Biosensors and Bioelectronics

ObjectTransdermal biosensors may provide an alternative to conventional blood-based biomarker measurement. Our purpose was to determine the binary correlation between transdermal (Infrasensor; RCE, Inc, Carlsbad, CA) and conventional blood-based measurements. MethodsThis was a secondary analysis from a previously published observational cardiac troponin I (cTnI) study performed to establish the upper reference level of cTnI, at 10 US hospitals. After obtaining informed consent, 2 cohorts of patients were enrolled: 1) those who completed a health assessment questionnaire and appeared healthy, and 2) those with a known elevated cTnI per the local hospital standard assay. All blood lab analyses were performed at the University of Maryland Medical Center, Baltimore, MD. Normal was defined as cTnI <53.48 ng/L (male) or 34.11 ng/L (female) using the Siemens Atellica IM assay (Siemens Medical Solutions, Mountain View, CA), NT-proBNP <450 pg/mL (>75 years) or <124 pg/mL (<75 years), creatinine >1.17 mg/dL (male) or >0.95 mg/dL (female), and HbA1c <6.4%. The Infrasensor was placed on the patients wrist for measurement and blood drawn for analysis at approximately the same time. ResultsOf 840 enrolled patients, the median (IQR) age was 46 (30,57), 416 (49.5%) were female, 10.36% Hispanic, 6.7% Asian, 12.9% African American, and 69.1% White. Elevated lab tests were 102 hscTnIs, 156 NTproBNPs, 37 HbA1Cs, and 163 creatinines. Significant binary correlations were found between all transdermal signals and the corresponding lab blood levels ConclusionInfrasensor transcutaneous measurement demonstrates similar results as that obtained from blood testing in the central laboratory. CapsuleThe Infrasensor (RCE, Inc, Carlsbad, CA, USA) is rapid point of care transcutaneous biomarker measurement device. This study evaluated its ability to provide qualitative results for troponin I, NTproBNP, creatinine, and HbA1c levels in 840 patients. Significant correlations were found between all transdermal signals and the corresponding binary lab blood levels.

Autologous CAR-T cell therapy has demonstrated remarkable clinical efficacy in hematologic malignancies, yet its broader application remains limited by complex, labor-intensive manufacturing and inconsistent product quality. We describe a novel microfluidic cell separation platform based on Deterministic Lateral Displacement (DLD), integrated into a fully automated, closed-system instrument (Curate System), capable of processing full leukopacks in under one hour. Compared to Ficoll(R)-based density gradient centrifugation, DLD processing yielded significantly higher leukocyte recovery (88% vs. 58%), superior platelet and red blood cell depletion, and reduced CD69 T-cell activation. Flow cytometric analysis revealed improved phenotypic preservation across key T-cell subsets, including naive and central memory populations. Cytokine profiling demonstrated enhanced washing efficiency, with markedly lower levels of biologic response modifiers such as RANTES and TGF-{beta}1. DLD-purified T cells exhibited enhanced expansion kinetics and greater yield, supporting improved manufacturing outcomes. These findings position DLD-based processing as a clinically relevant, scalable alternative to conventional methods, with potential to improve consistency, potency, and accessibility of CAR-T therapies.

BackgroundThere is a need for synthetic peptide-based serologic assays that exploit avidity to replace whole antigens while enabling low-cost diagnostics in resource-limited settings. ObjectiveTo evaluate the diagnostic accuracy of a polymeric peptide-based ELISA leveraging avidity to enhance signal. MethodA 15-member SARS-CoV-2 peptide library corresponding to multiple epitope clusters and proteins was screened by indirect ELISA using pooled sera from RT-PCR-confirmed COVID-19 patients to identify peptides with possible diagnostic utility. The identified lead candidate, S559, possessed terminal cysteine-substitution to allow disulfide polymerization, and the resulting avidity gain was evaluated by comparing the apparent dissociation constant (KDapp) before and after depolymerization with N-acetylcysteine. The performance of an optimized ELISA using S559 was evaluated on 1,222 prospectively collected COVID-19 serum samples and 218 biobanked pre-COVID control serum samples. ResultsPolymeric S559 with a KDapp of 29.26 nM-1was demonstrated to have a 218% avidity gain relative to the completely depolymerized form. At pre-defined thresholds, the optimized S559 ELISA has a sensitivity and specificity of 83.39% (95%CI: 81.18% and 85.43%) and 96.79% (95%CI: 93.50% and 98.70%), respectively. At post hoc thresholds determined by Youden index, sensitivity and specificity reached 95.01 (95% CI: 93.63% - 96.16%) and 100.00% (95% CI: 98.32% - 100.00%), respectively. ConclusionHomomultivalent epitope presentation using polymeric S559 allows a highly specific immunoassay using human sera that may have important value in detecting antibodies, whether for diagnosing infection, confirming vaccination status or conducting surveillance.

