Combination Of Biomarkers Research Articles

Novel Strategies in Diagnosing Heart Failure with Preserved Ejection Fraction: A Comprehensive Literature Review.

Heart failure (HF) with preserved ejection fraction (HFpEF) is a prevalent global condition affecting approximately 50% of the HF population. With the aging of the worldwide population, its incidence and prevalence are expected to rise even further. Unfortunately, until recently, no effective medications were available to reduce the high mortality and hospitalization rates associated with HFpEF, making it a significant unmet need in cardiovascular medicine. Although HFpEF is commonly defined as HF with normal ejection fraction and elevated left ventricular filling pressure, performing invasive hemodynamic assessments on every individual suspected of having HFpEF is neither feasible nor practical. Consequently, several clinical criteria and diagnostic tools have been proposed to aid in diagnosing HFpEF. Overall, these criteria and tools are designed to assist healthcare professionals in identifying and evaluating patients who may have HFpEF based on a combination of signs, symptoms, biomarkers, and non-invasive imaging findings. By employing these non-invasive diagnostic approaches, clinicians can make informed decisions regarding the best pharmacological and rehabilitation strategies for individuals with suspected HFpEF. This literature review aims to provide an overview of all currently available methods for diagnosing and monitoring this disabling condition.

Identification of Potential Blood-Based Biomarkers for Frailty by Using an Integrative Approach

Introduction: Although frailty is a geriatric syndrome that is associated with disability, hospitalization, and mortality, it can be reversible and preventable with the appropriate interventions. Additionally, as the current diagnostic criteria for frailty include only physical, psychological, cognitive, and social measurements, there is a need for promising blood-based molecular biomarkers to aid in the diagnosis of frailty. Methods: To identify candidate blood-based biomarkers that can enhance current diagnosis of frailty, we conducted a comprehensive analysis of clinical data, messenger RNA-sequencing (RNA-seq), and aging-related factors using a total of 104 older adults aged 65–90 years (61 frail subjects and 43 robust subjects) in a cross-sectional case-control study. Results: We identified two candidate biomarkers of frailty from the clinical data analysis, nine from the RNA-seq analysis, and six from the aging-related factors analysis. By using combinations of the candidate biomarkers and clinical information, we constructed risk prediction models. The best models used combinations that included skeletal muscle mass index measured by dual-energy X-ray absorptiometry (adjusted p = 0.026), GDF15 (adjusted p = 1.46E-03), adiponectin (adjusted p = 0.012), CXCL9 (adjusted p = 0.011), or apelin (adjusted p = 0.020) as the biomarker. These models achieved a high area under the curve of 0.95 in an independent validation cohort (95% confidence interval: 0.79–0.97). Our risk prediction models showed significantly higher areas under the curve than did models constructed using only basic clinical information (Welch’s t test p < 0.001). Conclusion: All five biomarkers showed statistically significant correlations with components of the frailty diagnostic criteria. We discovered several potential biomarkers for the diagnosis of frailty. Further refinement may lead to their future clinical use.

A comprehensive Exdia TRF-LFIA for simultaneous quantification of GFAP and NT-proBNP in distinguishing ischemic and hemorrhagic stroke

The goal of this study is to create a highly sensitive time-resolved fluorescence lateral flow immunoassay (TRF-LFIA) capable of concurrently measuring glial fibrillary acidic protein (GFAP) and the N-terminal fragment of B-type natriuretic peptide precursor (NT-proBNP). This assay is designed as a diagnostic tool and aims to provide an algorithm for stroke management, specifically for distinguishing between Ischemic stroke (IS) and Hemorrhagic stroke (HS). However, LFIA to quantify simultaneous serum NT-proBNP and GFAP are not yet available.We have developed and validated a novel TRF-LFIA for the simultaneous quantitative detection of NT-proBNP and GFAP. The sensitivity and reproducibility of the immunoassay were significantly improved by employing specific monoclonal antibodies linked to europium nanoparticles (EuNPs) that specifically target NT-proBNP and GFAP. The detection area on the nitrocellulose membrane featured sandwich-style complexes containing two test lines for NT-proBNP and GFAP, and one Control line. The fluorescence intensity of these test lines and control line was measured using an in-house developed Exdia TRF-Plus analyzer. As proof-of-concept, we enrolled patients suspected of having a stroke who were admitted within a specific time frame (6 h). A small amount of clinical specimen (serum) was used.To optimize the LFIA, an EuNPs conjugated antibodies were investigated to improve the detection sensitivity and decrease the background signal as well shorten the detection time. The Exdia TRF-LFIA cartridge offers a wide linear dynamic detection range, rapid detection, high sensitivity, and specificity. The limit of detection was determined to be 98 pg/mL for NT-proBNP and 68 pg/mL for GFAP, with minimal cross-reactivity. There were 200 clinical human serum samples that were used to evaluate this platform with high correlation. By combining the results of NT-proBNP and GFAP, we formulated an algorithm for the clinical assessment of Ischemic Stroke (IS) and Hemorrhagic Stroke (HS). According to our proposed algorithm, the combination of GFAP and NT-proBNP emerged as the most effective biomarker combination for distinguishing between IS and HS.Exdia TRF-LFIA shows great potential as a supplemental method for in vitro diagnostics in the laboratory or in other point-of-care testing (POCT) applications. Its development substantially decreases the diagnosis time for IS and HS. The proposed algorithm not only minimizes treatment delays but also lowers medical costs for patients.

Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations.

We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.

A Systemic Immune Inflammation Index and PD-L1 (SP142) Expression as a Potential Combined Biomarker of the Clinical Benefit of Chemo-Immunotherapy in Extensive-Stage Small-Cell Lung Cancer.

Background: No studies have identified combined biomarkers that may be more reasonable for the assessment of current chemo-immunotherapy in patients with extensive stage small-cell lung cancer (ES-SCLC). Methods: This study was conducted to investigate a combined biomarker with prognostic or predictive value in ES-SCLC. We determined the best independent prognostic biomarker among the four complete blood-count-derived inflammatory biomarkers (CBC-IBs). Subsequently, we analyzed the prognostic or predictive value of combining this independent CBC-IB with PD-L1 (SP142) expression. We prospectively assessed the SP142 analyses in tumor samples at diagnosis. Results: All in all, 55 patients with ES-SCLC were classified into four groups according to the systemic immune inflammation index (SII) (low/high) and SP142 (positive/negative). The best survival was observed in the low-SII/ SP142-positive group, whereas the worst survival was observed in the high-SII/SP142-negative group (p = 0.002). The combined SII-SP142 biomarker was better for predicting both survival and disease progression in patients with ES-SCLC. Conclusions: The combined SII-SP142 biomarker can be readily and universally obtained at a low cost in clinical practice, without requiring advanced genomics technology or specialized expertise. Although further studies are needed to confirm that the combined SII-SP142 biomarker is widely applicable, it should help clinicians to identify the best patients for combined chemotherapy with atezolizumab in ES-SCLC.

Combining multiple biomarkers linearly to minimize the Euclidean distance of the closest point on the receiver operating characteristic surface to the perfection corner in trichotomous settings.

The performance of individual biomarkers in discriminating between two groups, typically the healthy and the diseased, may be limited. Thus, there is interest in developing statistical methodologies for biomarker combinations with the aim of improving upon the individual discriminatory performance. There is extensive literature referring to biomarker combinations under the two-class setting. However, the corresponding literature under a three-class setting is limited. In our study, we provide parametric and nonparametric methods that allow investigators to optimally combine biomarkers that seek to discriminate between three classes by minimizing the Euclidean distance from the receiver operating characteristic surface to the perfection corner. Using this Euclidean distance as the objective function allows for estimation of the optimal combination coefficients along with the optimal cutoff values for the combined score. An advantage of the proposed methods is that they can accommodate biomarker data from all three groups simultaneously, as opposed to a pairwise analysis such as the one implied by the three-class Youden index. We illustrate that the derived true classification rates exhibit narrower confidence intervals than those derived from the Youden-based approach under a parametric, flexible parametric, and nonparametric kernel-based framework. We evaluate our approaches through extensive simulations and apply them to real data sets that refer to liver cancer patients.

Exhaled and Systemic Biomarkers to Aid the Diagnosis of Bronchial Asthma in Elite Water Sports Athletes.

