scholarly journals Combining Blood Gene Expression and Cellfree DNA to Diagnose Subclinical Rejection in Kidney Transplant Recipients

2021 ◽  
Vol 16 (10) ◽  
pp. 1539-1551
Author(s):  
Sookhyeon Park ◽  
Kexin Guo ◽  
Raymond L. Heilman ◽  
Emilio D. Poggio ◽  
David J. Taber ◽  
...  
Keyword(s):  
Gene Expression ◽  
Positive Predictive Value ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Predictive Value ◽  
Receiver Operating Curve ◽  
Blood Gene Expression ◽  
Content Type ◽  
Subclinical Rejection ◽  
Receiver Operating

Background and objectivesSubclinical acute rejection is associated with poor outcomes in kidney transplant recipients. As an alternative to surveillance biopsies, noninvasive screening has been established with a blood gene expression profile. Donor-derived cellfree DNA (cfDNA) has been used to detect rejection in patients with allograft dysfunction but not tested extensively in stable patients. We hypothesized that we could complement noninvasive diagnostic performance for subclinical rejection by combining a donor-derived cfDNA and a gene expression profile assay.Design, setting, participants, & measurementsWe performed a post hoc analysis of simultaneous blood gene expression profile and donor-derived cfDNA assays in 428 samples paired with surveillance biopsies from 208 subjects enrolled in an observational clinical trial (Clinical Trials in Organ Transplantation-08). Assay results were analyzed as binary variables, and then, their continuous scores were combined using logistic regression. The performance of each assay alone and in combination was compared.ResultsFor diagnosing subclinical rejection, the gene expression profile demonstrated a negative predictive value of 82%, a positive predictive value of 47%, a balanced accuracy of 64%, and an area under the receiver operating curve of 0.75. The donor-derived cfDNA assay showed similar negative predictive value (84%), positive predictive value (56%), balanced accuracy (68%), and area under the receiver operating curve (0.72). When both assays were negative, negative predictive value increased to 88%. When both assays were positive, positive predictive value increased to 81%. Combining assays using multivariable logistic regression, area under the receiver operating curve was 0.81, significantly higher than the gene expression profile (P<0.001) or donor-derived cfDNA alone (P=0.006). Notably, when cases were separated on the basis of rejection type, the gene expression profile was significantly better at detecting cellular rejection (area under the receiver operating curve, 0.80 versus 0.62; P=0.001), whereas the donor-derived cfDNA was significantly better at detecting antibody-mediated rejection (area under the receiver operating curve, 0.84 versus 0.71; P=0.003).ConclusionsA combination of blood-based biomarkers can improve detection and provide less invasive monitoring for subclinical rejection. In this study, the gene expression profile detected more cellular rejection, whereas donor-derived cfDNA detected more antibody-mediated rejection.

scholarly journals Development and Validation of a Diagnostic 35-Gene Expression Profile Test for Ambiguous or Difficult-to-Diagnose Suspicious Pigmented Skin Lesions

2020 ◽  
Vol 4 (6) ◽  
pp. 506-522
Author(s):  
Sarah Estrada ◽  
Jeffrey Shackelton ◽  
Nathan Cleaver ◽  
Natalie Depcik-Smith ◽  
Clay Cockerell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Predictive Value ◽  
Gene Selection ◽  
Skin Lesions ◽  
Neural Network Modeling ◽  
Intermediate Risk ◽  
Pigmented Lesions ◽  
Development And Validation

Purpose: A clinical hurdle for dermatopathology is the accurate diagnosis of melanocytic neoplasms. While histopathologic assessment is frequently sufficient, high rates of diagnostic discordance are reported. The development and validation of a 35-gene expression profile (35-GEP) test that accurately differentiates benign and malignant pigmented lesions is described. Methods: Lesion samples were reviewed by at least three independent dermatopathologists and included in the study if 2/3 or 3/3 diagnoses were concordant. Diagnostic utility of 76 genes was assessed with quantitative RT-PCR; neural network modeling and cross-validation were utilized for diagnostic gene selection using 200 benign nevi and 216 melanomas for training. To reflect the complex biology of melanocytic neoplasia, the 35-GEP test was developed to include an intermediate-risk zone. Results: Validation of the 35-GEP was performed in an independent set of 273 benign and 230 malignant lesions. The test demonstrated 99.1% sensitivity, 94.3% specificity, 93.6% positive predictive value and 99.2% negative predictive value. 96.4% of cases received a differential result and 3.6% had intermediate-risk. Conclusions: The 35-GEP test was developed to refine diagnoses of melanocytic neoplasms by providing clinicians with an objective tool. A test with these accuracy metrics could alleviate uncertainty in difficult-to-diagnose lesions leading to decreased unnecessary procedures while appropriately identifying at-risk patients.

