Established Clinical Risk Factors Research Articles

Adaptation and validation of a suicide-focused Word Sentence Association Paradigm to assess suicide-specific interpretation biases

The cognitive model of suicide proposes that biased cognitive processes contribute to suicidal thoughts and behaviors, and initial evidence suggests that attentional bias to suicide-congruent cues may predict suicidal behavior beyond established clinical risk factors. To date, much less work has explored the potential role of interpretation biases in the development and prediction of suicide risk. The present study assessed the psychometric properties, including reliability, concurrent validity, and predictive validity, of a novel adaptation of the Word Sentence Association Paradigm (WSAP) to assess suicide-suicide interpretation biases. In a sample of 239 psychiatric inpatients, including patients with no recent suicidal ideation or lifetime attempts (n = 35), those with recent suicide attempts (n = 29), and those with recent suicidal ideation (n = 174), participants completed the modified WSAP as well as self-reported suicidal ideation and attempts for the subsequent 6 months. Although the WSAP demonstrated good reliability, evidence of concurrent and prospective validity (in terms of self-reported suicidal ideation and attempts) was limited. Specifically, the clinical groups did not differ from one another on any measure of interpretation bias, nor did suicide-specific interpretation endorsements predict concurrent or future suicidal ideation when controlling for dysphoric interpretation bias. However, suicide-specific interpretation biases were uniquely associated with a history of a lifetime suicide attempt. Future work should further clarify the extent and specificity of the relationship between suicide-specific interpretation biases and clinical outcomes.

Quantitative Integrative Survival Prediction in Multiple Myeloma Patients Treated With Bortezomib-Based Induction, High-Dose Therapy and Autologous Stem Cell Transplantation.

Given the high heterogeneity in survival for patients with multiple myeloma, it would be clinically useful to quantitatively predict the individual survival instead of attributing patients to two to four risk groups as in current models, for example, revised International Staging System (R-ISS), R2-ISS, or Mayo-2022-score. Our aim was to develop a quantitative prediction tool for individual patient's 3-/5-year overall survival (OS) probability. We integrated established clinical and molecular risk factors into a comprehensive prognostic model and evaluated and validated its risk discrimination capabilities versus R-ISS, R2-ISS, and Mayo-2022-score. A nomogram for estimating OS probabilities was built on the basis of a Cox regression model. It allows one to translate the individual risk profile of a patient into 3-/5-year OS probabilities by attributing points to each prognostic factor and summing up all points. The nomogram was externally validated regarding discrimination and calibration. There was no obvious bias or overfitting of the prognostic index on the validation cohort. Resampling-based and external evaluation showed good calibration. The c-index of the model was similar on the training (0.76) and validation cohort (0.75) and significantly higher than for the R-ISS (P < .001) or R2-ISS (P < .01). In summary, we developed and validated individual quantitative nomogram-based OS prediction. Continuous risk assessment integrating molecular prognostic factors is superior to R-ISS, R2-ISS, or Mayo-2022-score alone.

#2715 Towards precise prediction of prognosis in ADPKD: a comprehensive biomarker analysis

