scholarly journals 3424 Serial Biomarker Monitoring Predicts Long Term Outcomes in Acute Graft Versus Host Disease

2019 ◽  
Vol 3 (s1) ◽  
pp. 114-114
Author(s):  
Hrishikesh Krishna Srinagesh ◽  
Hrishikesh Krishna Srinagesh ◽  
Urvi Kapoor ◽  
Mina Aziz ◽  
Kaitlyn Ben-David ◽  
...  
Keyword(s):  
Regression Model ◽  
Clinical Response ◽  
Corticosteroid Therapy ◽  
Acute Gvhd ◽  
Accurate Determination ◽  
Immunosuppressive Treatment ◽  
Response To Treatment ◽  
Training Set ◽  
Validation Set

OBJECTIVES/SPECIFIC AIMS: The first aim of the study is to evaluate the accuracy of serum biomarkers of acute GVHD measured after four weeks of corticosteroid therapy to predict 6 month NRM. The second aim of this study is to compare the accuracy of the biomarker algorithm to that of clinical response to corticosteroids after four weeks. The third aim of the study is to develop a novel regression model that uses weekly biomarker measurements over the first month of corticosteroid therapy to predict 6 month NRM. METHODS/STUDY POPULATION:. Patients who received HCT at one of 22 IRB-approved centers and provided blood samples to the Mount Sinai Acute GVHD International Consortium (MAGIC) biorepository and developed GVHD between January 2008 to May 2018 are included in this study. Patients were divided by time into a training set (Jan 2008-Dec 2015, n=233) for model development and a validation set (Jan 2015-May 2018, n=357) to evaluate the predictive performance of the model. The later time of the validation set was chosen deliberately to model contemporaneous GVHD treatment practices. The size of each group was designed so that there would be roughly equal numbers of deaths in both groups. RESULTS/ANTICIPATED RESULTS:. Serum concentrations of GVHD biomarkers after one month of corticosteroid therapy were measured in the validation set, and the predicted probability of NRM ($\hat{\rm p}$) was computed according to the previously published algorithm: $\log[-\log(1 - \hat{\rm p})]=-11.263 + 1.844({\rm logST}2)+ 0.577({\rm logREG}3\alpha)$. The performance of the biomarker algorithm was evaluated by creating receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) in the validation set. The AUC of the biomarker algorithm was a significantly better predictor of 6 month NRM than clinical response to treatment after four weeks of corticosteroids (0.84 vs. 0.64, p<0.001), which is a clinically relevant improvement in accuracy. To evaluate serial biomarker monitoring, serum biomarker concentrations will be measured weekly at five time points from treatment initiation to one month after corticosteroid therapy. We will use these values in the training set to develop a regression model for 6 month NRM that accounts for repeated biomarker measurements. The performance of this model will be tested in the validation set and the accuracy of the serial biomarker measurements will be compared to the accuracy of measuring biomarkers at the single time point after four weeks of corticosteroid therapy. An AUC improvement of 0.05 would be considered clinically significant. DISCUSSION/SIGNIFICANCE OF IMPACT: Clinical response to treatment after four weeks has been the standard endpoint in GVHD interventional trials for decades. If biomarkers measured at the same time more accurately predict long term mortality, this study would provide the basis for a novel endpoint in GVHD trials and enable more accurate determination of effect size of experimental interventions. An accurate biomarker algorithm will prove useful in guiding immunosuppressive treatment decisions for patients with GVHD. Patients identified by the algorithm as low-risk may benefit from reduced-dose corticosteroid therapy, potentially reducing lethal opportunistic infections. Patients identified as high-risk will be candidates for more intensive immunosuppression or investigational therapies. This precision medicine approach tailors therapy to the individual patient’s biology.