Our aim was to evaluate the accuracy of a combined airway inflammatory biomarker assessment in diagnosing asthma in elite water sports athletes. Members of the Hungarian Olympic and Junior Swim Team and elite athletes from other aquatic disciplines were assessed for asthma by objective lung function measurements, and blood eosinophil count (BEC), serum total immunoglobulin E (IgE), fractional exhaled nitric oxide (F ENO ) measurements, and skin prick testing were performed. A scoring system from BEC, F ENO , serum IgE, and skin test positivity was constructed by dichotomizing the variables and assigning a score of 1 if the variable is elevated. These scores were summed to produce a final composite score ranging from 0 to 4. A total of 48 participants were enrolled (age 21 ± 4 yr, 42% male), of which 22 were diagnosed with asthma. Serum total IgE and F ENO levels were higher in asthmatic individuals (68 [27-176] vs 24 [1-43], P = 0.01; 20 [17-26] vs 15 [11-22], P = 0.02), and positive prick test was also more frequent (55% vs 8%, P < 0.01). Asthmatic participants had higher composite variable scores (2 [1-3] vs 1 [0-1], P = 0.02). Receiver operating characteristic analysis showed that total IgE, F ENO , and composite variable were suitablefor identifying asthmatic participants (area under the curve = 0.72, P = 0.01; 0.70, P = 0.02, and 0.69, P = 0.03). A composite score of >2 reached a specificity of 96.2%, a sensitivity of 36.4%, and a likelihood ratio of 9.5. Logistic regression model revealed a strong association between the composite variable and the asthma diagnosis (OR = 2.71, 95% confidence interval = 1.17-6.23, P = 0.02). Our data highlight the diagnostic value of combined assessment of Th2-type inflammation in elite water sports athletes. The proposed scoring system may be helpful in ruling in asthma in this population upon clinical suspicion.

Combining biomarkers of BNIP3 L, S100B, NSE, and accessible measures to predict sepsis-associated encephalopathy: a prospective observational study

Background Accurate identification of delirium in sepsis patients is crucial for guiding clinical diagnosis and treatment. However, there are no accurate biomarkers and indicators at present. We aimed to identify which combinations of cognitive impairment-related biomarkers and other easily accessible assessments best predict delirium in sepsis patients. Methods One hundred and one sepsis patients were enrolled in a prospective study cohort. S100B, NSE, and BNIP3 L biomarkers were detected in plasma and cerebrospinal fluid and patients’ optic nerve sheath diameter (ONSD). The optimal biomarkers identified by Logistic regression are combined with other factors such as ONSD to filter out the perfect model to predict delirium in sepsis patients through Logistic regression, Naïve Bayes, decision tree, and neural network models. Main results Among all biomarkers, compared with BNIP3 L (AUC = .706, 95% CI = .597–.815) and NSE (AUC = .711, 95% CI = .609–.813) in cerebrospinal fluid, plasma S100B (AUC = .729, 95% CI = .626–.832) had the best discrimination performance for delirium in sepsis patients. Logistic regression analysis showed that the combination of cerebrospinal fluid BNIP3 L with plasma S100B, ONSD, neutrophils, and age provided the best discrimination to cognitive impairment in sepsis patients (accuracy = .901, specificity = .923, sensitivity = .911), which was better than Naïve Bayes, decision tree, and neural network models. Neutrophils, ONSD, and cerebrospinal fluid BNIP3 L were consistently the major contributors in a few models. Conclusions The logistic regression showed that the combination model was strongly correlated with cognitive dysfunction in sepsis patients.

Multiple biomarkers improve diagnostic accuracy across Lewy body and Alzheimer's disease spectra.

More than half of neurodegenerative disease patients have multiple pathologies at autopsy; however, most receive one diagnosis during life. We used the α-synuclein seed amplification assay (αSyn-SAA) and CSF biomarkers for amyloidosis and Alzheimer's disease (AD) neuropathological change (ADNC) to determine the frequency of co-pathologies in participants clinically diagnosed with Lewy body (LB) disease or AD. Using receiver operating characteristic analyses on retrospective CSF samples from 150 participants determined αSyn-SAA accuracy, sensitivity, and specificity for identifying clinically defined LB disease and predicting future change in clinical diagnosis. CSF biomarkers helped determine the frequency of concomitant Lewy body pathology, ADNC, and/or amyloidosis in participants with LB disease and AD, across clinical spectra. Following a decade-long follow-up, the clinically or autopsy-defined diagnosis changed for nine participants. αSyn-SAA demonstrated improved accuracy (91.3%), sensitivity (89.3%), and specificity (93.3%) for identifying LB disease compared to all non-LB disease, highlighting the limitations of clinical diagnosis alone. When examining biomarkers of co-pathology, amyloidosis was present in 18%, 48%, and 71% (χ2 (2) = 13.56, p = 0.001) and AD biomarkers were present in 0%, 8.7%, and 42.9% (χ2 (2) = 18.44, p < 0.001) of LB disease participants with different stages of cognitive impairment respectively. Co-occurring biomarkers for αSyn-SAA and amyloidosis were present in 12% and 14% of AD compared to 43% and 57% LB disease participants with different stages of cognitive impairment (χ2 (3) = 13.87, p = 0.003). Our study shows that using a combination of αSyn-SAA and AD biomarkers can identify people with αSyn, ADNC, and co-pathology better and earlier than traditional clinical diagnostic criteria alone.

