Search Results (154)

Background: Hepatocellular carcinoma remains a global health challenge with high mortality rates. The tumor immune microenvironment significantly impacts disease progression and survival. However, traditional analyses predominantly focus on single immune genes, overlooking the critical interplay among multiple immune gene signatures. Our study explores the prognostic significance of chemokine (C-C motif) ligand 5 (CCL5) expression and associated immune genes through an innovative combination of Autoregressive Integrated Moving Average (ARIMA) and Convolutional Neural Network (CNN) models. Methods: A time series dataset of CCL5 expression, comprising 230 liver cancer patients, was analyzed using an ARIMA model to capture its temporal dynamics. The residuals from the ARIMA model, combined with immune gene expression data, were utilized as input features for a CNN to predict survival outcomes. Survival analyses were conducted using the Cox proportional hazards model and Kaplan–Meier curves. Furthermore, the ARIMA-CNN framework’s results were systematically compared with traditional median-based stratification methods, establishing a benchmark for evaluating model efficacy and highlighting the enhanced predictive power of the proposed integrative approach. Results: CNN-extracted features demonstrated superior prognostic capability compared to traditional median-split analyses of single-gene datasets. Features derived from CD8⁺ T cells and effector T cells achieved a hazard ratio (HR) of 0.7324 (p = 0.0008) with a statistically significant log-rank p-value (0.0131), highlighting their critical role in anti-tumor immunity. Hierarchical clustering of immune genes further identified distinct survival associations. Notably, a cluster comprising B cells, Th2 cells, T cells, and NK cells demonstrated a moderate protective effect (HR: 0.8714, p = 0.1093) with a significant log-rank p-value (0.0233). Conversely, granulocytes, Tregs, macrophages, and myeloid-derived suppressor cells showed no significant survival association, emphasizing the complex regulatory landscape within the tumor immune microenvironment. Conclusions: Our study provides the first ARIMA-CNN framework for modeling gene expression and survival analysis, marking a significant innovation in integrating temporal dynamics and machine learning for biological data interpretation. This model offers deeper insights into the tumor immune microenvironment and underscores the potential for advancing precision immunotherapy strategies and identifying novel biomarkers, contributing significantly to innovative cancer management solutions. Full article

Human T-lymphotropic virus type 1 (HTLV-1) infection is associated with a spectrum of clinical outcomes, ranging from lifelong asymptomatic carriage to severe conditions such as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) and adult T-cell leukemia/lymphoma (ATLL). Although antibody responses are known to shape immune regulation, the functional relevance of IgG idiotype repertoires in HTLV-1 pathogenesis remains poorly understood. This study investigated the immunomodulatory effects of IgG from individuals with distinct HTLV-1 clinical outcomes. IgG was purified from pooled serum samples of asymptomatic carriers (ACs), HAM/TSP, and ATLL patients and used to stimulate peripheral blood mononuclear cells (PBMCs) from healthy donors. Cytokine production in CD4⁺, CD8⁺, and γδ T cells was assessed by flow cytometry. Additionally, proteome-wide IgG reactivity was evaluated using a human protein microarray encompassing over 21,000 proteins, and bioinformatic analyses were conducted to identify protein–protein interaction networks and tissue-specific autoreactivity. HAM/TSP-derived IgG selectively enhanced IFN-γ production in all T-cell subsets and suppressed IL-4 in CD4⁺ T cells. ATLL-derived IgG induced IL-9 and IL-13 production in CD4⁺ T cells, and both HAM/TSP and ATLL IgG elevated IL-13 levels in CD8⁺ T cells. Microarray data revealed distinct autoreactive IgG profiles across clinical groups, targeting immune-related proteins, apoptotic regulators, and proteins expressed in T cells, monocytes, and non-immune tissues such as brain and testis. Notably, no functional or structural clustering was observed in protein–protein interaction networks, suggesting these reactivities reflect complex, idiotype-specific immune alterations rather than compensatory responses. The present findings suggest that HTLV-1 infection may be associated with the development of distinct IgG repertoires that potentially modulate cytokine responses and exhibit broad reactivity toward human proteins. Such patterns could contribute to immune dysregulation and may partially explain the divergent clinical trajectories observed in HAM/TSP and ATLL. Further investigations are warranted to validate these observations at the individual level and to clarify their mechanistic relevance in disease progression. Full article

Amyloid cardiomyopathy (ACM), driven by transthyretin (TTR) and immunoglobulin light chain (LC) amyloid fibrils, remains a major clinical challenge due to limited mechanistic understanding and insufficient preclinical models. Human-induced pluripotent stem cells (iPSCs) have emerged as a transformative platform to model ACM, offering patient-specific and genetically controlled systems. In this review, we summarize recent advances in the use of iPSC-derived cardiomyocytes (iPSC-CMs) in both two-dimensional (2D) monolayer cultures and three-dimensional (3D) constructs—including spheroids, organoids, cardiac microtissues, and engineered heart tissues (EHTs)—for disease modeling, mechanistic research, and drug discovery. While 2D culture of iPSC-CMs reproduces hallmark proteotoxic phenotypes such as sarcomeric disorganization, oxidative stress, and apoptosis in ACM, 3D models provide enhanced physiological relevance through incorporating multicellularity, extracellular matrix interactions, and mechanical load-related features. Genome editing with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 further broadens the scope of iPSC-based models, enabling isogenic comparisons and the dissection of mutation-specific effects, particularly in transthyretin-related amyloidosis (ATTR). Despite limitations such as cellular immaturity and challenges in recapitulating aging-associated phenotypes, ongoing refinements in differentiation, maturation, and dynamic training of iPSC-cardiac models hold great promise for overcoming these barriers. Together, these advances position iPSC-based systems as powerful human-relevant platforms for modeling and elucidating disease mechanisms and accelerating therapeutic development to prevent ACM. Full article

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy, yet diagnosis still relies primarily on invasive bone-marrow procedures and advanced laboratory assays. Non-invasive, rapid, and cost-effective tools remain an unmet need. Fourier-transform infrared (FTIR) spectroscopy has shown promise for detecting cancer-associated biochemical changes in biofluids and cells. Methods: Serum from pediatric ALL patients and controls (n = 103; ALL = 45, controls = 58: healthy = 14, hematology controls = 44 with anemia, thrombocytopenia, leukopenia, and pancytopenia) was analyzed using FTIR. Spectra (800–1800, 2800–3500 cm⁻¹) were preprocessed with baseline correction, derivative filtering, and normalization. Group differences were assessed statistically, and logistic regression with stratified 10-fold cross-validation was applied; Receiver operating characteristic (ROC)\precision–recall (PR) analyses were based on out-of-fold predictions. Results: Distinct spectral alterations were observed between ALL and controls. Leukemia samples showed higher amide I (~1640 cm⁻¹) and amide II (~1545 cm⁻¹) absorbance, lower lipid-related bands (~1450, ~2920 cm⁻¹), and increased nucleic-acid–associated signals (~1080 cm⁻¹). Differences were significant (q < 0.05) with moderate effect sizes. Logistic regression achieved area under the curve (AUC) ≈ 0.80 with sensitivity ~0.73–0.84 across practical decision thresholds (0.50 → 0.30) and higher recall attainable at the expense of specificity. Principal component analysis (PCA)\hierarchical cluster analysis (HCA) indicated partial but consistent group separation, aligning with supervised performance. Conclusions: Serum FTIR spectroscopy shows promise for distinguishing pediatric ALL from controls by reflecting disease-related metabolic changes. The technique is rapid, label-free, and requires only small serum volumes. Our findings represent proof-of-concept, and validation in larger, multi-center studies is needed before clinical implementation can be considered. Full article