A Four Protein Plasma Fingerprint of Acute Graft Versus Host Disease (GVHD) Predicts Long Term Survival.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 38-38 ◽  
Author(s):  
Sophie Paczesny ◽  
Sung Choi ◽  
Thomas Braun ◽  
Carrie Kitko ◽  
Krijanovski Oleg ◽  
...  
Keyword(s):  
Acute Gvhd ◽  
Strong Association ◽  
Conditioning Regimen ◽  
Target Organ ◽  
Training Set ◽  
Term Survival ◽  
Long Term Survival ◽  
Validation Set

Abstract There are no independent laboratory diagnostic tests for acute GVHD. We first identified 7 potential plasma biomarkers (IL-2R-α, CRP, IL-8, TIMP-1, TNFR1, HGF, CA-19.9) of acute GVHD using a combination of proteomic approaches and antibody microarrays. We next conducted a retrospective analysis using plasma samples from 424 patients at the University of Michigan under IRB approval. We obtained samples at the first clinical signs of acute GVHD prior to treatment and at equivalent time points in patients without GVHD (Table 1). The median duration of follow-up was 420 days with a minimum follow-up of 180 days. Patients with veno-occlusive disease, idiopathic pneumonia syndrome, or septic shock were not included. We measured plasma levels of the 7 proteins by sequential ELISA. Logistic regression models with and without leave-one-out-cross-validation (LOOCV) tested the correlation of the laboratory values with the diagnosis of acute GVHD using area under the receiver-operating-characteristic (ROC) curves (AUC). The training set consisted of 282 randomly selected patients; the validation set included the remaining 142 patients. The final optimal fingerprint of four proteins excluded CRP because of its association with non-specific inflammation and included IL-2R-α, TNFR1, IL-8 and HGF, with AUCs of 0.91 and 0.89 in the training set (without and with LOOCV, respectively) and 0.86 in the validation set. The fingerprint had a strong association with grade of GVHD (p&lt;0.001) and target organ (p=0.002) at onset; interestingly, HGF had the strongest association. Using a predicted probability of acute GVHD of at least 50%, the fingerprint had a 72% sensitivity and 89% specificity. When we categorized the predicted risk of acute GVHD into low (0.00–0.59), moderate (0.60–094) and high (0.95–1.00), the plasma fingerprint predicted long-term survival (Figure 1, p&lt;0.001). We conclude that this plasma protein fingerprint has good sensitivity, high specificity, strong association with initial grade and target organ of acute GVHD, and effectively stratifies patients into three risk groups for GVHD that correlate with long term survival. Figure Figure Table 1: Patients characteristics GVHD- (N=242) GVHD+ (N=182) Age-yr Median (range) 45 (1–69) 49 (1–71) Donor type (%) MRD: 169 (70%) MRD: 105 (58%) URD: 73 (30%) URD: 77 (42%) Conditioning regimen Intensity (%) Full: 182 (75%) Full: 114 (63%) Reduced: 60 (25%) Reduced: 68 (37%) Day after BMT of samples : median (range) 30 (7–104) 29 (5–119) Grade at GVHD Onset (%) Grade 0 Grade 1 Grade 2 Grade 3–4 242 (57%) 48 (12%) 100 (24%) 34 (7%) Organ Target at GVHD Onset (%) n/a Skin Gut Liver Combined 119 (65%) 38 (21%) 7 (4%) 18 (10%)

scholarly journals MAGIC biomarkers predict long-term outcomes for steroid-resistant acute GVHD

Blood ◽  
2018 ◽  
Vol 131 (25) ◽  
pp. 2846-2855 ◽  
Author(s):  
Hannah Major-Monfried ◽  
Anne S. Renteria ◽  
Attaphol Pawarode ◽  
Pavan Reddy ◽  
Francis Ayuk ◽  
...  
Keyword(s):  
Clinical Response ◽  
Acute Gvhd ◽  
Steroid Treatment ◽  
Prognostic Biomarkers ◽  
Long Term Outcomes ◽  
Steroid Resistant ◽  
Key Points ◽  
Better Than

Key Points Biomarker scores generated after 1 week of steroid treatment of GVHD are prognostic. Biomarkers reflect prognosis better than early clinical response to GVHD treatment.

scholarly journals Graft-Versus-Host Disease–Free Antitumoral Signature After Allogeneic Donor Lymphocyte Injection Identified by Proteomics and Systems Biology

