Independent Validation Cohort Research Articles

Development and validation of a tool for assessing adherence to gluten free diet in patients with Celiac disease (Celiac Disease: Compliance Assessment Test; CD-CAT).

Life-long adherence to gluten-free diet (GFD) and its assessment is essential for celiac disease (CeD) patients. We have developed and validated a tool for assessing adherence to GFD which can be used by both physicians and dietitians. Phase-I: Development, content validation and assessment of reliability of tool. Phase-II: Validation of tool against standard dietary evaluation (SDE) (gold standard), IgA anti-tissue transglutaminase antibody (IgA-anti-tTG Ab) and gluten immunogenic peptides in urine (Urine-GIP). Overall, 380 biopsy-confirmed CeD patients [Derivation-cohort: n=100(Phase:I), n=210(Phase:II) and Independent validation cohort, n=70)were recruited. Of initial 90-point questionnaire, 84-items (CD-CAT.v1) were retained after content validation and pilot testing. In Phase:I, upon administering CD-CAT.v1on 100 patients, a comprehensive 35-items tool (CD-CAT.v2;α=0.86) was obtained after removing items with low test-retest reliability and item-rest correlation values. In Phase:II, upon administering CD-CAT.v2 on 210patients,22 items were removed having low correlation values (R<0.4) with SDE. Finally, a 13-item tool (CD-CAT.v3;α=0.84) was obtained with high criterion validity with SDE (r=0.806, p<0.001), moderate convergent validity with CDAT (r=0.602, p=0.007) and moderate to weak correlation with urine GIP (r=0.46, p=0.001) and IgA anti-tTG Ab (r=0.39, p=0.008), respectively. The final 13-item tool also strongly correlated with SDE (r=0.78, p<0.001) in an independent validation cohort of 70 CeD patients. Principal component analysis identified three relevant sub-scales with a cumulative-variance of 62%. The sensitivity and specificity of CD-CAT.v3 were 80% and 91%, respectively, with an area under curve of 0.905 with SDE. The obtained cut-off score of <19 from ROC-curve was further categorized as: 13=excellent,14-18=very good, 19-28=average and >28=poor adherence to GFD. CD-CAT is a new and rapid tool for monitoring dietary adherence to GFD with high sensitivity and specificity which can be administered by both physicians and dietitians.

Circulating microRNA as a Potential Biomarker for Skeletal Disease in Primary Hyperparathyroidism: A Case-control Study.

The goal of this study was to characterize the microRNA (miRNA) expression signatures in patients with PHPT and identify miRNA biomarkers of bone homeostasis. Primary hyperparathyroidism (PHPT) is associated with increased bone turnover and decreased bone mass. miRNA are markers of bone remodeling. We performed a prospective case-control study of post-menopausal females with PHPT and control subjects matched for race, age, and BMD. We collected clinical and biochemical data, assessed BMD by dual-energy X-ray absorptiometry, and measured 27 serum miRNAs related to bone remodeling. We used linear regression to assess the correlation between miRNA levels, conventional biochemical markers and BMD. A total of 135 subjects were evaluated, including 49 with PHPT (discovery group), 47 control patients without PHPT, and an independent validation cohort of 39 PHPT patients. Of 27 miRNAs evaluated, nine (miR-335-5p, miR-130b-3p, miR-125b-5p, miR-23a-3p, miR-152-3p, miR-582-5p, miR-144-5p, miR-320a and miR-19b-3p) were differentially expressed in PHPT compared to matched control subjects. All nine differentially expressed miRNAs significantly correlated with levels of serum parathyroid hormone (PTH), and eight of the nine correlated with calcium levels. No differentially expressed miRNAs were consistently correlated with markers of BMD. Subjects with PHPT segregate from controls based on the signature of these nine miRNAs on principle component analysis. These data suggest that PHPT is characterized by a unique miRNA signature that is distinct from postmenopausal and idiopathic osteoporosis. Levels of specific miRNAs significantly correlate with PTH, suggesting that bone remodeling in PHPT may be mediated in part by PTH-induced changes in miRNA.

Derivation and Validation of a Scoring System for Predicting Poor Outcome After Posterior Circulation Ischemic Stroke in China.

