Combination Of Biomarkers Research Articles

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.

Predicting the Outcome of Patients with Severe COVID-19 with Simple Inflammatory Biomarkers: The Utility of Novel Combined Scores-Results from a European Tertiary/Referral Centre.

Background: The clinical significance of combinations of inflammatory biomarkers in severe COVID-19 infection is yet to be proved. Although several studies have evaluated the prognostic value of biomarkers in patients with COVID-19, there are limited data regarding the value of the combination scores that could take full advantage of the prognostic value of several biomarkers and that could account for the heterogeneity of patients with severe COVID-19. We investigated the prognostic value of combination scores of admission values of inflammatory biomarkers in adults with severe COVID-19. Methods: Adults admitted to the Department of Respiratory Medicine of the UHL with severe COVID-19 (April-September 2021, NCT05145751) were included. Demographics, medical history, laboratory tests and outcome (high-flow nasal cannula (HFNC), admission to Intensive Care Unit (ICU) or death) were recorded. The optimal cut-off points of on admission values of C-reactive protein (CRP), CRP to lymphocyte ratio (CLR), lymphocyte to neutrophil ratio (LNR) and derived variation of neutrophil to lymphocyte ratio (dv-NLR (neutrophil/white blood count-lymphocyte)) for the predetermined outcome were defined. Based on the cut-off of CRP, LNR, dv-NLR and CLR, which were found to be predictors for HFNC, 3 scores were defined: CRP and LNR (C-CRP #1), CRP and dv-NLR (C-CRP #2), CRP and CLR (C-CRP #3). Likewise, based on the cut-off of CRP and CLR, which were found to be predictors for death, the score of CRP and CLR (C-CRP #3*) was defined. The combination scores were then classified as: 2 points (both biomarkers elevated); 1 point (one biomarker elevated) and 0 points (normal values). None of the biomarkers was predictive for the ICU admission, so no further analysis was performed. Binomial logistic regression analysis was used to establish the predictive role for each biomarker. Results: One hundred and fifteen patients (60% males, mean age 57.7 years) were included. Thirty-seven (32.2%) patients required HFNC, nine (7.8%) died and eight (7%) were admitted to ICU, respectively. As far as HFNC is concerned, the cut-off point was 3.2 for CRP, 0.231 for LNR, 0.90 for dv-NLR and 0.004 for CLR. Two points of C-CRP #1 and 2 points of C-CRP #3 predicted HFNC with a probability as high as 0.625 (p = 0.005) and 0.561 (p < 0.001), respectively. Moreover, 1 point of C-CRP #2 and 2 points of C-CRP #2 predicted HFNC with a probability of 0.333 and 0.562, respectively. For death, the optimal cut-off point for CRP was 1.11 and for CLR 3.2*1033. Two points of C-CRP #3* with an accuracy of 0.922 predicted mortality (p = 0.0038) in severe COVID-19. Conclusions: The combination scores of CRP and inflammatory biomarkers, based on admission values, are promising predictors for respiratory support using HFNC and for mortality in patients suffering from severe COVID-19 infection.

Longitudinal Measurements of Blood Biomarkers in Patients with Crohn's Disease or Ulcerative Colitis Treated with Infliximab: Only the Latest Values in the Induction Period Predict Treatment Failure.

Background: Few studies have incorporated longitudinal assessments or used combinations of blood biomarkers as predictors of loss of response to biologic therapy for patients with Crohn's disease (CD) or ulcerative colitis (UC). Methods: This is a population-based cohort study comprising Danish patients with CD or UC from 2008 to 2018. We used logistic regression to analyze whether levels and changes in levels of C-reactive protein (CRP), serum albumin, and hemoglobin, routinely measured during a 14-week infliximab induction period, predicted a change to another biologic medication or cessation of biologic therapy. Results: During the induction period, 2883 (1626 CD, 1257 UC) patients had 12,730, 12,040, and 13,538 specimens with CRP, serum albumin, and hemoglobin, respectively. In all, 284 patients (9.9%) switched to another biologic medication, and 139 (4.8%) ceased biologic therapy in the follow-up period. Only the most recent CRP and hemoglobin levels predicted the efficacy of infliximab treatment at approximately 14 weeks, a time point when the clinician often determines whether to continue treatment. Conclusion: Measurement of blood biomarkers prior to the clinical assessment does not predict the effectiveness of infliximab.

Metabolic profile of follicular fluid in patients with ovarian endometriosis undergoing IVF: a pilot study

Research QuestionEndometriosis is a common benign gynaecological disorder. A number of studies have confirmed that the occurrence of disease in endometriosis patients is associated with metabolic changes in biological fluids and tissues. We set out to investigate the metabolic characteristics of follicular fluid in patients with ovarian endometriosis undergoing in vitro fertilization (IVF). DesignThe current research project is an exploratory cohort study on endometriosis. A total of 19 infertile patients with ovarian endometriosis diagnosed by laparoscopy were enrolled in this study and 23 age- and BMI-matched controls (women with infertility due to male or tubal factors). All the patients underwent IVF treatment with a gonadotropin-releasing hormone antagonist protocol and completed follicular fluid (FF) collection at oocyte retrieval. The metabolomics of FF samples was analyzed by Ultra high performance liquid chromatography-orbitrap exploris-mass spectrometer (UHPLC-OE-MS). The best combination of biomarkers was then selected by performing stepwise logistic regression analysis with backward elimination. ResultsFifteen metabolites were identified as biomarkers associated with endometriosis. A final model containing 8-hydroxy-2-deoxyguanosine, biotin, n-acetyl-L-methionine and n-methylnicotinamide was constructed. Receiver operating characteristic analysis confirmed the value of these parameters in diagnosing endometriosis, with a sensitivity of 94.7% and a specificity of 95.7%. Analysis of fortification via the Kyoto Encyclopedia of Genes and Genome showed that 15 metabolites were enriched in 8 metabolic pathways. ConclusionMetabolomics based on UHPLC-OE-MS effectively characterized the metabolomics analysis of FF in patients with ovarian endometriosis. These findings may provide a new basis for a better understanding of how diseases progress and for the discovery of new biomarkers.

Combining Genomic Biomarkers to Guide Immunotherapy in Non-Small Cell Lung Cancer.

The clinical value of STK11, KEAP1, and EGFR alterations for guiding immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) remains controversial, as some patients with these proposed resistance biomarkers show durable ICB responses. More specific combinatorial biomarker approaches are urgently needed for this disease. To develop a combinatorial biomarker strategy with increased specificity for ICB unresponsiveness in NSCLC, we performed a comprehensive analysis of 254 patients with NSCLC treated with ligand programmed death-ligand 1 (PD-L1) blockade monotherapy, including a discovery cohort of 75 patients subjected to whole-genome sequencing (WGS), and an independent validation cohort of 169 patients subjected to tumor-normal large panel sequencing. The specificity of STK11/KEAP1/EGFR alterations for ICB unresponsiveness was assessed in the contexts of a low (<10 muts/Mb) or high (≥10 muts/Mb) tumor mutational burden (TMB). In low TMB cases, STK11/KEAP1/EGFR alterations were highly specific biomarkers for ICB resistance, with 0/15 (0.0%) and 1/34 (2.9%) biomarker-positive patients showing treatment benefit in the discovery and validation cohorts, respectively. This contrasted with high TMB cases, where 11/13 (85%) and 15/34 (44%) patients with at least one STK11/KEAP1/EGFR alteration showed durable treatment benefit in the discovery and validation cohorts, respectively. These findings were supported by analyses of progression-free survival and overall survival. The unexpected ICB responses in patients carrying resistance biomarkers in STK11, KEAP1, and EGFR were almost exclusively observed in patients with a high TMB. Considering these alterations in context, the TMB offered a highly specific combinatorial biomarker strategy for limiting overtreatment in NSCLC.

TROP-2, NECTIN-4 and predictive biomarkers in sarcomatoid and rhabdoid bladder urothelial carcinoma.