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaowen Liu ◽  
Zongliang Yue ◽  
Yimou Cao ◽  
Lauren Taylor ◽  
Qing Zhang ◽  
...  
Keyword(s):  
T Cells ◽  
Systems Biology ◽  
Graft Versus Host Disease ◽  
Acute Gvhd ◽  
Hematopoietic Cell Transplantation ◽  
Training Set ◽  
Activated T Cells ◽  
Graft Versus Host ◽  
Single Cell Profiling ◽  
Validation Set

PURPOSE As a tumor immunotherapy, allogeneic hematopoietic cell transplantation with subsequent donor lymphocyte injection (DLI) aims to induce the graft-versus-tumor (GVT) effect but often also leads to acute graft-versus-host disease (GVHD). Plasma tests that can predict the likelihood of GVT without GVHD are still needed. PATIENTS AND METHODS We first used an intact-protein analysis system to profile the plasma proteome post-DLI of patients who experienced GVT and acute GVHD for comparison with the proteome of patients who experienced GVT without GVHD in a training set. Our novel six-step systems biology analysis involved removing common proteins and GVHD-specific proteins, creating a protein-protein interaction network, calculating relevance and penalty scores, and visualizing candidate biomarkers in gene networks. We then performed a second proteomics experiment in a validation set of patients who experienced GVT without acute GVHD after DLI for comparison with the proteome of patients before DLI. We next combined the two experiments to define a biologically relevant signature of GVT without GVHD. An independent experiment with single-cell profiling in tumor antigen–activated T cells from a patient with post–hematopoietic cell transplantation relapse was performed. RESULTS The approach provided a list of 46 proteins in the training set, and 30 proteins in the validation set were associated with GVT without GVHD. The combination of the two experiments defined a unique 61-protein signature of GVT without GVHD. Finally, the single-cell profiling in activated T cells found 43 of the 61 genes. Novel markers, such as RPL23, ILF2, CD58, and CRTAM, were identified and could be extended to other antitumoral responses. CONCLUSION Our multiomic analysis provides, to our knowledge, the first human plasma signature for GVT without GVHD. Risk stratification on the basis of this signature would allow for customized treatment plans.

Oral pemphigus: long term behaviour and clinical response to treatment with deflazacort in sixteen cases

2000 ◽  
Vol 29 (4) ◽  
pp. 145-152 ◽  
Author(s):  
Michele D. Mignogna ◽  
Lorenzo Muzio ◽  
Roberto E. Mignogna ◽  
Roberto Carbone ◽  
Elvira Ruoppo ◽  
...  
Keyword(s):  
Clinical Response ◽  
Response To Treatment

Endoscopic third ventriculostomy in the treatment of childhood hydrocephalus in Uganda: report of a scoring system that predicts success

2010 ◽  
Vol 5 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Benjamin C. Warf ◽  
John Mugamba ◽  
Abhaya V. Kulkarni
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Endoscopic Third Ventriculostomy ◽  
Successful Outcome ◽  
Third Ventriculostomy ◽  
P Value ◽  
Training Set ◽  
Childhood Hydrocephalus ◽  
Validation Set

Object In Uganda, childhood hydrocephalus is common and difficult to treat. In some children, endoscopic third ventriculostomy (ETV) can be successful and avoid dependence on a shunt. This can be especially beneficial in Uganda, because of the high risk of infection and long-term failure associated with shunting. Therefore, the authors developed and validated a model to predict the chances of ETV success, taking into account the unique characteristics of a large sub-Saharan African population. Methods All children presenting with hydrocephalus at CURE Children's Hospital of Uganda (CCHU) between 2001 and 2007 were offered ETV as first-line treatment and were prospectively followed up. A multivariable logistic regression model was built using ETV success at 6 months as the outcome. The model was derived on 70% of the sample (training set) and validated on the remaining 30% (validation set). Results Endoscopic third ventriculostomy was attempted in 1406 patients. Of these, 427 were lost to follow-up prior to 6 months. In the remaining 979 patients, the ETV was aborted in 281 due to poor anatomy/visibility and in 310 the ETV failed during the first 6 months. Therefore, a total of 388 of 979 (39.6% and [55.6% of completed ETVs]) procedures were successful at 6 months. The mean age at ETV was 12.6 months, and 57.8% of cases were postinfectious in origin. The authors' logistic regression model contained the following significant variables: patient age at ETV, cause of hydrocephalus, and whether choroid plexus cauterization was performed. In the training set (676 patients) and validation set (303 patients), the model was able to accurately predict the probability of successful ETV (Hosmer-Lemeshow p value > 0.60 and C statistic > 0.70). The authors developed the simplified CCHU ETV Success Score that can be used in the field to predict the probability of ETV success. Conclusions The authors' model will allow clinicians to accurately identify children with a good chance of successful outcome with ETV, taking into account the unique characteristics and circumstances of the Ugandan population.