Guidelines for posterior circulation ischemic stroke (PCIS) treatment are lacking and outcome prediction is crucial for patients and clinicians. We aimed to develop and validate a prognostic score to predict the poor outcome for patients with PCIS. The score was developed from a prospective derivation cohort named the Third China National Stroke Registry (August 2015-March 2018) and validated in a spatiotemporal independent validation cohort (December 2017-March 2023) in China. Patients with PCIS with acute infarctions defined as hyperintense lesions on diffusion-weighted imaging were included in this study. The poor outcome was measured as modified Rankin scale (mRS) score 3-6 at 3 months after PCIS. Multivariable logistic regression analysis was used to identify predictors for poor outcome. The prognostic score, namely PCIS Outcome Score (PCISOS), was developed by assigning points to variables based on their relative β-coefficients in the logistic model. The PCISOS was derived from 3,294 patients (median age 62 [interquartile range (IQR) 55-70] years; 2,250 [68.3%] men) and validated in 501 patients (median age 61 [IQR 53-68] years; 404 [80.6%] men). Among them, 384 (11.7%) and 64 (12.8%) had poor outcome 3 months after stroke in respective cohorts. Age, mRS before admission, NIH Stroke Scale on admission, ischemic stroke history, infarction distribution, basilar artery, and posterior cerebral artery stenosis or occlusion were identified as independent predictors for poor outcome and included in PCISOS. This easy-to-use integer scoring system identified a marked risk gradient between 4 risk groups. PCISOS performed better than previous scores, with an excellent discrimination (C statistic) of 0.80 (95% CI 0.77-0.83) in the derivation cohort and 0.81 (95% CI 0.77-0.84) in the validation cohort. Calibration test showed high agreement between the predicted and observed outcomes in both cohorts. PCISOS can be applied for patients with PCIS with acute infarctions to predict functional outcome at 3 months post-PCIS. This simple tool helps clinicians to identify patients with PCIS with higher risk of poor outcome and provides reliable outcome expectations for patients. This information might be used for personalized rehabilitation plan and patient selection for future clinical trials to reduce disability and mortality.

Identification of a gene expression signature of vascular invasion and recurrence in stage I lung adenocarcinoma via bulk and spatial transcriptomics.

Microscopic vascular invasion (VI) is predictive of recurrence and benefit from lobectomy in stage I lung adenocarcinoma (LUAD) but is difficult to assess in resection specimens and cannot be accurately predicted prior to surgery. Thus, new biomarkers are needed to identify this aggressive subset of stage I LUAD tumors. To assess molecular and microenvironment features associated with angioinvasive LUAD we profiled 162 resected stage I tumors with and without VI by RNA-seq and explored spatial patterns of gene expression in a subset of 15 samples by high-resolution spatial transcriptomics (stRNA-seq). Despite the small size of invaded blood vessels, we identified a gene expression signature of VI from the bulk RNA-seq discovery cohort (n=103) and found that it was associated with VI foci, desmoplastic stroma, and high-grade patterns in our stRNA-seq data. We observed a stronger association with high-grade patterns from VI + compared with VI - tumors. Using the discovery cohort, we developed a transcriptomic predictor of VI, that in an independent validation cohort (n=60) was associated with VI (AUROC=0.86; p=5.42×10 -6 ) and predictive of recurrence-free survival (HR=1.98; p=0.024), even in VI - LUAD (HR=2.76; p=0.003). To determine our VI predictor's robustness to intra-tumor heterogeneity we used RNA-seq data from multi-region sampling of stage I LUAD cases in TRACERx, where the predictor scores showed high correlation (R=0.87, p<2.2×10 -16 ) between two randomly sampled regions of the same tumor. Our study suggests that VI-associated gene expression changes are detectable beyond the site of intravasation and can be used to predict the presence of VI. This may enable the prediction of angioinvasive LUAD from biopsy specimens, allowing for more tailored medical and surgical management of stage I LUAD.

Fracture prediction in a Swiss cohort.

Fracture prediction is essential in managing patients with osteoporosis, and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training and validating short- and long-term fracture risk prediction models in two cohorts. We used traditional and machine learning survival models to predict risks of vertebral, hip and any fractures on the basis of clinical risk factors, T-scores and treatment history among participants in a nationwide Swiss osteoporosis registry (N = 5944 postmenopausal women, median follow-up of 4.1years between January 2015 and October 2022; a total of 1190 fractures during follow-up). The independent validation cohort comprised 5474 postmenopausal women from the UK Biobank with 290 incident fractures during follow-up. Uno's C-index and the time-dependent area under the receiver operating characteristics curve were calculated to evaluate the performance of different machine learning models (Random survival forests and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86] for vertebral fractures, 0.83 [0.7, 0.94] for hip fractures and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estimations of up to 7years. In comparison, the 10-year fracture probability calculated with FRAX® Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations (SHAP) values were age, T-scores and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both traditional and machine learning models showed similar C-indices. We conclude that fracture risk can be improved by including the lumbar spine T-score, trabecular bone score, numbers of falls and recent fractures, and treatment information has a significant impact on fracture prediction.

Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.

This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (VWF, IL1RL1, DENND2B, MMP14, NAAA, and PANK1) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. DENND2B and PANK1 were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.

Analysis of PD-L1 promoter methylation combined with immunogenic context in pancreatic ductal adenocarcinoma

Despite the successful application of programmed cell death ligand 1 (PD-L1)-blocking strategies in some types of cancers and well-established prognostic indicators in pancreatic ductal adenocarcinoma (PDAC), the biological and clinical implications of the methylation status of PD-L1/PD-L2 in PDAC remain largely unknown. Therefore, this study aimed to explore the biological role of PD-L1/PD-L2 methylation and its association with clinicopathological features, clinical outcomes, and the immune microenvironment by analyzing the data on PD-L1/PD-L2 methylation and mRNA expression in PDAC cohorts obtained from the Cancer Genome Atlas and International Cancer Genome Consortium. The correlation between PD-L1 promoter methylation and PD-L1 expression and survival was further validated in an independent validation cohort (Peking Union Medical College Hospital [PUMCH] cohort) using pyrosequencing and immunohistochemistry. These results demonstrated that hypomethylation of the PD-L1 promoter was strongly associated with upregulated PD-L1 expression and shorter overall survival in PDAC. Multivariate Cox regression analyses revealed that the PD-L1 promoter methylation was an independent prognostic factor. PD-L1 promoter hypomethylation and high expression were related to aggressive clinical phenotypes. Moreover, both PD-L1 and PD-L2 methylation correlated with immune cell infiltration and the expression of immune checkpoint genes. PD-L1 promoter methylation status was further validated as an independent prognostic biomarker in patients with PDAC using the PUMCH cohort. The prognostic significance of PD-L1 promoter methylation was more discriminative in tumors with perineural/lymphovascular invasion and distant metastasis than in those without perineural/lymphovascular invasion and distant metastasis. In summary, the methylation status of the PD-L1 promoter is a promising biomarker for survival outcomes, immune infiltration, and the potential immune benefits of immunotherapy in PDAC.

Gut microbiota metabolism disturbance is associated with postoperative atrial fibrillation after coronary artery bypass grafting

Postoperative atrial fibrillation (POAF) is a common complication after coronary artery bypass grafting (CABG) surgery. Gut microbiota and its metabolites have been implicated in the development of AF. However, whether the gut–host metabolic interaction contributes to POAF is still unknown. This study aimed to investigate the POAF-associated gut microbiota metabolism biomarkers and related risk model. The POAF (N = 30) patients and non-POAF (N = 60) patients from the discovery cohort exhibited significantly different microbiome and metabolome profiles. The differentiated features were mainly implicated in the bile acids (BAs) and short-chain fatty acids metabolism, inflammation, and oxidative stress. Random forest analysis identified the combination of five secondary BAs showed a powerful performance on predicting POAF in the discovery cohort, highlighting significant values of area under the curve (AUC = 0.954) and correct classification rate (CCR, 93.3%). In addition, the five secondary BAs-based risk model also exhibited good performance in differentiating the POAF (N = 114) and non-POAF individuals (N = 253) in an independent validation cohort (AUC = 0.872; CCR = 90.4%). This work revealed perturbed microbial and metabolic traits in POAF, providing potential avenues for the prediction and prevention of POAF after CABG.

Leveraging cfDNA fragmentomic features in a stacked ensemble model for early detection of esophageal squamous cell carcinoma.