The surface protein TROP-2/TACSTD2 and the cell adhesion protein NECTIN-4/NECTIN4 are responsible for the efficacy of anticancer therapies based on antibody-drug conjugates (ADC) targeting intracellular microtubules. In contrast with common histologic subtypes of bladder urothelial carcinoma (BUC), little is known of TROP-2 and NECTIN-4 expression in sarcomatoid and rhabdoid BUC. In this study, we aimed to analyze TROP-2 and NECTIN-4 expression and additional predictive biomarkers by immunohistochemistry and fluorescence in situ hybridization (FISH) on 35 undifferentiated BUC (28 sarcomatoid and 7 rhabdoid). Wide genomic investigation was also performed on 411 BUC cases of the PanCancer Atlas, focusing on genes related to the microtubule pathways. Seven of 35 (20%) undifferentiated BUC showed expression of TROP-2. NECTIN-4 was expressed in 10 cases (29%). Seven cases (20%) co-expressed TROP-2 and NECTIN-4. HER-2 FISH was amplified in 5 cases (14%) while HER-2 immunoexpression was observed in 14 cases (40%). PD-L1 scored positive for combined proportion score (CPS) in 66% of cases and for tumor proportion score (TPS) in 51% of cases. Pan-NTRK1-2/3 was elevated in 9 cases (26%) and FGFR-2/3 was broken in 7 of 35 cases (20%). Of 28 sarcomatoid BUC, 9 (32%) were negative for all (TROP-2, NECTIN-4, PD-L1, HER-2, FGFR and pan-NTRK) biomarkers and 3 (11%) expressed all five biomarkers. Among cases with rhabdoid dedifferentiation, 1 of 7 (14%) showed activation of all biomarkers, whereas 2 of 7 (28%) showed none. The mRNA analysis identified microtubule-related genes and pathways suitable for combined ADC treatments in BUC. Sarcomatoid and rhabdoid BUC do harbor positive expression of the ADC targets TROP-2 or NECTIN-4 in a relatively modest subset of cases, whereas the majority do not. Different combinations of other positive biomarkers may help the choice of medical therapies. Overall, these findings have important clinical implications for targeted therapy for BUC.

A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma.

Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment. This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers. A retrospective case control, diagnostic accuracy study. This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively. This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.

Prediction performance of scoring systems after out-of-hospital cardiac arrest: A systematic review and meta-analysis.

Ongoing changes in post resuscitation medicine and society create a range of ethical challenges for clinicians. Withdrawal of life-sustaining treatment is a very sensitive, complex decision to be made by the treatment team and the relatives together. According to the guidelines, prognostication after cardiopulmonary resuscitation should be based on a combination of clinical examination, biomarkers, imaging, and electrophysiological testing. Several prognostic scores exist to predict neurological and mortality outcome in post-cardiac arrest patients. We aimed to perform a meta-analysis and systematic review of current scoring systems used after out-of-hospital cardiac arrest (OHCA). Our systematic search was conducted in four databases: Medline, Embase, Central and Scopus on 24th April 2023. The patient population consisted of successfully resuscitated adult patients after OHCA. We included all prognostic scoring systems in our analysis suitable to estimate neurologic function as the primary outcome and mortality as the secondary outcome. For each score and outcome, we collected the AUC (area under curve) values and their CIs (confidence iterval) and performed a random-effects meta-analysis to obtain pooled AUC estimates with 95% CI. To visualize the trade-off between sensitivity and specificity achieved using different thresholds, we created the Summary Receiver Operating Characteristic (SROC) curves. 24,479 records were identified, 51 of which met the selection criteria and were included in the qualitative analysis. Of these, 24 studies were included in the quantitative synthesis. The performance of CAHP (Cardiac Arrest Hospital Prognosis) (0.876 [0.853-0.898]) and OHCA (0.840 [0.824-0.856]) was good to predict neurological outcome at hospital discharge, and TTM (Targeted Temperature Management) (0.880 [0.844-0.916]), CAHP (0.843 [0.771-0.915]) and OHCA (0.811 [0.759-0.863]) scores predicted good the 6-month neurological outcome. We were able to confirm the superiority of the CAHP score especially in the high specificity range based on our sensitivity and specificity analysis. Based on our results CAHP is the most accurate scoring system for predicting the neurological outcome at hospital discharge and is a bit less accurate than TTM score for the 6-month outcome. We recommend the use of the CAHP scoring system in everyday clinical practice not only because of its accuracy and the best performance concerning specificity but also because of the rapid and easy availability of the necessary clinical data for the calculation.

