Dataset Information

Machine learning predicts stem cell transplant response in severe scleroderma.

ABSTRACT:

Objective

The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT clinical trial to scleroderma intrinsic subsets and tested the hypothesis that they predict long-term response to HSCT.

Methods

We analysed gene expression from PBCs of SCOT participants to identify differential treatment response. PBC gene expression data were generated from 63 SCOT participants at baseline and follow-up timepoints. Participants who completed treatment protocol were stratified by intrinsic gene expression subsets at baseline, evaluated for event-free survival (EFS) and analysed for differentially expressed genes (DEGs).

Results

Participants from the fibroproliferative subset on HSCT experienced significant improvement in EFS compared with fibroproliferative participants on CYC (p=0.0091). In contrast, EFS did not significantly differ between CYC and HSCT arms for the participants from the normal-like subset (p=0.77) or the inflammatory subset (p=0.1). At each timepoint, we observed considerably more DEGs in HSCT arm compared with CYC arm with HSCT arm showing significant changes in immune response pathways.

Conclusions

Participants from the fibroproliferative subset showed the most significant long-term benefit from HSCT compared with CYC. This study suggests that intrinsic subset stratification of patients may be used to identify patients with SSc who receive significant benefit from HSCT.

SUBMITTER: Franks JM

PROVIDER: S-EPMC8582621 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning predicts stem cell transplant response in severe scleroderma.

Franks Jennifer M JM Martyanov Viktor V Wang Yue Y Wood Tammara A TA Pinckney Ashley A Crofford Leslie J LJ Keyes-Elstein Lynette L Furst Daniel E DE Goldmuntz Ellen E Mayes Maureen D MD McSweeney Peter P Nash Richard A RA Sullivan Keith M KM Whitfield Michael L ML

Annals of the rheumatic diseases 20200915 12

<h4>Objective</h4>The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT clinical trial to scleroderma intrinsic subsets and tested the hypothesis that they predict long-term response to HSCT.<h4>Methods</h4>We analysed gene expression from PBCs of SCOT participants to identify differential treatment response. PBC ...[more]

PMID: 32933919

Similar Datasets

Project description:BackgroundDespite current therapies, diffuse cutaneous systemic sclerosis (scleroderma) often has a devastating outcome. We compared myeloablative CD34+ selected autologous hematopoietic stem-cell transplantation with immunosuppression by means of 12 monthly infusions of cyclophosphamide in patients with scleroderma.MethodsWe randomly assigned adults (18 to 69 years of age) with severe scleroderma to undergo myeloablative autologous stem-cell transplantation (36 participants) or to receive cyclophosphamide (39 participants). The primary end point was a global rank composite score comparing participants with each other on the basis of a hierarchy of disease features assessed at 54 months: death, event-free survival (survival without respiratory, renal, or cardiac failure), forced vital capacity, the score on the Disability Index of the Health Assessment Questionnaire, and the modified Rodnan skin score.ResultsIn the intention-to-treat population, global rank composite scores at 54 months showed the superiority of transplantation (67% of 1404 pairwise comparisons favored transplantation and 33% favored cyclophosphamide, P=0.01). In the per-protocol population (participants who received a transplant or completed ≥9 doses of cyclophosphamide), the rate of event-free survival at 54 months was 79% in the transplantation group and 50% in the cyclophosphamide group (P=0.02). At 72 months, Kaplan-Meier estimates of event-free survival (74% vs. 47%) and overall survival (86% vs. 51%) also favored transplantation (P=0.03 and 0.02, respectively). A total of 9% of the participants in the transplantation group had initiated disease-modifying antirheumatic drugs (DMARDs) by 54 months, as compared with 44% of those in the cyclophosphamide group (P=0.001). Treatment-related mortality in the transplantation group was 3% at 54 months and 6% at 72 months, as compared with 0% in the cyclophosphamide group.ConclusionsMyeloablative autologous hematopoietic stem-cell transplantation achieved long-term benefits in patients with scleroderma, including improved event-free and overall survival, at a cost of increased expected toxicity. Rates of treatment-related death and post-transplantation use of DMARDs were lower than those in previous reports of nonmyeloablative transplantation. (Funded by the National Institute of Allergy and Infectious Diseases and the National Institutes of Health; ClinicalTrials.gov number, NCT00114530 .).

Project description:Influenza virus is a major cause of acute hypoxemic respiratory failure. Early identification of patients who will suffer severe complications can help stratify patients for clinical trials and plan for resource use in case of pandemic. Objective:We aimed to identify which clinical variables best predict prolonged acute hypoxemic respiratory failure in influenza-infected critically ill children. Acute hypoxemic respiratory failure was defined using hypoxemia cutoffs from international consensus definitions of acute respiratory distress syndrome in patients with ventilatory support. Prolonged acute hypoxemic respiratory failure was defined by acute hypoxemic respiratory failure criteria still present at PICU day 7. Derivation Cohort:In this prospective multicenter study across 34 PICUs from November 2009 to April 2018, we included children (< 18 yr) without comorbid risk factors for severe disease. Validation Cohort:We used a Monte Carlo cross validation method with N 2 random train-test splits at a 70-30% proportion per model. Prediction Model:Using clinical data at admission (day 1) and closest to 8 am on PICU day 2, we calculated the area under the receiver operating characteristic curve using random forests machine learning algorithms and logistic regression. Results:We included 258 children (median age = 6.5 yr) and 11 (4.2%) died. By day 2, 65% (n = 165) had acute hypoxemic respiratory failure dropping to 26% (n = 67) with prolonged acute hypoxemic respiratory failure by day 7. Those with prolonged acute hypoxemic respiratory failure had a longer ICU stay (16.5 vs 4.0 d; p < 0.001) and higher mortality (13.4% vs 1.0%). A multivariable model using random forests with 10 admission and eight day 2 variables performed best (0.93 area under the receiver operating characteristic curve; 95 CI%: 0.90-0.95) where respiratory rate, Fio2, and pH on day 2 were the most important factors. Conclusions:In this prospective multicentric study, most children with influenza virus-related respiratory failure with prolonged acute hypoxemic respiratory failure can be identified early in their hospital course applying machine learning onto routine clinical data. Further validation is needed prior to bedside implementation.

