Biotechnology in modern medicine and pathology encompasses molecular diagnostics, multi-omics analysis, nanotechnology-enabled platforms, digital pathology, bioinformatics, and computational approaches that support disease detection, therapeutic development, and clinical decision-making. This review examines how these technologies have contributed to diagnostic workflows, therapeutic development, disease classification, and clinical decision-making in contemporary healthcare. The integration of molecular biology, nanotechnology, and computational sciences has expanded the evaluation of disease-related processes such as genetic variation, biomarker expression, tumor heterogeneity, immune regulation, and molecular pathway alterations in conditions including cancer, inherited disorders, infectious diseases, cardiovascular disease, and autoimmune disease. Therapeutic innovations such as gene editing, nanomedicine, immunotherapy, biologics, and targeted drug delivery systems have further supported mechanism-based treatment strategies by improving tissue targeting, reducing off-target toxicity, and enabling more individualized therapeutic planning. Artificial intelligence (AI) and bioinformatics approaches, including machine learning, deep learning, computational pathology, digital whole-slide image analysis, and omics-data integration, have supported biomarker discovery, disease classification, diagnostic image analysis, risk stratification, and pathology-based clinical decision support. Additionally, molecular and digital pathology have improved disease subclassification and prognostic assessment by integrating histomorphologic findings with molecular and computational data. Despite these advances, high costs, technical complexity, data standardization challenges, infrastructure limitations, and ethical concerns continue to restrict widespread clinical adoption. Future work should prioritize low-cost point-of-care molecular and biosensor platforms for resource-limited settings, interoperable standards for omics and digital pathology data, multicenter prospective validation of AI-assisted diagnostic tools and nanomedicine-based therapies, transparent reporting of algorithm provenance, and regulatory pathways addressing data privacy, clinical accountability, and equitable access. Overall, biotechnology represents an important component of precision healthcare, with the potential to strengthen diagnostic accuracy, targeted therapy, disease monitoring, and patient-centered clinical outcomes.
The tumour microenvironment (TME) contains a diverse mix of cells and components, including cancer cells, immune cells, connective tissue, and biochemical factors; all of these are constantly interacting as well as influencing both the progression and spread of cancer and the ability of the immune system to recognise and respond to it. Accumulating evidence indicates that immune cell trafficking in TMEs is a major factor in determining whether tumours are destroyed by immune defences or evade immune surveillance. However, advances in experimental techniques do not provide a complete picture of how immune-tumour cell interactions occur with respect to their spatial, temporal, and molecular characteristics. This review examines existing in silico tools for evaluating how immune cells migrate, communicate, and function in the TME. The components that affect how immune cells infiltrate tumours will be summarized (i.e., chemotactic gradients, adhesion molecules, extracellular matrix remodelling, hypoxia, and metabolic reprogramming), and their role in immune exclusion and the development of immune escape will be emphasized. Computational modelling techniques (e.g., agent-based models, ordinary and partial differential equation models, systems biology models, network biology models, and machine learning prediction models) enable multiscale simulation of immune dynamics. This capability helps further our understanding of how tumours escape immune surveillance and develop into malignancies. The use of bioinformatics databases and major bioinformatics resources such as TCGA, TIMER, TCIA, etc., that may assist in understanding the composition of the immune system and immunogenomics of tumours is assessed. To demonstrate the predictive ability of computational models to establish patterns of immune cell traffic and predict the efficacy of immunotherapy, we examine specific in silico analyses of distinct immune cell populations, such as Tumour-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs). Furthermore, integrating multi-omics and spatial transcriptomic datasets enables personalised modelling of potential responses to immune checkpoint therapy. Despite these advantages, precision immunotherapy faces many challenges, including data heterogeneity, model validation, and translation limitations, as well as future perspectives on precision immunotherapy using digital twin technology. Overall, the findings from this review support the increasing relevance of bioinformatics and computational science in understanding immune-TME interactions and developing novel cancer immunotherapies.