scholarly journals An Early Biomarker Algorithm Predicts Lethal Graft-Versus-Host Disease and Survival after Allogeneic Hematopoietic Cell Transplantation

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 509-509 ◽  
Author(s):  
Matthew J Hartwell ◽  
Umut Ozbek ◽  
Ernst Holler ◽  
Anne S. Renteria ◽  
Pavan R. Reddy ◽  
...  
Keyword(s):  
High Risk ◽  
Research Funding ◽  
Acute Gvhd ◽  
Conditioning Regimen ◽  
Training Set ◽  
Test Set ◽  
Graft Versus Host ◽  
Independent Validation ◽  
Blood Biomarker ◽  
Validation Set

Abstract No laboratory test can predict non-relapse mortality (NRM) after hematopoietic cellular transplantation (HCT) prior to the onset graft-versus-host disease (GVHD). Recently, we have shown that a signature of three GVHD plasma biomarkers (TNFR1, ST2, and REG3α) can predict response to GVHD therapy and NRM at the onset of clinical GVHD (Levine, Lancet Haem, 2015). Our goal in the current study was to identify a blood biomarker signature that could predict lethal GVHD and six-month NRM well in advance of the onset of GVHD symptoms. Patient samples on day +7 after HCT were obtained from 1,287 patients from 11 HCT centers in the Mount Sinai Acute GVHD International Consortium (MAGIC). Samples from two large centers (n = 929) were combined and randomly assigned to a training set (n = 620) and test set (n = 309). 358 patients from nine others centers constituted an independent validation set. The overall cumulative incidences of 6-month NRM were 11%, 12%, and 13% for the training, test, and validation sets respectively. The incidence of lethal GVHD, defined as death without preceding relapse while under steroid treatment for acute GVHD, were 18%, 24%, and 14% in the same groups, respectively. The median day of GVHD onset was 28 days in the training set and 29 days in the test and validation sets. We measured four GVHD related biomarkers [ST2, REG3α, TNFR1, and IL2Rα] in all samples and used the training set alone to develop competing risks regression models that used all 13 possible combinations of one to four biomarkers to predict 6-month NRM. The best algorithm, which we rigorously confirmed through Monte Carlo cross-validation of 75 different combinations of training sets, included ST2 and REG3α. No combination of one, three, or four biomarkers was superior to the combination of these two biomarkers. The day 7 algorithm identified high risk (HR) and low risk (LR) groups with 6-month NRMs of 28% and 7%, respectively (p<0.001) (Fig 1A). The relapse rates did not differ between risk groups so that overall survival (OS) was 60% for HR and 84% for LR (p<0.001) (Fig 1B). When applied to the test set (Fig 1C/D), the algorithm identified 54/309 (17%) of the patients as HR with an NRM of 33% vs 7% for LR patients (p<0.001) and 6-month OS of 57% and 81% for HR and LR patients, respectively (p<0.001). In the independent validation set (Fig 1 E/F), the algorithm identified 72/358 (20%) of the patients as HR with an NRM of 26% vs 10% for LR patients (p<0.001) and OS of 68% and 85% for HR and LR patients, respectively (p<0.001). High risk patients were three times more likely to die from GVHD than LR patients in each cohort (p<0.001) (Fig 2). The GI tract is the GVHD target organ that is most resistant to treatment and represents a major cause of NRM, and we observed twice as much severe GI GVHD (stage 3 or 4) in HR patients as in LR patients (p<0.001, data not shown). The algorithm successfully separated HR and LR strata for 6 month NRM in several groups with differing risks for GVHD and NRM, including donor type, degree. of HLA-match, age group, and conditioning regimen intensity (Fig 3). In conclusion, we have developed a blood biomarker algorithm that predicts the development of lethal GVHD seven days after HCT, which performed successfully in large multicenter validation sets. The GVH reaction is already in progress by day +7, even though clinical symptoms may not occur until days or weeks later. We speculate that the blood biomarker concentrations at this early time point reflect subclinical GI pathology, a notion that is reinforced by the fact that ST2 and REG3α, the two biomarkers in the algorithm, are closely associated with GI GVHD. The algorithm identified HR and LR strata in several patient groups with different overall risk for lethal GVHD (donor, HLA match, conditioning regimen intensity, age). This day +7 algorithm should prove useful in clinical BMT research by identifying patients at high risk for lethal GVHD who might benefit from aggressive preemptive treatment strategies. Disclosures Chen: Novartis: Research Funding; Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Jagasia:Therakos: Consultancy. Kitko:Therakos: Honoraria, Speakers Bureau. Kroeger:Novartis: Honoraria, Research Funding. Levine:Viracor: Patents & Royalties: GVHD biomarkers patent. Ferrara:Viracor: Patents & Royalties: GVHD biomarkers patent.