Project description:BackgroundNatural killer cells, interconnected with patient prognosis and treatment response, play a pivotal role in the tumor immune microenvironment and may serve as potential novel predictive biomarkers for renal cell carcinoma.MethodsClear cell renal cell carcinoma transcriptome data and the corresponding clinical data were obtained from the Cancer Genome Atlas (TCGA) database. Single-cell sequencing data were sourced from the Gene Expression Omnibus (GEO) database. A risk model was established by integrating ten different machine learning algorithms, which resulted in 101 combined models. The model with the highest average C-index was selected for further analysis, and was assessed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Meier survival analysis. The differences in immune infiltration fractions, clinicopathological features, and response to various targeted therapies and immunotherapy between high- and low-risk groups were investigated. Furthermore, qRT-PCR, IHC, colony formation test, CCK8 assay and flow cytometry were conducted to explore the expression pattern and function of ARHGAP9 in our own patient samples and renal cancer cell lines.ResultsTotally, 156 NK cell-related genes and 5189 prognosis-related genes were identified, and 36 genes of their intersection demonstrated prognostic value. A risk model with 18 genes was established by Coxboost plus plsRcox, which can accurately predict the prognosis of ccRCC patients. Significant correlations were determined between risk score and tumor malignancy and immune cell infiltration. Meanwhile, a combination of tumor mutation burden plus risk score could have higher accuracy of predicting clinical outcomes. Moreover, high-risk group patients were more likely to be responsive to targeted therapy but show no response to immunotherapy.ConclusionsIntricate signaling interactions between NK cells and various cellular subgroups were depicted and the developmental trajectory of NK cells was elucidated. A NK cells-related risk model was established, which can provide reliable prognostic information and identified patients with more probability of benefiting from therapy.

Project description:ImportanceSepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant (allo-HCT), and timely detection is crucial. However, the atypical presentation of sepsis within this population makes detection challenging, and existing clinical sepsis tools have limited prognostic value among this high-risk population.ObjectiveTo develop a full risk factor (demographic, transplant, clinical, and laboratory factors) and clinical factor-specific automated bacterial sepsis decision support tool for recipients of allo-HCT with potential bloodstream infections (PBIs).Design, setting, and participantsThis prognostic study used data from adult recipients of allo-HCT transplanted at the Fred Hutchinson Cancer Research Center, Seattle, Washington, between June 2010 and June 2019 randomly divided into 70% modeling and 30% validation data sets. Tools were developed using the area under the curve (AUC) optimized SuperLearner, and their performance was compared with existing clinical sepsis tools: National Early Warning Score (NEWS), quick Sequential Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS), using the validation data set. Data were analyzed between January and October of 2020.Main outcomes and measuresThe primary outcome was high-sepsis risk bacteremia (culture confirmed gram-negative species, Staphylococcus aureus, or Streptococcus spp bacteremia), and the secondary outcomes were 10- and 28-day mortality. Tool discrimination and calibration were examined using accuracy metrics and expected vs observed probabilities.ResultsBetween June 2010 and June 2019, 1943 recipients of allo-HCT received their first transplant, and 1594 recipients (median [interquartile range] age at transplant, 54 [43-63] years; 911 [57.2%] men; 1242 individuals [77.9%] identifying as White) experienced at least 1 PBI. Of 8131 observed PBIs, 238 (2.9%) were high-sepsis risk bacteremia. Compared with high-sepsis risk bacteremia, the full decision support tool had the highest AUC (0.85; 95% CI, 0.81-0.89), followed by the clinical factor-specific tool (0.72; 95% CI, 0.66-0.78). SIRS had the highest AUC of existing tools (0.64; 95% CI, 0.57-0.71). The full decision support tool had the highest AUCs for PBIs identified in inpatient (0.82; 95% CI, 0.76-0.89) and outpatient (0.82; 95% CI, 0.75-0.89) settings and for 10-day (0.85; 95% CI, 0.79-0.91) and 28-day (0.80; 95% CI, 0.75-0.84) mortality.Conclusions and relevanceThese findings suggest that compared with existing tools and the clinical factor-specific tool, the full decision support tool had superior prognostic accuracy for the primary (high-sepsis risk bacteremia) and secondary (short-term mortality) outcomes in inpatient and outpatient settings. If used at the time of culture collection, the full decision support tool may inform more timely sepsis detection among recipients of allo-HCT.

Project description:BackgroundPulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplantation with chronic graft-vs-host disease. These comorbid diseases can make the diagnosis of early lung graft-vs-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy.Study design and methodsWe conducted a single-center study of 66 patients with CT chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among posttransplantation lung complications solely from quantitative output.ResultsCompared with parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished four unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percentage air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with a specificity of 88%, sensitivity of 83%, accuracy of 86%, and an area under the receiver operating characteristic curve of 0.85.InterpretationOur machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome vs other lung diseases, including late pulmonary complications after hematopoietic cell transplantation.

Dataset Information

Machine learning predicts stem cell transplant response in severe scleroderma.

Objective

Methods

Results

Conclusions

Publications

Machine learning predicts stem cell transplant response in severe scleroderma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets