Molecular Biomarkers for Adrenoleukodystrophy: An Unmet Need

X-linked adrenoleukodystrophy (ALD) is an inherited progressive neurometabolic disease caused by mutations in the ABCD1 gene and the accumulation of very long-chain fatty acids in plasma and tissues. Patients present with heterogeneous clinical manifestations which can include adrenal insufficiency, myelopathy, and/or cerebral demyelination. In the absence of a genotype-phenotype correlation, the clinical outcome of an individual cannot be predicted and currently there are no molecular markers available to quantify disease severity. Therefore, there is an unmet clinical need for sensitive biomarkers to monitor and/or predict disease progression and evaluate therapy efficacy. The increasing amount of biological sample repositories (‘biobanking’) as well as the introduction of newborn screening creates a unique opportunity for identification and evaluation of new or existing biomarkers. Here we summarize and review the many studies that have been performed to identify and improve knowledge surrounding candidate molecular biomarkers for ALD. We also highlight several shortcomings of ALD biomarker studies, which often include a limited sample size, no collection of longitudinal data, and no validation of findings in an external cohort. Nonetheless, these studies have generated a list of interesting biomarker candidates and this review aspires to direct future biomarker research.

Differentiation of Pediatric-Onset Duchenne and Becker Muscular Dystrophy Subphenotypes Using the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet)

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) phenotypes are used to describe disease progression in affected individuals. However, considerable heterogeneity has been observed across and within these two phenotypes, suggesting a spectrum of severity rather than distinct conditions. Characterizing the phenotypes and subphenotypes aids researchers in the design of clinical studies and clinicians in providing anticipatory guidance to affected individuals and their families. Using data from the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet), we used K-means cluster analysis to group phenotypically similar males with pediatric-onset dystrophinopathy. We identified four dystrophinopathy clusters: Classical BMD, Classical DMD, late ambulatory DMD, and severe DMD. The clusters that we identified align with both ‘classical’ and ‘non-classical’ dystrophinopathy described in the literature. Individuals with dystrophinopathies have heterogenous clinical presentations that cluster into phenotypically similar groups. Use of clinically-derived phenotyping may provide a clearer understanding of disease trajectories, reduce variability in study results, and prevent exclusion of certain cohorts from analysis. Findings from studying subphenotypes may ultimately improve our ability to predict disease progression.

Kinetic Changes in Serum KL-6 Levels Predict Disease Progression in Patients with Systemic Sclerosis-Associated Interstitial Lung Disease: A Retrospective Observational Study

Background: The clinical course of patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) is highly variable. The Krebs von den Lungen-6 (KL-6) glycoprotein is a promising biomarker for reflectingepithelial injury. However, serum KL-6 and its association with the progression of SSc-ILD have been understudied. Methods: We reviewed 77 consecutive patients with SSc-ILD seen from 2004 to 2016. A longitudinal study of forced vital capacity (FVC), serum KL-6 levels, and the changes in KL-6 levels from baseline (ΔKL-6) was conducted. The progression of ILD was defined as ≥10% relative decline in FVC predicted or 5%–10% decline in FVC predicted along with radiological progression on chest computed tomography. The risk factors for ILD progression were assessed by univariate and multivariate regression.Results: The 77 study patients included 58 women (75%). The median age of the study patients was 56 years, and 59 (79%) patients had diffuse cutaneous SSc. During a 5-year follow-up period, 10 (13%) showed rapid progression of ILD within 2 years, 39 (51%) had overall progressionduring the 5 years, and 28 (36%) had stable disease. Most patients with progressive ILD showed elevationsin serum KL-6 levels over the initial 1-year follow-up period. The best cut-off value for ΔKL-6 that predicted progression of ILD was 193 U/mL (sensitivity 81.6%, specificity 92.9%). Multivariate analysis with adjustment revealed that diffuse cutaneous SSc(hazard ratio [HR] 3.1; 95% confidence interval [CI] 1.05-9.36]) and ΔKL-6 > 193 U/mL from baseline(HR, 4.7; 95% CI, 2.14-10.4)were independent predictors for progression of SSc-ILD.Conclusion: Changes in the KL-6 level can be useful for predicting disease progression in patients withSSc-ILD.

