Early Stratification Of Patients Research Articles

Melanoma’s New Frontier: Exploring the Latest Advances in Blood-Based Biomarkers for Melanoma

Melanoma is one of the most malignant cancers, and the global incidence of cutaneous melanoma is increasing. While melanomas are highly prone to metastasize if diagnosed late, early detection and treatment significantly reduce the risk of mortality. Identifying patients at higher risk of metastasis, who might benefit from early adjuvant therapies, is particularly important, especially with the advent of new melanoma treatments. Therefore, there is a pressing need to develop additional prognostic biomarkers for melanoma to improve early stratification of patients and accurately identify high-risk subgroups, ultimately enabling more effective personalized treatments. Recent advances in melanoma therapy, including targeted treatments and immunotherapy, have underscored the importance of biomarkers in determining prognosis and predicting treatment response. The clinical application of these markers holds the potential for significant advancements in melanoma management. Various tumor-derived genetic, proteomic, and cellular components are continuously released into the bloodstream of cancer patients. These molecules, including circulating tumor DNA and RNA, proteins, tumor cells, and immune cells, are emerging as practical and precise liquid biomarkers for cancer. In the current era of effective molecular-targeted therapies and immunotherapies, there is an urgent need to integrate these circulating biomarkers into clinical practice to facilitate personalized treatment. This review highlights recent discoveries in circulating melanoma biomarkers, explores the challenges and potentials of emerging technologies for liquid biomarker discovery, and discusses future directions in melanoma biomarker research.

Depression Treatment Trajectories and Associated Social Determinants: A Three-Year Follow-Up Study in 66,540 Older Adults Undergoing First-Time Depression Treatment in Denmark.

This study aimed to describe depression treatment patterns, identify unique trajectory groups using a group-based trajectory approach, and explore associated social determinants in older adults undergoing first-time depression treatment during a 3-year follow-up. This Danish register-based cohort study included all adults aged ≥ 65 who initiated depression treatment by redeeming first-time antidepressant prescriptions (no prescriptions in the last 10years) between 2006 and 2015. The outcome of interest during the 2-year follow-up was depression treatment, assessed as antidepressant prescriptions redemptions and psychiatric hospital contacts for depression. Latent class growth analyses were applied to model treatment trajectories during 3years. Multinomial logistic regression analyses were used to analyze adjusted associations between social determinates and trajectory group membership. Among the 66,540 older adults (55.2% females, mean age: 77.3years), we identified three distinct groups with unique patterns of depression treatment trajectories: 'brief-treatment' where individuals stopped depression treatment within 6months (33.7%); 'gradual-withdrawal' (26.5%) where treatment was gradually stopped over 2years; and 'persistent-treatment' where individuals continued depression treatment for the entire 3years (39.8%). Females, single-person households, and residents of less-urbanized regions were associated with higher odds of membership in the 'persistent-treatment' group, while older age, widowhood or separation, and non-Danish ethnicities were associated with lower odds. Three distinct patterns of depression treatment trajectories were identified in older adults, indicating differential clinical courses of depression-potentially influenced by social determinants, including sex, marital status, urban residence, and ethnicity. Early patient stratification and targeted interventions are crucial in depression care for older adults.

Specific imaging of CD8 + T-Cell dynamics with a nanobody radiotracer against human CD8β

PurposeWhile immunotherapy has revolutionized the oncology field, variations in therapy responsiveness limit the broad applicability of these therapies. Diagnostic imaging of immune cell, and specifically CD8+ T cell, dynamics could allow early patient stratification and result in improved therapy efficacy and safety. In this study, we report the development of a nanobody-based immunotracer for non-invasive SPECT and PET imaging of human CD8+ T-cell dynamics.MethodsNanobodies targeting human CD8β were generated by llama immunizations and subsequent biopanning. The lead anti-human CD8β nanobody was characterized on binding, specificity, stability and toxicity. The lead nanobody was labeled with technetium-99m, gallium-68 and copper-64 for non-invasive imaging of human T-cell lymphomas and CD8+ T cells in human CD8 transgenic mice and non-human primates by SPECT/CT or PET/CT. Repeated imaging of CD8+ T cells in MC38 tumor-bearing mice allowed visualization of CD8+ T-cell dynamics.ResultsThe nanobody-based immunotracer showed high affinity and specific binding to human CD8 without unwanted immune activation. CD8+ T cells were non-invasively visualized by SPECT and PET imaging in naïve and tumor-bearing mice and in naïve non-human primates with high sensitivity. The nanobody-based immunotracer showed enhanced specificity for CD8+ T cells and/or faster in vivo pharmacokinetics compared to previous human CD8-targeting immunotracers, allowing us to follow human CD8+ T-cell dynamics already at early timepoints.ConclusionThis study describes the development of a more specific human CD8+ T-cell-targeting immunotracer, allowing follow-up of immunotherapy responses by non-invasive imaging of human CD8+ T-cell dynamics.