scholarly journals Blood Gene Expression Profile Predicts Response to Antipsychotics

2018 ◽  
Vol 11 ◽  
Author(s):  
Jesus Sainz ◽  
Carlos Prieto ◽  
Fulgencio Ruso-Julve ◽  
Benedicto Crespo-Facorro
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Blood Gene Expression

scholarly journals Whole Blood Gene Expression Profile Associated with Spontaneous Preterm Birth in Women with Threatened Preterm Labor

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e96901 ◽  
Author(s):  
Yujing Jan Heng ◽  
Craig Edward Pennell ◽  
Hon Nian Chua ◽  
Jonathan Edward Perkins ◽  
Stephen James Lye
Keyword(s):  
Gene Expression ◽  
Preterm Birth ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Whole Blood ◽  
Preterm Labor ◽  
Spontaneous Preterm Birth ◽  
Blood Gene Expression ◽  
Threatened Preterm Labor

scholarly journals A 35-Gene Expression Profile Test for use in Suspicious Pigmented Lesions Impacts Clinical Management Decisions of Dermatopathologists and Dermatologists

2020 ◽  
Vol 4 (6) ◽  
pp. 523-533
Author(s):  
Aaron Farberg ◽  
Kelli Ahmed ◽  
Christine Bailey ◽  
Brooke Russell ◽  
Kelly Douglas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Predictive Value ◽  
Clinical Management ◽  
Histopathological Examination ◽  
Treatment Plan ◽  
Management Decisions ◽  
Pigmented Lesions ◽  
Test Result

Purpose: Histopathological examination is sufficient for diagnosis of many melanocytic neoplasms, however, diagnostic discordance is common between dermatopathologists. A timely and confident diagnosis is optimal, especially in cases where both benign and malignant melanocytic neoplasms are considered in the differential diagnosis as treatment plans diverge significantly. A 35-gene expression profile (GEP) test that classifies melanocytic lesions into categories (benign, intermediate-risk and malignant), has reported accuracy metrics of 99.1% sensitivity, 94.3% specificity, 93.6% positive predictive value and 99.2% negative predictive value in a validation cohort of 503 samples. The clinical utility of the 35-GEP is described. Methods: Dermatopathologists (n=6) and dermatologists (n=14) were queried regarding diagnostic challenges and patient management strategies in 60 difficult-to-diagnose melanocytic neoplasms. Participants reviewed each lesion twice, once without the 35-GEP result and once with. Responses were evaluated for consistent trends in the utilization of the 35-GEP test result. Results: Dermatopathologists utilized the 35-GEP result to refine their diagnoses by increasing overall lesion diagnostic concordance and confidence, while reducing additional work up requests. Dermatologists utilized the 35-GEP result to gauge overall prognosis and case difficulty. Alterations in office visit frequency, biopsies, and referrals to specialists were also influenced by the 35-GEP result and treatment plan modifications also matched the appropriate directionality of the 35-GEP result. Conclusions: The diagnosis of challenging melanocytic neoplasms and subsequent clinical management decisions are influenced by 35-GEP results in a manner that agrees with the test result. The utility of the test provides the opportunity to improve patient care.

scholarly journals Peripheral blood gene expression profile of atherosclerotic coronary artery disease in patients of different ethnicity in Malaysia

2012 ◽  
Vol 60 (3) ◽  
pp. 192-203 ◽  
Author(s):  
Mohd Hafiz Ngoo Abdullah ◽  
Zulhabri Othman ◽  
Hamdan Mohd Noor ◽  
Siti Suri Arshad ◽  
Ahmad Khairuddin Mohd Yusof ◽  
...  
Keyword(s):  
Gene Expression ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Peripheral Blood ◽  
Blood Gene Expression ◽  
Peripheral Blood Gene Expression ◽  
Artery Disease

Gene Expression Profile Alone Is Inadequate In Predicting Complete Responses In Multiple Myeloma

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 306-306
Author(s):  
Samir B. Amin ◽  
Stephane Minvielle ◽  
Bret Hanlon ◽  
Parantu K Shah ◽  
Cheng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Treatment Response ◽  
Board Of Directors ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Predictive Value ◽  
Response Prediction ◽  
Current Therapy ◽  
Advisory Committees

