlb.2024.100146. eCollection 2024 Jun.

ABSTRACT

As precision oncology has become a major part of the treatment landscape in oncology, liquid biopsies have developed as a particularly powerful tool as it surmounts several limitations of traditional tissue biopsies. These biopsies involve most commonly the isolation of circulating extracellular nucleic acids, including cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA), as well as circulating tumor cells (CTCs), typically from blood. The clinical applications of liquid biopsies are diverse, encompassing the initial diagnosis and cancer detection, the application as a tool for prognostication in early and advanced tumor settings, the identification of potentially actionable alterations, the monitoring of response and resistance under systemic therapy and the detection of resistance mechanisms, the differentiation of distinct immune checkpoint blockade response patterns through serial samples, the prediction of immune checkpoint blockade responses based on initial liquid biopsy characteristics and the assessment of tumor heterogeneity. Moreover, molecular relapse monitoring in early-stage cancers and the personalization of adjuvant or additive therapy via MRD have become a major field of research in recent years. Compared to tissue biopsies, liquid biopsies are less invasive and can be collected serially, offering real-time molecular insights. Furthermore, liquid biopsies may allow for a more holistic evaluation of a patient's disease, as they assess material from all tumor sites and can theoretically reflect tumor heterogeneity. Furthermore, quicker turnaround-time also constitutes an advantage of liquid biopsies. Disadvantages or hurdles include the challenge of detecting low amounts of tumor deposits in peripheral blood or other fluids and the potential of different amounts tumor-shedding from different metastatic sites, as well as potentially false-positive from clonal hematopoietic mutations of indeterminate potential (CHIP) mutations. The clinical utility of liquid biopsies still must be validated in most settings and further research has to be done. Clinal trials including alternate bodily fluids and leveraging AI-technology are expected to revolutionize the field of liquid biopsies.

PMID:40027149 | PMC:PMC11863819 | DOI:10.1016/j.jlb.2024.100146

J Liq Biopsy. 2024 Feb 10;4:100145. doi: 10.1016/j.jlb.2024.100145. eCollection 2024 Jun.

ABSTRACT

Blood-based biomarkers investigation does not require invasive tissue biopsies and may explore diverse tumoral components such as proteins, microRNAs, circulating tumor cells, ctDNA, and exosomes and may better reflect tumor molecular heterogeneity, either temporal or spatial. ctDNA is related to tumor burden and represents a more objective measure of the total body disease burden than imaging findings. ctDNA profiling can be therefore useful to determine minimal residual disease (MRD), which is defined as the remaining tumor cells or tumor-derived material after definitive treatment in patients with no clinical evidence of disease. The detection of MRD is highly predictive of future disease recurrence. Although detectable MRD is associated with a poor prognosis, it is not clear whether MRD detection can guide therapy escalation to improve patient outcomes. In this review, we present four cases of epidermal growth factor receptor (EGFR) mutant NSCLC patients who received standard of care curative treatment and periodic radiological assessment and liquid biopsy analyses were carried out as follow-up. A tumor-informed 52 genes Oncomine Pan-Cancer Cell-Free assay (Thermo Fisher Scientific, Waltham, MA, USA), was used to identify single-nucleotide variants (SNVs), indels, copy number variations (CNVs), and RNA fusions in blood based liquid biopsy ctDNA in three of four patients. In one patient the approach used was through Commercial Kit Cobas EGFR Mutation Test v2 CE-IVD (Roche Diagnostics SL) that identifies 42 mutations in the EGFR gene. In one patient, an actionable oncogene driver alteration was identified in the ctDNA analysis, four months after radical intent concurrent chemoradiotherapy and six weeks before radiological distant relapse was clearly confirmed. There is no evidence of ctDNA or radiological disease relapse in the other three patients. Finally, a review of the literature addressing the potential value of MRD detection in this clinical setting is presented and discussed as well.

PMID:40027142 | PMC:PMC11863883 | DOI:10.1016/j.jlb.2024.100145

bioRxiv [Preprint]. 2025 Feb 17:2025.02.13.637721. doi: 10.1101/2025.02.13.637721.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40027657 | PMC:PMC11870426 | DOI:10.1101/2025.02.13.637721

Semin Cancer Biol. 2025 Jun;111:76-88. doi: 10.1016/j.semcancer.2025.02.009. Epub 2025 Feb 20.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

PMID:39986585 | DOI:10.1016/j.semcancer.2025.02.009

Int Rev Cell Mol Biol. 2025;391:43-93. doi: 10.1016/bs.ircmb.2024.08.006. Epub 2024 Sep 7.

ABSTRACT

The sampling of circulating biomarkers provides an opportunity for non-invasive evaluation and monitoring of cancer activity. In modern day practice, this has typically been in the form of circulating tumor DNA (ctDNA) detected in plasma. The field of ctDNA has been a burgeoning technology, with prominent applications for blood-based cancer screening and in disease status assessment, especially after curative-intent surgery to evaluate for minimal residual disease (MRD). Clinical applications for the latter show an incredibly high sensitivity in certain cancer types with a need for additional studies to determine how much clinical decision-making should be adapted based on ctDNA results and which cancer types, stages, and treatments are best informed by ctDNA results. This chapter provides an overview of ctDNA detection as tool for cancer screening, detecting MRD, and/or molecularly characterizing a cancer, highlighting the rapidly amassing research as a prognostic biomarker and emerging data on ctDNA as a predictive biomarker.

PMID:39939078 | DOI:10.1016/bs.ircmb.2024.08.006

Nat Comput Sci. 2025 Feb 7. doi: 10.1038/s43588-024-00764-8. Online ahead of print.

ABSTRACT

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

PMID:39920506 | DOI:10.1038/s43588-024-00764-8

Front Microbiol. 2025 Jan 22;15:1516667. doi: 10.3389/fmicb.2024.1516667. eCollection 2024.

ABSTRACT

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

PMID:39911715 | PMC:PMC11794229 | DOI:10.3389/fmicb.2024.1516667

Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.

ABSTRACT

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013

Discov Oncol. 2025 Jan 29;16(1):100. doi: 10.1007/s12672-025-01826-7.

ABSTRACT

Non-small cell lung cancer (NSCLC) remains a dire disease being the first cause of cancer death among both genders. Early-stage NSCLC often has better treatment outcomes despite it being a highly heterogeneous disease. So far, the neo-adjuvant chemotherapy strategies have led to a small benefit with an improvement of 5% in overall survival as an absolute benefit. Recently, the introduction of immune checkpoint inhibitors combined with chemotherapy has shown robust efficacy in terms of event-free survival and overall survival. Thus, these combinations are today considered a new standard of care in early-stage NSCLC. The application of these strategies to all-comer population lead to confounding definitive results regarding the efficacy and predictive biomarkers are urgently needed balancing the promise of healing than toxicities. At present, the clinical staging TNM system guides the clinical choice, however it is not entirely sufficient. Circulant tumoral DNA (ctDNA) emerged as a promising prognostic and predictive biomarker that may guide the future perioperative strategy and pave the way to personalized medicine also in this exciting field. This narrative review aims to put in the context the employment of ctDNA, give some perspective and suggestions weighing the pros and cons of this technique for our tomorrow clinical practice.

PMID:39881042 | PMC:PMC11780067 | DOI:10.1007/s12672-025-01826-7