Abstract 3510: A radiogenomic approach for triple-negative breast cancer risk stratification

Abstract Background: Triple-negative breast cancer (TNBC) is an aggressive disease that accounts for 15-20% of all breast cancers. Expressions of ER, PR and HER2 receptors are lacking in this disease, and thus targeted therapies are not effective. TNBC has a shorter relapse-free survival, higher metastasis rate and decreased overall survival compared with other breast cancers. However, when undergoing standard treatment, some patients respond well, while others have poor outcome, suggesting TNBC heterogeneity. Early stratification of patients with long versus short survival could identify the subgroup of patients who would not benefit from exposure to toxicity of chemotherapy treatment. Here, we developed a non-invasive radiogenomic approach for TNBC risk stratification. Methods: A transcriptomic-based prognostic gene signature was previously developed using the TCGA-BRCA cohort (n=860). Briefly, LASSO Cox regression model analysis with the ‘glmnet’ R package was used to identify the transcriptomic signature gene-set consisting of 50 genes. We tested this signature to prognosticate overall survival in a Stanford cohort (n=63) and a previously published SCANB cohort (n=604). The patients were stratified into high- and low-risk groups based on the median risk-score. Next, we developed a machine learning model that identified a radiomic feature set to predict the prognostic transcriptomic risk-groups. Radiomic features were extracted from pre-treatment breast MRI. Radiomics features were extracted using PyRadiomics. The model utilized Decision Tree Classifier and LeaveOneOut method was used for cross-validation. Results: The transcriptomic signature low-risk group was significantly associated with improved overall survival in the two TNBC cohorts, with hazard ratios of 0.11 [95% CI: 0.01-0.88] for the Stanford cohort and 0.71 [95% CI: 0.52-0.97] for the SCANB cohort (log-rank p-values p=0.012 and p=0.032, respectively). Including this transcriptomic signature in a multivariate analysis, which adjusted for clinical features (patient age, grade, stage and Ki67%), the transcriptomic prognostic signature remained a significant prognostic factor (p<0.05). The radiomic feature set (consisting of 20 features) predicted the high- and low-risk transcriptomic groups with a mean accuracy of 72.2% and a mean AUROC of 71%. The precision, F1 and recall scores were 67%, 74% and 82%, respectively. In an independent dataset consisting of 116 Stanford TNBC patients, we used this model to predict risk groups based on the MRI radiomics features, and evaluated the prognostic effects of predicted risk groups. The overall survival of the predicted high-risk group was significantly poorer than the predicted low-risk group (p=0.013). Conclusions: We present a prognostic model that can non-invasively stratify TNBC patients for low versus high mortality risk using radiomic features derived from pre-treatment patient MRI data. Citation Format: Humaira Noor, Yuanning Zheng, Adam Mantz, Ryle Zhou, Andrew Kozlov, Wendy B. DeMartini, Shu-tian Chen, Satoko Okamoto, Debra Ikeda, Sarah Mattonen, Sandy Napel, Melinda L. Telli, George Sledge, Allison Kurian, Mina Satoyoshi, Olivier Gevaert, Haruka Itakura. A radiogenomic approach for triple-negative breast cancer risk stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3510.

Abstract 5200: Multiparametric MRI along with machine learning informs on molecular underpinnings, prognosis, and treatment response in pediatric low grade glioma

Abstract We report a comprehensive radiogenomic analysis, including multiparametric MRI and RNA sequencing of 502 tumors across 13 histological types of pediatric low-grade glioma (PLGG). Through an unsupervised analysis of treatment-naïve imaging, we were able to identify 3 distinct imaging clusters that were associated with different molecular pathways according to transcriptomic pathway analysis. Using xCell enrichment scores as input for clustering, we discovered three distinct immunological groups based on immune cell infiltration. The results suggested the presence of an ‘immune-hot” cluster within the tumors with a poorer prognosis, which may potentially benefit from emerging immunotherapies. We subsequently generated a radiomic signature that was able to predict immunological profiles. We also developed a clinicoradiomic predictive model that can assess patients’ risk of progression and predict treatment response. Finally, we evaluated the transcriptomic pathways that were most predictive of clinicoradiomic risk scores and identified the biological pathways most predictive of risk of progression in PLGG, when adjusting for molecular subtype, race, reported gender, age at diagnosis, and the anatomic location of the tumor. This is the first large-scale radiogenomic analysis in PLGG that can aid in prognostication, assessing patients’ risk of progression, predicting treatment response in conventional therapies, and early stratification of patients to identify potential candidates for targeted therapies. Citation Format: Ali Nabavizadeh, Adam Kraya, Komal Rathi, Meen Chul Kim, Nastaran Khalili, Ariana Familiar, Varun Kesherwani, Hannah Anderson, Deep Gandhi, Aria Mahtabfar, Sina Bagheri, Xiaoyan Huang, Yuankun Zhu, Alexander Sickler, Saksham Phul, Sabine Mueller, Mateusz P Koptyra, Philip B Storm, Jeff Ware, Yuanquan Song, Michael J Fisher, Arastoo Vossough, Christos Davatzikos, Adam C Resnick, Anahita Fathi Kazerooni. Multiparametric MRI along with machine learning informs on molecular underpinnings, prognosis, and treatment response in pediatric low grade glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5200.