Clinicopathologic features, concurrent genomic alterations, and clinical outcomes of patients with KRAS G12D mutations in resected lung adenocarcinoma

BackgroundIn light of the ongoing clinical development of KRAS G12D-specific inhibitors, we sought to investigate the clinicopathologic, co-occurring genomic features and outcomes of patients with KRAS G12D-mutant lung adenocarcinoma. Methods3828 patients with completely resected primary lung adenocarcinomas were examined for KRAS mutations between 2008 and 2020. The association between KRAS G12D and clinicopathologic features, molecular profiles, and outcomes was investigated. Results65 patients (1.7%) with KRAS G12D-mutant lung adenocarcinoma were identified. KRAS G12D mutation was more frequent in males, former/current smokers, radiologic solid tumors, and invasive mucinous adenocarcinoma. TP53 and STK11 were the two most frequent concomitant mutations in the KRAS G12D group. KRAS G12D mutation did not appear to be a prognostic factor in resected stage I-III lung adenocarcinomas, while KRAS non-G12D mutation was related to worse survival, especially in stage I tumors. KRAS G12D mutations were associated with positive but low (1–49%) PD-L1 expression compared to negative (<1%), while KRAS non-G12D mutation was associated with high PD-L1 expression (≥50%). TP53 co-mutation indicated higher PD-L1 expression, while STK11 co-mutation had a negligible impact on PD-L1 expression. Furthermore, data mining of MSK datasets from cBioPortal revealed that KRAS G12D and SKT11 co-mutation were associated with a diminished response to immunotherapy. ConclusionsKRAS G12D-mutant lung adenocarcinoma harbored unique clinicopathologic and genomic characteristics. Despite not being prognostic in resected lung adenocarcinoma, KRAS G12D might be a valuable biomarker in combination with certain co-mutations for identifying relevant subgroups of patients that could eventually influence treatment regimens

There Is Still a Need for a Comprehensive Investigation of the Health Consequences of Exposure to Urban Air with Special Regard to Particulate Matter (PM) and Cardiovascular Effects

Air pollution is a foremost public health problem worldwide. The pulmonary effects of air pollution have been well established for decades, and substantial epidemiological evidence is consistently showing that both acute and chronic exposure to air pollution is linked to cardiovascular morbidity and mortality. The underlying cause for this link is, however, still unknown, and many questions remain open. Most of the epidemiological studies focusing on health consequences of exposure to urban air used data from air monitoring stations or—when applying personal sampling or monitoring—measured a limited number of components to assess the exposure. On the other hand, studies conducting a decent exposure characterization and measuring a relatively large number of components with personal sampling had little or no focus on the effects and investigated only a few biomarkers. The number of research studies on this topic is huge, but at the same time, it seems as if there was no need for a comprehensive examination of the effects of urban air pollution on health. Researchers and research supporting organizations, in their fascination with the search for “novelty” and “beyond state-of-the-art”, do not seem to be aware of that they will never be able to assemble the whole picture from the puzzle pieces of research activities focusing only on certain aspects. Without a comprehensive investigation, we might never be able to answer such questions as (i) which of the urban air pollutants are forerunners in causing health effects, especially cardiovascular effects? (ii) Which biomarkers have the best predictor performance in early effects? (iii) Are there biomarkers or combinations of biomarkers that can help determine the degree of individual susceptibility and sensitivity and the dependence of these by different factors (e.g., age, gender, and physical activity)? The authors of this article aim to go through the already investigated—at the same time, essential—elements of the topic and propose a more comprehensive study, which, of course, can be expanded, debated, and improved.

