Stratification Of Patients Research Articles

Prediction of hypertensive disorders in pregnant women in the «gray» risk zone following combined first-trimester screening

Introduction. In obstetrics, hypertensive disorders of pregnancy (HDP) including preeclampsia (РЕ) are one of the primary causes resulting in critical cases and maternal mortality. HDP prediction is a milestone that allows preventing complications as well as reducing the number of most common relevant complications of pregnancy. Existing algorithms that predict PE risk distribute the risks in such a way that a considerable number of patients fall into the category of false negative results, and, consequently, receive no timely prevention and proper follow-up. In particular, this cohort usually consists of patients with borderline high risks, who may be designated as a medium risk group or located in a “gray” zone. Aim: to develop a prognostic model for risk stratification in female patients with borderline to high developing PE risk based on combined first-trimester screening. Materials and Methods. A prospective comparative study included 1089 female patients who underwent a combined screening at 11–14 weeks of gestation. Group 1 consisted of female patients at high РЕ risk (1:100 and greater), while female patients at moderate risk (1:101–1:250) and low risk (below 1:250) were included into Group 2 and Group 3, respectively. All pregnant women underwent examination including assessed anamnestic, general clinical and laboratory data, mean blood pressure (BP), uterine artery pulsatility index, serum level of human chorionic gonadotropin beta-subunits (β-hCG), placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A). Results. An impact of various factors on risk of developing hypertensive disorders in pregnancy was assessed by binary logistic regression by identifying most significant among them and generating a statistical prediction model – the prognostic index of hypertensive disorders in pregnancy. The latter included: obstetric history, body mass index, PlGF, mean ВР, and alanine aminotransferase level. The sensitivity and specificity comprised 91.2 and 53.6 %, respectively, and the method effectivenesswas 81.8 %. Conclusion. The method proposed for HDP prediction is a second-line approach that may be used in clinical practice to stratify patients with borderline high risk of developing PE.

Abstract A023: Stratification of bladder cancer patients with the use of flow cytometry is increasing the diagnostic potential of molecular classifiers

Abstract Recent research suggests the potential categorization of bladder cancer (BLCa) patients into molecular subtypes to aid in the selection of appropriate treatment strategies, be it conventional, targeted, or a combination thereof. However, the practical implementation of molecular classifiers has faced obstacles due to the intricate nature of gene regulation, tumor heterogeneity, and challenges related to tumor sampling caused by substantial stromal infiltration and tumor multifocality. Hence, there is a pressing need for more straightforward marker-based methodologies to assist in genomic and transcriptomic subtyping efforts. Our study addressed this gap by establishing and validating a panel of antibodies for application in flow cytometry (FCM) to enhance the stratification of BLCa patients. We curated a set of 17 FCM markers capable of delineating four cell clusters (epithelial, immune, endothelial, and stromal) and defining tumor classification (basal and luminal), immune infiltration, and hot-target expression on BLCa tissue samples. This FCM panel was deployed on ex vivo tissue samples treated with chemotherapy and targeted therapy agents. Initially tested on a cohort of 10 BLCa tissue samples, our validation of the FCM panel revealed correlations between marker expression and clinical-pathological features, such as the presence of metastasis and heightened immune and stromal infiltration. Notably, samples exhibiting lower claudin-4 levels displayed increased immune infiltration, while those with metastases at diagnosis showcased elevated basal marker expression. The translational potential of the FCM panel was demonstrated in ex vivo experiments where tissue slices from six patients were subjected to chemotherapy and targeted therapy agents, leading to observable shifts in marker expression post-treatment, particularly pronounced with chemotherapy. Noteworthy effects included reductions in epithelial cells expressing FGFR3 and EGFR following targeted therapy with erdafitinib or lapatinib, alongside alterations in the basal/luminal cell ratio indicated by increased CD44, CD90, and CD239 marker expressions. Our findings underscore the value of FCM in categorizing BLCa patients based on specific marker expression, supplementing conventional histological analyses to enhance cancer diagnosis, guide personalized therapeutic approaches, and monitor treatment efficacy. Citation Format: Martina Radic, Federico La Manna, Martina Minoli, Panagiotis Chouvardas, George N. Thalmann, Roland Seiler-Blarer, Marianna Kruithof-de Julio, Bernhard Kiss. Stratification of bladder cancer patients with the use of flow cytometry is increasing the diagnostic potential of molecular classifiers [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr A023.