Abstract 7625: The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus

Abstract Thymidine Kinase (TK) plays a basic role in DNA synthesis and cell proliferation. The enzyme is highly cell cycle dependent and correlates with cell growth state. Numerous studies have consistently shown elevated TK activity (TKa) in the blood of patients with solid tumors and active disease. DiviTum® TKa, a clinically and analytically validated FDA cleared assay, measures and quantifies TKa in serum or plasma in humans and animals. TKa as a biomarker offers valuable information at all stages of drug development, from drug efficacy and dose response studies in cell culture and xenograft models, to patient selection and treatment monitoring in clinical trials. The launch of FDA Project Optimus highlights the need for new biomarkers to assist in drug development to identify optimal dose. Using TKa to measure the impact on cell proliferation as a drug evaluation endpoint can confirm mechanism of action efficacy and a minimally effective dose of a new compound rather than a maximally tolerated dose. TKa can be used as a pharmacodynamic tool to improve study endpoints. TKa data in cultured cells and mouse xenografts demonstrates that a complete shutdown of tumor cell proliferation can be observed well before and at much lower drug-doses than when cell death or toxicity is used as the dose determination endpoint. Increased dosing of CDK4/6 inhibitors demonstrates immediate impact on TKa levels in cell lines and provides signals of drug effect at doses where cell viability is unaffected. After initiation of samuraciclib treatment, a CDK7 inhibitor, a significant reduction in TKa levels in blood from metastatic breast cancer (MBC) patients was observed, indicating inhibition of cell cycle progression. For patients on samuraciclib therapy and with stable disease before progression, TKa levels were significantly lower than pre-treatment (p=0.0038). Post-progression TKa levels increased to significantly higher levels than during therapy including the last sample measured before progression (p=0.014). Baseline blood TKa levels are predictive for progression free survival (PFS) allowing for the early stratification of patients with long vs short PFS. In patients with MBC, TKa levels during the first treatment cycle are strongly predictive for PFS. Suppressed TKa levels after 2 and 4 weeks of CDK4/6 inhibitor therapy predict longer PFS than high TKa levels at the same time points (HR 5.65; p< 0.0001). MBC patients monitored >3 months and showing low TKa levels have very low likelihood of progression within 30- to 60 days, <2% and <6%, respectively. TKa is a translational liquid biomarker that bridges results between preclinical and clinical studies, providing fundamental information for drug development decision making. Citation Format: Mattias Bergqvist, Hanna Ritzén, Amy Williams. The liquid biomarker thymidine kinase activity reflecting proliferation rate can provide guidance in drug development and address Project Optimus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7625.

Abstract PR016: Largescale organoid panel drug screening to short-track clinically-relevant output

Abstract Introduction Patient-derived organoids (PDOs) have emerged over the last decade as representative in vitro replicas of tumor biology, bringing increased accuracy in predicting clinical outcomes based on pre-clinical results. Large and deeply characterized organoid biobanks, covering broad varieties in indications and genetic backgrounds, have been generated that allow large scale (combination) drug testing, reliable biomarker predictions, and improved drug repurposing and patient stratification studies. However, relatively long timelines and high expenses hold the organoids back to live up to their promise. To overcome these hurdles, we have optimized processes, and here present a largescale organoid panel drug screening platform consisting of over 50 organoid models that predicts drug responses across at least seven indications in a robust, but also fast and cost-efficient manner. Methods Colorectal, breast, lung, pancreatic, ovarian, cervix and melanoma organoid models were selected for subtype and driver mutation variety, banked in large batches, and preserved in an assay-ready format to reduce lead-in time and organoid expansion phase. Drug responses of the full organoid panel were characterized with standard of care compounds (cisplatin, gemcitabine and paclitaxel) and AMG510 targeted therapy (KRAS G12C inhibitor, sotorasib) by measuring ATP levels after a 5-day exposure using a 9-dose curve. Experiments were executed using automated liquid handling equipment to standardize procedures and increase consistency. Control variability (CV), Z-factors and assay windows were calculated to assess assay performance, and IC50 values and area under the curves (AUC) were calculated to express drug sensitivity. Experiments were repeated in multiple labs to test for assay robustness. Results The largescale panel screening platform exhibits a high average assay performance, with high Z-factors (>0.6), low intra- and interplate variability (CV <15%) and reproducible IC50 outputs. Data report of the full panel was prepared within 6 weeks of project kick-off; shortening timelines compared to regular organoid screens over 6-fold, from months to weeks. Using IC50 values and AUC, drug sensitivity testing distinguished sensitive from partially and insensitive models, and models with specific genetic features (KRAS G12C vs G12X) behaved as expected when a AMG510 therapy was tested. Conclusion and Discussion Assay-ready organoid technology-based largescale panel screening as presented here has resulted in efficient timeline and cost reductions and will therefore further harness the large potential of PDO technology in the drug development pipeline. Largescale organoid panel screening enables identification of drug synergy by testing drug combinations. With the majority of included models also being available as PDX models, it will facilitate a seamless translation into subsequent in vivo studies. Moreover, combining panel screens with advanced biomarker analysis will allow early patient stratification, further aiding clinical development of novel oncology drugs. Citation Format: Liza Wijler, Annelot Staes, Linda van Seeters, Lama Alhaj Hasan, Bram Herpers, Leo Price, Mariusz Madej, Michiel Fokkelman, Xiaoxi Xu, Marrit Putker. Largescale organoid panel drug screening to short-track clinically-relevant output [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr PR016.

