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2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi143-vi144
Author(s):  
Omaditya Khanna ◽  
Anahita Fathi Kazerooni ◽  
Jose A Garcia ◽  
Chiharu Sako ◽  
Sherjeel Arif ◽  
...  

Abstract PURPOSE Although WHO grade I meningiomas are considered ‘benign’ tumors, an elevated Ki-67 is one crucial factor that has been shown to influence clinical outcomes. In this study, we use standard pre-operative MRI and develop a machine learning (ML) model to predict the Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed of 306 patients that underwent surgical resection. The mean and median Ki-67 of tumor specimens were 4.84 ± 4.03% (range: 0.3–33.6) and 3.7% (Q1:2.3%, Q3:6%), respectively. Pre-operative MRI was used to perform radiomic feature extraction (N=2,520) followed by ML modeling using least absolute shrinkage and selection operator (LASSO) wrapped with support vector machine (SVM) through nested cross-validation on a discovery cohort (N=230), to stratify tumors based on Ki-67 < 5% and ≥ 5%. A replication cohort (N=76) was kept ‘unseen’ in order to provide insights regarding the generalizability of our predictive model. RESULTS A total of 60 radiomic features extracted from seven different MRI sequences were used in the final model. With this model, an AUC of 0.84 (95% CI: 0.78-0.90), with associated sensitivity and specificity of 84.1% and 73.3%, respectively, were achieved in the discovery cohort. The selected features in the trained predictive model were then applied to the subjects of the replication cohort and the model was applied independently in this cohort. An AUC of 0.83 (95% CI: 0.73-0.94), with a sensitivity of 82.6% and specificity of 85.5% was obtained for this independent testing. Furthermore, the model performed commendably when applied to all skull base and non-skull base tumors in our patient cohort, evidenced by comparable AUC values of 0.86 and 0.83, respectively. CONCLUSION The results of this study may provide enhanced diagnostics to the surgeon pre-operatively such that it can guide surgical strategy and individual patient treatment paradigms.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi7-vi7
Author(s):  
Anahita Fathi Kazerooni ◽  
Sanjay Saxena ◽  
Danni Tu ◽  
Erik Toorens ◽  
Vishnu Bashyam ◽  
...  

Abstract PURPOSE Multi-omics data integration captures tumor characteristics at multiple scales [i.e., microscopic (genomics and epigenetics), macroscopic (radiomics), clinical manifestation], provides a more comprehensive assessment of patient’s risk, and facilitates personalized therapies. In this work, we investigated the synergistic value of such multiple data sources for risk stratification and prediction of overall survival in IDH-wildtype glioblastoma tumors. METHODS Quantitative conventional and deep radiomics were extracted from pre-operative multi-parametric structural MRI (T1, T1Gd, T2, T2-FLAIR) of 501 patients with newly diagnosed glioblastoma. 389/501 and 112/501 patients formed our discovery and replication cohorts, respectively. Conventional radiomics were extracted from CaPTk, and deep radiomics from a pre-trained VGG-19 model. Multivariate SVM classification was performed on the discovery cohort to stratify patients in high, medium, and low-risk groups, using recursive feature elimination and 5-fold cross-validation. This model was independently tested on the replication cohort, and a radiomic-based survival prediction index (SPIradiomics) was calculated for each patient. Multi-stage integration of omics data, i.e., clinical (age, gender, extent of resection (EOR)), SPIradiomics, epigenetics (MGMT promoter methylation), and genomics (27 clinically relevant gene mutations via next-generation sequencing (NGS)), was performed using multivariate Cox proportional hazards (Cox-PH) model for stratification of the risk in the replication cohort. RESULTS Cox-PH modeling resulted in a concordance index (c-index) of 0.65 (95% CI:0.6–0.7) for clinical data, 0.67 (95% CI:0.62–0.72) for clinical and epigenetics, 0.70 (95% CI:0.65–0.75) for clinical and radiomics, 0.72 (95% CI:0.68–0.77) for clinical, epigenetics, and radiomics, and 0.75 (95% CI:0.71 – 0.78) for the multi-omics combination of all data; highlighting the added value of each layer of information in prediction of the patient’s risk. CONCLUSION Our results reinforce the synergistic value of integrated diagnostic methods for improving risk assessment of patients with glioblastoma that may pave the path towards a more personalized treatment planning.