Abstract PR011: Imaging mass cytometry captures patient heterogeneity enabling BCG response stratification in non-muscle invasive bladder cancer

Abstract BCG immunotherapy is the standard of care for high-risk non-muscle invasive bladder cancer (NMIBC). The mechanisms of response to BCG are not fully understood. Patient response to BCG is highly variable with ~30-40% of individuals experiencing recurring or progressing disease after treatment within 24 months. Prognostic markers of response to treatment are not fully understood and remain insufficient for patient stratification. In order to develop an understanding of BCG response by comprehensive assessment of the tumor context. We performed single cell spatial proteomic profiling via imaging mass cytometry (IMC) on tissue microarrays of tumor biopsies from 53 bladder cancer patients (36 responding and 17 non-responding). We then validated our finding on an external cohort of whole pathology slides from 10 patients (4 responding and 6 non-responding). The IMC panel was developed based on scRNAseq profiles of bladder tumor samples to provide an unbiased cellular context of NMIBC. Additionally, IMC data is accompanied by hematoxylin and eosin (H&E) images of adjacent cuts and annotated by a clinical pathologist to distinguish tumor, immune, and stromal regions. We first developed a convolutional neural network (CNN) to reproduce pathologist annotations of tumor, stroma, and immune regions from H&E data with high accuracy (AUC>93%). We then registered IMC and annotated H&E images to train a random forest model to predict pathologist annotations using IMC data (AUC>95%). To determine the cellular composition of TMA spots, we used sparse non-negative matrix factorization (sNMF) to encode and cluster IMC data within tumor, stroma, and immune regions. NMF analysis revealed that a component defined primarily by KRT5, CD11b, and CD14, specifically within tumor-annotated regions adjoining COL1A1+ stromal regions, is a prognostic marker of poor response (p-value<0.05). This is consistent with previous findings that tumor-associated myeloid cells indicate poor prognosis. Additional NMF components of the immune annotated region showed weak associations with response. Immune regions defined by CD68, CD11b, CD31, and PDPN corresponded with poor BCG response, indicating myeloid-stromal-vascular interactions outside the tumor influence outcome, while vascular immune regions defined by high aSMA presence correspond with better response. Collectively, combining NMF components to generate a single response score for responding and non-responding patients (p-value<0.05) proved more effective at patient stratification than any single NMF component alone. The external cohort validated our random forest model for separating tumor, stroma, and immune regions, our sNMF model for clustering IMC images, and our response score (p-value<0.1). Citation Format: Ali Foroughi Pour, Dylan Baker, Santhosh Sivajothi, Bonnie Choy, Khyati Meghani, Yanni Yu, Leigh A. Fall, Benjamin Ristau, Joshua J. Meeks, Paul Robson, Jeffrey H. Chuang. Imaging mass cytometry captures patient heterogeneity enabling BCG response stratification in non-muscle invasive bladder cancer [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR011.

Use of Immune Profiling Panel to assess the immune response of septic patients for prediction of worsening as a composite endpoint

Sepsis induces intense, dynamic and heterogeneous host response modulations. Despite improvement of patient management, the risk of mortality and healthcare-associated infections remains high. Treatments to counterbalance immune response are under evaluation, but effective biomarkers are still lacking to perform patient stratification. The design of the present study was defined to alleviate the limitations of existing literature: we selected patients who survived the initial hyperinflammatory response and are still hospitalized at day 5–7 after ICU admission. Using the Immune Profiling Panel (IPP), a fully automated RT-qPCR multiplex prototype, we optimized a machine learning model combining the IPP gene expression levels for the identification of patients at high risk of worsening, a composite endpoint defined as death or secondary infection, within one week after sampling. This was done on 332 sepsis patients selected from two retrospective studies. The IPP model identified a high-risk group comprising 30% of patients, with a significant increased proportion of worsening events at day 28 compared to the low-risk group (49% vs. 28%, respectively). These preliminary results underline the potential clinical application of IPP for sepsis patient stratification in a personalized medicine perspective, that will be confirmed in a larger prospective multicenter study.