Background: We aimed to identify key molecular drivers of gastric cancer progression and poor prognosis by integrating multi-omics analyses with experimental validation. Methods: Single-cell RNA-seq data were clustered to delineate major cell types. InferCNV identified tumor epithelial cells, and reclustering revealed a malignant subset with poor prognosis. The overlap between subset markers and The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) upregulated differentially expressed genes (DEGs) was modeled with univariate, LASSO-, and multivariate Cox to derive a prognostic signature. Patients were stratified according to signature scores, and group differences in survival and immunologic features were compared. Spatial transcriptomics defined the localization patterns of key signature genes. In vitro functional assays (CCK-8, colony formation, EdU incorporation, flow cytometry, Transwell migration and invasion, and wound healing) confirmed the pivotal role of SRI. Results: Reclustering of tumor epithelial cells yielded seven subsets (C0–C6), with C5 displaying marked malignant features and correlating with poor prognosis in multiple cohorts. Intersecting 208 genes yielded a five-gene signature (ASCL2, REPIN1, CXCL3, TMEM176A, SRI). The signature stratified patients into high- and low-risk groups. The high-risk cohort exhibited significantly poorer survival, distinct immune-infiltration patterns, elevated immune-evasion scores, and a reduced predicted response to immunotherapy. Single-cell and spatial transcriptomics localized TMEM176A to fibroblasts and SRI to the tumor epithelium. Finally, in vitro knockdown of SRI inhibited tumor cell proliferation, migration and invasion. Conclusions: Our multi-omics approach identified a malignant epithelial subset, C5, and a five-gene signature that stratifies gastric cancer prognosis and immune response. Functional assays showed that SRI knockdown impairs tumor cell growth, migration and invasion. Full article

Background: Primary hepatocellular carcinoma (HCC) and liver metastases differ in origin, progression, and therapeutic response, yet a direct high-resolution spatial comparison of their tumor microenvironments (TMEs) within the liver has not previously been performed. Methods: We applied high-definition spatial transcriptomics to fresh-frozen specimens of one HCC and one liver metastasis (>16,000 genes per sample, >97% mapping rates) as a proof-of-principle two-specimen study, cross-validated in human proteomics and patients’ survival datasets. Transcriptional clustering revealed spatially distinct compartments, rare cell states, and pathway alterations, which were further compared against an independent systemic dataset. Results: HCC displayed an ordered lineage architecture, with transformed hepatocyte-like tumor cells broadly dispersed across the tissue and more differentiated hepatocyte-derived cells restricted to localized zones. By contrast, liver metastases showed two sharply compartmentalized domains: an invasion zone, where proliferative stem-like tumor cells occupied TAM-rich boundaries adjacent to hypoxia-adapted tumor-core cells, and a plasticity zone, which formed a heterogeneous niche of cancer–testis antigen–positive germline-like cells. Across both tumor types, we detected a conserved metabolic program of “porphyrin overdrive,” defined by reduced cytochrome P450 expression, enhanced oxidative phosphorylation gene expression, and upregulation of FLVCR1 and ALOX5, reflecting coordinated rewiring of heme and lipid metabolism. Conclusions: In this pilot study, HCC and liver metastases demonstrated fundamentally different spatial architectures, with metastases uniquely harboring a germline/neural-like plasticity hub. Despite these organizational contrasts, both tumor types converged on a shared program of metabolic rewiring, highlighting potential therapeutic targets that link local tumor niches to systemic host–tumor interactions. Full article

Background and Objectives: Lupus nephritis (LN) is a major cause of mortality and morbidity in patients with systemic lupus erythematosus (SLE). Biomarkers derived from blood, urine, and multi-omics techniques are essential for enabling access to less invasive methods for LN evaluation and personalized precision medicine. Materials and Methods: The purpose of this work was to review the studies that addressed the potential role of urinary and serological biomarkers for the diagnosis, disease activity, response to treatment, and renal outcome of adult patients with LN, published over the past decade, and summarize their results with a particular emphasis being directed towards the available traditional tools. Results: Traditional biomarkers used for the diagnosis and surveillance of LN are proteinuria, urinary sediment, estimated glomerular filtration rate (eGFR), anti-double-stranded deoxyribonucleic acid (anti-dsDNA), anti-C1q, and serum complement levels. Anti-dsDNA, serum C3, and proteinuria are the conventional biomarkers with the strongest clinical evidence, with overall moderate ability in predicting LN from non-renal SLE, disease activity, renal flares, response to therapy, and prognosis. The last decade has brought significant progress in our understanding regarding the pathogenesis of LN and, consequently, several molecules, either alone or in combination panels, have emerged as potential novel biomarkers, some of them outperforming conventional biomarkers. Promising results have been suggested for urinary activated leukocyte cell adhesion molecule (ALCAM), soluble cluster of differentiation 163 (CD163), C-X-C motif chemokine ligand 10 (CXCL10), monocyte chemoattractant protein 1 (MCP-1), neutrophil gelatinase-associated lipocalin (NGAL), tumor necrosis factor-like weak inducer of apoptosis (TWEAK), and vascular cell adhesion molecule 1 (VCAM-1). Conclusions: Despite the intensive research of the last decade, no novel biomarker has entered clinical practice, and we continue to rely on traditional biomarkers to assess non-invasively LN and guide its treatment. Novel biomarkers should be validated in multiple longitudinal independent cohorts, compared with conventional biomarkers, and integrated with renal histology information in order to optimize the management of LN patients. Full article