Abstract Background and Aims Determining prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) heavily relies on the height-adjusted total kidney volume (HtTKV) Mayo classification. This classification predicts kidney outcomes on a group level but lacks precision in individual patients. Recently, several urine and blood biomarkers have demonstrated prognostic value in ADPKD, independent of HtTKV and baseline eGFR. This study compares the prognostic value of various biomarkers to aid selection when developing novel risk stratification tools. Method Included were participants of the DIPAK studies with a follow-up duration of &amp;gt;1 year and ≥3 eGFR measurements. We excluded patients with diabetes mellitus and patients using tolvaptan or somatostatin analogues. At baseline, urine samples were analyzed for albumin, urea, creatinine, β2-microglobulin (β2-MG), monocyte chemotactic protein-1 (MCP-1), TmP/GFR (tubular maximum reabsorption rate of phosphate to GFR), and blood was drawn to measure creatinine, urea (for assessment of urinary-to-plasma urea ratio; UP urea), glucagon, fibroblast growth factor 23 (FGF-23) and copeptin. Albumin, β2-MG and MCP-1 were divided by urinary creatinine concentration to account for concentration effects. The cross-sectional and longitudinal association with baseline eGFR and eGFR decline over time was analyzed using multivariate linear regression and repeated measures linear mixed model regression. Furthermore, logistic regression models were tested to identify patients with rapidly-progressive disease (eGFR slope ≤ −3.0 ml/min/1.73 m2/year). Lastly, multivariate Cox regression analysis was conducted to evaluate the time to the combined endpoint of 30% decline in eGFR, reaching an eGFR ≤ 15 ml/min/1.73 m2, initiation of dialysis or receiving a kidney transplant. Results 565 patients were included with a mean age of 47 ± 11 years, eGFR of 64 ± 27 ml/min/1.73 m2 and HtTKV of 858 [IQR: 540, 1305] ml/m. During a mean follow-up of 5.2 ± 2.2 years, the mean eGFR decline was −3.38 ± 2.37 mL/min/1.73 m2/year. Of these patients, 283 (50.1%) had rapidly progressive disease and 267 (47.3%) reached the combined kidney endpoint. All biomarkers except glucagon were associated with baseline eGFR and eGFR decline over time after adjustment for established clinical risk factors for disease progression (sex, PKD mutation and baseline age and eGFR). After adjustment for the same covariates, albumin/creatinine, MCP-1/creatinine, FGF-23, UP-urea and copeptin were predictive of rapid disease progression. The area under the curve (AUC) value for predicting rapidly progressive disease for the Mayo HtTKV classification alone was 0.694, which could be improved to 0.766 (P &amp;lt; .001) by a risk prediction model that also included the aforementioned predictive clinical risk factors. The introduction of several biomarkers further improved this clinical model, including albumin/creatinine (AUC 0.794, p = 0.03), MCP-1/creatinine (AUC 0.804, p = 0.04) and copeptin (AUC 0.789, p = 0.05). Similar results were seen in multivariate Cox regression models after adjustment for covariates, with independent predictors of the combined endpoint being again albumin/creatinine (P &amp;lt; .001), MCP-1 creatinine (P &amp;lt; .001), copeptin (p = 0.012), but now also UP-urea (p = 0.008) and FGF-23 (P &amp;lt; .001). These findings appeared robust in subgroup analyses of 200 patients with Mayo HtTKV class 1C and 122 patients with early disease (age ≤ 40 years and eGFR ≥ 60 ml/min/1.73 m2 at baseline), in whom predicting prognosis is most challenging. Conclusion In patients with ADPKD, albumin/creatinine, MCP-1/creatinine, FGF-23 and copeptin are consistent predictors of kidney outcomes after adjustment for established clinical risk factors. Our data suggest that these biomarkers should be prioritized when developing novel multimarker models to enhance precision of risk prediction in ADPKD.

Predicting the development of vulvar lichen sclerosus

Relevance. The issue of timely diagnosis and treatment of vulvar lichen sclerosus has become especially acute in recent years due to the “rejuvenation” of the disease and the risk of its malignancy. In this regard, it is urgent to search for effective methods for predicting and early detection of the disease. The aim of the study - to develop amodel for predicting vulvar lichen sclerosus based on established clinical and anamnestic risk factors. Materials and Methods. The prospective case-control study included 404 women aged 20 to 70 years, of which 344 were patients with vulvar lichen sclerosus and 60 were women without vulvar diseases. At the first stage, acomparative statistical correlation analysis of the clinical and anamnestic data of the subjects was carried out using the Spearman correlation coefficient (R0.15), Chi-square tests, Phi and Cramer statistics, the Mann-­Whitney U test and the Student t test (p 0.05). The data obtained were used to develop aneural network model for predicting vulvar lichen sclerosus in the second stage of the study. Results and Discussion. Based on established reliably significant (p0.05) obstetric-­gynecological, somatic, infectious, hygienic and household factors influencing the risk of developing vulvar lichen sclerosus (Rindicator - from 0.16 to 0.38 confirms the statistical significance of correlations), aneural network model for predicting vulvar lichen sclerosus was developed (the percentage of correct classification on the test sample is the maximum possible value - 100%) and acomputer program was written that automates the procedure for predicting the disease. Conclusion. The neural network model for predicting the disease, developed on the basis of reliably (p0.05) significant risk factors for vulvar lichen sclerosus, has high prognostic properties, and acomputer program written on its basis allows the doctor in amatter of minutes to identify the patient at risk for the development of vulvar lichen sclerosus and give she needs preventive recommendations aimed at preventing or early detection of the disease.

Novel Polygenic Risk Score and Established Clinical Risk Factors for Risk Estimation of Aortic Stenosis