2021 ◽  
Author(s):  
Hugo Pomares-Millan ◽  
Naemieh Atabaki-Pasdar ◽  
Ingegerd Johansson ◽  
Alaitz Poveda ◽  
Paul W Franks

Background: Lifestyle exposures play a major role in the development of disease, yet people vary in their susceptibility. A critical step towards precision medicine is identifying individuals who are resilient or sensitive to the environment, and, assess whether the allocation to these predicted groups are more or less likely to develop cardiometabolic disease. Methods: We have used repeated data from the VHU study (n=35440) to identify sensitive and resilient individuals using prediction intervals at the 5th and 95th quantile. Three exposure susceptibility groups were derived per cardiometabolic score using quantile regression forests in the training dataset; next, in the validation dataset, we assessed the different risk of the groups using Cox proportional hazard models for CVD and diabetes. Results: The results of our study suggest that, after ~10 y of follow-up, individuals with sensitivity to the environmental exposures associated with systolic and diastolic blood pressure, blood lipids, and glucose were at higher risk of developing cardiometabolic disease. Moreover, when hazards were pooled with the replication cohort, for those individuals sensitive to the exposures associated with blood pressure traits, the hazards remained significant. Conclusions: Identifying individuals who are predicted to be sensitive are at higher risk of developing disease, this population may be a clinical target for prevention or early intervention and public health strategies.


Author(s):  
Guixin Wu ◽  
Jie Liu ◽  
Minghao Liu ◽  
Qiya Huang ◽  
Jieyun Ruan ◽  
...  

Background: The presence of variants in OBSCN was identified to be linked to hypertrophic cardiomyopathy (HCM), but whether OBSCN truncating variants were associated with HCM remained unknown. Methods: Whole-exome sequencing was performed in 986 patients with HCM and 761 non-HCM controls to search for OBSCN truncating variants, and the result was tested in a replication cohort consisting of 529 patients with HCM and 307 controls. The association of the OBSCN truncating variants with baseline characteristics and prognosis of patients with HCM were ascertained. Results: There were 28 qualifying truncating variants in the OBSCN gene detected in 26 (2.6%) patients with HCM and 6 (0.8%) controls. The OBSCN truncating variants were more prevalent in patients with HCM than controls (odds ratio, 3.4, P =0.004). This association was confirmed in the replication cohort (odds ratio, 3.8, P =0.024). The combined effects of the two cohorts estimated the odds ratio to be 3.58 ( P <0.001). Patients with or without OBSCN truncating variants shared similar demographic and echocardiographic variables at baseline. During 3.3±2.4 years (4795 patient-years) follow-up, the patients with OBSCN truncating variants were more likely to experience cardiovascular death (adjusted hazard ratio, 3.1 [95% CI, 1.40–6.70], P =0.005) and all-cause death (adjusted hazard ratio, 2.63 [95% CI, 1.21–5.71], P =0.015). Conclusions: Our data indicated that OBSCN truncating variants contributed to the disease-onset of HCM, and increased the risk of malignant events in patients with HCM.


2021 ◽  
Author(s):  
Anahita Fathi Kazerooni ◽  
Sanjay Saxena ◽  
Erik Toorens ◽  
Danni Tu ◽  
Vishnu Bashyam ◽  
...  