Blood-based biomarkers are central to diagnostics, yet current approaches depend on invasive sampling and centralized laboratory infrastructure. At the same time, womens reproductive health remains severely under-monitored: most clinically relevant biomarkers are rarely measured outside fertility clinics, leaving millions without accessible, continuous insight into their reproductive lifespan. Anti-Mullerian hormone (AMH), a key indicator of ovarian reserve and overall reproductive function, still requires venous blood collection and specialized analysis, creating a major barrier to early detection, routine monitoring, and population-level screening. Here, we present a lateral flow assay (LFA) enabling direct AMH detection in unprocessed menstrual blood. The assay uses covalently conjugated 150 nm gold nanoshells to achieve sensitive colorimetric detection within the clinically relevant 0-10 ng/mL range. Results can be visually interpreted by naked-eye detection or quantified via a smartphone-based machine-learning algorithm for semi-quantitative assessment. The LFA performance correlates strongly with clinical chemistry lab-based analyses and can be seamlessly integrated into point-of-care formats, including wearable menstruation pads as well as simple dipstick tests. This technology provides a non-invasive, affordable, and robust solution for decentralized, regular monitoring of ovarian health.

A critical challenge in endocrine neurosurgery is intraoperative discrimination between normal pituitary tissue and pituitary neuroendocrine tumors (PitNETs). Suggesting the universal persistence of near-infrared autofluorescence (NIRAF) in endocrine organs and inspired by routine clinical use of NIRAF for parathyroid gland identification, we discovered that pituitary NIRAF can be employed for label-free transsphenoidal surgery guidance. Ex vivo confocal spectral imaging of 33 specimens identified secretory granules as the dominant long-wavelength fluorescence source and showed that normal pituitary had higher granule content than PitNETs. For the first time, we made use of the pituitary NIRAF during surgery and assessed its performance for pituitary/adenoma separation in vivo for 27 surgeries and showed near-perfect separability between pituitary and non-pituitary measurement sites with ROC-AUC of 0.98. The obtained results clearly demonstrate that the suggested method, based on the solid microscopic background, has the potential for clinical translation and paves the way for enhanced gland preservation during resection.

We present a new method for lung pathology detection in blood plasma, including lung cancer staging. Raman spectroscopy uses inelastically scattered laser light to obtain molecular information in a reagent-free manner. Obtaining Raman spectral data from liquid samples has long proven challenging, but we have developed a novel tool for obtaining spectra from 60 l liquid samples within two minutes: Raman of Well-based Samples (ROWS). With a low-cost ROWS device, we analyzed 372 blood plasma samples from a national biobank, including controls (n=92), patients with stage I-II lung cancer (n=99), stage III-IV cancer (n=46), benign tumours (n=36) and other lung conditions (n=99). Machine learning models were built to assess lung cancer stage and lung pathology presence. ROWS achieves up to 94% sensitivity, 90% specificity and 93% accuracy depending on classification. ROWS proves a robust method for rapid, low-cost, user-friendly, point-of-care lung pathology analysis in small quantities of blood plasma. One Sentence SummaryA new way of detecting lung cancer in small volumes of liquid blood plasma was developed using laser-based Raman spectroscopy and metallic wells.