Association between Fibrinogen-to-Albumin Ratio and Adverse Stroke Outcomes among Patients with Acute Ischemic Stroke

Introduction: Fibrinogen-to-albumin ratio (FAR) is implicated in prothrombotic states and is associated with an increased risk of acute ischemic stroke (AIS). However, studies investigating whether the prothrombotic effect of FAR is associated with long-term adverse outcomes in patients with AIS are lacking. Therefore, we aimed to investigate the association based on the Third China National Stroke Registry (CNSR-III). Methods: Patients with AIS with complete laboratory data for fibrinogen and albumin in the CNSR-III were included in this study. The primary outcomes were poor functional outcomes (modified Rankin scale score 3–6) at 12 months, including disability (modified Rankin scale score 3–5), all-cause death, recurrent stroke, and combined vascular events within 1 year. Univariate and multivariate logistic or Cox regression analyses were used to investigate the association between FAR quartiles and adverse outcomes. Results: A total of 8,984 patients with AIS were enrolled in this study. After 1-year follow-up, 238 patients were lost to follow-up. A total of 1,230 (14.06%) patients had poor functional outcomes; 932 (10.37%) and 981 (10.92%) experienced stroke recurrence and combined vascular events, respectively. The adjusted odds ratios/hazard ratios and 95% confidence intervals of the highest quartile of the FAR (>11.44) were 1.64 (1.35–2.00) for poor functional outcomes, 1.68 (1.34–2.10) for disability, 1.40 (1.02–1.94) for all-cause death, 1.11 (0.92–1.34) for stroke recurrence, and 1.11 (0.92–1.33) for combined vascular event, respectively. Conclusion: High FAR (>11.44) increased the risk of short- and long-term poor functional outcomes, including disability and all-cause death among patients with AIS. The FAR may play an important role in the early stratification of patients with AIS.

Increased levels of platelet function-related miR-16-5p predicts increased severity of ischemic stroke: in silico validation analysis

Abstract Background Ischemic stroke (IS) is one of the most frequent causes of death. Since miRNAs have been illustrated to play an important role in various processes through regulation of multiple genes, and platelet function, their utility as novel biomarkers should be determined. We aimed to identify and validate circulating miRNAs, modulated in acute ischemic stroke (IS) patients in order to distinguish the most specific as biomarkers to facilitate diagnosis and prognosis. Method Sixty patients with acute IS (24-h and 7-days after IS) and 30 age- and gender-matched individuals free of stroke with multiple risk factors for cardiovascular disease. Studied miRNAs were selected based on bioinformatic analysis that was previously published by our team (1-2-3). PlasmaRNA was extracted; quality RNA was assessed: fluorometric assay; RT-PCR for miRNAs expression measurement p<0.05. Results Let-7e expression 7-day of IS was significantly lower than both controls and first day of IS (p=0.007, p=0.001, res). miR-125a-3p was lower both at day-1 and day-7 of IS compared to controls (p=0.008, p=0.020, res). miR-125a-5p on the other hand, had significantly higher expression at day-1 compared to controls (p=0.002). Both miR-125a-3p and -5p showed diagnostic value in ROC curve analysis (AUC= 0.705, AUC= 0.709, res). Patients with moderate stroke (based on NIHSS) had significantly higher miR-16-5p expression levels compared to patients with minor stroke at the first day of IS (AUC: 0.718, (95% CI, 0.59-0.85) p = 0.004). High baseline expression of miR-16-5p and diabetes mellitus are independent predictors of increased severity of stroke to a multivariate analysis (OR: 4.341; 95% CI, 1.15–16.42; p = 0.031 and OR: 4.653; 95% CI, 1.07–20.19; p = 0.040, res). Conclusion In this study we validated miRNAs based on our previous bioinformatic analysis which could serve as novel, platelet-related predictive biomarkers of the IS severity, and can be used for early stratification of patients and prediction of severity of disease development in an individual.

292. SPECIFIC METHYLATION MARKERS ASSOCIATED WITH CHANGES IN GENE EXPRESSION DISTINGUISH EAC AND SUB-TYPES OF BE PATIENTS