Long-Term Colectomy Rate After Intensive Intravenous Corticosteroid Therapy for Ulcerative Colitis Prior to the Immunosuppressive Treatment Era

2007 ◽  
Vol 102 (11) ◽  
pp. 2513-2519 ◽  
Author(s):  
Anders Gustavsson ◽  
Jonas Halfvarson ◽  
Anders Magnuson ◽  
Hanna Sandberg-Gertzén ◽  
Curt Tysk ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Corticosteroid Therapy ◽  
Immunosuppressive Treatment ◽  
Intravenous Corticosteroid

scholarly journals Detection of EGFR Mutations by TaqMan Mutation Detection Assays Powered by Competitive Allele-Specific TaqMan PCR Technology

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Cristin Roma ◽  
Claudia Esposito ◽  
Anna Maria Rachiglio ◽  
Raffaella Pasquale ◽  
Alessia Iannaccone ◽  
...  
Keyword(s):  
Mutation Detection ◽  
Response To Treatment ◽  
Egfr Mutations ◽  
Clinical Samples ◽  
Training Set ◽  
Similar Frequency ◽  
Highly Sensitive ◽  
Allele Specific ◽  
Taqman Pcr ◽  
Validation Set

Epidermal growth factor receptor (EGFR) mutations in non-small-cell lung cancer (NSCLC) are predictive of response to treatment with tyrosine kinase inhibitors. Competitive Allele-Specific TaqMan PCR (castPCR) is a highly sensitive and specific technology. EGFR mutations were assessed by TaqMan Mutation Detection Assays (TMDA) based on castPCR technology in 64 tumor samples: a training set of 30 NSCLC and 6 colorectal carcinoma (CRC) samples and a validation set of 28 NSCLC cases. The sensitivity and specificity of this method were compared with routine diagnostic techniques including direct sequencing and the EGFR Therascreen RGQ kit. Analysis of the training set allowed the identification of the threshold value for data analysis (0.2); the maximum cycle threshold (Ct=37); and the cut-off ΔCt value (7) for the EGFR TMDA. By using these parameters, castPCR technology identified both training and validation set EGFR mutations with similar frequency as compared with the Therascreen kit. Sequencing detected rare mutations that are not identified by either castPCR or Therascreen, but in samples with low tumor cell content it failed to detect common mutations that were revealed by real-time PCR based methods. In conclusion, our data suggest that castPCR is highly sensitive and specific to detect EGFR mutations in NSCLC clinical samples.