Chimeric Antigen Receptor (CAR)-T cell therapy has transformed the treatment landscape of cancer, yet major challenges remain in enhancing efficacy, reducing adverse effects, and expanding accessibility. Autologous CAR-T cells, derived from individual patients, have achieved remarkable clinical success in hematologic malignancies; however, their highly personalized nature limits scalability, increases costs, and delays timely treatment. Allogeneic CAR-T cells generated from healthy donors provide an “off-the-shelf” alternative but face two critical immune barriers: graft-versus-host disease (GvHD), caused by donor T-cell receptor (TCR) recognition of host tissues, and host-versus-graft rejection, mediated by recipient immune responses against donor HLA molecules. Recent advances in genome engineering, particularly Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9, allow precise modification of donor T cells to overcome these limitations. For example, TRAC gene knockout eliminates TCR expression, preventing GvHD, while disruption of HLA molecules reduces immunogenicity without impairing cytotoxicity. Beyond hematologic cancers, CRISPR-edited allogeneic CAR-T cells targeting the NKG2D receptor have shown promise in preclinical studies and early-phase trials. NKG2D CAR-T cells recognize stress ligands (MICA/B, ULBP1–6) expressed on over 80% of diverse solid tumors, including pancreatic and ovarian cancers, thereby broadening therapeutic applicability. Nevertheless, the genomic editing process carries risks of off-target effects, including potential disruption of tumor suppressor genes and oncogenes, underscoring the need for stringent safety and quality control. This review examines the distinguishing features of allogeneic versus autologous CAR-T therapy, with a particular focus on NKG2D-based allogeneic CAR-T approaches for solid tumors. We summarize current strategies to mitigate immune barriers, discuss practical manufacturing challenges, and analyze available clinical data on NKG2D CAR-T trials. Collectively, these insights underscore both the promise and the hurdles of developing safe, universal, and scalable allogeneic CAR-T therapies for solid malignancies. Full article

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor. Due to its aggressive nature and resistance to conventional treatments, there is a pressing need to develop novel and more effective therapies for this deadly malignancy. Here, we explored the therapeutic potential of the DNA minor groove binders trabectedin (TRB) and lurbinectedin (LUR) for the treatment of iCCA using cell lines, spheroids, cancer-associated fibroblasts (CAFs), patient-derived tumor organoids (PDOs), and the chicken chorioallantoic membrane (CAM) in vivo model. TRB and, more substantially, LUR, significantly inhibited cell growth in iCCA cell lines, spheroids, CAFs, and PDOs at very low nanomolar concentrations. Specifically, the two drugs significantly reduced proliferation, triggered apoptosis, and caused DNA damage in iCCA cells. At the metabolic level, TRB and LUR decreased mitochondrial respiration and glycolysis. At the molecular level, the two compounds effectively downregulated the mammalian target of rapamycin complex 1 (mTORC1) and Hippo/YAP pathways and suppressed the expression of yes-associated protein 1 (YAP1), cellular myelocytomatosis oncogene (c-Myc), E2F transcription factor 1 (E2F1), Bromodomain-containing protein 4 (BRD4), TEA domain transcription factor 4 (TEAD4), and cluster of differentiation 7 (CD7) proto-oncogenes. Furthermore, LUR significantly restrained the in vivo growth of iCCA cells in the CAM model. Our data indicate that TRB and LUR possess strong anti-proliferative and pro-apoptotic activities and could represent promising therapeutic agents for the treatment of iCCA. Full article