Abstract A025: Integrating genomic alterations and histopathological features for enhanced risk stratification in non-muscle invasive bladder cancer

Abstract Urothelial carcinoma is the most prevalent type of bladder cancer, where non-muscle invasive bladder cancer (NMIBC) accounts for about 75% of the initial diagnosis. Recognizing the limitations of current WHO04/16 grading and TNM staging systems, this study investigates genomic alterations, mitotic activity index (MAI) and immunohistochemical (IHC) markers CK20, p53, and CD25 to identify better prognostic biomarkers in NMIBC. In this retrospective population-based study of NMIBC patients (n=287), a targeted next-generation sequencing (NGS) panel analysis was performed using Oncomine™ Focus Assay on Ion GeneStudio S5 platform to identify genomic changes, including single nucleotide variations (SNVs) and copy number variations (CNVs) across 52 genes. The results revealed that the most frequent gene variants were identified in FGFR3 (n=183, 45%) and PIK3CA (n=98, 24%), while AKT1 (n=39, 20%), ERBB2 (n=24, 12%), MYCN (n=23, 12%) and CCND1 (n=22, 11%) were among the most amplified genes. Distinct genetic associations were observed among Ta, T1, low-grade (LG), and high-grade (HG) tumors. Ta tumors were significantly associated with FGFR3 and PIK3CA mutations, while T1 tumors were linked to ERBB2 mutations (p<0.001). Additionally, LG tumors were enriched with FGFR3 mutations, while HG tumors were significantly associated with FGFR1, CCND1, and ERBB2 mutations (p<0.001). In the analysis of tumor recurrence, patients with FGFR3 mutations had significantly shorter recurrence-free survival (RFS) (p=0.033). Analysis of stage progression revealed a significant association between FGFR3 mutations and improved PFS (p<0.001), while MYC, ERBB2 (p<0.001), and MYCN (p=0.011) were linked to shorter PFS. In gene set enrichment analysis, gene sets like PIK3CA and ERBB2 (p=0.003), and PIK3CA and MYC (p=0.005), were inversely associated with progression-free survival (PFS). In the multivariate Cox regression analysis, MAI was the strongest predictor for PFS (p<0.001). The study emphasizes the potential of combining genomic alterations with histopathological features for enhanced risk stratification in NMIBC patients. Citation Format: Melinda Lillesand, Vebjørn Kvikstad, Einar Gudlaugsson, Ivar Skaland, Aida Slewa Johannessen, Almaz Nigatu Tesfahun, Sigmund Vegard Sperstad, Emiel A.M. Janssen, Marie Austdal. Integrating genomic alterations and histopathological features for enhanced risk stratification in non-muscle invasive bladder cancer [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr A025.

Imaging-Based Deep Learning for Predicting Desmoid Tumor Progression.

Desmoid tumors (DTs) are non-metastasizing and locally aggressive soft-tissue mesenchymal neoplasms. Those that become enlarged often become locally invasive and cause significant morbidity. DTs have a varied pattern of clinical presentation, with up to 50-60% not growing after diagnosis and 20-30% shrinking or even disappearing after initial progression. Enlarging tumors are considered unstable and progressive. The management of symptomatic and enlarging DTs is challenging, and primarily consists of chemotherapy. Despite wide surgical resection, DTs carry a rate of local recurrence as high as 50%. There is a consensus that contrast-enhanced magnetic resonance imaging (MRI) or, alternatively, computerized tomography (CT) is the preferred modality for monitoring DTs. Each uses Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), which measures the largest diameter on axial, sagittal, or coronal series. This approach, however, reportedly lacks accuracy in detecting response to therapy and fails to detect tumor progression, thus calling for more sophisticated methods. The objective of this study was to detect unique features identified by deep learning that correlate with the future clinical course of the disease. Between 2006 and 2019, 51 patients (mean age 41.22 ± 15.5 years) who had a tissue diagnosis of DT were included in this retrospective single-center study. Each had undergone at least three MRI examinations (including a pretreatment baseline study), and each was followed by orthopedic oncology specialists for a median of 38.83 months (IQR 44.38). Tumor segmentations were performed on a T2 fat-suppressed treatment-naive MRI sequence, after which the segmented lesion was extracted to a three-dimensional file together with its DICOM file and run through deep learning software. The results of the algorithm were then compared to clinical data collected from the patients' medical files. There were 28 males (13 stable) and 23 females (15 stable) whose ages ranged from 19.07 to 83.33 years. The model was able to independently predict clinical progression as measured from the baseline MRI with an overall accuracy of 93% (93 ± 0.04) and ROC of 0.89 ± 0.08. Artificial intelligence may contribute to risk stratification and clinical decision-making in patients with DT by predicting which patients are likely to progress.