Antibody Profiling of Pan-Cancer Viral Proteome Reveals Biomarkers for Nasopharyngeal Carcinoma Diagnosis and Prognosis

Diagnosing, predicting disease outcome, and identifying effective treatment targets for virus-related cancers are lacking. Protein biomarkers have the potential to bridge the gap between prevention and treatment for these types of cancers. While it has been shown that certain antibodies against EBV proteins could be used to detect nasopharyngeal carcinoma (NPC), antibodies targeting are solely a tiny part of the about 80 proteins expressed by the EBV genome. Furthermore, it remains unclear what role other viruses play in NPC since many diseases are the result of multiple viral infections. For the first time, this study measured both IgA and IgG antibody responses against 646 viral proteins from 23 viruses in patients with NPC and control subjects using nucleic acid programmable protein arrays. Candidate seromarkers were then validated by ELISA using 1665 serum samples from three clinical cohorts. We demonstrated that the levels of five candidate seromarkers (EBV-BLLF3-IgA, EBV-BLRF2-IgA, EBV-BLRF2-IgG, EBV-BDLF1-IgA, EBV-BDLF1-IgG) in NPC patients were significantly elevated than controls. Additional examination revealed that NPC could be successfully diagnosed by combining the clinical biomarker EBNA1-IgA with the five anti-EBV antibodies. The sensitivity of the six-antibody signature at 95% specificity to diagnose NPC was comparable to the current clinically-approved biomarker combination, VCA-IgA, and EBNA1-IgA. However, the recombinant antigens of the five antibodies are easier to produce and standardize compared to the native viral VCA proteins. This suggests the potential replacement of the traditional VCA-IgA assay with the 5-antibodies combination to screen and diagnose NPC. Additionally, we investigated the prognostic significance of these seromarkers titers in NPC. We showed that NPC patients with elevated BLLF3-IgA and BDLF1-IgA titers in their serum exhibited significantly poorer disease-free survival, suggesting the potential of these two seromarkers as prognostic indicators of NPC. These findings will help develop serological tests to detect and treat NPC in the future.

Non-invasive biomarkers of disease activity and organ damage in ANCA-associated vasculitis: a systematic review

BackgroundIn anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), histopathological assessment of affected tissue is often necessary for diagnosis and assessment of disease extent. There is a requirement for validated non-invasive biomarkers...

Likelihood ratio combination of multiple biomarkers viasmoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer.

BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response ​rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Longitudinal Associations Between Persistent Post-Concussion Symptoms and Blood Biomarkers of Inflammation and CNS-Injury After Mild Traumatic Brain Injury.