Glycated hemoglobin (HbA1c) is a central biomarker for long-term glycemic control and diabetes management, traditionally quantified using laboratory-intensive chromatographic or immunochemical assays. As the global burden of diabetes continues to rise, there is growing interest in alternative, scalable approaches capable of rapid biochemical assessment. Fourier-transform infrared (FTIR) spectroscopy offers a reagent-free method that captures molecular signatures of protein glycation, but translating complex spectra into clinically interpretable HbA1c values requires robust analytical frameworks. Here, we present a complementary multi-model strategy for predicting HbA1c from FTIR spectra of whole blood. Using 685 blood samples with matched reference HbA1c measurements, we evaluated three analytically distinct yet synergistic approaches: partial least squares regression (PLSR), peak-resolved curve fitting based on pseudo-Voigt functions combined with H2O AutoML, and a convolutional neural network (CNN). PLSR and CNN models were trained on biologically informative spectral regions (800-1800 cm-{superscript 1} and 2800-3400 cm-{superscript 1}), while curve fitting focused on the fingerprint region (1000-1720 cm-{superscript 1}) to extract interpretable biochemical parameters. PLSR achieved the highest predictive accuracy (R{superscript 2} = 0.76), closely followed by the CNN (R{superscript 2} = 0.73), reflecting their ability to capture global linear and nonlinear spectral relationships. Although curve fitting yielded lower predictive performance (R{superscript 2} = 0.59), its peak-level decomposition enabled mechanistic interpretation of glycation-related changes. Explainable AI analysis using SHAP identified lipid- and protein-associated vibrations, carbohydrate-linked glycation bands, and amide-region structural features as key contributors to HbA1c prediction. Rather than treating these approaches as competing alternatives, our results demonstrate that their integration provides a more informative framework than any single model alone. By combining predictive performance with biochemical interpretability, this multi-model FTIR strategy highlights a scalable and mechanistically grounded pathway toward non-invasive HbA1c assessment and broader metabolic screening in diabetes monitoring. The code for this study is freely available at https://github.com/MelnychenkoM/ftir-hba1c-prediction.

Shigella flexneri 2a is the most common cause of shigellosis, a major public health concern in developing countries. Rapid and reliable diagnostic tools are critical for timely outbreak detection and management. Leveraging Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, we developed a CRISPR-Cas12a-based assay for the rapid and specific detection of S. flexneri 2a and validated its performance using stool specimens from patients. Two guide RNAs targeting the gtrII and gtrX genes-unique markers of the S. flexneri 2a serotype-were designed to ensure specificity. Recombinase Polymerase Amplification (RPA) was coupled with Cas12a-mediated collateral cleavage for signal amplification, with detection by fluorescence or lateral flow. Analytical sensitivity, specificity, and clinical accuracy were compared with conventional PCR using purified DNA and 588 clinical stool specimens. The CRISPR-Cas12a assay achieved a detection limit of 10 copies/uL, comparable to PCR, and showed 100% analytical specificity without cross-reactivity to other bacteria. The isothermal reaction operated at room temperature and was completed within one hour. Both readouts allowed visual interpretation without specialized equipment. Clinical validation demonstrated a diagnostic sensitivity of 95% and specificity of 98%, confirming robust performance. This study provides two key advances: it establishes a CRISPR-Cas12a assay specifically targeting S. flexneri 2a, the predominant serotype, and validates it using a large clinical cohort. The assays simplicity, speed, and high diagnostic accuracy make it a valuable tool for clinical diagnostics and field-based surveillance in resource-limited settings. IMPORTANCERapid and accessible diagnostics are essential for effective management of infectious diseases such as shigellosis. We developed a CRISPR-Cas12a-based assay that specifically detects Shigella flexneri 2a, the predominant serotype responsible for the global disease burden. This assay integrates isothermal amplification with CRISPR-mediated detection to achieve single-copy sensitivity within one hour, eliminating the need for complex instrumentation. Dual fluorescence and lateral-flow readouts enable flexible use in both clinical laboratories and low-resource settings. The methods simplicity, accuracy, and adaptability demonstrate the practical potential of CRISPR diagnostics for point-of-care applications. By enabling rapid, on-site identification of S. flexneri 2a, this approach can significantly improve clinical diagnosis and strengthen public health responses to enteric pathogen outbreaks.