Aims: Surgery remains the only definitive treatment option for abdominal aortic aneurysms (AAA), as no conclusive evidence supports drug effectiveness in preventing AAA growth. Although type 2 diabetes (T2D) is an important cardiovascular risk factor, patients with T2D show reduced AAA presence and growth, associated with metformin use. We aimed to investigate the potential benefits of metformin on AAA using proteomics and in vitro experiments. Methods: Proteomics analysis using tandem mass spectrometry was performed on aortic smooth muscle cells (SMCs) from non-pathological controls (C-SMC, n = 8), non-diabetic (ND, n = 19) and diabetic (D, n = 5) AAA patients. Key findings were subsequently validated in aortic tissue using mass spectrometry-based proteomics. SMCs were cultured with/without metformin and analyzed. Results: Comparison of the proteome of SMCs from ND-AAA patients with controls revealed a reduction in proteins associated with metabolic processes and mitochondrial function. Cytoskeletal and extracellular matrix (ECM) proteins were elevated in ND-AAA-SMCs versus C-SMCs, with a similar cluster of mechanosensitive proteins being increased in ND-AAA-SMCs versus D-AAA-SMCs. D-AAA-SMCs showed an improved metabolic and antioxidant profile, enriched in pentose phosphate pathway proteins responsible for NAD(P)H generation (G6PD, PGD) and NAD(P)H-dependent antioxidants (NQO1, CBR1, AKR1C1, AKR1B1, GSTM1), all regulated by NRF2, an antioxidant transcription factor. Over half of the proteins identified in the protein–protein interaction network, constructed from proteins with higher expression in D-AAA SMCs versus ND-AAA SMCs, were verified in D-AAA aortic tissue. In vitro, metformin causes a shift from aerobic to anaerobic metabolism, increased AMPK activation and elevated mitochondrial biogenesis, indicated by increased PGC-1α expression. Metformin increased the gene expression of PGD, CBR1 and the protein expression of NQO1, with enhanced translocation of pNRF2 to the nucleus, due to reduced KEAP1 as negative regulator of NRF2. Consequently, metformin enhanced the gene expression of well-known antioxidant regulators SOD2 and CAT. Conclusions: This study identified significant differences in the proteome of SMCs derived from controls, ND-AAA and D-AAA patients. It highlights distinct pathways in relation to mechanosensing, metabolism and redox balance as therapeutic targets of metformin that may underlie its inhibition of AAA progression. Full article

Background/Objectives: Breast cancer (BC) is the most prevalent cancer worldwide, with approximately 40% of early-stage BC patients developing recurrence despite initial treatments. Current diagnostic methods, such as mammography and solid tissue biopsies, face limitations in sensitivity, accessibility, and the ability to characterize tumor heterogeneity or monitor systemic disease progression. Methods: To address these gaps, this study investigates a fully automated analysis workflow using data derived from fluorescent Whole-Slide Imaging (fWSI) for detecting and classifying rare cells (circulating tumor and tumor microenvironment cells) in peripheral blood samples. Our methodology integrates supervised machine learning algorithms for rare event detection, immunofluorescence-based classification, and statistical quantification of cellular features. Results: Using a fWSI dataset of 534 cancer and non-cancer peripheral blood samples, the automated model demonstrated high concordance with manual annotation, achieving up to 98.9% accuracy and a precision-sensitivity AUC of 83.2%. Morphometric analysis of rare cells identified significant differences between normal donors, early-stage BC, and late-stage BC cohorts, with distinct clusters emerging in late-stage BC. Conclusions: These findings highlight the potential of liquid biopsy and algorithmic approaches for improving BC diagnostics and staging, offering a scalable, minimally invasive solution to enhance clinical decision-making. Future work aims to refine the automated framework to minimize errors and improve the robustness across diverse cohorts. Full article