The aim of our study was to investigate the biological underpinnings of persistent post-concussion symptoms (PPCS) at 3 months following mild traumatic brain injury (mTBI). Patients (n = 192, age 16-60 years) with mTBI, defined as Glasgow Coma Scale (GCS) score between 13 and 15, loss of consciousness (LOC) <30 min, and post-traumatic amnesia (PTA) <24 h were included. Blood samples were collected at admission (within 72 h), 2 weeks, and 3 months. Concentrations of blood biomarkers associated with central nervous system (CNS) damage (glial fibrillary acidic protein [GFAP], neurofilament light [NFL], and tau) and inflammation (interferon gamma [IFNγ], interleukin [IL]-8, eotaxin, macrophage inflammatory protein-1-beta [MIP]-1β, monocyte chemoattractant protein [MCP]-1, interferon-gamma-inducible protein [IP]-10, IL-17A, IL-9, tumor necrosis factor [TNF], basic fibroblast growth factor [FGF]-basic platelet-derived growth factor [PDGF], and IL-1 receptor antagonist [IL-1ra]) were obtained. Demographic and injury-related factors investigated were age, sex, GCS score, LOC, PTA duration, traumatic intracranial finding on magnetic resonance imaging (MRI; within 72 h), and extracranial injuries. Delta values, that is, time-point differences in biomarker concentrations between 2 weeks minus admission and 3 months minus admission, were also calculated. PPCS was assessed with the British Columbia Post-Concussion Symptom Inventory (BC-PSI). In single variable analyses, longer PTA duration and a higher proportion of intracranial findings on MRI were found in the PPCS group, but no single biomarker differentiated those with PPCS from those without. In multi-variable models, female sex, longer PTA duration, MRI findings, and lower GCS scores were associated with increased risk of PPCS. Inflammation markers, but not GFAP, NFL, or tau, were associated with PPCS. At admission, higher concentrations of IL-8 and IL-9 and lower concentrations of TNF, IL-17a, and MCP-1 were associated with greater likelihood of PPCS; at 2 weeks, higher IL-8 and lower IFNγ were associated with PPCS; at 3 months, higher PDGF was associated with PPCS. Higher delta values of PDGF, IL-17A, and FGF-basic at 2 weeks compared with admission, MCP-1 at 3 months compared with admission, and TNF at 2 weeks and 3 months compared with admission were associated with greater likelihood of PPCS. Higher IL-9 delta values at both time-point comparisons were negatively associated with PPCS. Discriminability of individual CNS-injury and inflammation biomarkers for PPCS was around chance level, whereas the optimal combination of biomarkers yielded areas under the curve (AUCs) between 0.62 and 0.73. We demonstrate a role of biological factors on PPCS, including both positive and negative effects of inflammation biomarkers that differed based on sampling time-point after mTBI. PPCS was associated more with acute inflammatory processes, rather than ongoing inflammation or CNS-injury biomarkers. However, the modest discriminative ability of the models suggests other factors are more important in the development of PPCS.

Lipids in Psychiatric Disorders: Functional and Potential Diagnostic Role as Blood Biomarkers.

Lipids are a crucial component of the human brain, serving important structural and functional roles. They are involved in cell function, myelination of neuronal projections, neurotransmission, neural plasticity, energy metabolism, and neuroinflammation. Despite their significance, the role of lipids in the development of mental disorders has not been well understood. This review focused on the potential use of lipids as blood biomarkers for common mental illnesses, such as major depressive disorder, anxiety disorders, bipolar disorder, and schizophrenia. This review also discussed the impact of commonly used psychiatric medications, such as neuroleptics and antidepressants, on lipid metabolism. The obtained data suggested that lipid biomarkers could be useful for diagnosing psychiatric diseases, but further research is needed to better understand the associations between blood lipids and mental disorders and to identify specific biomarker combinations for each disease.

Circulating Monocyte Chemoattractant Protein-1 (MCP-1) in Patients with Primary Biliary Cholangitis.

Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease that leads to the destruction of the intrahepatic bile ducts. While the inflammatory process can be mediated by monocyte chemotactic protein-1 (MCP-1), the importance of circulating MCP-1 as a biomarker is unclear. Our aim was to assess the diagnostic significance of the serum concentrations of MCP-1 in PBC patients. We compared circulating MCP-1 with biochemical, immunological and histological parameters. Serum samples were collected from 120 PBC patients, 60 pathologic controls and 30 healthy donors. MCP-1 levels were determined by using commercial enzyme-linked immunosorbent assay (ELISA). Elevated serum MCP-1 levels were detected in 66% of PBC patients with a specificity of 97%. Significantly higher levels of MCP-1 protein were found in the sera of patients with PBC than in the group of healthy individuals-410.2 pg/mL vs. 176.0 pg/mL, p < 0.01). Patients with higher concentrations of alkaline phosphatase also had higher levels of MCP-1 (r = 0.4, p < 0.01). In accordance with Ludwig's classification, a positive correlation of serum MCP-1 concentration with the degree of fibrosis was observed, OR = 6.1, p = 0.0003. We compared the MCP-1 with procollagen type III, hyaluronic acid (HA), FIB-4 index, APRI and collagen type IV when predicting the advance of liver fibrosis. Circulating MCP-1 is better correlated with liver fibrosis and is also associated with the occurrence of specific antimitochondrial autoantibodies and specific anti-nuclear autoantibodies-anti-gp210. MPC-1 can be considered to be a tool for diagnosing the degree of fibrosis in PBC, and combinations of MCP-1 and other specific biomarkers could support the diagnosis of PBC.