Polygenic risk scores (PRSs) have proven to be as strong as or stronger than established clinical risk factors for many cardiovascular phenotypes. Whether this is true for aortic stenosis remains unknown. To develop a novel aortic stenosis PRS and compare its aortic stenosis risk estimation to established clinical risk factors. This was a longitudinal cohort study using data from the Million Veteran Program (MVP; 2011-2020), UK Biobank (2006-2010), and 6 Thrombolysis in Myocardial Infarction (TIMI) trials, including DECLARE-TIMI 58 (2013-2018), FOURIER (TIMI 59; 2013-2017), PEGASUS-TIMI 54 (2010-2014), SAVOR-TIMI 53 (2010-2013), SOLID-TIMI 52 (2009-2014), and ENGAGE AF-TIMI 48 (2008-2013), which were a mix of population-based and randomized clinical trials. Individuals from UK Biobank and the MVP meeting a previously validated case/control definition for aortic stenosis were included. All individuals from TIMI trials were included unless they had a documented preexisting aortic valve replacement. Analysis took place from January 2022 to December 2023. PRS for aortic stenosis (developed using data from MVP and validated in UK Biobank) and other previously validated cardiovascular PRSs, defined either as a continuous variable or as low (bottom 20%), intermediate, and high (top 20%), and clinical risk factors. Aortic stenosis (defined using International Classification of Diseases or Current Procedural Terminology codes in UK Biobank and MVP or safety event data in the TIMI trials). The median (IQR) age in MVP was 67 (57-73) years, and 135 140 of 147 104 participants (92%) were male. The median (IQR) age in the TIMI trials was 66 (54-78) years, and 45 524 of 59 866 participants (71%) were male. The best aortic stenosis PRS incorporated 5 170 041 single-nucleotide variants and was associated with aortic stenosis in both the MVP testing sample (odds ratio, 1.41; 95% CI, 1.37-1.45 per 1 SD PRS; P = 4.6 × 10-116) and TIMI trials (hazard ratio, 1.44; 95% CI, 1.27-1.62 per 1 SD PRS; P = 3.2 × 10-9). Among genetic and clinical risk factors, the aortic stenosis PRS performed comparably to most risk factors besides age, and within a given age range, the combination of clinical and genetic risk factors was additive, providing a 3- to 4-fold increased gradient of risk of aortic stenosis. However, the addition of the aortic stenosis PRS to a model including clinical risk factors only improved risk discrimination of aortic stenosis by 0.01 to 0.02 (C index in MVP: 0.78 with clinical risk factors, 0.79 with risk factors and aortic stenosis PRS; C index in TIMI: 0.71 with clinical risk factors, 0.73 with risk factors and aortic stenosis PRS). This study developed and validated 1 of the first aortic stenosis PRSs. While aortic stenosis genetic risk was independent from clinical risk factors and performed comparably to all other risk factors besides age, genetic risk resulted in only a small improvement in overall aortic stenosis risk discrimination beyond age and clinical risk factors. This work sets the stage for further development of an aortic stenosis PRS.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine. In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables. We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors. Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work. The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

Unfavorable social determinants of health and mortality risk by cardiovascular disease status: Findings from a National Study of United States Adults

The association between cumulative burden of unfavorable social determinants of health (SDoH) and all-cause mortality has not been assessed by atherosclerotic cardiovascular disease (ASCVD) status on a population level in the United States. We assessed the association between cumulative social disadvantage and all-cause mortality by ASCVD status in the National Health Interview Survey, linked to the National Death Index. In models adjusted for established clinical risk factors, individuals experiencing the highest level of social disadvantage (SDoH-Q4) had over 1.5 (aHR=1.55; 95%CI=1.22, 1.96) and 2-fold (aHR=2.21; 95% CI=1.91, 2.56) fold increased risk of mortality relative to those with the most favorable social profile (SDoH-Q1), respectively for adults with and without ASCVD; those experiencing co-occurring ASCVD and high social disadvantage had up to four-fold higher risk of mortality (aHR=3.81; 95%CI=3.36, 4.32). These findings emphasize the importance of a healthcare model that prioritizes efforts to identify and address key social and environmental barriers to health and wellbeing, particularly in individuals experiencing the double jeopardy of clinical and social risk.

Entirely noninvasive outcome prediction in central nervous system lymphomas using circulating tumor DNA

AbstractState-of-the-art response assessment of central nervous system lymphoma (CNSL) by magnetic resonance imaging is challenging and an insufficient predictor of treatment outcomes. Accordingly, the development of novel risk stratification strategies in CNSL is a high unmet medical need. We applied ultrasensitive circulating tumor DNA (ctDNA) sequencing to 146 plasma and cerebrospinal fluid (CSF) samples from 67 patients, aiming to develop an entirely noninvasive dynamic risk model considering clinical and molecular features of CNSL. Our ultrasensitive method allowed for the detection of CNSL-derived mutations in plasma ctDNA with high concordance to CSF and tumor tissue. Undetectable plasma ctDNA at baseline was associated with favorable outcomes. We tracked tumor-specific mutations in plasma-derived ctDNA over time and developed a novel CNSL biomarker based on this information: peripheral residual disease (PRD). Persistence of PRD after treatment was highly predictive of relapse. Integrating established baseline clinical risk factors with assessment of radiographic response and PRD during treatment resulted in the development and independent validation of a novel tool for risk stratification: molecular prognostic index for CNSL (MOP-C). MOP-C proved to be highly predictive of outcomes in patients with CNSL (failure-free survival hazard ratio per risk group of 6.60; 95% confidence interval, 3.12-13.97; P < .0001) and is publicly available at www.mop-c.com. Our results highlight the role of ctDNA sequencing in CNSL. MOP-C has the potential to improve the current standard of clinical risk stratification and radiographic response assessment in patients with CNSL, ultimately paving the way toward individualized treatment.

Association of epicardial adipose tissue on magnetic resonance imaging with cardiovascular outcomes: Quality over quantity?