Despite advances in genetic testing, many 46,XY Disorders of sex development (DSD) cases remain unsolved after whole-exome sequencing (WES). This study intended to explore rare variants in patients with micropenis, cryptorchidism, or hypospadias using bioinformatics analysis to identify potential pathogenic contributors and pathways underlying 46,XY DSD. A total of 35 patients with specific phenotypes (micropenis/cryptorchidism/hypospadias) and negative whole-exome sequencing results were enrolled. Bioinformatics analysis methods (SKAT-O test and GO enrichment) were applied to identify the putative loss-of-function (pLoF) variation, including nonsense, frameshift, and canonical splice-site variants, and predicted deleterious missense variants (CADD Phred > 20). Literature was reviewed to explore the correlation of detected candidate genes/pathways and 46,XY disorder of sex development. After variant quality filtering, we identified 307,638 pLoF variants and 127,857 predicted deleterious missense variants across all samples. In subgroup A (micropenis, n = 21), we identified 146,268 pLoF variants and 104,746 predicted deleterious missense variants. In subgroup B (cryptorchidism, n = 10), we identified 111,172 pLoF variants and 77,244 predicted deleterious missense variants. In subgroup C (hypospadias, n = 4), we identified 50,198 pLoF variants and 23,111 predicted deleterious missense variants. Using SKAT-O with an initial screening threshold of p < 0.005 (FDR q < 0.05), we obtained 67 candidate genes from the pLoF variant set and 59 candidate genes from the predicted deleterious missense variant set in subgroup A; 81 and 11 candidate genes, respectively, in subgroup B; and 17 and 0 candidate genes, respectively, in subgroup C. Assessment of rare variants helps further explore the genetic contributors to 46,XY disorder of sex development and provide potential candidate genes and associated pathways.
Somatic variant calling, the identification of mutations in non-germline cells acquired over an individual's lifetime, is critical for studying diseases, including cancer, and for developing precision oncology strategies. Traditional somatic variant calling methods rely on linear reference genomes, which do not adequately capture human genetic diversity and result in reference bias, compromising the accuracy of somatic variant detection. Recently developed graph-based human pangenome reference represents diverse genetic variants across human populations and has promised to drive advances in many genetics and genomics studies. In this study, we introduced Pansoma, a novel pangenome-native and machine learning-based tool specifically designed for somatic variant calling using a pangenome graph reference. Pansoma performs somatic variant detection from both short- and long-read sequencing data by learning tensor representations of alignment on graph nodes rather than on a linear reference. Pansoma outputs variant representations anchored to the pangenome graph paths and conventional somatic variant calls remapped to the linear reference. Additionally, we provide accompanying bioinformatics tools tailored for graph-based genomic data management and variant calling results analysis. Benchmarking shows that Pansoma not only improves tumor-only somatic variant detection but also preserves graph-specific variant representations that are not directly recoverable from linear- reference outputs.
The gut microbiota constitutes a highly complex ecosystem that plays a pivotal role in maintaining human health and modulating the immune response. Dysbiosis, an imbalance in the composition of gut microbiota, has been linked to the development and progression of multiple immune-mediated inflammatory diseases (IMIDs) and allergic disorders. Advances in sequencing and bioinformatics have enabled a deeper understanding of microbial diversity and function, revealing the crucial role of metabolites such as short-chain fatty acids (SCFAs) and secondary bile acids in sustaining epithelial integrity and immune tolerance. Altered microbiota profiles are associated with increased intestinal permeability, systemic inflammation, and autoimmune conditions including multiple sclerosis, rheumatoid arthritis, and inflammatory eye disease. Early-life establishment of microbiota is also critical, influencing susceptibility to asthma, food allergy, and other atopic conditions, such as drug hypersensitivity. Emerging therapeutic strategies aim to restore microbial balance through interventions such as the administration of probiotics, prebiotics, symbiotics, or postbiotics and fecal microbiota transplantation. Preliminary clinical evidence suggests these interventions may enhance barrier function, modulate inflammation, and improve clinical outcomes, although methodological heterogeneity and limited sample sizes constrain their application. Specific findings highlight that modulation of SCFAs and targeted bacterial taxa may influence disease activity and response to treatment. Future research should focus on identifying disease-specific microbial signatures, optimizing therapeutic formulations, and standardizing protocols for microbiota-based interventions. Integrating multiomics analyses and artificial intelligence will be essential if we are to develop predictive biomarkers and personalized therapies. Modulation of the microbiota is therefore positioned as a promising avenue in the prevention and management of IMIDs and allergic diseases.