Abstract Background Esophageal adenocarcinoma (EAC) is one of the most deadly tumors in the world, owing to its aggressive nature and late detection. Barrett’s esophagus (BE), a premalignant condition, is the only known precursor of EAC. We focused on finding epigenetic changes such as differentially methylated regions within EAC and BE patients to demonstrate their biological significance in relation to patient stratification and early detection. Methods A differential methylation analysis pipeline was used to identify significant CpGs between 23 EAC, 62 BE, 49 normal squamous (SQ) and 7 fundus samples (GSE81334). These CpGs of interest were validated in the second dataset from an independent cohort (GSE104707) and the TCGA ESCA project dataset. RNASeq and methylation datasets from TCGA were integrated to find overlapping biomarkers that may reveal biological control of methylation. We further performed GO and pathway analysis of differentially methylated CpGs and cnetplot, correlating the obtained CpGs with affected genes and Gene Ontologies and pathways. Results We identified and validated 505 differentially methylated CpGs. Using the obtained markers, we were able to separate the normal samples from the EAC samples. BE samples were divided into 2 distinct sub-classes clustering either with normal or EAC, indicating potential for early patient stratification. With the integrated analysis of differentially methylated probes and gene expression, 108 regions were identified to be correlated, indicating epigenetic control of the regions. GO enrichment analysis revealed these regions to belong to biological processes related to cancer development. Further integration of mutation signatures revealed this signal to be independent of known alterations. Conclusion(s): Methylation based clustering reveals markers showing distinct biological signals in BE and EAC patients and provides methylation status of distinct BE and EAC subtypes. Identified biomarkers show their relevance in early cancer detection and may exhibit strong potential in BE prognosis and better patients’ stratification. Characterization of these subtypes could aid in choosing more appropriate management strategies and better outcomes and improved survival of the patients.

Metabolic Associated Fatty Liver Disease (MAFLD) and COVID-19 Infection: An Independent Predictor of Poor Disease Outcome?

Background and Objectives: Early reports on COVID-19 infection suggested that the SARS-CoV-2 virus solely attacks respiratory tract cells. As the pandemic spread, it became clear that the infection is multiorganic. Metabolic associated fatty liver disease (MAFLD) is a chronic liver disease strongly associated with insulin resistance and diabetes. The aim of this study was to assess a possible interplay between MAFLD and COVID-19 infection and its implication in COVID-19 outcome. Materials and Methods: A retrospective observational study, including 130 COVID-19 positive patients was conducted. MAFLD diagnosis was made based on the International Consensus criteria. Patients were divided into two groups, group A (MAFLD) and group B (nonMAFLD). Anthropometric and laboratory analysis were obtained. COVID-19 severity was assessed using the NEWS2 score. Disease outcome was threefold and regarded as discharged, patients who required mechanical ventilation (MV), and deceased patients. Results: MAFLD prevalence was 42%, 67% of patients were discharged, and 19% needed MV. Mortality rate was 14%. MAFLD patients were significantly younger (p < 0.001), and had higher body mass index (p < 0.05), respiratory rate (p < 0.05) and systolic blood pressure (p < 0.05) than nonMAFLD patients. Regarding metabolic syndrome and inflammatory markers: group A had significantly higher glycemia at admission (p = 0.008), lower HDL-c (p < 0.01), higher triglycerides (p < 0.01), CRP (p < 0.001), IL-6 (p < 0.05) and ferritin (p < 0.05) than group B. MAFLD was associated with more prevalent type 2 diabetes (p = 0.035) and hypertension (p < 0.05). MAFLD patients had a more severe disease course (NEWS2 score, 6.5 ± 0.5 vs. 3 ± 1.0, p < 0.05). MAFLD presence was associated with lower patient discharge (p < 0.01) and increased need for MV (p = 0.024). Multiple regression analysis showed that BMI (p = 0.045), IL-6 (p = 0.03), and MAFLD (p < 0.05) are significant independent risk factors for a poor COVID-19 outcome. Conclusions: The prevalence of MAFLD is relatively high. MAFLD patients had a more severe COVID-19 clinical course and worse disease outcome. Our results imply that early patient stratification and risk assessment are mandatory in order to avoid poor outcomes.

Concomitant Electrophysiological Study with Transcatheter Aortic Valve Implantation to Predict Risk of Atrioventricular Block

Introduction: Data on the impact of left bundle-branch block after transcatheter aortic valve implantation (TAVI) are scarce, and treatment has been individualized. Based on this, the electrophysiological study (EPS) concomitant with TAVI may be a strategy for the early stratification of patients needing permanent pacemaker implantation (PPM). Objective: To describe the use of EPS in risk stratification of a definitive pacemaker in patients undergoing TAVI. Materials and methods: Data from seven patients with indications for TAVI due to critical aortic stenosis were retrospectively evaluated. The EPS was performed with a quadripolar diagnostic catheter in His bundle to measure the His-ventricle (HV) interval. Measurement of HV at 70 ms or above was used for discussion on PPM implant indication. Results: Four analyzed patients evolved with left bundle-branch block after TAVI. PPM implantation was indicated for one patient, and the surgery was performed uneventfully during the same hospital stay. Before TAVI, the HV interval ranged from 46 to 58 ms (mean = 53.2 ms), increasing to 52 to 84 ms (mean = 62.8 ms) immediately after valve intervention. Conclusion: The strategy of EPS during TAVI is viable to stratify patients early according to the risk of 2nd or 3rd-degree atrioventricular block, allowing adequate treatment.