Abstract Background Quantity of epicardial adipose tissue (EAT) has been associated with poor cardiovascular outcomes. As suggested by computed tomography (CT) studies the quality of EAT may be of incremental prognostic value. Cardiac magnetic resonance (CMR) is the gold-standard for tissue characterization, however, has never been applied for EAT quality assessment. Objectives To investigate EAT quality measured on CMR T1 mapping as predictor of outcome in an all-comer cohort. Methods We measured EAT area (EATA) and T1 time (EAT-T1) in a four-chamber views (Figure 1) and tested for association with clinical, demographic, and laboratory parameters using linear regression models. We used Cox-regression analyses to test the association between EATA and EAT-T1 with a composite endpoint of non-fatal myocardial infarction, heart failure hospitalization, and all-cause death. Results A total of 966 participants were included (47.2% female, mean age: 58.4years). Mean EATA was 7.3cm2 and mean EAT-T1 was 268ms. On linear regression EAT-T1 was not associated with markers obesity, dyslipidemia or co-morbidities such as diabetes (p&amp;gt;0.05 for all). During a follow-up of 57.7 months, a total of 280 (29.0%) events occurred. EAT-T1 was independently associated (adj. HR: 1.003, 95%-CI: 1.000 – 1.005, p=0.029, Figure 2) with the composite endpoint when adjusted for established clinical risk factors including age, sex, natriuretic peptide levels, left and right ventricular function, arterial hypertension, body mass index, and coronary artery disease. Conclusion In an all-comer CMR cohort, EAT quality, as measured on CMR T1-times, but not EAT quantity is independently associated with a composite endpoint of non-fatal myocardial infarction, heart failure hospitalization, and all-cause death.Figure 1.Regions of interestFigure 2.Kaplan Meier Curves

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime.

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

Aortic valve stenosis in familial hypercholesterolaemic: Should we systematically screen?

The optimal timing for mitral valve (MV) surgery in asymptomatic patients with primary mitral regurgitation (MR) remains a matter of debate. Myocardial contraction fraction (MCF) − the ratio of the left ventricular (LV) stroke volume to that of the myocardial volume − is a volumetric measure of LV myocardial shortening independent of size or geometry.To assess the relationship between MCF and outcome in patients with significant chronic primary MR due to prolapse managed in contemporary practice.Clinical, Doppler-echocardiographic and outcome data prospectively collected in 174 patients (mean age 62 years, 27% women) with significant primary MR and no or mild symptoms were analysed. The impact of MCF< or ≥30% on cardiac events (cardiovascular death, acute heart failure or MV surgery) was studied.During an estimated median follow-up of 49 (22–77) months, cardiac events occurred in 115 (66%) patients. The 4-year estimates of survival free from cardiac events were 21 ± 5% for patients with MCF <30% and 40 ± 6% for those with ≥30% (P < 0.001). MCF <30% was associated with a considerable increased risk of cardiac events after adjustment for established clinical risk factors, MR severity and current recommended class I triggers for MV surgery (adjusted hazard ratio: 2.33, 95% confidence interval: 1.51−3.58; P < 0.001). Moreover, MCF < 30% improved the predictive performance of models, with better global fit, reclassification and discrimination.MCF < 30% is strongly associated with occurrence of cardiac events in patients with significant primary MR due to prolapse. Further studies are needed to assess the direct impact of MCF on patient management and outcomes.

A Genomic Risk Score Identifies Individuals at High Risk for Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) has an estimated heritability of 29%. We developed a genomic risk score for ICH and determined its predictive power in comparison to standard clinical risk factors. We combined genome-wide association data from individuals of European ancestry for ICH and related traits in a meta-genomic risk score ([metaGRS]; 2.6 million variants). We tested associations with ICH and its predictive performance in addition to clinical risk factors in a held-out validation dataset (842 cases and 796 controls). We tested associations with risk of incident ICH in the population-based UK Biobank cohort (486 784 individuals, 1526 events, median follow-up 11.3 years). One SD increment in the metaGRS was significantly associated with 31% higher odds for ICH (95% CI, 1.16-1.48) in age-, sex- and clinical risk factor-adjusted models. The metaGRS identified individuals with almost 5-fold higher odds for ICH in the top score percentile (odds ratio, 4.83 [95% CI, 1.56-21.2]). Predictive models for ICH incorporating the metaGRS in addition to clinical predictors showed superior performance compared to the clinical risk factors alone (c-index, 0.695 versus 0.686). The metaGRS showed similar associations for lobar and nonlobar ICH, independent of the known APOE risk locus for lobar ICH. In the UK Biobank, the metaGRS was associated with higher risk of incident ICH (hazard ratio, 1.15 [95% CI, 1.09-1.21]). The associations were significant within both a relatively high-risk population of antithrombotic medications users, as well as among a relatively low-risk population with a good control of vascular risk factors and no use of anticoagulants. We developed and validated a genomic risk score that predicts lifetime risk of ICH beyond established clinical risk factors among individuals of European ancestry. Whether implementation of the score in risk prognostication models for high-risk populations, such as patients under antithrombotic treatment, could improve clinical decision making should be explored in future studies.