Adipose tissue enlargement in obesity leads to hypoxia, which may promote premature aging. This study aimed to understand the hypoxic response in 3D cultures of SGBS cells, a model for brown-like adipose tissue expressing uncoupling protein 1 (UCP1). Single-nucleus RNA sequencing of SGBS organoids revealed a heterogeneous composition and sub-population-specific responses to hypoxia. The analysis identified a cluster of transcriptional repression, indicating dying cells, and implied a role of ferroptosis in this model. Further experiments with SGBS cells and white adipose tissue-derived stem/progenitor cells showed that Acyl-CoA synthetase long-chain family member 4 (ACSL4), a key enzyme in ferroptosis, is expressed only in the presence of browning factors. Hypoxia downregulated ACSL4 protein in SGBS organoids but induced an inflammaging phenotype. Analysis of brown-like epicardial adipose tissue from cardiac surgery patients revealed a significant positive correlation of ACSL4 mRNA with UCP1 and hypoxia-inducible pro-inflammatory markers, while ACSL4 protein appeared to be inversely correlated. In conclusion, this study demonstrates that adipocytes’ capability to undergo ACSL4-mediated ferroptosis is linked to brown-like adipogenesis, suggesting an opportunity to modulate ferroptotic signaling in adipose tissue. The dual role of hypoxia by inhibiting ACSL4 but promoting inflammaging indicates a relationship between ferroptosis and aging that warrants further investigation. Full article

Early prediction of lymph node metastasis (LNM) following neoadjuvant therapy (NAT) is crucial for timely treatment optimization in esophageal squamous cell carcinoma (ESCC). This study developed and validated a computed tomography-based radiomic model for predicting pathologically confirmed LNM status at the time of surgery in ESCC patients after NAT. A total of 469 ESCC patients from Sun Yat-sen University Cancer Center were retrospectively enrolled and randomized into a training cohort (n = 328) and a test cohort (n = 141). Three signatures were constructed: the tumor-habitat-based signature (Habitat_Rad), derived from radiomic features of three tumor subregions identified via K-means clustering; the multiple instance learning-based signature (MIL_Rad), combining features from 2.5D deep learning models; and the clinicoradiological signature (Clinic), developed through multivariate logistic regression. A combined radiomic nomogram integrating these signatures outperformed the individual models, achieving areas under the curve (AUCs) of 0.929 (95% CI, 0.901–0.957) and 0.852 (95% CI, 0.778–0.925) in the training and test cohorts, respectively. The decision curve analysis confirmed a high net clinical benefit, highlighting the nomogram’s potential for accurate LNM prediction after NAT and guiding individualized therapy. Full article

Background: The progressive course of coronavirus disease 2019 (COVID-19) is more frequently observed in individuals with obesity, diabetes, pulmonary and cardiovascular disease, or arterial hypertension. Many patients with these conditions are prescribed statins to treat hypercholesterolaemia. However, statins exhibit additional pleiotropic effects. The present study aims to investigate the effects of all eight currently existing statins on the expression of genes whose products have been reported to be directly associated with complicated COVID-19 disease. Methods: We extended the interpretation of the whole-genome DNA microarray analyses of pancreatic cancer cells MiaPaCa-2 and whole-transcriptome analyses of adipose tissue-derived mesenchymal stem cells AD-MSC that we had performed in the past. From the number of genes with altered expression induced by statins, we focused on those reported to be involved in a complicated course of COVID-19, including APOE and ACE2, genes encoding proteins involved in innate antiviral immunity and respiratory failure genes. Results: Although we did not observe statin-induced changes in the expression of APOE, ACE2 and any of the six genes clustered in the locus associated with respiratory failure in patients with COVID-19, some statins induced changes in the expression of genes encoding their interaction partners. Among genes associated with the immune system, all statins, which are effective in vitro affected the expression of genes encoding IL-6 and IL-8 and interaction partners of NF-kB, which may influence the duration of viral persistence. Conclusions: Statins act on multiple pathways simultaneously, some of which support COVID-19 development, while others suppress it. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article