Although advances have been made in intrahepatic cholangiocarcinoma (ICC) treatment, ICC prognosis remains unsatisfactory. Thus, in-depth exploration of ICC pathogenesis to identify new therapeutic targets is crucial for optimizing treatment strategies. Acquisition of single-cell sequencing data encompassing ICC tumor tissues and adjacent normal tissues was performed via the GEO database. Key cell populations and genes were screened through cell annotation, differential expression gene analysis, and KEGG enrichment analysis. In constructed in vitro cell models, qPCR and western blot were respectively implemented to capture mRNA and protein expression levels. Cell migration, proliferation, and invasion abilities were assessed via wound healing, EdU, and Transwell invasion assays. RIP assay was implemented to evaluate RNA-protein interactions, and MeRIP-qPCR was employed to detect m6A modification levels on RNA. Bioinformatics analysis revealed an evident increase in the proportion of cholangiocytes in ICC tumor tissues compared to adjacent normal tissues. Within the IGF2BP1 + Malignant subpopulation, which had the highest malignancy score, the expression of IGF2BP1 and FN1 was significantly upregulated. Cellular experiments demonstrated that IGF2BP1 recognizes the m6A modification on FN1 mRNA, thereby stabilizing its expression. Furthermore, significant enhancement of ICC cell proliferation, migration, and invasion capabilities resulted from IGF2BP1 overexpression, while FN1 knockdown markedly attenuated these promoting effects. Through recognition of the m6A modification on FN1 mRNA, IGF2BP1 facilitates the proliferative, migratory, and invasive capabilities of ICC cells.
Drought is a major abiotic stress limiting the growth and ecological adaptation of tropical and subtropical trees. The SnRK2 gene family is a core regulator in ABA signaling and drought response pathways. However, genome-wide identification and functional characterization of the SnRK2 family remain unclear in Bombax ceiba, a typical drought-tolerant tropical pioneer tree species with important ecological and economic value. We performed genome-wide identification of the BcSnRK2 gene family using bioinformatics approaches. Phylogenetic relationships, gene structures, conserved motifs, cis-acting elements, chromosomal localization, and protein structures were systematically analyzed. Subcellular localization was verified by transient expression in Nicotiana benthamiana. Tissue-specific expression and drought-responsive patterns were detected by qRT-PCR under 10% PEG6000 treatment. Protein-protein interaction networks were predicted using the STRING database. A total of nine BcSnRK2 genes were identified and unevenly distributed across eight chromosomes. All BcSnRK2 proteins contained conserved kinase domains and shared a highly conserved exon-intron structure. Promoter regions harbored abundant ABA-responsive and stress-related cis-elements. BcSnRK2 genes exhibited distinct tissue-specific expression profiles. All genes were significantly induced by drought stress in a tissue- and time-dependent manner, with BcSnRK2.9 and BcSnRK2.7 showing strong and sustained activation in shoots and BcSnRK2.7 and BcSnRK2.3 responding prominently in roots. BcSnRK2 proteins were localized in the cytoplasm, plasma membrane, and nucleus, and were predicted to interact with core components of the ABA signaling pathway. The BcSnRK2 family exhibits evolutionary conservation and functional divergence in Bombax ceiba. The compact size of the SnRK2 family, conserved structural features, and distinct tissue-specific drought response patterns are consistent with a streamlined stress signaling system that may contribute to the ecological adaptation of Bombax ceiba in seasonally dry tropical environments, although formal evolutionary analyses are required to establish adaptive significance. This study provides valuable gene resources for drought resistance breeding of woody plants and advances the understanding of stress signaling mechanisms in tropical trees.