Simplifying prediction of disease progression in pre-symptomatic type 1 diabetes using a single blood sample

Aims/hypothesis Accurate prediction of disease progression in individuals with pre-symptomatic type 1 diabetes has potential to prevent ketoacidosis and accelerate development of disease-modifying therapies. Current tools for predicting risk require multiple blood samples taken during an OGTT. Our aim was to develop and validate a simpler tool based on a single blood draw. Methods Models to predict disease progression using a single OGTT time point (0, 30, 60, 90 or 120 min) were developed using TrialNet data collected from relatives with type 1 diabetes and validated in independent populations at high genetic risk of type 1 diabetes (TrialNet, Diabetes Prevention Trial–Type 1, The Environmental Determinants of Diabetes in the Young [1]) and in a general population of Bavarian children who participated in Fr1da. Results Cox proportional hazards models combining plasma glucose, C-peptide, sex, age, BMI, HbA1c and insulinoma antigen-2 autoantibody status predicted disease progression in all populations. In TrialNet, the AUC for receiver operating characteristic curves for models named M60, M90 and M120, based on sampling at 60, 90 and 120 min, was 0.760, 0.761 and 0.745, respectively. These were not significantly different from the AUC of 0.760 for the gold standard Diabetes Prevention Trial Risk Score, which requires five OGTT blood samples. In TEDDY, where only 120 min blood sampling had been performed, the M120 AUC was 0.865. In Fr1da, the M120 AUC of 0.742 was significantly greater than the M60 AUC of 0.615. Conclusions/interpretation Prediction models based on a single OGTT blood draw accurately predict disease progression from stage 1 or 2 to stage 3 type 1 diabetes. The operational simplicity of M120, its validity across different at-risk populations and the requirement for 120 min sampling to stage type 1 diabetes suggest M120 could be readily applied to decrease the cost and complexity of risk stratification. Graphical abstract

Plasma Neurofilament Light Chain Predicts Cognitive Progression in Prodromal and Clinical Dementia with Lewy Bodies

Plasma neurofilament light chain (NfL) is a marker of neuronal damage in different neurological disorders and might predict disease progression in dementia with Lewy bodies (DLB). The study enrolled 45 controls and 44 DLB patients (including 17 prodromal cases) who underwent an extensive assessment at baseline and at 2 years follow-up. At baseline, plasma NfL levels were higher in both probable DLB and prodromal cases compared to controls. Plasma NfL emerged as the best predictor of cognitive decline compared to age, sex, and baseline severity variables. The study supports the role of plasma NfL as a useful prognostic biomarker from the early stages of DLB.

ARID1A mutation to predict disease progression during first-line chemotherapy in biliary tract cancer patients.