Abstract Background. Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in newly diagnosed, treatment-naïve, IDH-wildtype GBM patients, by combining conventional and deep learning methods.Methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. SVM classifiers were trained on the discovery cohort (n=404) to categorize patient groups of high-risk (OS<6 months) vs all, and low-risk (OS≥18 months) vs all. The trained patient stratification model was independently tested in the replication cohort (n=112) and a patient-wise survival prediction index (SPIradiomics) was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information.Results. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed AUCs of 0.78 (95%CI:0.70–0.85)/0.75 (95%CI:0.64–0.79) and 0.75 (95%CI: 0.65–0.84)/0.63 (95%CI: 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95%CI:0.6–0.7) for clinical data, 0.70 (95%CI:0.65–0.75) for clinical and radiomics, 0.72 (95%CI:0.68–0.77) for clinical, MGMT methylation, and radiomics, and 0.75 (95%CI:0.72–0.79) for the combination of all omics, i.e., clinical, MGMT methylation, radiomics, and genomics.Conclusions. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM. Our multi-omic survival prediction tool is easily scalable and can be used for more effective clinical trial stratification.


Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Faheem W. Guirgis ◽  
Lauren Page Black ◽  
Morgan Henson ◽  
Guillaume Labilloy ◽  
Carmen Smotherman ◽  
...  

Abstract Objective Approximately one-third of sepsis patients experience poor outcomes including chronic critical illness (CCI, intensive care unit (ICU) stay > 14 days) or early death (in-hospital death within 14 days). We sought to characterize lipoprotein predictive ability for poor outcomes and contribution to sepsis heterogeneity. Design Prospective cohort study with independent replication cohort. Setting Emergency department and surgical ICU at two hospitals. Patients Sepsis patients presenting within 24 h. Methods Measures included cholesterol levels (total cholesterol, high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDL-C]), triglycerides, paraoxonase-1 (PON-1), and apolipoprotein A-I (Apo A-I) in the first 24 h. Inflammatory and endothelial markers, and sequential organ failure assessment (SOFA) scores were also measured. LASSO selection assessed predictive ability for outcomes. Unsupervised clustering was used to investigate the contribution of lipid variation to sepsis heterogeneity. Measurements and main results 172 patients were enrolled. Most (~ 67%, 114/172) rapidly recovered, while ~ 23% (41/172) developed CCI, and ~ 10% (17/172) had early death. ApoA-I, LDL-C, mechanical ventilation, vasopressor use, and Charlson Comorbidity Score were significant predictors of CCI/early death in LASSO models. Unsupervised clustering yielded two discernible phenotypes. The Hypolipoprotein phenotype was characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1), higher SOFA scores, and worse clinical outcomes (45% rapid recovery, 40% CCI, 16% early death; 28-day mortality, 21%). The Normolipoprotein cluster patients had higher cholesterol levels, less endothelial dysfunction, lower SOFA scores and better outcomes (79% rapid recovery, 15% CCI, 6% early death; 28-day mortality, 15%). Phenotypes were validated in an independent replication cohort (N = 86) with greater sepsis severity, which similarly demonstrated lower HDL-C, ApoA-I, and higher ICAM-1 in the Hypolipoprotein cluster and worse outcomes (46% rapid recovery, 23% CCI, 31% early death; 28-day mortality, 42%). Normolipoprotein patients in the replication cohort had better outcomes (55% rapid recovery, 32% CCI, 13% early death; 28-day mortality, 28%) Top features for cluster discrimination were HDL-C, ApoA-I, total SOFA score, total cholesterol level, and ICAM-1. Conclusions Lipoproteins predicted poor sepsis outcomes. A Hypolipoprotein sepsis phenotype was identified and characterized by lower lipoprotein levels, increased endothelial dysfunction (ICAM-1) and organ failure, and worse clinical outcomes.


Author(s):  
Karin Bolin ◽  
Juliana Imgenberg-Kreuz ◽  
Dag Leonard ◽  
Johanna K. Sandling ◽  
Andrei Alexsson ◽  
...  