4054 Background: In this study, we developed a stacked ensemble model that leverages cell-free DNA (cfDNA) fragmentation for the early detection of esophageal squamous cell carcinoma (ESCC). The model combined four fragmentomics features obtained from whole genome sequencing (WGS) and employed four machine learning algorithms. We evaluated the model’s generalizability in an independent validation cohort and an external cohort collected at different center. Additionally, the model’s robustness and repeatability were assessed across low coverage and repeated measured samples. The results underscore the promising potential of our model as an effective strategy for early diagnosis and management of ESCC in clinical settings. Methods: 256 healthy individuals and 243 patients diagnosed with esophageal cancer were enrolled in this study, including 47 healthy participants and 44 patients in the external cohort. Plasma samples from all participants were profiled by whole-genome sequencing (WGS). Fragmentomic features encompassed copy number variation (CNV), fragmentation size coverage (FSC), fragmentation size distribution (FSD), and nucleosome positioning (NP) were incorporated alongside machine learning models to develop an optimized classification model. The model generated a cancer score ranging from 0 to 1 for each cancer and noncancer sample, with a score closer to 1 indicating a higher probability of cancer. The performance of this model was assessed using an independent validation cohort and an external cohort, and model’s robustness and reproducibility were examined. Results: A stacked ensemble model was developed by integrating four cfDNA features and four machine learning algorithms. This integrated model exhibiting remarkable sensitivity of 91.8% (89/97) and specificity of 98.1% (102/104), and AUC of 0.986 in the independent validation cohorts using the cut-off of 0.69 selected at a specificity level of 98% (105) in the training cohort. The external cohort demonstrated a sensitivity of 86.4% (38/44) and a specificity of 95.7% (45/47) with the same cut-off. The model's performance remained consistent even at low sequencing coverage depths of 0.5× (AUC 0.978), providing valuable insights into the resilience and stability of our methodology and ascertained its practical applicability in scenarios with limited sequencing resources or constraints. Furthermore, our model demonstrated sensitivity in identifying early pathological features, with a sensitivity of 94.1% (16/17) for stage I and 91.4% (53/58) for tumors smaller than 3 cm. Conclusions: A stacked ensemble model was implemented in this study, utilizing cfDNA fragmentomics features with exceptional sensitivity for early detection of esophageal squamous cell carcinoma. The anticipated impact of this model is the enhancement of early detection strategies for esophageal cancer in clinical setting.

Cancer clinical trial participation in socioeconomically vulnerable patients: A risk model to aid in targeted interventions.

1529 Background: In patients with cancer, those with lower income are 30% less likely to participate in clinical trials. Lower income individuals face direct and indirect costs that can make participation in a clinical trial prohibitive. However, a broader evaluation of specific behavioral, information, insurance, and area-level variables that could contribute to this disparity has not been conducted. We aimed to develop and validate a model to identify socioeconomically vulnerable patients at high risk of non-participation in clinical trials. Methods: We used data from the Health Information National Trends Survey (HINTS), a national cross-sectional survey about knowledge of, attitudes toward, and use of health-related information. We analyzed HINTS survey databases that included questions about whether patients with cancer participated in a clinical trial (survey years 2014, 2017, and 2020). We examined 21 different demographic, socioeconomic, behavioral, geographic, and health information questions. We derived a risk model to predict clinical trial participation in a random set of 60% of participants using best subset selection with k-fold cross validation. The derived model was validated in the remaining 40% of participants. Logistic regression was used. Results: We examined N=1,023 participants with household income &lt;$75,000, the current median in the U.S. In the training dataset of n=614 participants, a model with 5 variables was identified. Non-Hispanic White patients and patients without a college education, with high levels of distrust, with high levels of anxiety or depression, and from non-urban areas were all at lower risk of trial participation. We summed the adverse risk factors for all individuals; a risk score with 4 levels was constructed based on distribution quartiles. In the independent validation cohort (n=409), each increase in level of adverse risk factors was associated with a 42% reduction in the odds of trial participation (OR=0.58, 95%-CI, 0.40-0.84, p=.004), indicating successful model validation. Among all individuals, trial participation rates decreased from 18.6% to 7.5% to 4.6% to 2.8%, respectively, as the number of adverse risk factors increased (in quartiles) from 0-1 to 2 to 3 to 4-5. Individuals with 4 or 5 risk factors (vs. those with 0-1 factors) were 87% less likely to participate (OR=0.13, 95% CI, 0.05-0.31, p&lt;.0001). Conclusions: We developed and validated a 5-variable risk model that identified a large set of lower income individuals at lower risk of trial participation. In a first-time observation, psychosocial variables were shown to be meaningful predictors of lower trial participation. These findings could aide in the early identification of patients who may benefit from additional support to navigate the treatment trial decision making process, in the name of more equitable participation in trials for all patients.

Systemic low-grade C-reactive protein is associated with proximal symptom spread in carpal tunnel syndrome.