Personalized vaccines provide the advantage of patient-specific antigen selection to optimize immune responses, a strategy extensively explored in oncology through neoantigen-targeted peptide, mRNA, and dendritic cell platforms. Peptide vaccines provide simplicity and stability though often elicit limited cytotoxic T-cell responses. What is more, mRNA vaccines lead to rapid, multiplexed neoantigen delivery, endogenous antigen processing and eventually improved immunogenic coverage. Dendritic cell-based vaccines have the potency to prime potent T-cells although this technology requires labor-intensive manufacturing and extensive production timelines. Integration with immune checkpoint inhibitors, adoptive cell therapies, and oncolytic viruses further enhances efficacy, suggesting that rational combinations may be more effective than single modalities. Recent advances in sequencing, computational epitope prediction, and bioinformatics pipelines have facilitated neoantigen prioritization and DC vaccine design, enabling more rapid and precise personalization. Hybrid vaccination strategies, such as ex-vivo mRNA-electroporated dendritic cells and in-vivo DC-targeted platforms, bridge the gap between manufacturing feasibility and potent immune activation. Emerging technologies, including AI-driven neoepitope prediction, receptor-targeted antigen delivery, biomaterial-based modulation, and distributed mRNA manufacturing, seem to be promising approaches to accelerate personalized vaccine development in future. From another point of view, lessons learned from the COVID-19 pandemic accelerated the development, large-scale deployment, and validation of mRNA vaccine platforms for infectious diseases. Host HLA diversity, prior immune history, and viral evolution create heterogeneity in immune responses, highlighting opportunities for semi-personalized or adaptive strategies. In this review, we provide a landscape of personalized vaccines, with a focus on DC-based platforms, and explore translational lessons for viral pathogens. A conceptual framework linking cancer immunotherapy and infectious disease preparedness is proposed, emphasizing hybrid personalization approaches, rapid manufacturing, and AI-enabled epitope selection. This perspective highlights how convergence of immunology, computational biology, and advanced vaccine technologies could expand the scope of personalized vaccination, from oncology to future epidemic and pandemic scenarios as well as the current challenges.
Plants are continuously exposed to a wide range of abiotic stresses, including drought, salinity, temperature extremes, nutrient deficiency, and heavy metal toxicity, which severely constrain growth and productivity. To cope with these challenges, plants have evolved sophisticated regulatory networks involving phytohormones and non-coding RNAs (ncRNAs). This review provides a comprehensive overview of the biogenesis and functional roles of major ncRNA classes' microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) and their dynamic interplay with phytohormonal signaling pathways under stress conditions. We highlight how ncRNAs modulate key hormonal pathways, including abscisic acid, auxin, jasmonic acid, ethylene, and gibberellins, to fine-tune stress-responsive gene expression and maintain cellular homeostasis. Special emphasis is placed on nutrient stress and heavy metal toxicity, where ncRNA-mediated regulation influences ion transport, autophagy, reactive oxygen species detoxification, and metabolic reprogramming. Emerging evidence on circRNAs as miRNA sponges further reveals an additional regulatory layer linking developmental processes with stress adaptation. The review also integrates recent advances in high-throughput sequencing, multi-omics approaches, and bioinformatics tools that have enabled large-scale identification and functional annotation of stress-responsive ncRNAs. Furthermore, the application of artificial intelligence and machine learning models in predicting ncRNA target interactions and regulatory networks is discussed as a transformative approach in plant stress biology. Collectively, this synthesis provides a systems-level understanding of ncRNA-phytohormone cross-talk and underscores its potential in developing climate-resilient crops through advanced molecular and computational strategies.
Understanding the causal effects of genetic mutations is essential for explaining fitness variation, forecasting evolutionary trajectories and assessing extinction risk, yet remains a fundamental challenge, particularly in natural populations. While amino acid substitutions can alter protein structure and function, mutations affecting gene regulation can also have significant fitness consequences. In this Opinion Piece, we argue that epigenetic mechanisms, given their central role in gene regulation, probably modulate the deleteriousness of mutations. Drawing on evidence from humans and model organisms, we identify three ways in which epigenetic mechanisms might interact with deleterious mutations. Specifically, we hypothesize that epigenetic regulation may: (i) be disrupted by deleterious mutations in non-coding regions and epigenetic regulator genes; (ii) buffer the expression of deleterious mutations; and (iii) contribute to the repair and purging of deleterious mutations. Advances in next- and third-generation sequencing and bioinformatics now allow these hypotheses to be empirically tested in wild populations. As many species face ongoing population declines, unravelling how epigenetic mechanisms influence the functional effects of mutations is vital for understanding fitness variation, guiding evolutionary predictions and informing conservation strategies. This article is part of the theme issue 'Ecological epigenetics at the intersection of behaviour and life history variation in non-model animals'.
Type 1 diabetes mellitus (T1DM) is a multifactorial disease wherein a genetic predisposition, upon exposure to environmental factors, triggers seroconversion and subsequent beta-cell destruction. The global incidence of T1DM has risen in recent decades, underscoring the significant role environmental factors play in actuating inherent genetic risk. However, the absence of an identifiable unique triggering factor complicates the identification of risk groups. Advances in bioinformatics and epigenetic research are opening new opportunities for early diagnosis, prevention, and novel therapies for the condition. A comprehensive analysis of complex molecular pathways will make it possible to develop algorithms for the early diagnosis, disease course prediction, and therapy personalization based on a patient's genetic profile. This review systematizes current data on the genetic and epigenetic heterogeneity of autoimmune diabetes and its potential triggers. It assesses the regional and ethnic disparities in T1DM incidence globally and within the Russian Federation, and it discusses the potential of clinical-genetic models for disease prediction.
In recent years, the number of omics studies aimed at characterizing fungal communities in fermented foods, such as table olives, has steadily increased. Moreover, advances in bioinformatics and artificial intelligence have provided new resources for analysing these datasets. Among these tools, supervised machine learning (ML) models, particularly tree-based algorithms, are especially valuable for their interpretability and their ability to identify underlying patterns. In this study, we evaluated three target variables of table olive samples (processing type, country of origin, and olive cultivar) by implementing three models built with tree-based algorithms, specifically Classification and Regression Trees (CART), Random Forest (RF), and eXtreme Gradient Boosting (XGB). The initial dataset consisted of 872 samples of fungal metataxonomic data obtained from diverse table olive sources. The RF models were the most accurate, achieving an overall accuracy above 75% and a kappa coefficient greater than 0.65. The highest accuracy and robustness were obtained for the model classifying country of origin, with 89% accuracy and a kappa coefficient of 0.86. Furthermore, the use of tree-based models enabled the identification of the fungal genera that contributed most to sample classification. Among the most important genera detected, we noticed Citeromyces, Candida, Pichia, Zygotorulaspora, Taphrina, Wickerhamomyces, Saccharomyces, Starmerella, Aureobasidium, and Dekkera. This approach demonstrates the potential for application of this methodology in the table olive sector and other fermented foods, where the industrial implementation of ML techniques based in omic data could enhance traceability, authenticity, and quality control.
As global ecosystems and food systems face unprecedented anthropogenic and climatic challenges, there is a demand for an integrated understanding of biological systems. Proteomics has emerged as a definitive approach offering a direct view of the molecular phenotype, yet it is traditionally separated into plant and animal disciplines. With recent advances in mass spectrometry (MS) and bioinformatics tools, this prospective review proposes that combining a One Health proteomics approach with deep-learning data analysis can revolutionize global food security, animal productivity, and ecosystem health by uncovering proteoform signatures that drive resilience across life. The potential of a unified One Health proteomic framework, highlighting major developments, including 4D proteomics, Data-Independent Acquisition (DIA), and single-cell resolution, and emphasizes their capacity to resolve the complex proteoform landscape across kingdoms. Review emphasizes the applications of proteogenomics as a cross-disciplinary tool to improve genome annotations, explain evolutionary differences, discover biomarkers in animals and resolve complex signaling networks in plants under stress. Nevertheless, contemporary proteogenomics methods still show limitations in their ability to comprehensively resolve proteoforms due to the fact that the use of peptide-based approaches makes it difficult to fully appreciate the post-translational modifications specific to each protein isoform. We show that One Health proteomics will provide a transformative roadmap for deciphering the functional proteoform signatures that underpin resilience across the tree of life.