4105 Background: Biliary tract cancer (BTC) is a retractable disease showing a dismal prognosis with therapeutic resistance. There are clinical unmet needs on predicting therapeutic response and precise strategy for the patient classification according to clinically relevant tumor biology in the patients with BTC. We aimed to identify clinically detectable genomic alteration predicting therapeutic response after first-line chemotherapy in BTC using real-world data. Methods: A comprehensive genomic analysis of multi-institutional cohorts of BTC cases was performed using next-generation sequencing (NGS) with targeted DNA panel and patients’ clinicopathologic data. Results: A total of 200 BTC patients with NGS panel tests from three cancer centers were included in this study. The genomic alteration of TP53 (55.5%), KRAS (23%), ARID1A (10%), and ERBB2 amplification (10%) were the most frequent alteration events in the BTC. Pathologically-proved BTC including extrahepatic (n = 52), ampulla of Vater (n = 4), gallbladder (n = 56), intrahepatic (n = 88) cancers showed a distinct pattern of genomic alterations in terms of ARID1A for extrahepatic BTC and ERBB2 amplification, RB1, ARID2 for GB cancer, and KRAS, IDH1, PBRM1, BAP1 for intrahepatic BTC respectively (chi-square test, P < 0.05). The oncologic outcomes for progression-free and overall survival were significantly stratified according to the best response after the first-line chemotherapy (log-rank test, P < 0.001). The logistic regression test revealed that ARID1A, BRCA2, and STK11 could significantly predict disease progression during first-line chemotherapy. ARID1A, especially, was the only independent predictive marker in the multivariate regression model in total BTC (OR 3.91, 95%CI 1.25-11.66, P = 0.015) and extrahepatic BTC (OR 5.71, 95%CI 1.23-28.98, P = 0.027). The predictive performance of significant genomic alteration was enhanced with the tumor marker CA19-9 (DeLong’s test, Z = 1.933, P = 0.053, AUC 0.73, 95%CI 0.623-0.837). Conclusions: Clinically available NGS test showed distinct genomic alterations in terms of different deterioration patterns for oncogenic molecular pathways according to the anatomic locations of BTC. Integrative analysis using the data for genomic alteration and therapeutic response for the first-line chemotherapy uncover that the patients with ARID1A mutation show a significant disease progression rate during initial treatment for BTC, especially in the extrahepatic BTC. Prospective translational studies revealing underlying biology and precision strategy should be followed to improve the therapeutic response of BTC.

AB0420 CIRCULATING MONOCYTES HAVE DISTINCT PHYSICAL PROPERTIES THAT CORRELATE WITH DISEASE ACTIVITY AND SEVERITY AND PREDICT PROGRESSION IN SYSTEMIC SCLEROSIS

Background:Systemic sclerosis (SSc) is associated with high morbidity and is one of the autoimmune rheumatic diseases with the highest mortality. However, tools to evaluate disease activity, response to treatment or to predict disease progression are scarce. Dysregulated immune responses are major pathogenic players at the onset and in the progression of SSc. Recent evidence demonstrates that mechanical properties of circulating leukocytes reflect their states and functions, and during activation ensure their adaptation to the changing physical requirements (e.g. softening to extravasate and migrate in the tissues) (1). Real-time fluorescence and deformability cytometry (RT-FDC) is a novel technique that allows the identification of cells from a heterogenous population by marker expression, with their subsequent mechanical phenotyping in a high-throughput manner (2, 3).Objectives:Here we characterized the physical properties of circulating immune cells in SSc patients, aiming to identify disease-related changes in their phenotypes, clinical correlates of these changes and their potential to predict disease progression.Methods:51 patients fulfilling the 2013 ACR/EULAR classification criteria for SSc and 17 age- and sex-matched healthy controls were included in the study. Blood was collected from the donors between 05.2019 and 10.2020. Peripheral blood mononuclear cells (PBMCs) were isolated and stained with antibodies against major circulating lymphoid (CD8+, CD4+ T cells, B cells, NK cells, NKT-like cells) and myeloid subpopulations (classical, intermediate and inflammatory monocytes, conventional dendritic cells and plasmacytoid dendritic cells). Each subpopulation was identified in RT-FDC by standard gating based on its marker expression and its area, deformation and apparent Young’s modulus (a measure of cell stiffness) were determined. The analysis was conducted using a custom Python script. For the patients included, demographic and clinical data were collected at every visit. Correlations with clinical parameters were analyzed in R.Results:All three subpopulations of monocytes identified by expression of HLA-DR, CD14 and/or CD16 had higher deformation and cross-sectional area in SSc patients as compared to healthy controls. From the SSc patients, monocytes had higher deformation and area in those with diffuse cutaneous SSc, extensive lung fibrosis and active disease as compared to those with limited cutaneous SSc, limited lung fibrosis and stable disease, respectively. Moreover, monocyte deformation and area significantly correlated with the EUSTAR activity index, with mRSS, with the extent of lung involvement on HR-CT (positive correlation), with DLCO and FVC (negative correlation). Follow-up data collected one year after the measurements showed that a higher monocyte deformation and cross-sectional area at baseline predicts future progression of lung disease, defined according to the INBUILD study, as well as future progression of skin fibrosis.Conclusion:We demonstrated that circulating subsets of monocytes in SSc patients show an increase in deformation and cross-sectional area, that these changes correlate with current disease activity and can identify patients with high risk of future progression of skin or lung fibrosis. These changes might reflect an activated state of circulating monocytes in SSc that facilitate their tissue migration. Mechanical phenotyping of monocytes by RT-FDC might thus serve as a useful tool for clinical evaluation of SSc patients.References:[1]Bashant KR, Toepfner N, Day CJ, Mehta NN, Kaplan MJ, Summers C, et al. The mechanics of myeloid cells. Biol Cell. 2020;112(4):103-12.[2]Otto O, Rosendahl P, Mietke A, Golfier S, Herold C, Klaue D, et al. Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat Methods. 2015;12(3):199-202, 4 p following.[3]Rosendahl P, Plak K, Jacobi A, Kraeter M, Toepfner N, Otto O, et al. Real-time fluorescence and deformability cytometry. Nat Methods. 2018;15(5):355-8.Disclosure of Interests:Alexandru-Emil Matei: None declared, Kubánková Markéta: None declared, Andrea-Hermina Györfi: None declared, Evgenia Boxberger: None declared, Despina Soteriou: None declared, Maria Papava: None declared, Julia Muth: None declared, Martin Kräter: None declared, Georg Schett: None declared, Jochen Guck: None declared, Jörg H.W. Distler Consultant of: Actelion, Active Biotech, Anamar, ARXX, Bayer Pharma, Boehringer Ingelheim, Celgene, Galapagos, GSK, Inventiva, JB Therapeutics, Medac, Pfizer, RuiYi and UCB, Grant/research support from: Anamar, Active Biotech, Array Biopharma, aTyr, BMS, Bayer Pharma, Boehringer Ingelheim, Celgene, Galapagos, GSK, Inventiva, Novartis, Sanofi-Aventis, RedX, UCB, Employee of: Stock owner of 4D Science and Scientific head of FibroCure