Clinical significance of myocardial contraction fraction in significant primary mitral regurgitation

BackgroundThe optimal timing for mitral valve (MV) surgery in asymptomatic patients with primary mitral regurgitation (MR) remains a matter of debate. Myocardial contraction fraction (MCF) − the ratio of the left ventricular (LV) stroke volume to that of the myocardial volume − is a volumetric measure of LV myocardial shortening independent of size or geometry. AimTo assess the relationship between MCF and outcome in patients with significant chronic primary MR due to prolapse managed in contemporary practice. MethodsClinical, Doppler-echocardiographic and outcome data prospectively collected in 174 patients (mean age 62 years, 27% women) with significant primary MR and no or mild symptoms were analysed. The impact of MCF< or ≥30% on cardiac events (cardiovascular death, acute heart failure or MV surgery) was studied. ResultsDuring an estimated median follow-up of 49 (22–77) months, cardiac events occurred in 115 (66%) patients. The 4-year estimates of survival free from cardiac events were 21±5% for patients with MCF <30% and 40±6% for those with ≥30% (P<0.001). MCF <30% was associated with a considerable increased risk of cardiac events after adjustment for established clinical risk factors, MR severity and current recommended class I triggers for MV surgery (adjusted hazard ratio: 2.33, 95% confidence interval: 1.51−3.58; P<0.001). Moreover, MCF<30% improved the predictive performance of models, with better global fit, reclassification and discrimination. ConclusionsMCF<30% is strongly associated with occurrence of cardiac events in patients with significant primary MR due to prolapse. Further studies are needed to assess the direct impact of MCF on patient management and outcomes.

A low risk of recurrence after breast-conserving surgery for DCIS: A single-institution experience

Background: Previously published studies report up to 30% recurrence rates after DCIS, so it would be desirable to identify those women at risk for recurrence and adapt adjuvant management. This study aimed to identify the locoregional recurrence rate after breast conserving surgery (BCS) for DCIS, and to evaluate the possible role of immunohistochemical (IHC) staining in predicting the risk of recurrence. Patients and methods: In a retrospective cohort study, patients who underwent BCS for pure DCIS were identified. Data on well-established clinical-pathological risk factors and development of locoregional recurrence was gathered from patient files. In addition, IHC stains of ER, PR, HER2, p53, and ki67 were performed on original tumor samples. Univariable Cox regression analyses were performed to identify possible risk factors for locoregional recurrence. Results: 190 patients were included. At a median follow-up time of 12.8 years fifteen (8%) patients developed locoregional recurrence: 7 invasive cancer and 8 DCIS. These recurrences were diagnosed within a range of 1.7 to 19.6 years after the initial diagnosis. Univariable Cox regression analysis did only show a significant association between p53 and locoregional recurrence. Our re-excision rate to obtain free margins was 30.5%, and 90% received radiotherapy. Endocrine treatment was not used. Conclusions: At 12.8 years follow-up, patients with DCIS treated with BCS have a very low locoregional recurrence of 8%. Although we could demonstrate that increased p53 expression is a risk factor for locoregional recurrence, we think this is of little clinical value in our population with such a low recurrence rate. Microabstract: With a published recurrence rate up to 30% after DCIS, it would be desirable to identify those at risk to adapt treatment and follow-up. We aimed to evaluate the role of immunohistochemical staining to determine the risk of locoregional recurrence, in addition to established clinical and pathological risk factors. At a median follow-up of 12.8 years, we found a locoregional recurrence rate of 8%. Increased expression of p53 is associated with an increased risk of locoregional recurrence.

Circulating Tumor DNA Profiling for Detection, Risk Stratification, and Classification of Brain Lymphomas.

Clinical outcomes of patients with CNS lymphomas (CNSLs) are remarkably heterogeneous, yet identification of patients at high risk for treatment failure is challenging. Furthermore, CNSL diagnosis often remains unconfirmed because of contraindications for invasive stereotactic biopsies. Therefore, improved biomarkers are needed to better stratify patients into risk groups, predict treatment response, and noninvasively identify CNSL. We explored the value of circulating tumor DNA (ctDNA) for early outcome prediction, measurable residual disease monitoring, and surgery-free CNSL identification by applying ultrasensitive targeted next-generation sequencing to a total of 306 tumor, plasma, and CSF specimens from 136 patients with brain cancers, including 92 patients with CNSL. Before therapy, ctDNA was detectable in 78% of plasma and 100% of CSF samples. Patients with positive ctDNA in pretreatment plasma had significantly shorter progression-free survival (PFS, P < .0001, log-rank test) and overall survival (OS, P = .0001, log-rank test). In multivariate analyses including established clinical and radiographic risk factors, pretreatment plasma ctDNA concentrations were independently prognostic of clinical outcomes (PFS HR, 1.4; 95% CI, 1.0 to 1.9; P = .03; OS HR, 1.6; 95% CI, 1.1 to 2.2; P = .006). Moreover, measurable residual disease detection by plasma ctDNA monitoring during treatment identified patients with particularly poor prognosis following curative-intent immunochemotherapy (PFS, P = .0002; OS, P = .004, log-rank test). Finally, we developed a proof-of-principle machine learning approach for biopsy-free CNSL identification from ctDNA, showing sensitivities of 59% (CSF) and 25% (plasma) with high positive predictive value. We demonstrate robust and ultrasensitive detection of ctDNA at various disease milestones in CNSL. Our findings highlight the role of ctDNA as a noninvasive biomarker and its potential value for personalized risk stratification and treatment guidance in patients with CNSL.