Alteration of circulating ACE2-network related miRNAs in patients with COVID-19

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Medical University of Warsaw and São Paulo Research Foundation (FAPESP). Introduction Coronavirus disease 2019 (COVID-19) is associated with an increased risk of mortality and adverse cardiovascular (CV) occurrences, especially in a group of patients with CV diseases (CVD). SARS-CoV-2 virus enters cells through angiotensin converting enzyme 2 (ACE2) cell membrane enzymes, leading to the downregulation in the ACE2-related pathways. Based on our previous in silico [1] results we hypothesized that alteration in the ACE2 interaction network caused by virus infection can affect the expression of miRNAs and influence severity of the disease. Purpose In this study we aimed to analyse the diagnostic and predictive utility of ACE2-related miRNAs, which were identified in our previous in silico analysis (miR-10b-5p, miR-124-3p, miR-200b-3p, miR-26b-5p, miR-302c-5p) in patients with COVID-19. We also aimed to unravel the functions of analyzed miRNAs, by using machine learning based tools, including SHAP for clinical data analysis, and bioinformatic tools for data mining, target predictions and enrichment analyses. Methods Blood samples and clinical data of 79 COVID-19 patients were collected at three different time points, including the day of admission, 7-days and 21-days after admission, as well as one time-point of 32 healthy volunteers. The composite endpoint was hospitalisation length of stay (&amp;gt;21 days) and/or death in follow-up. Results Delta low miR-200b-3p expression (after 7-days of admission) presents predictive utility in assessment of the hospital length of stay and/or death in ROC curve analysis (AUC:0.730, p=0.002) (Fig. 1). Delta low miR-200b-3p expression, together with diabetes mellitus (DM) are independent predictors of increased hospital length of stay and/or death (OR= 5.775; CI= 1.572- 21.214; p= 0.008, OR= 4.888; CI= 1.001- 23.858; p= 0.050, respectively). The expression levels of miR-26b-5p and miR-10b-5p in COVID-19 patients were found lower at the baseline, 7 and 21-days after admission compared to the healthy controls (p&amp;lt;0.0001 for all time points). SHAP analysis indicated levels of miR 200b-3p (day 7th), miR-302c-5p (day 7th), CRP (day 7th), neutrophils (day 0), and D-Dimer (day 0) as the most promising predictors of long hospitalisation for the COVID-19 patients. Pathway enrichment analysis showed that among top shared pathways between targets of analyzed miRNAs were IL-2 signaling pathway, and Pathways in cancer. miR-200b-3p showed regulation of COVID-19-related targets associated with T cell protein tyrosine phosphatase and HIF-1 transcriptional activity in hypoxia, key pathways in COVID-19. Bioinformatics analysis pointed out the role of those miRNAs in multiple CVD phenotypes associated with COVID-19 (Fig. 2). Conclusions In this study we validated and characterized miRNAs which could serve as novel, predictive biomarkers of the COVID-19 long term hospitalisation and can be used for early stratification of patients and prediction of severity of infection development in an individual.

Unraveling novel crosstalk between distinct neutrophil subsets and soluble plasma factors in sepsis and ARDS

Abstract Sepsis is a life-threatening condition caused by an aberrant immune response to infection and subsequent organ dysfunction. Further, due to shared mechanisms such as severe inflammation and endothelial dysfunction, sepsis remains the most common etiology in acute respiratory distress syndrome (ARDS) patients. Despite multiple efforts to identify a distinct signature to support the development of new therapeutics, sepsis remains a significant unmet clinical need. While singular components of the dysregulated immune response have been explored, studies linking different data layers are lacking. Using an integrated systems immunology approach, we evaluated neutrophil phenotypes and related changes in soluble plasma factors in sepsis versus healthy donors. Results from our studies identify differentially expressed mature and immature neutrophil subsets in sepsis patients. Further, we show that expression of neutrophil CD10 is associated with factors such as pentraxin-3, angiopoietin-2, and lysophosphatidylcholines, in the sepsis cohort. These findings detail a novel correlation between increased presence of immature neutrophils and endothelial dysfunction/metabolic alterations in sepsis patients. Additionally, we profiled the neutrophil landscape and evaluated levels of soluble plasma factors, in a subset of ARDS patients and healthy donors. Here, we present distinct findings that reveal a novel crosstalk between neutrophil subsets and parameters indicative of endothelial dysfunction, in sepsis and ARDS patients. Overall, our study reinforces the importance of data integration and provides novel insights that could help inform future mechanistic studies, early patient stratification and treatment options.

Abstract 625: Alteration Of Circulating Ace2-network Related Micrornas In Patients With Covid-19