Neuropathic pain is a highly prevalent condition associated with persistent disability. Some patients with neuropathic pain experience symptom spread outside neuroanatomical boundaries; these patients report more severe sensory symptoms and greater disability. However, the mechanisms behind such symptom spread are not fully understood. We used pre-surgical carpal tunnel syndrome (CTS) as a human model system of neuropathic pain to identify differences in the concentration of serologic inflammatory mediators between patients with CTS with territorial symptoms and those with proximal symptom spread to either the elbow or shoulder/neck. We performed a post-hoc analysis, comparing levels of serologic inflammatory mediators in a discovery cohort among 3 symptoms spread profiles (n = 55; n = 25 no spread, n = 21 spread to elbow, n = 9 spread to shoulder/neck). We then de-novo analysed the significantly dysregulated mediators in an independent validation cohort (n = 72; n = 34 no spread, n = 16 spread to elbow, n = 22 spread to shoulder/neck). The discovery cohort revealed higher serum concentrations of C-reactive protein (CRP) and interleukin-6 in patients with any symptom spread proximal to the wrist; interferon-γ was higher in patients with symptom spread to the elbow compared with those without proximal spread. The validation study replicated the association of higher CRP concentrations in patients with proximal spread to the elbow (no spread: median [interquartile range] 2.5 [5.4]; spread to elbow 6.2 [4.6]; spread to shoulder/neck 2.6 [3.7], P = 0.006). No other markers replicated in the validation cohort. Our findings suggest that proximal symptom spread in the context of neuropathic symptoms is associated with low-grade inflammation.

A liquid biopsy-based assay for classifying breast cancer from benign nodule.

e12572 Background: Breast cancer (BRCA) ranks as the most frequently diagnosed non-skin cancer and stands as the second leading cause of cancer-related mortality in women in the United States. Globally, 2.3 million women were diagnosed with breast cancer in 2020, resulting in 685,000 deaths. Therefore, accurate classification of breast cancer could contribute to immediate treatment and improve outcomes for individuals diagnosed with breast cancer. Our study introduces an ensemble model utilizing the distinctive characteristics of cell-free DNA (cfDNA) fragmentation from whole genome sequencing (WGS) for the classification of BRCA and benign nodule. Methods: The study cohort consisted of 214 individuals diagnosed with breast cancer (BRCA) and 211 individuals with benign breast nodule. Employing whole-genome sequencing (WGS), we subjected plasma samples from all participants to comprehensive fragmentomic profiling. Three fragmentomic features: copy number variation (CNV), fragmentation size coverage (FSC), and fragmentation size distribution (FSD), were utilized by machine learning algorithms. An ensemble model integrated results from each algorithm then generated a cancer score ranging from 0 to 1 for each sample. The model was trained exclusively using the training cohort and the model’s performance was assessed in the independent validation cohort. Results: Our ensemble model demonstrated a sensitivity of 80.2% and a specificity of 95.3% in training cohort, with an AUC of 0.966. In the validation cohort, the model maintained its effectiveness, with a sensitivity of 73.6%, a specificity of 89.3% supported by an AUC of 0.919. Notably, the model showed various predictive ability across different stages of breast cancer. In the training cohort, it exhibited a sensitivity of 76.4% for Stage I, 81.8% for Stage II, and 88.9% for Stage III, indicating superior predictive power at advanced progression levels. Additionally, the model demonstrated a consistent sensitivity of 75% for Stage I in the validation cohort, affirming its reliable performance across independent datasets. When stratified by molecular subtypes, the model showed relatively lower scores and sensitivity BRCA with HR+/HER2+, compared to HR-/HER2+ and HR+/HER2- subgroups, as well as Triple-Negative Breast Cancer. Finally, the model generated unbiased predictions across various histology subtypes, accurately discerning ductal carcinoma and other BRCA histology subtypes in training and validation cohorts. Conclusions: The ensemble model that leverages non-invasive liquid biopsy-based assay for the accurate classification of BRCA and benign nodule provides a critical opportunity for effective intervention for cancer patients. This approach, facilitating swift and appropriate treatments, and optimizing the allocation of healthcare resources, underscores its significance in the ongoing pursuit of enhancing breast cancer care and outcomes.