Neuroimaging to Facilitate Clinical Trials in Huntington’s Disease: Current Opinion from the EHDN Imaging Working Group

Neuroimaging is increasingly being included in clinical trials of Huntington’s disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Multimodal data integration for enhanced longitudinal prediction for cardiac and cerebrovascular events following initial diagnosis of obstructive sleep apnea syndrome.

Obstructive sleep apnea syndrome (OSAS), a prevalent condition, often coexists with intricate metabolic issues and is frequently associated with negative cardiovascular outcomes. We developed a longitudinal prediction model integrating multimodal data for cardiovascular risk stratification of patients with an initial diagnosis of OSAS. We reviewed the data of patients with new-onset OSAS who underwent diagnostic polysomnography between 2018-19. Patients were treated using standard treatment regimens according to clinical practice guidelines. Over a median follow-up of 32 months, 98/729 participants (13.4%) experienced our composite outcome. At a ratio of 7:3, cases were randomly divided into development (n = 510) and validation (n = 219) cohorts. A prediction nomogram was created using six clinical factors - sex, age, diabetes mellitus, history of coronary artery disease, triglyceride-glucose index, and apnea-hypopnea index. The prediction nomogram showed excellent discriminatory power, based on Harrell's C-index values of 0.826 (95% confidence interval (CI) = 0.779-0.873) for the development cohort and 0.877 (95% CI = 0.824-0.93) for the validation cohort. Moreover, comparing the predicted and observed major adverse cardiac and cerebrovascular events in both development and validation cohorts indicated that the prediction nomogram was well-calibrated. Decision curve analysis demonstrated the good clinical applicability of the prediction nomogram. Our findings demonstrated the construction of an innovative visualisation tool that utilises various types of data to predict poor outcomes in Chinese patients diagnosed with OSAS, providing accurate and personalised therapy. Chinese Clinical Trial Registry ChiCTR2300075727.

PET/CT Imaging of Infectious Diseases: Overview of Novel Radiopharmaceuticals

Infectious diseases represent one of the most common causes of hospital admission worldwide. The diagnostic work-up requires a complex clinical approach, including laboratory data, CT and MRI, other imaging tools, and microbiologic cultures. PET/CT with 18F-FDG can support the clinical diagnosis, allowing visualization of increased glucose metabolism in activated macrophages and monocytes; this tracer presents limits in differentiating between aseptic inflammation and infection. Novel PET radiopharmaceuticals have been developed to overcome these limits; 11C/18F-labeled bacterial agents, several 68Ga-labeled molecules, and white blood cells labeled with 18F-FDG are emerging PET tracers under study, showing interesting preliminary results. The best choice among these tracers can be unclear. This overview aims to discuss the most common diagnostic applications of 18F-FDG PET/CT in infectious diseases and, as a counterpoint, to describe and debate the advantages and peculiarities of the latest PET radiopharmaceuticals in the field of infectious diseases, which will probably improve the diagnosis and prognostic stratification of patients with active infectious diseases.

Profiles of motivational impairment and their relationship to functional decline in frontotemporal dementia.

Motivational disturbances are pervasive in frontotemporal dementia (FTD) and impact negatively on everyday functioning. Despite mounting evidence of anhedonia in FTD, it remains unclear how such changes fit within the broader motivational symptom profile of FTD, or how anhedonia relates to functional outcomes. Here we sought to comprehensively characterize motivational disturbances in FTD and their respective relationships with functional impairment. A cross-sectional study design was used including 211 participants-68 behavioral-variant FTD (bvFTD), 32 semantic dementia (SD), 43 Alzheimer's disease (AD), and 68 healthy older control participants. Anhedonia severity was measured using the Snaith-Hamilton Pleasure Scale while severity of apathy was assessed across Emotional, Executive, and Initiation dimensions using the Dimensional Apathy Scale. Functional impairment was established using the FTD Functional Rating Scale (FRS). Distinct motivational profiles emerged in each dementia syndrome: a domain-general motivational impairment in bvFTD; a predominantly anhedonic profile in SD; and more pronounced initiation and executive apathy in AD. Correlation analyses revealed differential associations between motivational symptoms and severity of functional impairment in each group. Executive apathy was associated with functional impairment in bvFTD, while anhedonia was strongly correlated with functional decline in SD. Finally, executive and emotional apathy were associated with functional decline in AD. Our study indicates distinct profiles of apathy and anhedonia in FTD syndromes, which in turn are differentially associated with functional decline. This detailed characterization of motivational phenotypes can inform patient stratification for targeted interventions to improve functional outcomes.

Determining the optimal diagnostic and risk stratification approaches for people with hypertension in two rural populations in Kenya and The Gambia: a study protocol for IHCoR-Africa Work Package 2

Background Sub-Saharan Africa (SSA) has one of the highest prevalences of hypertension worldwide. The impact of hypertension is of particular concern in rural SSA, where access to clinics and hospitals is limited. Improvements in the management of people with hypertension in rural SSA could be achieved by sharing diagnosis and care tasks between the clinic and the community. To develop such a community-centred programme we need optimal approaches to identify and risk stratify patients with elevated blood pressure. The aim of the study is to improve the evidence base for diagnosis and risk estimation for a community-centred hypertension programme in two rural settings in SSA. Methods We will conduct a cross-sectional study of 1250 adult participants in Kilifi, Kenya and Kiang West, The Gambia. The study has five objectives which will determine the: (1) accuracy of three blood pressure (BP) measurement methods performed by community health workers in identifying people with hypertension in rural SSA, compared to the reference standard method; (2) relationship between systolic BP and cardiovascular risk factors; (3) prevalence of hypertension-mediated organ damage (HMOD); (4) accuracy of innovative point-of-care (POC) technologies to identify patients with HMOD; and (5) cost-effectiveness of different combinations of BP and HMOD measurements for directing hypertension treatment initiation. Expected findings This study will determine the accuracy of three methods for community BP measurement and POC technologies for HMOD assessment. Using the optimal methods in this setting it will estimate the prevalence of hypertension and provide the best estimate to date of HMOD prevalence in SSA populations. The cost-effectiveness of decision-making approaches for initiating treatment of hypertension will be modelled. These results will inform the development of a community-centred programme to improve care for hypertensive patients living in rural SSA. Existing community engagement networks will be used to disseminated within the research setting.