IntroductionBreast Cancer (BC) remains a significant clinical challenge, and despite well-established screening strategies, new biomarkers could improve BC detection, treatment and management. Urine represents a headily accessible liquid biopsy for diagnosis and extracellular vesicle (EV) transfer of oncogenic proteins, RNAs, and metabolites that promote tumor growth, invasion, metastasis, and immune evasion. AimsTo compare the whole urine and urinary EV metabolomes and identify BC specific metabolite changes. MethodologyUrine samples were collected from four participant groups: breast cancer (BC) patients (n = 42), individuals with breast benign disease (BBD; n = 3), symptom controls (SC; n = 4), and healthy controls (HC; n = 6). EVs were isolated using differential centrifugation, ultrafiltration, and size-exclusion chromatography (SEC), and their morphology was confirmed by transmission electron microscopy (TEM). Metabolites from whole urine and from EVs derived from the same samples were extracted using methanol-water (70:30, v/v) and analyzed by direct-infusion mass spectrometry (DI-MS) in both positive and negative ESI modes. Metabolic features were processed with BinneR and annotated using the HMDB and KEGG databases. Integrated multi-omics analysis of whole-urine and EV-associated metabolomes was performed using the DIABLO framework within the MixOmics package in R platform. ResultsDI-MS profiling detected a broad spectrum of metabolites in both whole-urine and EV-derived fractions. Multivariate analyses revealed a clear separation of breast cancer (BC) patients from healthy controls and non-cancer groups in both matrices. Whole EV metabolites with area under the curves (AUC) of > 0.7 included glyceryl phosphoryl derivatives, N-eicosapentaenoyl species, sphinganine-1-phosphate and tetracosahexaenoic acid. EV-enriched metabolites included carnitine, histidine and adenosine monophosphate. DIABLO-based integrative analysis suggested that urinary and EV metabolomes were broadly similar with the discrete putative metabolite biomarkers representing minor, but specific changes with BC. ConclusionsThe whole urine and EV metabolomes suggested a small number of metabolite changes that were specific to BC. This could indicate that the urinary EVs describe distinctive aspects of the breast carcinogenic process.

Ovarian cancer is the deadliest of gynecological cancers and surgery is often necessary for a final diagnosis. Benign cases could be managed more conservatively, avoiding the risks and complications associated with surgery, if accurate diagnostic biomarkers existed. Underlying differences between circulating protein biomarkers and tumor gene expression also restricts interpretation and prioritization of potential biomarkers for diagnosis and potential drug targets. Here, high-throughput affinity plasma proteomics data encompassing over 5400 proteins in plasma from 404 women from two independent Swedish cohorts were analyzed alone and combined with total RNA sequencing in corresponding benign and malignant tumor tissue. A subset of 191 proteins previously identified as differentially expressed between benign and malignant conditions were used to perform correlation analyses, revealing similar patterns between groups but much stronger signals in malignant cases. Comparison with known protein interactions from the STRING database revealed a highly interconnected network consisting of 154 proteins in plasma. Differential correlation analysis (DCA) was performed on the full set of 5414 proteins and for their corresponding tumor RNA expression. DCA identified 31 plasma proteins with significant differential correlations (adjusted p < 0.05, {Delta}R > 0.5) and 759 tumor transcript pairs with significantly differentially correlating RNA expression. Distinct protein-protein correlation patterns in plasma were discovered and validated with notable differences between benign and malignant tumors. In general, these patterns were distinct from those detected on gene expression level in tumor tissue. In conclusion, our findings reveal clear differences in plasma protein co-regulation, with distinct correlation patterns between malignant and benign cases. The differences between results obtained in tumor transcriptomics and plasma proteomics results from the same patients warrants further studies into the tumor microenvironment to understand the function of promising protein biomarker candidates and the potential of these as future drug targets.

Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is conventionally diagnosed using electrocardiography and serial blood biomarker measurements. We investigated a non-invasive, bloodless, and electrode-free diagnostic strategy using a wrist-worn infrared spectrophotometric biosensor (Infrasensor). In a prospective, multicenter study of 595 patients with suspected NSTE-ACS enrolled across 13 sites in two countries, participants were stratified into five analytical cohorts. With 200 multi-ethnic controls and a leave-one-cohort-out external validation, a machine learning model detected high-grade coronary obstruction with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI: 0.84-0.90), 90% specificity, and 84% positive predictive value--surpassing standard risk scores. A secondary model predicted freedom from NSTE-ACS and adverse outcomes over 30 days with an AUC of 0.89 (95% CI: 0.87-0.92), 99% sensitivity, and 96% negative predictive value. These findings demonstrate the potential of the Infrasensor as a rapid, scalable point-of-care tool for early risk stratification in NSTE-ACS.

BackgroundHER2-positive (HER2+) cancers are associated with aggressive tumor development but also high response rates to targeted blockade treatments of the HER-2/neu signaling pathway leading to improved clinical outcome for the patient. Current clinical analysis of the HER2 status primarily relies on solid tumor biopsies low-suitable for continuous real-time monitoring needed for possible adjustment of the treatment, while serum tests targeting blood-circulating HER-2/neu fragments often show conflicting tumor-serum relations. MethodsA cellulase-linked aptamer sandwich assay was used for detection of total urokinase plasminogen activator (uPA) and its different forms in serum of cancer patients and healthy individuals. Serum uPA levels were correlated with solid biopsy results and relevant clinical data extracted from electronic patient records, and FDG-PET/CT scanning. ResultsWe show that serum uPA allows precise stratification of patients with HER2+ cancers and cancers with HER2 borderline expression. Serum levels of total uPA 96.6% accurately informed about HER2+ tumor status in a cohort of 85 patients, with a HER2+ cut-off value of 0.976 ng mL-1. ConclusionsThe established liquid biopsy test for serum uPA has potential for accurate diagnosis and staging of patients with HER2+ cancers and "borderline" cancers requiring further confirmatory (or rejection) testing.

Multiple myeloma (MM) evolves from monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM) with annual progression rates of 1% and 10%, respectively. Current risk models dont fully capture the underlying dynamic molecular processes. We hypothesized that urinary peptides reflect disease-specific microenvironmental alterations in plasma cell dyscrasias. To test this hypothesis, capillary electrophoresis-mass spectrometry CE-MS was applied to profile the urinary peptidome of 314 individuals, including a discovery group (42 MGUS, 27 SMM, 14 MM), an independent validation group (45 MGUS, 9 SMM, 7 MM, 9 with plasmacytoma), 86 without underlying malignancy, and 75 patients with impaired kidney function. 121 peptides were significantly altered between MM and MGUS and displayed a monotonic abundance trend across the MGUS-SMM-MM continuum. These peptides predominantly derived from collagens, beta-2 microglobulin, alpha-1 antitrypsin, and antithrombin-III. Integration of these 121 peptides into a support vector machine classifier achieved an area under the curve of 0.94 (0.85-0.99; 95% CI) in the independent validation cohort, with 100% sensitivity and 82% specificity for MM detection. The finding that urinary peptides enable non-invasive molecular discrimination of MM from precursor states represents a solid basis for a prospective evaluation in prognosis and detection of progression. Significance StatementProgression from monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM) to active multiple myeloma (MM) remains difficult to predict in routine clinical practice. Current risk assessment based on the International Myeloma Working Group (IMWG) criteria primarily relies on clinical and biochemical variables. This study identifies myeloma-specific urinary peptide signatures reflecting extracellular matrix (ECM) remodeling. In a cohort of 314 patients, CE-MS-based urinary peptidomic analysis yielded a 121-peptide ECM-derived classifier that accurately differentiated active MM from precursor conditions, achieving 100% sensitivity and 82% specificity upon independent validation. Importantly, gradual changes in peptide abundance with disease evolution from MGUS to SMM to MM suggest that urinary ECM-related peptide fragments reflect stage-associated molecular changes across the MGUS-SMM-MM continuum. These findings represent a solid basis for the evaluation of the value of this classifier in predicting progression in a prospective study.