Prognosis recovery score of apical surgery Guided Bone Regeneration using cone beam computed tomography and digital bioinformatics

ObjectiveGuided Bone Regeneration (GBR) is a dental surgical procedure that uses barrier membranes to prevent soft tissue invasion and conduct new bone growth. This study aimed to define a Prognosis Recovery score (PR score) to objectively classify post-surgery responders from non-responder patients who underwent GBR using Cone Beam Computed Tomography (CBCT). MethodsThis prospective-observational-longitudinal-cohort study recruited 250 individuals who were assigned to: Conventional-Apical-Surgery (CAS, n = 39), Apical-Surgery using human fascia lata Membrane placement (ASM, n = 42), and Apical-Surgery using human fascia lata Membrane placement and lyophilized allograft Bone powder (ASMB, n = 39); and Apical-Surgery using collagen membrane Porcine origin and Bovine Bone-matrix (ASPBB, n = 130), an independent external validation cohort. Surgery was performed, and evolution was monitored by CBCTs at 0, 6-, 12-, 18-, and 24 months post-surgery. ResultsNormalized lesion volumes were calculated, and non-linear time evolution morphology curves were characterized. The three-time evolution bone growth patterns were: a linear tendency (PR0), “S'' shaped log-logistic (PR1), and “C" cellular growth (PR2). The treatment success rates were PR2-46 %, PR2-88 %, and PR2-95 %/PR1-5% for CAS, ASM, and ASMB groups. The xenograft ASPBB counterpart achieved PR2-92 % and PR1-8%. The score PR had a sensitivity, specificity, and accuracy of 100 %. ConclusionsPatients' treatment success can be quantitatively, objectively, and precisely predicted with the Prognosis Recovery score (using only two CBCTs), according to their biological response to allograft or xenograft materials (time-evolution bone growth curves), reducing cost and radiation exposure. Clinical significanceThrough digital imaging and bioinformatic analysis of bone regeneration observed in CBCTs, we defined a Prognosis Recovery (PR) score using only two CBCT volume assessments (0 and 6 months). The PR score allowed us to define three time-evolution curves depending on the biomaterials used and to classify patients in a quantitative, objective, and accurate way.

Plasma extracellular vesicle tau and TDP-43 as diagnostic biomarkers in FTD and ALS

Minimally invasive biomarkers are urgently needed to detect molecular pathology in frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Here, we show that plasma extracellular vesicles (EVs) contain quantifiable amounts of TDP-43 and full-length tau, which allow the quantification of 3-repeat (3R) and 4-repeat (4R) tau isoforms. Plasma EV TDP-43 levels and EV 3R/4R tau ratios were determined in a cohort of 704 patients, including 37 genetically and 31 neuropathologically proven cases. Diagnostic groups comprised patients with TDP-43 proteinopathy ALS, 4R tauopathy progressive supranuclear palsy, behavior variant FTD (bvFTD) as a group with either tau or TDP-43 pathology, and healthy controls. EV tau ratios were low in progressive supranuclear palsy and high in bvFTD with tau pathology. EV TDP-43 levels were high in ALS and in bvFTD with TDP-43 pathology. Both markers discriminated between the diagnostic groups with area under the curve values >0.9, and between TDP-43 and tau pathology in bvFTD. Both markers strongly correlated with neurodegeneration, and clinical and neuropsychological markers of disease severity. Findings were replicated in an independent validation cohort of 292 patients including 34 genetically confirmed cases. Taken together, the combination of EV TDP-43 levels and EV 3R/4R tau ratios may aid the molecular diagnosis of FTD, FTD spectrum disorders and ALS, providing a potential biomarker to monitor disease progression and target engagement in clinical trials.

Assessment of plasma cell-free DNA fragmentation for multi-cancer early detection: An independent clinical validation study.