ACE2 interaction network caused by virus infection can affect the expression levels of miRNAs and influence severity of COVID19. This study aimed to analyse the diagnostic/predictive utility of ACE2-related miRNAs, identified by in silico analysis: miR10b, miR124, miR200b-3p, miR26b, miR302c-5p in patients with COVID-19, and aimed to unravel the functions of miRNAs, by using machine learning-SHAP analysis for clinical data and bioinformatic tools. Blood samples and clinical data of 79 patients and 32 healthy were collected at; day of admission, 7days and 21days after admission. Endpoint was hospitalisation length of stay (&gt;21days) and/or death in follow-up. Delta low miR200b-3p expression (7days-admission) presents predictive utility in assessment of the hospital length of stay and/or death (AUC:0.73,p=0.002). Logistic regression analysis showed that delta low miR-200b-3p, diabetes are independent predictors of increased hospital length of stay and/or death (OR=5.8;CI=0.57-21.21;p= 0.008, OR= 4.9;CI=1-23.9;p=0.04). MiR-26b-5p and miR10b in patients were found lower at the baseline, 7 and 21days after admission compared to healthy controls (p&lt;0.0001 for all time points). SHAP analysis showed miR200b-3p (day7), miR302c-5p (day7), CRP (day7), neutrophils (day0), and DDimer (day0) as most promising predictors of long hospitalisation. Pathway-enrichment showed the following top pathways of the interleukin-2 signaling pathway, and cancer pathways. MiR200b-3p showed regulation of COVID19-related targets associated with Tcell protein tyrosine phosphatase and HIF-1 transcriptional activity in hypoxia, key pathways in COVID19. Bioinformatics analysis showed miRNAs roles in multiple CVDs phenotypes associated with COVID19. We validated/characterized miRNAs which can serve as novel, predictive biomarkers of the long term COVID19 hospitalisation and can be used for early stratification of patients and prediction of severity of infection.

COVID-19 Biomarkers at the Crossroad between Patient Stratification and Targeted Therapy: The Role of Validated and Proposed Parameters.

Clinical knowledge about SARS-CoV-2 infection mechanisms and COVID-19 pathophysiology have enormously increased during the pandemic. Nevertheless, because of the great heterogeneity of disease manifestations, a precise patient stratification at admission is still difficult, thus rendering a rational allocation of limited medical resources as well as a tailored therapeutic approach challenging. To date, many hematologic biomarkers have been validated to support the early triage of SARS-CoV-2-positive patients and to monitor their disease progression. Among them, some indices have proven to be not only predictive parameters, but also direct or indirect pharmacological targets, thus allowing for a more tailored approach to single-patient symptoms, especially in those with severe progressive disease. While many blood test-derived parameters quickly entered routine clinical practice, other circulating biomarkers have been proposed by several researchers who have investigated their reliability in specific patient cohorts. Despite their usefulness in specific contexts as well as their potential interest as therapeutic targets, such experimental markers have not been implemented in routine clinical practice, mainly due to their higher costs and low availability in general hospital settings. This narrative review will present an overview of the most commonly adopted biomarkers in clinical practice and of the most promising ones emerging from specific population studies. Considering that each of the validated markers reflects a specific aspect of COVID-19 evolution, embedding new highly informative markers into routine clinical testing could help not only in early patient stratification, but also in guiding a timely and tailored method of therapeutic intervention.

Abstract 198: Pan-cancer assay-ready organoid drug screening with robust, reproducible and clinically-relevant output

Abstract Introduction The pharmaceutical industry has mostly relied on 2D cancer cell lines and 3D spheroids for in vitro testing, but poor correlation of pre-clinical and clinical outcomes has driven the development of more predictive models. Patient-derived organoids (PDOs) have emerged as representative in vitro avatars of tumor biology, allowing the generation of biobanks covering a large variety in indications and genetic backgrounds. Here, we assess the robustness of our drug screening platform by testing the reproducibility of our organoid assays within and between organoid batches, read-outs, and different labs. We present an assay-ready organoid platform, allowing short timelines, repeated assays from a single batch of organoids, high throughput assays and large panel screens. Methods Colorectal, breast, lung, pancreatic, ovarian, cervix and melanoma PDO models were selected for subtype and driver mutation variety, banked in large batches, and preserved in assay-ready format. Outgrowth and drug responses of these batches were compared to those of organoids produced in the classic method. Experiments were executed using automated liquid handling equipment to standardize procedures and increase consistency. Performance of the drug screens were assessed by repeated drug sensitivity testing and by calculating intra- and interplate variability, control variability (CV), Z-factor, assay windows and IC50 values. Moreover, drug responses were tested in both celltiterglo-based and high content imaging-based assays. Results Assay performance of the organoid drug testing platform was high, with high Z-factors (&amp;gt;0.6), and low intra- and interplate variability (CV &amp;lt;15%) indicating reproducible assays. Both the classic and assay-ready organoid technologies resulted in highly reproducible IC50outputs, also when assays were executed months apart or in different labs. Timelines were shortened from months to weeks, and the reduced logistical burden allows screening of large organoid panels of &amp;gt;50 models. Drug sensitivity testing in our fast-paced panel screening platform distinguished sensitive from partially and insensitive models, illustrating how organoids allow patient stratification. Conclusion and Discussion The assay-ready organoid technology presented here has further improved our already highly predictive and reproducible organoid drug testing platform. The resulting short timelines and large panel screening capabilities will further unlock the great potential of PDO technology. Using large organoid panels in pre-clinical drug development and in combination with biomarker analysis will allow identification of responsive indications, subtypes, and genotypes, and for early patient stratification. Moreover, as most models presented in our panel screen platform are also available as PDX models, it allows for easy translation into in vivo follow-up studies. Citation Format: Liza Wijler, Jara Garcia Mateos, My Nguyen, Annelot Staes, Linda van Seters, Lama Alhaj Hasan, Victor Tiroille, Bram Herpers, Leo Price, Mariusz Madej, Michiel Fokkelman, Marrit Putker. Pan-cancer assay-ready organoid drug screening with robust, reproducible and clinically-relevant output [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 198.