1978 Donanemab in early symptomatic Alzheimer’s Disease: Efficacy and safety in TRAILBLAZER-ALZ 2, Phase 3 Randomized Clinical Trial

Abstract Introduction In TRAILBLAZER-ALZ donanemab (DONA) cleared brain amyloid plaques, significantly slowing disease progression in early symptomatic Alzheimer’s disease (ESAD). Methods: TRAILBLAZER-ALZ2 enrolled participants with ESAD and amyloid and tau pathology by positron-emission tomography, randomizing (multicenter) those with low/medium-tau (n=1182) and high-tau (n=552) (missing tau n=2). Participants (randomized double-blind,1:1) received DONA (n=860)/placebo (n=876) IV every 4w for 72w. DONA participants meeting amyloid clearance treatment completion criteria at 24/52w had blinded switched to placebo. Primary outcomes Integrated AD Rating Scale(iADRS) change from baseline at 76w in low/medium-tau or combined (low/medium- and high-tau) populations. Statistical testing allocated most power (80% α spend) to low/medium-tau population outcomes, with the remainder for combined population outcomes, including clinical and biomarker assessments. Results In the low/medium-tau population iADRS change at 76w: −6.02 (DONA) and −9.27 (placebo) (difference 3.25; 95%CI, 1.88-4.62; P&amp;lt;0.001), 35.1% slowing of disease progression. Change in Clinical Dementia Rating Scale (CDR)–Sum of Boxes: 1.20 (DONA) and 1.88 (placebo) (difference −0.67; 95% CI −0.95 to −0.40; P&amp;lt;0.001), 36.0% slowing. Participants receiving DONA experienced 38.6% less risk of progressing to next disease stage vs placebo over 76w (CDR-Global score, HR=0.61; P&amp;lt;0.001). Amyloid clearance at 24/52/76w: achieved in 34.2%/71.3%/80.1% DONA-treated participants. Significant, positive results were observed in the combined population. Serious AEs: 17.4% (DONA), and 15.8% (placebo), with 3 deaths among DONA patients who experienced serious amyloid-related imaging abnormalities (ARIA). AEs with DONA included ARIA-E (24.0%, 6.1% symptomatic); ARIA-H (31.4%); infusion-related reactions (8.7%). Conclusion DONA treatment significantly slowed clinical progression at 76w with a safety profile consistent with earlier studies. Presented: AAIC2023.

Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease.

Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.

Biomarkers of Brain Ischemia (Review)

Introduction. Strokes remain the second leading cause of death and the third leading cause of disability. Additional serum biomarker testing should be used to better diagnose transient ischemic attack (TIA), but most neurospecific biomarkers have low prognostic specificity and sensitivity. Timely identification of TIA and differential diagnosis of stroke in the first hour will ensure a shorter period of patient recovery and reduce the risk of mortality and disability. Serum biomarker studies should be included to overcome the difficulty of diagnosing TIA.Text. Neurospecific biomarkers such as S100B, GFAP, and NSE are used to diagnose acute ischemic damage to glial cells and neurons. S100B and GFAP are detected in astrocytes and NSE in neurons and cells of the neuroendocrine system. Elevated serum concentrations of these biomarkers are associated with various pathological conditions such as strokes and brain injuries and other central nervous system (CNS) lesions. Dynamic monitoring of biomarker concentrations makes it possible to evaluate the efficacy of the ongoing therapy and to identify predictors of patient deterioration for prompt correction of therapeutic procedures. To create a diagnostic panel it is necessary to study metabolic processes in ischemic tissue, taking into account concomitant diagnoses and results of neuroimaging, and to use breakthrough advances in machine learning and big data.Conclusion. The review showed that none of the assessed biomarkers can be recommended for the diagnosis of cerebral circulation disorders, but the combination of several neurospecific biomarkers can significantly improve diagnostic efficiency and find application in the differential diagnosis of stroke, intracranial hematoma, and other brain lesions for the purpose of early pharmacotherapy of CNS lesions and as surrogate endpoints during clinical trials.