e15037 Background: Cell-free DNA (cfDNA) in the circulation has gained significant attention due to its potential applications for non-invasive early cancer detection. Fragmentomics of cfDNA is emerging as a promising field of biomarker research, exhibiting the capability to sensitively detect multiple cancer types. In previous retrospective research, we developed an approach called PatternWGS for comprehensive analysis of cfDNA fragmentation patterns and presented a multi-cancer detection model. In this study, we aim to evaluate the performance of PatternWGS in an independent validation cohort including lung, breast, colorectal, liver, and gastric cancers from a new clinical center. Methods: Participants with confirmed diagnosis of lung, breast, colorectal or liver cancer were enrolled in the cancer group. Non-cancer participants without known presence of malignancies were recruited from the same clinical center. Plasma samples from patients with lung (n=47), breast (n=19), colorectal (n=14), liver (n=8) and gastric (n=6) cancers, along with 101 non-cancer individuals were collected in this study. cfDNA was isolated from plasma and stored within a month prior to the whole genome sequencing. The characteristics of cfDNA fragmentation integrating fragment size profiles, motif patterns, genomic coverage distributions were utilized to evaluate the performance for cancer signal detection. In addition, the performance of estimated cfDNA tumor fraction, using copy number aberrations (ichorCNA), was compared with our approach. Results: 94 patients with one of five types of cancers and 101 non-cancer controls were prospectively enrolled between July 2023 and November 2023. Among the cancer group, 64% were at early stages (Stage I: 48%, Stage II: 16%). A significantly higher proportion of shorter fragments was observed in cancer group, and the degree increased with increasing cancer stage. Overall sensitivity across cancer types and stages was 72.3%, and the specificity for non-cancer controls was 80%. As expected, the sensitivity of cancer detection is higher in late-stage patients compared to those in the early-stages (61% in stage I, 80% in stage II, 84.6% in stage III, and 91.7% in stage IV). The sensitivity of detecting lung (72% had stage I cancer), breast, colorectal, liver, and gastric cancers reached 57.4%, 84.2%, 85.7%, 87.5% and 100% respectively. The detection performance using cfDNA tumor fraction by ichorCNA demonstrated a relatively low overall sensitivity of 28.7% (24.6% for stage I-II, 44% for stage III-IV). Conclusions: This independent clinical validation study, which focused on exploring plasma cfDNA fragmentation, demonstrated ideal performance in early-stage cancer detection. The cfDNA fragmentation-based approach exhibited a superior sensitivity in detecting early cancer signals compared with copy number aberrations-based approach.

Developing a predictive model for metastatic potential in pancreatic neuroendocrine tumor.

Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive. Use machine learning to develop predictive models of PNET metastatic potential dependent upon transcriptomic signature. RNA-sequencing data were analyzed from 95 surgically-resected primary PNETs in an international cohort. Two cohorts were generated with equally balanced metastatic PNET composition. Machine learning was used to create predictive models distinguishing between localized and metastatic tumors. Models were validated on an independent cohort of 29 formalin-fixed, paraffin-embedded samples using NanoString nCounter®, a clinically-available mRNA quantification platform. Gene expression analysis identified concordant differentially expressed genes between the two cohorts. Gene set enrichment analysis identified additional genes that contributed to enriched biologic pathways in metastatic PNETs. Expression values for these genes were combined with an additional 7 genes known to contribute to PNET oncogenesis and prognosis, including ARX and PDX1. Eight specific genes (AURKA, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1, ZPLD1) were identified as sufficient to classify the metastatic status with high sensitivity (87.5% - 93.8%) and specificity (78.1% - 96.9%). These models remained predictive of the metastatic phenotype using NanoString nCounter® on the independent validation cohort, achieving a median AUROC of 0.886. We identified and validated an eight-gene panel predictive of the metastatic phenotype in PNETs, which can be detected using the clinically-available NanoString nCounter® system. This panel should be studied prospectively to determine its utility in guiding operative versus non-operative management.

Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD

Accurate non-invasive biomarkers to diagnose metabolic dysfunction-associated steatotic liver disease (MASLD)-related fibrosis are urgently needed. This study applies a translational approach to develop a blood-based biomarker panel for fibrosis detection in MASLD. A molecular gene expression signature identified from a diet-induced MASLD mouse model (LDLr−/−.Leiden) is translated into human blood-based biomarkers based on liver biopsy transcriptomic profiles and protein levels in MASLD patient serum samples. The resulting biomarker panel consists of IGFBP7, SSc5D and Sema4D. LightGBM modeling using this panel demonstrates high accuracy in predicting MASLD fibrosis stage (F0/F1: AUC = 0.82; F2: AUC = 0.89; F3/F4: AUC = 0.87), which is replicated in an independent validation cohort. The overall accuracy of the model outperforms predictions by the existing markers Fib-4, APRI and FibroScan. In conclusion, here we show a disease mechanism-related blood-based biomarker panel with three biomarkers which is able to identify MASLD patients with mild or advanced hepatic fibrosis with high accuracy.