Stratification of Allogeneic Hematopoietic Cell Transplant By Risk of Developing Veno-Occlusive Disease: A Model for Assigning a Risk Score

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 983-983 ◽  
Author(s):  
Christopher Strouse ◽  
Ying Zhang ◽  
Mei-Jie Zhang ◽  
Alyssa DiGilio ◽  
Marcelo C. Pasquini ◽  
...  
Keyword(s):  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Primary Outcome ◽  
Conditioning Regimen ◽  
Occlusive Disease ◽  
Hematopoietic Cell ◽  
Training Set ◽  
Allogeneic Hct ◽  
Validation Set

Abstract Background Hepatic veno-occlusive disease (VOD) is a rare complication of hematopoietic cell transplantation (HCT) that is associated with high mortality. Developing a clinical risk score to identify patients at high risk for VOD would inform clinical decision-making and aid efforts to study preventive strategies. Methods We retrospectively analyzed data on 13,097 patients receiving allogeneic HCT as reported by 221 centers to the Center for International Blood & Marrow Transplant Research between 2008-2013. All ages, disease types (malignant, non-malignant), and conditioning regimen intensities were included. The cohort was randomly divided into training and validation sets. The primary outcome was development of VOD by day+100 post HCT with or without multi-organ failure as reported by the reporting institutions. A multivariate logistic regression model was built using the training set to identify independent prognostic factors associated with the primary outcome. Age was analyzed as a continuous variable within 5 strata (Table 1). A risk score was constructed in the training set using the significant factors from the regression model: the magnitude of the odds ratios (OR) determined the magnitude of the risk score term coefficients. Risk scores were calculated by addition of the risk score coefficients corresponding to each patients' characteristics. The score's predictive ability was confirmed in the validation set. Results Baseline characteristics are shown in Table 1. The training and validation sets were similar across all characteristics. VOD incidence at day+100 was 4.9% (n=637). VOD diagnosis was established by biopsy/autopsy (n=139), Seattle criteria (n=115) or Baltimore criteria (n=289) in 543 patients (85% of patients). Diagnostic information was missing on 3 patients. The remaining 91 patients were diagnosed using other clinical evidence (e.g. ascites, ultrasound). The multivariate model identified 6 independent prognostic variables: age, hepatitis B/C (HBV/HCV) serology, Karnofsky performance score (KPS), disease type/status, conditioning regimen and sirolimus use. See Table 1 for non-significant variables assessed. Age was inversely associated with development of VOD across all strata; only the <10 and 20-40 strata were statistically significant (Figure 1). For ages <10 years, 10-20, 20-40, 40-60, and 60-80 the ORs were 1.10 (95% confidence interval [CI] 1.03-1.17, p=0.004), 1.01 (95% CI: 0.95 - 1.07, p=0.81), 1.04 (95% CI: 1.01-1.08, p=0.01), 1.01 (95% CI: 0.98-1.04, p=0.61), and 1.05 (95% CI: 0.96-1.15, p=0.28), corresponding to a 10%, 1%, 4%, 1% and 5% increased risk of VOD for each year decrease in age within their respective strata. Myeloablative (MAC) regimens were associated with higher risk of VOD, and busulfan-based (BU) MAC regimens guided by pharmacokinetic (PK) monitoring were associated with higher risk than those without PK guidance. KPS <90% (OR 1.47, 95% CI 1.11-1.95, p=0.007), HBV+ serology with or without HCV+ serology (OR 2.19, 95% CI 1.35-3.56, p=0.002), and use of prophylactic sirolimus (OR 2.39, 95% CI 1.54-3.71, p = 0.0001) were associated with higher risk. Within the training set, each patient's risk score was calculated and the patients were stratified into 4 groups according to their risk score percentile: low (A, ≤40th percentile), intermediate (B, >40th, ≤80th percentile), high (C, >80th, ≤90th percentile) and very high risk (D, >90th percentile) with ORs for development of VOD in the training and validation sets as shown in Table 2 and Figures 2 and 3. The incidences of VOD within groups A, B, C and D in the training set were 1.15%, 4.34%, 8.70%, and 17.84%, respectively; the corresponding incidences in the validation set were 1.96%, 4.43%, 9.72%, and 14.33%, respectively. There was no significant difference in the c statistic by set (p = 0.07). Conclusion The risk score successfully stratified allogeneic HCT patients by risk of developing VOD and was validated in an independent subset of patients. The model demonstrates strong discriminatory ability to identify a high risk cohort to focus efforts for further study. Disclosures Villa: Jazz Pharmaceuticals: Employment, Equity Ownership.

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