This small pilot feasibility study shows that reverse phase protein array (RPPA) technology is a useful tool for targeted proteomics analysis in human ovarian follicular fluid. RPPA supplements mass spectrometry approaches that are currently used by providing functional signal transduction data that drive cellular biology. Herein, we present the first report of using RPPA in follicular fluid to elucidate protein signaling pathways. The results show potential associations between follicular fluid proteins measured with RPPA and reproductive outcomes from in vitro fertilization, including oocyte maturity, oocyte fertilization, embryo quality, and pregnancy. This study provides evidence that RPPA is a feasible approach to be used in clinical studies of reproductive endpoints. However, a larger study of RPPA to identify diagnostic and prognostic follicular fluid protein biomarkers of infertility is needed.

Point-of-care (POC) blood testing enables rapid, decentralized diagnostics with transformative promise, yet its innovation landscape remains poorly mapped. To this end, we focused on features that we believe are key to make progress in areas of precision healthcare and predictive medicine, such as longitudinal data collection and data analytics integration. While no review can be complete, this work attempts to address this gap by analyzing 86 POC blood testing devices worldwide and proposing a unified framework to compare them across technology principles, diagnostic breadth, usability, regulatory pathway, deployment feasibility (via a custom index), and data/AI integration. Electrochemical biosensors were the single largest platform (29.1%), strongly associated with glucose testing ({chi}2=237.8, p<0.001), while spectroscopic and microfluidic systems remained niche due to higher costs and specialized requirements. Regulatory approval skewed toward moderate risk (44.2% FDA II; 27.4% IVDR C), while approval times lengthened with risk class (e.g., IVDR D {approx}540 days). A trade-off was observed between usability and panel breadth: tools for home or low-resource settings emphasize simplicity and affordability, whereas clinical systems expand diagnostic range at higher complexity and cost. Deployment feasibility scores favored handhelds, while benchtops were penalized by workflow and capital demands, and microfluidics by consumables. Innovation clusters in North America, Europe, and East Asia reinforce global leadership and disparities.

Determining physiological stress at high resolution is crucial across diverse settings to enable informed decision-making in the context of health and disease. Saliva-based targeted multi-omics testing provides a powerful, non-invasive method to quantify physiological stress and circadian dynamics at high-frequency. In a laboratory crossover trial with 24-hour sampling comprising 413 saliva samples, we demonstrate high analytical reliability, distinct molecular individuality, and robust acute and delayed responses to physical exercise across proteins, metabolites, and lipids. Moreover, we present the most comprehensive existing dataset describing 24-hour molecular kinetics across these three omics layers. Leveraging this controlled setting, we applied machine learning to single-timepoint saliva samples to accurately predict recent physical exercise both immediately after and 24 hours later. Next, we translated this analytical framework to a real-world longitudinal setting of elite football players monitored over 16 months, comprising over 12,000 saliva samples. Despite increased biological and contextual variability, the model retained robust discrimination between exercise and rest on the following day. Based on prediction probabilities, we introduce a saliva-based internal strain metric, that captures internal load and can be harnessed to monitor physical exercise and recovery. Model robustness was further supported through out-of-sample validation using previously unseen observations. Our findings demonstrate that saliva-based targeted multi-omics reliably captures physical exercise and recovery states in both laboratory and real-world environments, providing a scalable framework for monitoring physical performance. This non-invasive approach holds broad potential for physiological monitoring and can serve as a blueprint for health- and disease-related contexts.