Abstract Abstract 306 Current therapy for multiple myeloma (MM) remains empiric. With advancement in the understanding of its molecular basis, newer therapies are emerging faster than ever with increasing the difficulty in the selection of treatment regime to maximize response and minimize the rising cost of therapy. In recent years, treatment response prediction using gene expression profiling is being evaluated to identify expression signature that can classify patients likely to benefit from chemotherapy, e.g., there are several multi-gene expression assays available to predict treatment responses. However, expression signatures and predictive power vary significantly among these assays. Here, we have assessed the ability of gene expression profile to predict complete response (CR) in patients with MM. We evaluated 128 newly-diagnosed patients with MM enrolled on IFM protocol and treated uniformly with high-dose melphalan followed by autologous stem cell transplant. Seventy one of 128 patients (56%) had achieved CR while the rest 57 (44%) had partial response (PR) or less to this therapeutic intervention. CD138+ MM cells collected at the time of diagnosis were profiled for gene expression and processed using the dChip and aroma.affymetrix module in R software. We have used all common machine learning packages in R/Bioconductor and BRB-array Tools software to build response signature models; the packages used but not limited to were: Decision tree, Support Vector Machines (SVM), Prediction Analysis of Microarray (PAM), K-Nearest Neighbors, Bayesian Additive Regression Trees (BART), Lasso, Ridge regression, amongst others. For accurate assessment of model prediction ability, the dataset was split into training and test sets. Classifier gene models were built, trained and evaluated using K-fold cross-validation followed by model selection based on minimum prediction error. We built several models using different classification methods and experimented with gene inclusion criteria in our datasets according to those features most differentially expressed between CR and non-CR patients. Final model from each of these methods was applied to test dataset to predict CR vs non-CR, and prediction results were evaluated using area under the ROC curve (AUC) as a predictive measure. The maximum AUC among all the training-testing splits was 0.63. The true positive rate (Sensitivity) to correctly predict CR case reached maximum 70% or more at the cost of higher false negative, which is to misclassify a patient as non-CR who might have responded to the treatment. Among the number of methods employed, our best predictive capability provided 66% sensitivity, 60% specificity, 67% positive predictive value and 59% negative predictive value. Importantly, comparing real CR proportion (71/128 = 56%) with that of predicted by the best model (66%), no statistically significant difference was observed (Chi-square; p-value: 0.09). We observe similar results using two independent datasets available in public data repository. Based on our analysis, we recognize and in fact foresee that the expression profile alone has limited ability to predict treatment response especially when response rate is high. This lack of predictability using current approach of response prediction with gene expression alone may be related to several limitations, like alternate splicing, miRNA-based gene regulation, post-translational modifications, binary distribution of response status, inherent variability of new samples, and developing unified signature without consideration of myeloma subtypes. A comprehensive model needs to be developed using global genomic changes to have meaningful output for clinical application. Disclosures: Munshi: Millennium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Onyx: Membership on an entity's Board of Directors or advisory committees.

Blood-based multi-tissue gene expression inference with Bayesian ridge regression

2020 ◽  
Vol 36 (12) ◽  
pp. 3788-3794
Author(s):  
Wenjian Xu ◽  
Xuanshi Liu ◽  
Fei Leng ◽  
Wei Li
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Ridge Regression ◽  
Whole Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression ◽  
Tissue Specific ◽  
Tissue Gene Expression

Abstract Motivation Gene expression profiling is widely used in basic and cancer research but still not feasible in many clinical applications because tissues, such as brain samples, are difficult and not ethnical to collect. Gene expression in uncollected tissues can be computationally inferred using genotype and expression quantitative trait loci. No methods can infer unmeasured gene expression of multiple tissues with single tissue gene expression profile as input. Results Here, we present a Bayesian ridge regression-based method (B-GEX) to infer gene expression profiles of multiple tissues from blood gene expression profile. For each gene in a tissue, a low-dimensional feature vector was extracted from whole blood gene expression profile by feature selection. We used GTEx RNAseq data of 16 tissues to train inference models to capture the cross-tissue expression correlations between each target gene in a tissue and its preselected feature genes in peripheral blood. We compared B-GEX with least square regression, LASSO regression and ridge regression. B-GEX outperforms the other three models in most tissues in terms of mean absolute error, Pearson correlation coefficient and root-mean-squared error. Moreover, B-GEX infers expression level of tissue-specific genes as well as those of non-tissue-specific genes in all tissues. Unlike previous methods, which require genomic features or gene expression profiles of multiple tissues, our model only requires whole blood expression profile as input. B-GEX helps gain insights into gene expressions of uncollected tissues from more accessible data of blood. Availability and implementation B-GEX is available at https://github.com/xuwenjian85/B-GEX. Supplementary information Supplementary data are available at Bioinformatics online.

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