Combining Glial Fibrillary Acidic Protein and Neurofilament Light Chain for the Diagnosis of Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent brain disorder affecting more than 2% of the world's population. Due to the lack of well-specific biomarkers, it is difficult to distinguish MDD from other diseases with similar clinical symptoms (such as Alzheimer's disease and cerebral thrombosis). In this work, we provided a strategy to address this issue by constructing a combinatorial biomarker of serum glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL). To achieve the convenient and sensitive detection of two proteins, we developed an electrochemical immunosandwich sensor using two metal-ion-doped carbon dots (Pb-CDs and Cu-CDs) as probes for signal output. Each probe contains approximately 300 Pb2+ or 200 Cu2+, providing excellent signal amplification. This method achieved detection limits of 0.3 pg mL-1 for GFAP and 0.2 pg mL-1 for NFL, lower than most of the reported detection limits. Analysis of real serum samples showed that the concentration ratio of GFAP to NFL, which is associated with the relative degree of brain inflammation and neurodegeneration, is suitable for not only distinguishing MDD from healthy individuals but also specifically distinguishing MDD from Alzheimer's disease and cerebral thrombosis. The good specificity gives the combinatorial GFAP/NFL biomarker broad application prospects in the screening, diagnosis, and treatment of MDD.

Genomic heterogeneity at baseline is associated with T790M resistance mutations in EGFR-mutated lung cancer treated with the first-/second-generation tyrosine kinase inhibitors.

This study analyzed whether extended molecular profiling can predict the development of epidermal growth factor receptor (EGFR) gene T790M mutation, which is the most frequent resistance alteration in non-small cell lung cancer (NSCLC) after treatment with the first-/second-generation (1G/2G) EGFR inhibitors (tyrosine kinase inhibitors [TKIs]), but only weakly associated with clinical characteristics. Whole exome sequencing (WES) was performed on pretreatment tumor tissue with matched normal samples from NSCLC patients with (n = 25, detected in tissue or blood rebiopsies) or without (n = 14, negative tissue rebiopsies only) subsequent EGFR p.T790M mutation after treatment with 1G/2G EGFR TKI. Several complex genetic biomarkers were assessed using bioinformatic methods. After treatment with first-line afatinib (44%) or erlotinib/gefitinib (56%), median progression-free survival and overall survival were 12.1 and 33.7 months, respectively. Clinical and tumor genetic characteristics, including age (median, 66 years), sex (74% female), smoking (69% never/light smokers), EGFR mutation type (72% exon 19 deletions), and TP53 mutations (41%) were not significantly associated with T790M mutation (p > 0.05). By contrast, complex biomarkers including tumor mutational burden, the clock-like mutation signature SBS1 + 5, tumor ploidy, and markers of subclonality including mutant-allele tumor heterogeneity, subclonal copy number changes, and median tumor-adjusted variant allele frequency were significantly higher at baseline in tumors with subsequent T790M mutation (all p < 0.05). Each marker alone could predict subsequent development of T790M with an area under the curve (AUC) of 0.72-0.77, but the small number of cases did not allow confirmation of better performance for biomarker combinations in leave-one-out cross-validated logistic regression (AUC 0.69, 95% confidence interval: 0.50-0.87). Extended molecular profiling with WES at initial diagnosis reveals several complex biomarkers associated with subsequent development of T790M resistance mutation in NSCLC patients receiving first-/second-generation TKIs as the first-line therapy. Larger prospective studies will be necessary to define a forecasting model.