TRANSOESOPHAGEAL ELECTROPHYSIOLOGICAL STUDY IN PEDIATRIC PATIENTS WITH VENTRICULAR PRE–EXCITATION

Abstract Introduction The transoesophageal electrophysiological study (SETE) is a simple, semi–invasive and highly reliable test for risk stratification in patients with ventricular pre–excitation. In paediatric subjects, symptoms may be nonspecific or poorly reported; hence the usefulness of the study in guiding the therapeutic strategy. The guidelines also suggest performing it in all asymptomatic patients with ventricular preexcitation above 8 years of age.Purpose of the study: to evaluate the inducibility of re–entry tachycardia at SETE in paediatric patients with ventricular pre–excitation. Materials and Methods We observed 21 patients aged between a few days of life and 12 years (average age 11 years); 14 of them had constant and 7 intermittent pre–excitations. The latter were subjected to the study as they reported dubious symptoms either due to sports activities or due to a previous diagnosis of neonatal TPS. The study was performed by carrying out the protocols of continuous atrial stimulation at increasing frequencies, programmed atrial stimulation with single, double, and triple extra stimulus and atrial stimulation with burst. Results Of the 21 patients, 8 (38.1%) were inducible. Of the 14 patients with constant ventricular pre–excitation, 5 had tachycardia upon return to the study (35.7%), 4 of which were symptomatic due to previous TPS. Therefore, only one patient with constant and asymptomatic ventricular pre–excitation was inducible. Of the 7 patients with intermittent ventricular preexcitation (documented by Holter or exercise test), 3 developed TPS at SETE (42.8%): all these patients complained of heart palpitations but did not have a documented arrhythmia. On the contrary, all patients with intermittent ventricular preexcitation and asymptomatic were found to be non–inducible. In patients in whom it was possible to calculate the effective antegrade refractory period of the anomalous pathway, shorter refractory period showed greater inducibility. Non–sustained atrial fibrillation was induced in only two patients. Conclusions SETE demonstrates a good correlation between symptoms and inducibility especially in patients with intermittent ventricular pre–excitation. In fact, if the manifest pre–excitation disappears with growth in about a third of patients, the retrograde conduction capacity of the pathway often persists. The study also allows us to identify patients with constant pre–excitation but pathway at low risk.

BRUGADA SYNDROME: NEWEST DATA ON RISK STRATIFICATION

Abstract Background Brugada Syndrome (BrS) is a rare genetically based channelopathy responsible for malignant arrhythmias and sudden cardiac death (SCD) in young adults. Objectives The aims was to evaluate prognostic indicators useful for accurate arrhythmic risk stratification in BrS by studying genetic, clinical, anamnestic, instrumental parameters, and those derived from electrophysiological study (EPS) and intracavitary mapping. Materials and Methods 209 patients with Brugada type 1 were recruited between 01/01/2018 and 01/10/2023. Results In the first study, predictive factors related to high arrhythmic risk from the data obtained for the total population of 209 Brugada patients enrolled in our case series include a positive history of cardiogenic syncope (p value 0.000041), having had a resuscitated cardiac arrest (p value 0.00025), presenting positive EPS for inducibility of ventricular tachycardia (p value 0.00032), having a family history of sudden cardiac death (p value < 0.00001), and presenting positive cardiac mapping and MRI for pathological alterations (both with p value > 0.00001). From the data obtained for the subset of 80 Brugada patients with ICD in our case series, predictive factors related to high arrhythmic risk are different from the previously analyzed population: presenting spontaneous type 1 ECG (p value 0.0015), having had a resuscitated cardiac arrest (p value 0.014028), having a family history of sudden cardiac death (p value < 0.00001), and presenting positive mapping, genetics, and cardiac MRI for pathological alterations. Positive EPS and syncope in this subset do not appear to be risk factors, unlike the population of all 209 enrolled patients. Conclusions Multiparametric scores, including the mentioned markers, have been introduced in clinical practice for a better approach in selection. However, new markers are needed to improve risk stratification in BrS patients, especially those in the so–called "gray zone" with intermediate risk. In the future, it will be evaluated through experimental models and genotype–phenotype integration whether such electrical heterogeneity is also associated with specific genetic or structural abnormalities and may represent a new therapeutic target for etiological treatment in BrS patients. In conclusion, there is an urgent need to develop new methods for risk stratification in Brugada syndrome that are sufficiently sensitive, specific, reproducible, and easy to apply.

ERIC recommendations for TP53 mutation analysis in chronic lymphocytic leukemia-2024 update.