Cardiovascular Risk Factors in Children and Adolescents with Type 1 Diabetes Mellitus: The Role of Insulin Resistance and Associated Genetic Variants

Introduction: Type 1 diabetes (T1D) is associated with an increased risk of cardiovascular disease. Insulin resistance is an important cardiovascular risk factor (CVRF), also in subjects with T1D, but the influence of the genetic predisposition of insulin resistance on cardiovascular risk is still unknown in T1D. We aimed to determine whether a genetic score composed of six variants, previously associated with insulin resistance and type 2 diabetes (T2D) risk, associates with insulin sensitivity and known CVRFs in children and adolescents with T1D. Materials and Methods: 330 children and adolescents (174 males; mean age 15.7 ± 3.5 years) with T1D were genotyped for the following genetic variants: rs1801278 (IRS1), rs1044498 (ENPP1), rs2295490 (TRIB3), rs1801282 (PPARG), rs780094 (GCKR), and rs35767 (IGF1). An additive genetic risk score (GRS) and cardiovascular risk score (CVRS) were calculated. Anthropometric, glycemic control, insulin sensitivity, blood pressure, and biochemical parameters were assessed. Multivariate regression between evaluated phenotypes and GRS was performed. Results: We found a significant association between the GRS and estimated insulin sensitivity (β = −0.027 [−0.040 to −0.013], R² = 0.86, p≤ 0.001), diastolic blood pressure (β = 0.68 [0.08–1.27], R² = 0.20, p = 0.026), triglycerides (β = 4.26 [1.74–6.77], R² = 0.13, p = 0.001), waist to height ratio (β = 0.003 [0.001–0.006], R² = 0.75, p = 0.010), non-HDL-cholesterol (β = 3.63 [1.39–5.87], R² = 0.12, p = 0.002), and CVRS (β = 0.063 [0.008–0.118], R² = 0.19, p = 0.025), independent of age, sex, BMI, pubertal stage, diabetes duration, glycated hemoglobin, type of treatment, and total insulin requirement. The addition of the GRS to established clinical risk factors significantly improved the discriminatory capability of the regression model for predicting subjects with more CVRFs (C-statistic 0.89 [95% CI: 0.84–0.95] versus 0.83 (0.73–0.93); p = 0.037). Conclusions: Insulin resistance and T2D risk-associated genetic variants influence insulin sensitivity and known CVRFs in children and adolescents with T1D.

Exploration of Menarche, Dietary Iron, and Additional Risk Factors on the Development of Iron Deficiency in Adolescent Girls

Background: Iron deficiency (ID) is estimated to affect between 9 and 16% of adolescent girls in the United States. It occurs during a critical time of increased iron demands related to blood volume expansion, muscle mass increase, and menstrual blood loss. Black girls are disproportionately affected, likely due to social determinants of health. ID is associated with decreased cognitive function, exercise capacity, and fatigue. Unrecognized ID can progress to complications of severe anemia. Despite readily available therapies including both oral and intravenous iron, no universal screening recommendations or evidence-based prediction tools exist to identify adolescent girls at risk for ID. Our objective was to determine the prevalence of, timing of, and clinical risk factors for ID in a large, biracial cohort of adolescent girls. Methods: Existing clinical data and stored serum specimens were obtained from the National Heart Lung and Blood Institute (NHLBI) Growth and Health Study (NGHS). The NGHS was a 10-year longitudinal study initiated in 1985 that enrolled 2,379 girls (51% Black, 49% White) from three cities (San Francisco, Cincinnati, Washington DC). Subjects participated in annual visits with serum samples obtained at Years 1, 3, 5, 7, and 10 with a retention rate of 89% at the 10th annual visit. Clinical data included socioeconomic data, body mass index (BMI), age at menarche, menstrual and pregnancy history, use of oral contraceptive medications, and dietary iron intake. We hypothesized that >10% of NGHS subjects would meet criteria for ID. We further hypothesized that longer duration from onset of menarche, low dietary iron intake, history of pregnancy, and obesity (defined as BMI >30) would be associated with ID. Available serum samples at Year 10 (n=692) were analyzed with Q-Plex Human Micronutrient Array ELISA kits (Quansys Biosciences) to measure iron-related parameters including serum ferritin, soluble transferrin receptor (sTfR1), sTfR1:ferritin index, and C-reactive protein. Subjects with ferritin >150 ng/mL and CRP >5 mg/L were excluded. ID was defined as serum ferritin <15 ng/mL, sTfR1 >5 mg/L, and sTfR1:ferritin index >2. Univariate logistic regression models assessed for the association of ID with established clinical risk factors. Results: Clinical and laboratory data from 510 subjects were included in the final analysis (median age 19 years, 51.4% Black). Of those, 83 (n=16.3%) were found to be iron deficient. ID was not associated with racial group, poverty threshold, BMI, age at menarche, history of pregnancy, oral contraceptive pill use, or average daily iron intake (TABLE). Subjects with later onset of menarche tended to have lower ferritin (OR 1.27 [1.05-1.54], p<0.015, FIGURE). Conclusion: The prevalence of ID in subjects 18 to 19 years of age enrolled in the NGHS was consistent with higher values of previously published prevalence estimates in adolescent girls. Established risk factors for the development of ID, such as earlier onset of menarche, were not clearly associated with ID in this cohort. These findings suggest that universal screening, rather than targeted screening, may be important for the early identification of all girls with ID. Additional clinical data and serum samples from Years 5 and 7 of the NGHS will be obtained to determine the prevalence of ID in the same cohort of subjects at earlier ages and determine changes in iron status over time. The results from this study will inform the development of a risk prediction tool as well as evidenced-based screening recommendations for ID in adolescent girls. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Multi-Omics Profiling of JMML HSPCs Reveals Onco-Fetal Reprogramming and Identifies Novel Prognostic Biomarkers and Therapeutic Targets in High-Risk JMML

Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative neoplasm of early childhood, which is characterized by highly heterogenous clinical outcomes ranging from spontaneous resolution to early relapse after hematopoietic stem cell transplantation (HSCT). Established clinical risk factors as well as characteristic mutational patterns do not fully explain this heterogeneity. Recently, we and others identified DNA methylation subgroups in JMML, which were shown to be the only significant factor for predicting overall survival (Schönung et al., Clinical Cancer Research 2021). Together, these findings suggest a functional role for DNA methylation in the molecular pathogenesis of JMML. To elucidate the impact of aberrant DNA methylation on pathogenesis and disease progression, we leveraged a multi-modal molecular analysis approach. We integrated flow cytometry data with single-cell RNA-seq and ultra-low input whole-genome bisulfite sequencing (WGBS) data in order to dissect the hematopoietic system across patients from all epigenetic subgroups (n=8). With this approach, we aimed to identify novel candidate prognostic biomarkers and therapeutic targets for high-risk JMML patients. We observed conservation of epigenetic subgroups already in the hematopoietic stem and progenitor cell (HSPC) compartment, suggesting that differential methylation might be key to understand pathogenesis and course of the disease in JMML. In addition, single-cell transcriptomics revealed disease-specific aberrations to be most pronounced in the HSPC compartment relative to all other hematopoietic cell types. Consequently, we focused our work on JMML HSPCs, which revealed a high degree of immunophenotypic, transcriptomic and methylomic heterogeneity across JMML risk groups. This included the expression of proto-oncogenes belonging to the AP-1 family, CTNNB1 and MECOM (EVI1). Furthermore, we observed divergent lineage priming across JMML subgroups which affected lymphoid, myeloid and erythroid differentiation programs. Remarkably, subgroup-specific aberrations comprised upregulation of fetal developmental programs in HSPCs from high-risk JMML patients. This is of particular interest regarding the cell-of-origin in JMML, since mutations activating the RAS signaling pathway have been identified in newborn blood samples from children who later developed JMML (Behnert et al., Leukemia 2022). Comparing the methylome data of HSPCs from JMML patients with HSPCs from healthy individuals at different developmental stages (fetal, perinatal, juvenile and adult) suggested progressive epigenomic reprogramming of leukemic HSPCs towards high-risk JMML. As a consequence, we observed signatures of both accelerated aging and aberrant activation of onco-fetal programs. This included the downregulation of postnatal HSPC markers such as AVP and vice versa the upregulation of prenatal markers such as HMGA2 in high-risk HSPCs, which was associated with differential methylation of these genes. Moreover, binding motifs of central hematopoietic and developmental transcription factors such as RUNX1, FLI1, CDX4, and several HOX genes were not only enriched in differentially methylated regions but were also found to be differentially methylated themselves. Systematic comparison of JMML HSPCs with their healthy nearest normal counterparts revealed disease-specific expression of several cell surface markers. The expression patterns appeared to be regulated in a subgroup-specific manner, which nominates these factors as novel prognostic biomarkers in JMML. Moreover, we identified surface markers that were not only differentially methylated but also known drug targets in other entities, which is why we selected these genes as high confidence candidates for further functional validation. In a preclinical patient-derived xenograft (PDX) mouse model, targeted treatment with a therapeutic antibody against one of these candidates efficiently depleted high-risk JMML HSPCs and improved survival of JMML-PDX mice. In conclusion, the personalized molecular analysis of JMML HSPCs disentangled disease-specific heterogeneity and provided novel insights into tumorigenesis. Subgroup-specific epigenetic dysregulation included onco-fetal reprogramming of JMML HSPCs and revealed novel prognostic biomarkers and promising novel drug targets specifically for high-risk JMML.