Distinct subsets of neutrophils crosstalk with cytokines and metabolites in patients with sepsis

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Despite continued efforts to understand the pathophysiology of sepsis, no effective therapies are currently available. While singular components of the aberrant immune response have been investigated, comprehensive studies linking different data layers are lacking. Using an integrated systems immunology approach, we evaluated neutrophil phenotypes and concomitant changes in cytokines and metabolites in patients with sepsis. Our findings identify differentially expressed mature and immature neutrophil subsets in patients with sepsis. These subsets correlate with various proteins, metabolites, and lipids, including pentraxin-3, angiopoietin-2, and lysophosphatidylcholines, in patients with sepsis. These results enabled the construction of a statistical model based on weighted multi-omics linear regression analysis for sepsis biomarker identification. These findings could help inform early patient stratification and treatment options, and facilitate further mechanistic studies targeting the trifecta of surface marker expression, cytokines, and metabolites.

The Association of PLAUR Genotype and Soluble suPAR Serum Level with COVID-19-Related Lung Damage Severity.

Uncovering the risk factors for acute respiratory disease coronavirus 2019 (COVID-19) severity may help to provide a valuable tool for early patient stratification and proper treatment implementation, improving the patient outcome and lowering the burden on the healthcare system. Here we report the results of a single-center retrospective cohort study on 151 severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-infected symptomatic hospitalized adult patients. We assessed the association of several blood test measurements, soluble urokinase receptor (uPAR) serum level and specific single nucleotide polymorphisms of ACE (I/D), NOS3 (rs2070744, rs1799983), SERPINE1 (rs1799768), PLAU (rs2227564) and PLAUR (rs344781, rs2302524) genes, with the disease severity classified by the percentage of lung involvement on computerized tomography scans. Our findings reveal that the T/C genotype of PLAUR rs2302524 was independently associated with a less severe lung damage (odds ratio 0.258 [0.071-0.811]). Along with high C-reactive protein, fibrinogen and soluble uPAR serum levels turned out to be independently associated with more severe lung damage in COVID-19 patients. The identified factors may be further employed as predictors of a possibly severe COVID-19 clinical course.

Abstract C028: The lung pro-thrombotic niche drives cancer-associated thromboembolism and metastasis via extracellular vesicle ITGB2

Abstract Thromboembolism (TE) is a common complication in cancer patients and the second leading cause of cancer-related deaths. The incidence of TE varies in different cancer types, with the highest risk in pancreatic ductal adenocarcinoma (PDAC) and in advanced-stage and metastatic cancers. Despite the benefits associated with anti-coagulant therapy for symptomatic TE, the prevention of TE still remains an unmet clinical need due to lack of biomarkers predictive of TE risk and the bleeding risk associated with the routine use of anti-coagulants. Small extracellular vesicles (sEV) mediate cell-to-cell communication. Cancer cells and the tumor microenvironment release large numbers of sEV into the blood circulation and sEVs have displayed a therapeutic and predictive value in systemic diseases. However, the role of sEVs in cancer-associated TE remains to be investigated. Here we show that sEVs from (pre)metastatic lungs of mice with melanoma, breast, lung, and PDAC induce TE in mice and express high levels of integrin beta 2 (ITGB2), while sEVs from tumor cell lines, primary tumors, or other metastasis-bearing organs did not show any pro-thrombotic properties. A specific subtype of interstitial macrophages infiltrating (pre-)metastatic lungs were the main source of ITGB2+ pro-thrombotic sEVs. Blockade of ITGB2 on lung-derived sEVs, or systemically in mice, prevented EV-induced platelet aggregation and TE, and reduced metastasis. Examination of the mechanisms of ITGB2-induced TE showed that EV-associated ITGB2 interacts directly or through fibrin with different binding partners on platelets, and induce their activation and aggregation. Importantly, we found that levels of ITGB2 on sEVs are elevated in the plasma of PDAC patients prior (&amp;lt;30 days) to TE events in comparison to PDAC patients with no history of TE, and thus might serve as prognostic biomarker of TE. Together, our results provide the first evidence of the establishment of a pro-thrombotic lung niche in PDAC as well as other cancer types. Moreover, we identify EV-associated ITGB2 as a new target for the prevention and/or treatment of TE, as well as a potential “liquid biopsy” analyte for the early stratification of patients at high risk of TE. Citation Format: Serena Lucotti, Yusuke Ogitani, Candia M. Kenific, Linda Bojmar, Michele Cioffi, Pernille Lauritzen, Henrik Molina, Soeren Heissel, Henry B. Lengel, Xiaohong Jing, Haiying Zhang, Irina Matei, Eileen M. O'Reilly, William R. Jarnagin, David Jones, James B. Bussel, David Kelsen, Jacqueline F. Bromberg, Diane M. Simeone, David Lyden. The lung pro-thrombotic niche drives cancer-associated thromboembolism and metastasis via extracellular vesicle ITGB2 [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C028.