Predictive biomarkers of rapidly developing insulin deficiency in children with type 1 diabetes

IntroductionThe rate of progression to complete insulin deficiency varies greatly in type 1 diabetes. This constitutes a challenge, especially when randomizing patients in intervention trials aiming to preserve beta cell...

Scoring system for diagnosis and pretreatment risk assessment of neuroblastoma using urinary biomarker combinations.

The urinary catecholamine metabolites, homovanillic acid (HVA) and vanillylmandelic acid (VMA), are used for the adjunctive diagnosis of neuroblastomas. We aimed to develop a scoring system for the diagnosis and pretreatment risk assessment of neuroblastoma, incorporating age and other urinary catecholamine metabolite combinations. Urine samples from 227 controls (227 samples) and 68 patients with neuroblastoma (228 samples) were evaluated. First, the catecholamine metabolites vanillactic acid (VLA) and 3-methoxytyramine sulfate (MTS) were identified as urinary marker candidates through comprehensive analysis using liquid chromatography-mass spectrometry. The concentrations of these marker candidates and conventional markers were then compared among controls, patients, and numerous risk groups to develop a scoring system. Participants were classified into four groups: control, low risk, intermediate risk, and high risk, and the proportional odds model was fitted using the L2-penalized maximum likelihood method, incorporating age on a monthly scale for adjustment. This scoring model using the novel urine catecholamine metabolite combinations, VLA and MTS, had greater area under the curve values than the model using HVA and VMA for diagnosis (0.978 vs. 0.964), pretreatment risk assessment (low and intermediate risk vs. high risk: 0.866 vs. 0.724; low risk vs. intermediate and high risk: 0.871 vs. 0.680), and prognostic factors (MYCN status: 0.741 vs. 0.369, histology: 0.932 vs. 0.747). The new system also had greater accuracy in detecting missing high-risk neuroblastomas, and in predicting the pretreatment risk at the time of screening. The new scoring system employing VLA and MTS has the potential to replace the conventional adjunctive diagnostic method using HVA and VMA.

Large-Scale Cancer Genomic Analysis Reveals Significant Disparities between Microsatellite Instability and Tumor Mutational Burden.

Microsatellite instability (MSI) and tumor mutational burden (TMB) are predictive biomarkers for pan-cancer immunotherapy. The interrelationship between MSI-high (MSI-H) and TMB-high (TMB-H) in human cancers and their predictive value for immunotherapy in lung cancer remain unclear. We analyzed somatic mutation data from the Genomics Evidence Neoplasia Information Exchange (n = 46,320) to determine the relationship between MSI-H and TMB-H in human cancers using adjusted multivariate regression models. Patient survival was examined using the Cox proportional hazards model. The association between MSI and genetic mutations was assessed. Patients (31-89%) with MSI-H had TMB-low phenotypes across 22 cancer types. Colorectal and stomach cancers showed the strongest association between TMB and MSI. TMB-H patients with lung cancer who received immunotherapy exhibited significantly higher overall survival [HR, 0.61; 95% confidence interval (CI), 0.44-0.86] and progression-free survival (HR, 0.65; 95% CI, 0.47-0.91) compared to the TMB-low group; no significant benefit was observed in the MSI-H group. Patients with TMB and MSI phenotypes showed further improvement in overall survival and PFS. We identified several mutated genes associated with MSI-H phenotypes, including known mismatch repair genes and novel mutated genes, such as ARID1A and ARID1B. Our results demonstrate that TMB-H and/or a combination of MSI-H can serve as biomarkers for immunotherapies in lung cancer. These findings suggest that distinct or combined biomarkers should be considered for immunotherapy in human cancers because notable discrepancies exist between MSI-H and TMB-H across different cancer types.