Cancers of the Head and Neck (HNC) ranks seventh most abundant cancer category according to global incidence. thus posing a pertinent health hallenge. Shift in the homeostatic relationship of head and neck microbiome, causes microbial metabolic dysbiosis. Consequently, there is an increase in the pathobiome and pathogenic functions potentiating initiation and progression of carcinogenesis. Infection, inflammation and immune mediation trigger the pathogenic mechanisms. Accordingly, periodontitis perpetrated by unsatisfactory oral hygiene is connected to initiation and progression of HNC supported by substantial evidence. Further, mechanistic evidence is emerging on pathogenesis of bacteria-mediated carcinogenesis via toxins, carcinogenic metabolites and inflammatory cytokines with a view to possible treatments to halt progression of cancers. Advancements in surgical management techniques and adjuvant radiotherapy treatment, chemotherapy and emerging therapies such as immunotherapy, have not significantly increased overall disease free survival rates of most of HNCs. Early detection of cancers therefore, facilitates favorable outcomes such as better survival rates. Nevertheless, traditional invasive diagnostic approaches such as tissue biopsy gives rise to pain and discomfort to the patient In contrast, microbiome based diagnostic approaches, underpinned by salivary and mouth rinse microbiome analyses offers promising non-invasive, screening tools for early detection of HNC. This is augmented by advances in next generation sequencing, third generation sequencing, bioinformatics and machine learning technologies. Current developments in metagenomics, transcriptomics along with metabolomics enhanced harnessing the immense potential saliva possesses as a valuable screening and diagnostic tool, not only for cancer detection but for a range of diseases such as gastrointestinal diseases, autoimmune and metabolic disorders. Microbiome signatures in risk assessment of HNC is emerging as a new dimension in personalized risk assessment, risk stratification and care based pathways. Salivary microbiome analyses provides a promising approach for risk stratification, early stratification, through to assessment of prognosis, treatment success and survival of HNC patients suggested by accumulating evidence. Against this backdrop, we aim to provide an overview of microbiome based diagnostic approaches exploring new dimensions of detection and identification of HNC specific microbial biomarkers, microbial signatures, screening tools, primary diagnostic biomarkers, prognostic markers and interpersonal microbiome in the arena of personalized medicine.
Antimicrobial resistance is a constant threat to global public health, requiring innovative strategies for therapeutic target identification. Hence, this narrative review discusses the application of structural modeling and artificial intelligence in the functional prediction of proteins encoded by multidrug-resistant bacterial genomes. Tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating the annotation of hypothetical proteins and the identification of conserved domains and catalytic sites. These computational approaches bridge the gap between genomic data and biological function, accelerating drug discovery and guiding design of new antimicrobial bioactive compounds. Despite notable advances, several challenges have persisted regarding experimental validation and genomic variability, revealing an opportunity to integrate artificial intelligence-driven modeling with bioinformatics as a transformative method for better understanding resistance mechanisms and prioritizing novel therapeutic targets.
Breast cancer (BC) is a heterogeneous malignancy with diverse molecular subtypes and variable clinical outcomes. Despite diagnostic and therapeutic advances, recurrence and metastasis contribute to poor prognosis in subsets of patients. Regulators of G protein signaling (RGS) proteins, negative modulators of G protein-coupled receptor (GPCR) pathways, influence tumor progression, but their expression profiles, genomic alterations, immune associations, and prognostic roles in BC remain incompletely understood. This study systematically investigated the RGS family to identify potential biomarkers and therapeutic targets. Transcriptomic and clinical data from TCGA and seven independent GEO datasets were evaluated. A random-effects meta-analysis established cross-cohort expression consensus. Diagnostic value was assessed via ROC curve analysis. A prognostic signature was constructed using LASSO and multivariate Cox regression. Genomic alterations, DNA methylation, immune mapping, and pharmacogenomic profiling (DepMap/Broad Institute) were comprehensively analyzed. Meta-analysis identified eight robustly dysregulated RGS genes across BC cohorts. A combined 8-gene panel demonstrated diagnostic accuracy (AUC = 0.98). Furthermore, a LASSO-derived 6-gene signature successfully stratified patients into high- and low-risk prognostic groups (p < 0.0001). Immune infiltration profiling, validated by scRNA-seq, confirmed that RGS18 expression is robustly correlated with immune cells and originates predominantly from the tumor microenvironment rather than malignant cells. This study identifies the RGS gene family as a multidimensional framework for BC stratification. Through meta-analysis, we confirmed that RGS3 and RGS4 act as independent oncogenic risk factors, while RGS1 and RGS18 serve as key immunoregulatory biomarkers. We established a high-accuracy 8-gene diagnostic panel (AUC = 0.98) and a 6-gene prognostic signature (RGS1, 2, 3, 10, 16, 19) that independently predicts patient survival. Our findings reveal that these dysregulations are driven by genomic amplifications and CpG methylation. These results position the RGS family as robust clinical biomarkers and actionable targets for precision oncology.