Fluorescence lifetime imaging ophthalmoscopy permits in vivo assessment of retinal metabolism but has remained limited by insufficient cellular resolution in the human eye. Here we present adaptive optics-enhanced fluorescence lifetime imaging ophthalmoscopy (AOFLIO), a method for single-cell-resolved, in vivo structural and metabolic imaging of the human retinal pigment epithelium (RPE). Through real-time correction of ocular wavefront aberrations, precisely synchronized adaptive optics reflectance and lifetime image acquisition via a phase-locked loop-based timing architecture, and sub-pixel photon registration that localizes individual autofluorescence photons with high spatial precision, AOFLIO directly resolves the RPE cell mosaic and measures autofluorescence decay using the same photons, enabling direct structural-functional correlation at the single-cell level. We demonstrate single-cell RPE lifetime mapping in healthy subjects and reveal altered metabolic signatures and fine characterization of RPE metabolic in age-related macular degeneration. AOFLIO establishes a platform for cellular-scale metabolic imaging in the living human eye.

Clear cell renal cell carcinoma (ccRCC) is the leading cause of kidney cancer-related death, but how the tumor microenvironment shapes patient survival is not completely understood. Here, we describe the characterization of ccRCC tumor ecosystems from 498 patients using imaging mass cytometry with a focus on tumor, myeloid, and T cell landscapes. Data from more than 3 million single cells is analyzed using machine-learning to identify key ecosystem features that outperform basic clinical data for predicting patient survival. We define three survival ecotypes of ccRCC: Poor ecotypes, correlate with the worst survival, have high levels of ICAM1 and CD44 expression in tumor cells and are enriched in M2-like macrophages and interactions of exhausted CD8+ T cells with macrophages. Favorable ecotypes are characterized by high levels of VHL on tumor cells and of HLADR on myeloid cells and contain Th1-like CD4+ T cells. Medium ecotypes have the highest endothelial cell density and various immune-to-tumor interactions. Multi-omic characterization of these ecotypes using targeted genomic sequencing and metabolic imaging reveals distinct genomic and metabolic features, including BAP1 mutations in Poor and VHL monodriver/wild-type status in Favorable patients. We show that deep learning allows ecotype prediction directly from standard pathology H&E images. We validate the ecotypes and their associated molecular characteristics with orthogonal omics data across five clinical cohorts and more than 2,500 patients. These analyses highlight an overall survival benefit for Medium patients treated with immunotherapy. In summary, our study distills the survival-relevant information encoded in the ccRCC tumor microenvironment into prognostic survival ecotypes, which may inform clinical decision making in the future.

BackgroundEarly cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1H-NMR-based metabolomics can identify cancer with high accuracy. SCAN2 tested whether integrating metabolomics with glycomics improves discrimination in a clinically complex, real-world population. MethodsSerum from 369 SCAN patients (59 cancers) was analysed using AXINON(R)lipoFIT(R)-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FindingsIntegration of glycomics with metabolomics improved discrimination, achieving an AUC of 0.88 in a refined cohort excluding dominant comorbidities. Cancer-associated bi- and tri-antennary glycans, including FA2G2S1, FA2BG1, and M5A1G1S1, differentiated cancer cases. A classifier targeting metastatic disease achieved an AUC of 0.80. Joint probability analysis preserved cancer-associated metabolic signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.8%. InterpretationThese findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and demonstrate that integrating metabolomics with glycomics enhances cancer detection in patients with non-specific symptoms. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing.

Immune effector cell-associated neurotoxicity syndrome (ICANS) is a common and life-threatening complication of chimeric antigen receptor (CAR) T-cell therapy, with early detection being critical for timely intervention and improved outcomes. Cytokines such as interleukin-6 (IL-6) are key mediators of the inflammatory cascade underlying ICANS pathogenesis, but prospective clinical evidence for their predictive value is limited. Here we quantify IL-6 levels in a prospective cohort of 40 CAR-T patients (270 serum samples), using a simple in-house microfluidic bead immunoassay. IL-6 levels measured by our assay were significantly associated with ICANS onset. Specifically, each [~]3.4-fold increase in IL-6 levels was linked to a 74% increase in the odds of developing ICANS the following day, independent of other clinical variables. Overall, we show the prognostic value of IL-6 for next-day ICANS, demonstrate the potential of frequent cytokine measurement to guide CAR-T patient management, and develop a simple experimental method to perform such monitoring.