In chronic lymphocytic leukemia (CLL), analysis of TP53 aberrations (deletion and/or mutation) is a crucial part of treatment decision-making algorithms. Technological and treatment advances have resulted in the need for an update of the last recommendations for TP53 analysis in CLL, published by ERIC, the European Research Initiative on CLL, in 2018. Based on the current knowledge of the relevance of low-burden TP53-mutated clones, a specific variant allele frequency (VAF) cut-off for reporting TP53 mutations is no longer recommended, but instead, the need for thorough method validation by the reporting laboratory is emphasized. The result of TP53 analyses should always be interpreted within the context of available laboratory and clinical information, treatment indication, and therapeutic options. Methodological aspects of introducing next-generation sequencing (NGS) in routine practice are discussed with a focus on reliable detection of low-burden clones. Furthermore, potential interpretation challenges are presented, and a simplified algorithm for the classification of TP53 variants in CLL is provided, representing a consensus based on previously published guidelines. Finally, the reporting requirements are highlighted, including a template for clinical reports of TP53 aberrations. These recommendations are intended to assist diagnosticians in the correct assessment of TP53 mutation status, but also physicians in the appropriate understanding of the lab reports, thus decreasing the risk of misinterpretation and incorrect management of patients in routine practice whilst also leading to improved stratification of patients with CLL in clinical trials.

Predictive value of Systematic Coronary Risk Evaluation 2-Diabetes risk model and arterial stiffness for cardiovascular events in the Asian population with type 2 diabetes mellitus.

Individuals with diabetes are at high risk of developing cardiovascular events. The present study investigated the predictive value of the cardio-ankle vascular index (CAVI) when added to the Systematic Coronary Risk Evaluation 2-Diabetes (SCORE2-Diabetes) risk algorithm to predict cardiovascular events in the Asian population. The SCORE2-Diabetes risk was assessed in 1,502 patients with diabetes, aged 40-69 years. Then, we further stratified each 10-year risk category with a CAVI value of 9.0. The primary outcomes (composite of all causes of death, myocardial infarction, stroke and hospitalization for heart failure) were assessed over 5 years. The mean age of the population was 59.8 ± 6.4 years. The proportion of 10-year risk according to the SCORE2-Diabetes risk of low, moderate, high and very high risk identified at 7.2, 30.0, 27.2 and 35.6%, respectively. The mean CAVI value was 8.4 ± 1.4, and approximately 35.4% of the patients had CAVI ≥9.0. The SCORE2-Diabetes risk algorithm independently predicted the primary outcomes in patients with diabetes (hazard ratio 1.18, 95% confidence interval [CI] 1.13-1.22), whereas CAVI did not (hazard ratio 1.03, 95% CI 0.89-1.18). The C-index for the primary outcomes of the SCORE2-Diabetes risk algorithm alone was 0.72 (95% CI 0.67-0.77). The combination of SCORE2-Diabetes and CAVI, both in the continuous value and risk groups, did not improve discrimination (C-index 0.72, 95% CI 0.67-0.77 and 0.68, 95% CI 0.64-0.74, respectively). Adding the CAVI to the SCORE2-Diabetes risk algorithm did not improve individual risk stratification in patients with diabetes.

Three-dimensional telomere profiling predicts risk of progression in smoldering multiple myeloma.

Smoldering multiple myeloma (SMM) is a precursor stage that precedes multiple myeloma (MM). SMM is heterogenous with nearly 40% of patients progressing to MM in the first 5 years. The high rate of progression of SMM patients highlights the need for early intervention, which underscores the importance of identifying SMM patients with the highest risk of progression. Several risk stratification models showed utility in identifying high-risk SMM patients; however, these systems showed limited sensitivity. To date, identifying high-risk SMM patients remains an important clinical need. In this study, we present the 3-dimensional telomere profiling as a structural biomarker capable of stratifying SMM patients as a function of genomic instability. Quantifying telomere dysfunction using the TeloView technology showed utility in risk stratification of cancer patients, particularly hematological malignancies. In this study, we analyzed 168 SMM patients. We report an AUC in ROC analysis of 0.8 using a subset of the patients as a training dataset. We then conducted a blind validation on a different cohort and demonstrated a positive predictive value of 85% and negative predictive value of 73%, with sensitivity and specificity of 83% and 76%, respectively. We examined the correlation between the TeloView prediction and the 20-2-20 scoring system, and cytogenetic abnormalities. We report a correlation of 53% with the 20-2-20 scores and over 60% correlation with cytogenetic abnormalities. The result of this study presents the telomere profiling as an effective biomarker able to stratify SMM patients to their respective risk groups with high sensitivity and specificity.