Identification of depression in patients with acute coronary syndrome using multiple serum biomarkers

BackgroundBiomarkers for depression in patients with acute coronary syndrome (ACS) have not been identified. MethodsThis study evaluated multiple serum biomarkers for depressive disorders after ACS. Thirteen serum biomarkers associated with seven functional systems, along with sociodemographic/clinical characteristics, were evaluated in 969 patients within 2 weeks after ACS onset (acute phase). In total, 711 patients were evaluated for depressive disorder using DSM-IV criteria 1 year later (chronic phase). Logistic regression was used for the analysis. ResultsDepressive disorders were observed in 378 patients (39.0%) in the acute phase of ACS and 183 patients (25.7%) in the chronic phase. The weighted scores of five serum biomarkers (high-sensitivity C-reactive protein, interleukin-6, homocysteine, troponin I, and creatine kinase-MB) were significantly associated with depressive disorder diagnosis in the acute phase, and the weighted scores of three other biomarkers (tumor necrosis factor-alpha, interleukin-1 beta, and homocysteine) were significantly associated with depressive disorders in the chronic phase, in a dose-dependent manner after adjusting for relevant covariates (all P-values <0.001). ConclusionsThe combination of several serum biomarkers exhibited robust associations with depressive disorders in both the acute and chronic phases of ACS.

Development of Continuous Assessment of Muscle Quality and Frailty in Older Patients Using Multiparametric Combinations of Ultrasound and Blood Biomarkers: Protocol for the ECOFRAIL Study.

Frailty resulting from the loss of muscle quality can potentially be delayed through early detection and physical exercise interventions. There is a demand for cost-effective tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessments. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional, and microstructural muscle properties, while biological assays derived from blood samples are associated with functional information. This study aims to assess multiparametric combinations of ultrasound and blood-based biomarkers to offer a cross-sectional evaluation of the patient frailty phenotype and to track changes in muscle quality associated with supervised exercise programs. This prospective observational multicenter study will include patients aged 70 years and older who are capable of providing informed consent. We aim to recruit 100 patients from hospital environments and 100 from primary care facilities. Each patient will undergo at least two examinations (baseline and follow-up), totaling a minimum of 400 examinations. In hospital environments, 50 patients will be measured before/after a 16-week individualized and supervised exercise program, while another 50 patients will be followed up after the same period without intervention. Primary care patients will undergo a 1-year follow-up evaluation. The primary objective is to compare cross-sectional evaluations of physical performance, functional capacity, body composition, and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based assays. We will analyze ultrasound raw data obtained with a point-of-care device, along with a set of biomarkers previously associated with frailty, using quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. Additionally, we will examine the sensitivity of these biomarkers to detect short-term muscle quality changes and functional improvement after a supervised exercise intervention compared with usual care. At the time of manuscript submission, the enrollment of volunteers is ongoing. Recruitment started on March 1, 2022, and ends on June 30, 2024. The outlined study protocol will integrate portable technologies, using quantitative muscle ultrasound and blood biomarkers, to facilitate an objective cross-sectional assessment of muscle quality in both hospital and primary care settings. The primary objective is to generate data that can be used to explore associations between biomarker combinations and the cross-sectional clinical assessment of frailty and sarcopenia. Additionally, the study aims to investigate musculoskeletal changes following multicomponent physical exercise programs. ClinicalTrials.gov NCT05294757; https://clinicaltrials.gov/ct2/show/NCT05294757. DERR1-10.2196/50325.

Tumor beta2-microglobulin and HLA-A expression is increased by immunotherapy and can predict response to CIT in association with other biomarkers.

Downregulation of MHC class I expression and/or defects in the antigen presentation pathways are commonly reported in human cancers. Numerous studies previously have explored extensively the molecular mechanisms that underlie HLA-class I and Beta2-Microglobulin (B2M) downregulation. However, the techniques presently available to detect expression of MHC class I proteins lack the robustness, specificity and sensitivity needed for systematic integration and analysis in clinical trials. Furthermore, the dynamics of HLA-class I and B2M expression have not been comprehensively studied as a potential biomarker for immunotherapy. Using novel, validated, immunohistochemistry (IHC)-based methods for quantifying B2M and HLA-A in tumor samples from diverse cancer types, we have determined loss of B2M and HLA-A proteins in 336 archived, primary specimens and 329 biopsies from metastatic patients collected during Roche-sponsored Phase 1 clinical trials investigating novel immunotherapy candidates as monotherapy or in combination with CPI. Up to 56% of cases with B2M or HLA-A loss were noted in the investigated tumor types. The frequency of loss was dependent on indication and stage of disease and revealed heterogeneous expression patterns across patients. B2M and HLA-A loss was increased in metastatic lesions compared to primary tumors, indicating selection of MHC class I low clones in metastatic and refractory tumor cells. High on-treatment B2M expression correlated with successful clinical outcome (RECIST), while high baseline B2M did not. A treatment-induced increase of B2M expression was noted in most of the patients with low B2M levels at baseline. The triple biomarker combination of B2M, CD8 and PDL1 strongly improved response prediction to cancer immunotherapy. Our results indicate that B2M and HLA-A loss occurs frequently in tumors and is reversed in most instances following immunotherapy which supports the conclusion that MHC class I loss is not the dominant resistance mechanism to CPI treatment. This investigation reveals a highly dynamic expression of HLA-A and B2M in tumors affected by indication, metastatic status, immunophenotype and immunotherapy treatment. Baseline expression levels of B2M on tumors may be of utility as a constituent of a biomarker panel used for selecting patients for immunotherapy clinical trials.

Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes

Establishing biomarkers to predict multiple sclerosis diagnosis and prognosis has been challenging using a single biomarker approach. We hypothesised that a combination of biomarkers would increase the accuracy of prediction models to differentiate multiple sclerosis from other neurological disorders and enhance prognostication for people with multiple sclerosis. We measured 24 fluid biomarkers in the blood and cerebrospinal fluid of 77 people with multiple sclerosis and 80 people with other neurological disorders, using ELISA or Single Molecule Array assays. Primary outcomes were multiple sclerosis versus any other diagnosis, time to first relapse, and time to disability milestone (Expanded Disability Status Scale 6), adjusted for age and sex. Multivariate prediction models were calculated using the area under the curve value for diagnostic prediction, and concordance statistics (the percentage of each pair of events that are correctly ordered in time for each of the Cox regression models) for prognostic predictions. Predictions using combinations of biomarkers were considerably better than single biomarker predictions. The combination of cerebrospinal fluid [chitinase-3-like-1 + TNF-receptor-1 + CD27] and serum [osteopontin + MCP-1] had an area under the curve of 0.97 for diagnosis of multiple sclerosis, compared to the best discriminative single marker in blood (osteopontin: area under the curve 0.84) and in cerebrospinal fluid (chitinase-3-like-1 area under the curve 0.84). Prediction for time to next relapse was optimal with a combination of cerebrospinal fluid[vitamin D binding protein + Factor I + C1inhibitor] + serum[Factor B + Interleukin-4 + C1inhibitor] (concordance 0.80), and time to Expanded Disability Status Scale 6 with cerebrospinal fluid [C9 + Neurofilament-light] + serum[chitinase-3-like-1 + CCL27 + vitamin D binding protein + C1inhibitor] (concordance 0.98). A combination of fluid biomarkers has a higher accuracy to differentiate multiple sclerosis from other neurological disorders and significantly improved the prediction of the development of sustained disability in multiple sclerosis. Serum models rivalled those of cerebrospinal fluid, holding promise for a non-invasive approach. The utility of our biomarker models can only be established by robust validation in different and varied cohorts.

Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning.

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.

Tumor Copy Number Alteration Burden as a Predictor for Resistance to Immune Checkpoint Blockade across Different Cancer Types.

Immune checkpoint blockade (ICB) benefits only a subset of advanced cancer patients, and predictive biomarkers for immunotherapy response are needed. Recently, copy number alteration (CNA) burden has been proposed to predict ICB resistance. We assessed this finding using the publicly accessible data for 1661 ICB-treated patients whose tumors were profiled by MSK-IMPACT, an approved targeted assay in clinical care. We tested the hypothesis that the continuous increase in CNA burden is associated with poor overall survival following ICB. In addition, we hypothesized that the combinatorial biomarkers of tumor mutational burden (TMB) and CNA burden would better stratify patients for immune status and ICB response. Of the 1661 cases, 79% (n = 1307) were treated with anti PD-1/PD-L1 and the remaining 21% (n = 354) with anti CTLA-4 or the combination of both. In a multivariate analysis, increase in CNA burden was associated with poor overall survival [HR = 1.52, 95% CI (1.01-2.30), p = 0.04]. The combination of biomarkers TMB and CNA burden stratified patients into four clinically distinct subsets among which "LowTMB/HighCNA" showed the worst survival (p < 0.0001). The four patient subsets had unique CNA profiles and enriched pathways, which could predict transcriptional and phenotypic effects related to immune signaling and CD8+ T-cell abundance in the tumor microenvironment. CNA burden was associated with poor overall survival in patients receiving ICB and could improve patient stratification when incorporated with TMB. These findings may guide patient selection for immunotherapy or alternative strategies.