Prognostic value of combined MTV and ADC derived from baseline FDG PET/MRI in aggressive non-Hodgkins lymphoma

PurposeThe aim of this prospective study was to investigate the prognostic value of metabolic tumor volume (MTV) and apparent diffusion coefficient (ADC) from baseline FDG PET/MRI compared to established clinical risk factors in terms of progression free survival (PFS) at 2 years in a cohort of diffuse large B-cell Lymphoma (DLBCL) and high-grade-B-cell lymphoma (HGBCL).MethodsThirty-three patients and their baseline PET/MRI examinations were included. Images were read by two pairs of nuclear medicine physicians and radiologists for defining lymphoma lesions. MTV was computed on PET, and up to six lymphoma target lesions with restricted diffusion was defined for each PET/MRI examination. Minimum ADC (ADCmin) and the corresponding mean ADC (ADCmean) from the target lesion with the lowest ADCmin were included in the analyses. For the combined PET/MRI parameters, the ratio between MTV and the target lesion with the lowest ADCmin (MTV/ADCmin) and the corresponding ADCmean (MTV/ADCmean) was calculated for each patient. Clinical, histological, and PET/MRI parameters were compared between the treatment failure and treatment response group, while survival analyses for each variable was performed by using univariate Cox regression. In case of significant variables in the Cox regression analyses, Kaplan-Meier survival analyses with log-rank test was used to study the effect of the variables on PFS.ResultsECOC PS scale ≥2 (p = 0.05) and ADCmean (p = 0.05) were significantly different between the treatment failure group (n = 6) and those with treatment response (n = 27). Survival analyses showed that ADCmean was associated with PFS (p = 0.02, [HR 2.3 for 1 SD increase]), while combining MTV and ADC did not predict outcome. In addition, ECOG PS ≥2 (p = 0.01, [HR 13.3]) and histology of HGBCL (p = 0.02 [HR 7.6]) was significantly associated with PFS.ConclusionsADCmean derived from baseline MRI could be a prognostic imaging biomarker for DLBCL and HGBCL. Baseline staging with PET/MRI could therefore give supplementary prognostic information compared to today’s standard PET/CT.

P18.01.A The applicability of established clinical and histopathological risk factors for tumor recurrence during long-term perioperative care in meningioma patients

Abstract Background Risk factors for the prediction of late-onset tumor recurrence in meningioma patients are sparse but needed to estimate duration and control intervals during long-term follow-up in meningioma patients. In this study, we therefore analyzed the value of established risk factors for postoperative meningioma recurrence for the prediction of long-term prognosis in a large, single-center series. Material and methods Correlations of established clinical (age, sex, tumor location, extent of resection), radiological (postoperative tumor volume) and histopathological variables (WHO grade, brain invasion) with tumor relapse were analyzed separately after 3, 5, and 10 years following microsurgery for primary diagnosed intracranial meningioma between 1991 and 2021 in uni- and multivariate analyses. The prognostic value was compared to findings in the entire cohort. Results Within a median follow-up of 29 months (range: 0-307 months), recurrence was observed in 141 patients (12%) after a median PFS of 36 months. PFS among the entire cohort (n=1218) at 3, 5, 10 and 15 years postoperatively were 90%, 84%, 74% and 70%, respectively. Among all patients included, skull base location (HR: 1.51, 95%CI 1.05-2.16; p=.026), Simpson ≥IV resections (HR: 2.41, 95%CI 1.52-3.84; p&amp;lt;.001), high-grade histology (HR: 3.70, 95%CI 2.50-5.47; p&amp;lt;.001) and male gender (HR: 1.46, 95%CI 1.01-2.11; p=.042) were independent risk factors for recurrence. Skull base location (HR: 1.92, 95%CI 1.17-3.17; p=.010 and HR: 2.02, 95%CI 1.04-3.95; p=.038) and high-grade histology (HR: 1.87, 95%CI 1.04-3.38; p=.038 and HR: 2.29, 95%CI 1.07-4.01; p=.034) but not subtotal resection (HR: 1.53, 95%CI .68-3.45; p=.303 and HR: 1.75, 95%CI .52-5.96; p=.369) remained independently correlated with recurrence after an event-free PFS of at least three (n=485) and five years (n=346), respectively. Similarly, postoperative tumor volume was related with recurrence in the entire cohort (p&amp;lt;.001) but not beyond a follow-up of ≥ three years (p&amp;gt;.05). In 147 patients with a follow-up of ≥ ten years, ten recurrences occurred, and no correlation was found with any of the analyzed variables. Conclusion Skull base tumor location and high-grade histology but not the extent of resection should be considered when planning follow-up duration and intervals following ≥5 years after meningioma surgery. Tumor relapses following more than ten years after surgery are very rare, and corresponding predictors are lacking.