Individualized Risk for CKD Progression among US Adults.

Chronic kidney disease (CKD) is currently defined using glomerular filtration rate (GFR) or albuminuria. This is based on the relative risk of mortality and kidney outcomes compared to a healthy population and does not consider an individual's absolute risk of CKD progression. Using National Health and Nutrition Examination Survey data from 1999-2020, we characterized the individual-level absolute 3-year risk of ≥40% decline in estimated GFR (eGFR, mL/min/1.73 m2) or kidney failure (3-year risk) among US adults. We categorized the 3-year risk and considered ≥5% high risk. Among 199.81 million US adults, 8.42 million (4%) had a 3-year risk ≥5%, including 1.04 million adults without CKD (eGFR ≥60 and albuminuria <30 mg/g). These high-risk adults without CKD as currently defined had risk factors including hypertension (98%), heart failure (72%), and diabetes (44%). 15.51 million adults had CKD with preserved eGFR (eGFR ≥60 and albuminuria ≥30 mg/g)-3.73 million had a 3-year risk ≥5%, 41% of whom did not have diabetes and thus would not be screened for albuminuria using current screening recommendations. The 3-year risk of CKD progression was low (risk <5%) in 94% of the 5.66 million US adults with CKD stage G3a-A1 (eGFR 45 to <60 and albuminuria <30 mg/g). Assessment of the individual's absolute risk of CKD progression allows further risk stratification of patients with CKD and identifies individuals without CKD, as currently defined, that are at high risk of CKD progression.

Exploring the molecular landscape of cancer of unknown primary: A comparative analysis with other metastatic cancers.

Cancer of unknown primary (CUP) tumors are biologically very heterogeneous, which complicates stratification of patients for treatment. Consequently, these patients face limited treatment options and a poor prognosis. With this study, we aim to expand on the current knowledge of CUP biology by analyzing two cohorts: a well-characterized cohort of 44 CUP patients, and 213 metastatic patients with known primary. These cohorts were treated at the same institution and characterized by identical molecular assessments. Through comparative analysis of genomic and transcriptomic data, we found that CUP tumors were characterized by high expression of immune-related genes and pathways compared to other metastatic tumors. Moreover, CUP tumors uniformly demonstrated high levels of tumor-infiltrating leukocytes and circulating Tcells, indicating a strong immune response. Finally, the genetic landscape of CUP tumors resembled that of other metastatic cancers and demonstrated mutations in established cancer genes. In conclusion, CUP tumors possess a distinct immunophenotype that distinguishes them from other metastatic cancers. These results may suggest an immune response in CUP that facilitates metastatic tumor growth while limiting growth of the primary tumor.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Stellae-123 gene expression signature improved risk stratification in taiwanese acute myeloid leukemia patients

The European Leukemia Net recommendations provide valuable guidance in treatment decisions of patients with acute myeloid leukemia (AML). However, the genetic complexity and heterogeneity of AML are not fully covered, notwithstanding that gene expression analysis is crucial in the risk stratification of AML. The Stellae-123 score, an AI-based model that captures gene expression patterns, has demonstrated robust survival predictions in AML patients across four western-population cohorts. This study aims to evaluate the applicability of Stellae-123 in a Taiwanese cohort. The Stellae-123 model was applied to 304 de novo AML patients diagnosed and treated at the National Taiwan University Hospital. We find that the pretrained (BeatAML-based) model achieved c-indexes of 0.631 and 0.632 for the prediction of overall survival (OS) and relapse-free survival (RFS), respectively. Model retraining within our cohort further improve the cross-validated c-indexes to 0.667 and 0.667 for OS and RFS prediction, respectively. Multivariable analysis identify both pretrained and retrained models as independent prognostic biomarkers. We further show that incorporating age, Stellae-123, and ELN classification remarkably improves risk stratification, revealing c-indices of 0.73 and 0.728 for OS and RFS, respectively. In summary, the Stellae-123 gene expression signature is a valuable prognostic tool for AML patients and model retraining can improve the accuracy and applicability of the model in different populations.