This SuperSeries is composed of the SubSeries listed below.
Development and clinical validity of a novel blood-based molecular biomarker for subclinical acute rejection following kidney transplant.
Specimen part
View SamplesSub-clinical acute rejection (subAR) in kidney transplant recipients (KTR) leads to chronic rejection and graft loss. Non-invasive biomarkers are needed to detect subAR. 307 KTR were enrolled into a multi-center observational study. Precise clinical phenotypes (CP) were used to define subAR. Differential gene expression (DGE) data from peripheral blood samples paired with surveillance biopsies were used to train a Random Forests (RF) model to develop a gene expression profile (GEP) for subAR. A separate cohort of paired samples was used to validate the GEP. Clinical endpoints and gene pathway mapping were used to assess clinical validity and biologic relevance. DGE data from 530 samples (130 subAR) collected from 250 KTR yielded a RF model: AUC 0.85; 0.84 after internal validation with bootstrap resampling. We selected a predicted probability threshold favoring specificity and NPV (87% and 88%) over sensitivity and PPV (64% and 61%, respectively). We tested the locked model/threshold on a separate cohort of 138 KTR undergoing surveillance biopsies at our institution (rejection 42; no rejection 96): NPV 78%; PPV 51%; AUC 0.66. Both the CP and GEP of subAR within the first 12 months following transplantation were independently associated with worse graft outcomes at 24 months, including de novo donor-specific antibody (DSA). Serial GEP tracked with response to treatment of subAR. DGE data from both cohorts mapped to gene pathways indicative of allograft rejection.
Development and clinical validity of a novel blood-based molecular biomarker for subclinical acute rejection following kidney transplant.
Specimen part
View SamplesSub-clinical acute rejection (subAR) in kidney transplant recipients (KTR) leads to chronic rejection and graft loss. Non-invasive biomarkers are needed to detect subAR. 307 KTR were enrolled into a multi-center observational study. Precise clinical phenotypes (CP) were used to define subAR. Differential gene expression (DGE) data from peripheral blood samples paired with surveillance biopsies were used to train a Random Forests (RF) model to develop a gene expression profile (GEP) for subAR. A separate cohort of paired samples was used to validate the GEP. Clinical endpoints and gene pathway mapping were used to assess clinical validity and biologic relevance. DGE data from 530 samples (130 subAR) collected from 250 KTR yielded a RF model: AUC 0.85; 0.84 after internal validation with bootstrap resampling. We selected a predicted probability threshold favoring specificity and NPV (87% and 88%) over sensitivity and PPV (64% and 61%, respectively). We tested the locked model/threshold on a separate cohort of 138 KTR undergoing surveillance biopsies at our institution (rejection 42; no rejection 96): NPV 78%; PPV 51%; AUC 0.66. Both the CP and GEP of subAR within the first 12 months following transplantation were independently associated with worse graft outcomes at 24 months, including de novo donor-specific antibody (DSA). Serial GEP tracked with response to treatment of subAR. DGE data from both cohorts mapped to gene pathways indicative of allograft rejection.
Development and clinical validity of a novel blood-based molecular biomarker for subclinical acute rejection following kidney transplant.
No sample metadata fields
View SamplesA cross sectional study using data collected at the time of liver biopsy, the final eligibility assessment for participation in iWITH (NCT01638559), an immunosuppression withdrawal trial.
Evidence of Chronic Allograft Injury in Liver Biopsies From Long-term Pediatric Recipients of Liver Transplants.
Specimen part
View SamplesFibromyalgia (FM) is a common pain disorder characterized by dysregulation in the processing of pain. Although FM has similarities with other rheumatologic pain disorders, the search for objective markers has not been successful. In the current study we analyzed gene expression in the whole blood of 70 fibromyalgia patients and 70 healthy matched controls. Global molecular profiling revealed an upregulation of several inflammatory molecules in FM patients and downregulation of specific pathways related to hypersensitivity and allergy. There was a differential expression of genes in known pathways for pain processing, such as glutamine/glutamate signaling and axonal development. We also identified a panel of candidate gene expression-based classifiers that could establish an objective blood-based molecular diagnostic to objectively identify FM patients and guide design and testing of new therapies. Ten classifier probesets (CPA3, C11orf83, LOC100131943, RGS17, PARD3B, ANKRD20A9P, TTLL7, C8orf12, KAT2B and RIOK3) provided a diagnostic sensitivity of 95% and a specificity of 96%. Molecular scores developed from these classifiers were able to clearly distinguish FM patients from healthy controls. An understanding of molecular dysregulation in fibromyalgia is in its infancy; however the results described herein indicate blood global gene expression profiling provides many testable hypotheses that deserve further exploration.
Genome-wide expression profiling in the peripheral blood of patients with fibromyalgia.
Specimen part, Disease
View Samples