TP53 mutation and MET amplification in circulating tumor DNA analysis predict disease progression in patients with advanced gastric cancer

Background Gastric cancer (GC) is a heterogeneous disease that encompasses various molecular subtypes. The molecular mutation characteristics of circulating tumor DNA (ctDNA) in advanced gastric cancer (AGC), especially the clinical utility of TP53 mutation and MET amplification in ctDNA need to be further explored. Objectives The aim of this study was mainly to assess the clinical utility of TP53 mutation and MET amplification in ctDNA as biomarkers for monitoring disease progression of AGC. Patients and Methods We used multigene NGS-panel technology to study the characteristics of ctDNA gene mutations and screen the key mutant genes in AGC patients. The Kaplan-Meier method was used to calculate the survival probability and log-rank test was used to compare the survival curves of TP53 mutation and MET amplification in ctDNA of AGC patients. The survival time was set from the blood test time to the follow-up time to observe the relationship between the monitoring index and tumor prognosis. Results We performed mutation detection on ctDNA in 23 patients with AGC and identified the top 20 mutant genes. The five most frequently mutated genes were TP53 (55%), EGFR (20%), ERBB2 (20%), MET (15%) and APC (10%). TP53 was the most common mutated gene (55%) and MET had a higher frequency of mutations (15%) in our study. Kaplan-Meier analysis showed that patients with TP53 mutant in ctDNA had shorter overall survival (OS) than these with TP53 wild (P < 0.001). The Allele frequency (AF) of TP53 mutations in patient number 1 was higher in the second time (0.94%) than in the first time (0.36%); the AF of TP53 mutations in patient number 16 was from scratch (0∼0.26%). In addition, the AF of TP53 mutations in patients who survive was relatively low (P = 0.047). Simultaneously, Kaplan-Meier analysis showed that patients with MET amplification also had shorter OS than these with MET without amplification (P < 0.001). Conclusion TP53 and MET are the two common frequently mutant genes in ctDNA of AGC patients.TP53 mutation and MET amplification in ctDNA could predict disease progression of AGC patients.