Pediatric solid high-risk malignancies mostly lack established molecular biomarkers for early detection, minimal residual disease assessment, or treatment monitoring. Challenges include small patient numbers, limited sample volumes, low tumor mutational burden, and few recurrent alterations. Within the multicenter pediatric precision oncology program INFORM, we prospectively collected liquid biopsies from 130 pediatric patients and optimized cell-free DNA isolation and analysis. Whole-genome, whole-exome, and targeted panel sequencing were performed using liquid biopsy-adapted protocols. Integrating tissue-derived molecular profiles and orthogonal validation revealed that low-coverage whole-genome sequencing reliably detects circulating tumor DNA. An in silico ctDNA estimation score, combining fragment length and genome segment alterations, improved sensitivity and specificity to 95%, enabling plasma-based tumor detection in 93% of patients. Whole-exome and panel sequencing effectively identified clinically relevant, potentially druggable molecular targets. However, their utility varied substantially across different tumor entities, underscoring the need for entity-specific considerations in the interpretation and application of these methodologies. In-depth analyses demonstrated liquid biopsy's potential to track tumor evolution, identifying common tumor ancestors and refining patient stratification. This study advances liquid biopsy methodologies in pediatric oncology and provides a rationale that, as SNVs are more sensitively captured by panel sequencing and WES, while CNVs are better represented by lcWGS and WES. The underlying tumor genomic profile should guide the selection of liquid biopsy assays to optimize clinical decision-making. Systematic liquid biopsy analyses within the pediatric precision oncology INFORM registry enabled a real-world, multicenter comparison of sequencing approaches across high-risk malignancies. By optimizing preanalytical and bioinformatic tools for pediatric settings, we improved plasma-based cancer detection, molecular tumor characterization, and identification of targetable alterations, laying the groundwork for integration into personalized medicine programs and clinical trials.
Therapy-induced senescence (TIS) is an important response to anticancer treatments in ovarian cancer, which can trigger the senescence-associated secretory phenotype (SASP) that may contribute to epithelial-mesenchymal transition (EMT), tumor migration, invasion, and adhesion. This review delineates the molecular mechanisms through linking TIS-mediated SASP to EMT and evaluates emerging therapeutic opportunities. We discuss key regulatory pathways including cGAS-STING, NF-κB, p53, and STAT3 that orchestrate SASP components such as IL-6, IL-8, and TGF-β to activate EMT transcription factors. Bioinformatics analyses of clinical datasets identify SASP factors (IL-6, AREG, c-JUN, TIMP1, THBS1) as critical EMT mediators, with hub genes including COL4A2, COL5A2, PDGFRB, and FBN1 involved in ECM remodeling and cell adhesion. SASP promotes ovarian cancer cell adhesion through MMP-mediated ECM degradation, release of matrix-bound growth factors, generation of adhesive fragments, and induction of mesothelial-mesenchymal transition. We evaluate emerging therapeutic strategies targeting this axis, including senolytics to eliminate senescent cells, senomorphics to suppress SASP secretion, and targeted inhibition of specific SASP components. Although preclinical advances are promising, challenges in model systems and real-time SASP monitoring remain; future integrated multi-omics analyses and personalized therapeutic approaches are needed to translate these findings into improved ovarian cancer outcomes.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies, typically associated with late-stage diagnosis and limited survival despite advances in systemic therapy. Long-term survival in metastatic PDAC is exceedingly rare, and treatment strategies are limited by the intrinsic resistance of the tumor to standard therapies. However, emerging evidence suggests that molecular subtypes may exhibit distinct biological behaviors and may be amenable to new therapeutic options. We report the remarkable long-term survival of a 43-year-old male with a blank medical history, diagnosed with metastatic PDAC in 2018. The patient presented with postprandial upper abdominal discomfort that progressed to obstructive jaundice, leading to the discovery of a pancreatic head mass with metastatic lymphadenopathy. Despite the poor prognosis typically associated with metastatic PDAC, the patient achieved significant disease control through FOLFIRINOX chemotherapy and underwent surgical resection following complications from vascular involvement. Next-generation sequencing (NGS) revealed a rare KRAS-wildtype tumor with microsatellite instability (MSI) and a high tumor mutational burden (TMB). Moreover, a long-standing suspicion of Lynch syndrome could finally be confirmed. This case highlights the potential impact of personalized treatment and molecular tumor profiling in reshaping the outlook for PDAC patients.