Post-ablation atrial arrhythmogenic channels predict atrial fibrillation recurrence - iatrogenic substrate revisited

Abstract Background Fibrosis is a hallmark of atrial fibrillation (AF) arrhythmogenic substrate, and the extent of atrial fibrosis, as determined by late gadolinium enhancement (LGE)-MRI, predicts AF recurrence after ablation. However, ablation fundamentally alters the individual composition of fibrotic tissue, and inhomogeneous ablation lesions may constitute an arrhythmogenic substrate themselves. From ventricular arrhythmias (VA) we know that it is not the extent of scar tissue that determines arrhythmogenicity, but rather the scar-pervading channels of surviving tissue with residual conduction. Purpose To investigate post-ablation arrhythmogenic substrate in terms of atrial scar channels using LGE-MRI. Methods Patients with PVI-only AF ablation were included. All patients received a systematic 12-months follow-up and an LGE-MRI 3 months post-ablation. Scar channels were defined as corridors of borderzone tissue (residual conduction), protected by dense scar. Channels were automatically identified using a software algorithm that has been extensively validated in the ventricle and was now adapted for the atrium. To differentiate healthy tissue, borderzone and dense scar, LGE was quantified based on the signal intensity ratios of each voxel relative to the blood pool. Signal intensity ratio thresholds defining dense scar and borderzone, were then empirically tested to enhance the predictive value of the identified channels. The algorithm was then applied to an independent validation cohort using the empirically determined thresholds. Results 150 patients were included. 10 different combinations of signal intensity ratio thresholds defining borderzone tissue and dense scar were empirically tested regarding the prediction of potentially arrhythmogenic channels in 50 patients (derivation cohort). For each of the threshold combinations, the number of predicted channels was linked to 12-months arrhythmia-free survival using logistic regression. The computed channels best predicted 12-months arrhythmia recurrence, when based on signal intensity ratios of 1.1-1.32 (borderzone) and &amp;gt;1.32 (dense scar). This threshold combination was then validated in an independent cohort of 100 patients. Patients with scar channels had significantly lower arrhythmia-free survival than those without (55% vs. 88%, p=0.016, Fig. 1). The number of detected scar channels was predictive of 12-months arrythmia recurrence (OR 4.9; p=0.012) – independent of other factors like LA diameter, AF type or the extent of native atrial fibrosis (pre-ablation LGE-MRI). Of note, the number of gaps in the PV-encircling lesions was not predictive of AF recurrence. Conclusions LGE-MRI-detected post-ablation scar channels predict recurrent AF after PVI and may be a surrogate of iatrogenic substrate. In light of large-area ablations with PFA and other single-shot devices fueling a trend towards more and more extensive lesions, the concept of iatrogenic arrhythmogenic substrate may have to be reconsidered.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma.

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

Multi-omic analysis identifies hypoalbuminemia as independent biomarker of poor outcome upon PD-1 blockade in metastatic melanoma

We evaluated the prognostic value of hypoalbuminemia in context of various biomarkers at baseline, including clinical, genomic, transcriptomic, and blood-based markers, in patients with metastatic melanoma treated with anti-PD-1 monotherapy or anti-PD-1/anti-CTLA-4 combination therapy (n = 178). An independent validation cohort (n = 79) was used to validate the performance of hypoalbuminemia compared to serum LDH (lactate dehydrogenase) levels. Pre-treatment hypoalbuminemia emerged as the strongest predictor of poor outcome for both OS (HR = 4.01, 95% CI 2.10–7.67, Cox P = 2.63e−05) and PFS (HR = 3.72, 95% CI 2.06–6.73, Cox P = 1.38e−05) in univariate analysis. In multivariate analysis, the association of hypoalbuminemia with PFS was independent of serum LDH, IFN-γ signature expression, TMB, age, ECOG PS, treatment line, treatment type (combination or monotherapy), brain and liver metastasis (HR = 2.76, 95% CI 1.24–6.13, Cox P = 0.0131). Our validation cohort confirmed the prognostic power of hypoalbuminemia for OS (HR = 1.98, 95% CI 1.16–3.38; Cox P = 0.0127) and was complementary to serum LDH in analyses for both OS (LDH-adjusted HR = 2.12, 95% CI 1.2–3.72, Cox P = 0.00925) and PFS (LDH-adjusted HR = 1.91, 95% CI 1.08–3.38, Cox P = 0.0261). In conclusion, pretreatment hypoalbuminemia was a powerful predictor of outcome in ICI in melanoma and showed remarkable complementarity to previously established biomarkers, including high LDH.