AbstractThe genetic background of lupus nephritis (LN) has not been completely elucidated. We performed a case-only study of 2886 SLE patients, including 947 (33%) with LN. Renal biopsies were available from 396 patients. The discovery cohort (Sweden, n = 1091) and replication cohort 1 (US, n = 962) were genotyped on the Immunochip and replication cohort 2 (Denmark/Norway, n = 833) on a custom array. Patients with LN, proliferative nephritis, or LN with end-stage renal disease were compared with SLE without nephritis. Six loci were associated with LN (p < 1 × 10−4, NFKBIA, CACNA1S, ITGA1, BANK1, OR2Y, and ACER3) in the discovery cohort. Variants in BANK1 showed the strongest association with LN in replication cohort 1 (p = 9.5 × 10−4) and proliferative nephritis in a meta-analysis of discovery and replication cohort 1. There was a weak association between BANK1 and LN in replication cohort 2 (p = 0.052), and in the meta-analysis of all three cohorts the association was strengthened (p = 2.2 × 10−7). DNA methylation data in 180 LN patients demonstrated methylation quantitative trait loci (meQTL) effects between a CpG site and BANK1 variants. To conclude, we describe genetic variations in BANK1 associated with LN and evidence for genetic regulation of DNA methylation within the BANK1 locus. This indicates a role for BANK1 in LN pathogenesis.


2021 ◽  
Vol 12 ◽  
Author(s):  
Richard Ahn ◽  
Damjan Vukcevic ◽  
Allan Motyer ◽  
Joanne Nititham ◽  
David McG. Squire ◽  
...  

Killer cell immunoglobulin-like receptors (KIR) regulate immune responses in NK and CD8+ T cells via interaction with HLA ligands. KIR genes, including KIR2DS1, KIR3DL1, and KIR3DS1 have previously been implicated in psoriasis susceptibility. However, these previous studies were constrained to small sample sizes, in part due to the time and expense required for direct genotyping of KIR genes. Here, we implemented KIR*IMP to impute KIR copy number from single-nucleotide polymorphisms (SNPs) on chromosome 19 in the discovery cohort (n=11,912) from the PAGE consortium, University of California San Francisco, and the University of Dundee, and in a replication cohort (n=66,357) from Kaiser Permanente Northern California. Stratified multivariate logistic regression that accounted for patient ancestry and high-risk HLA alleles revealed that KIR2DL2 copy number was significantly associated with psoriasis in the discovery cohort (p ≤ 0.05). The KIR2DL2 copy number association was replicated in the Kaiser Permanente replication cohort. This is the first reported association of KIR2DL2 copy number with psoriasis and highlights the importance of KIR genetics in the pathogenesis of psoriasis.


Author(s):  
Pyry N Sipilä ◽  
Nelli Heikkilä ◽  
Joni V Lindbohm ◽  
Christian Hakulinen ◽  
Jussi Vahtera ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ravi Kumar Dutta ◽  
Malin Larsson ◽  
Thomas Arnesen ◽  
Anette Heie ◽  
Martin Walz ◽  
...  

AbstractAldosterone-producing adenomas (APAs) are a major cause of primary aldosteronism (PA) and are characterized by constitutively producing aldosterone, which leads to hypertension. Several mutations have been identified in ion channels or ion channel-associated genes that result in APAs. To date, no studies have used a genome-wide association study (GWAS) approach to search for predisposing loci for APAs. Thus, we investigated Scandinavian APA cases (n = 35) and Swedish controls (n = 60) in a GWAS and discovered a susceptibility locus on chromosome Xq13.3 (rs2224095, OR = 7.9, 95% CI = 2.8–22.4, P = 1 × 10–7) in a 4-Mb region that was significantly associated with APA. Direct genotyping of sentinel SNP rs2224095 in a replication cohort of APAs (n = 83) and a control group (n = 740) revealed persistently strong significance (OR = 6.1, 95% CI = 3.5–10.6, p < 0.0005). We sequenced an adjacent gene, MAGEE1, of the sentinel SNP and identified a rare variant in one APA, p.Gly327Glu, which is complementary to other mutations in our primary cohort. Expression quantitative trait loci (eQTL) were investigated on the X-chromosome, and 24 trans-eQTL were identified. Some of the genes identified by trans-eQTL point towards a novel mechanistic explanation for the association of the SNPs with APAs. In conclusion, our study provides further insights into the genetic basis of APAs.


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