Dataset Information

Plasma proteome profiling of cardiotoxicity in patients with diffuse large B-cell lymphoma.

ABSTRACT:

Background

Cardiovascular toxicity is a notorious complication of doxorubicin (DXR) therapy for diffuse large B-cell lymphoma (DLBCL). Although surveillance of well-known biological markers for cardiovascular disease (CVD) as NTproBNP and Troponins may be helpful, there are no established markers to monitor for evolving CVD during treatment. New possibilities have arisen with the emergence of newer techniques allowing for analysis of plasma proteins that can be associated with cardiovascular disease. Proximity Extension Assay is one of them.

Objectives

We aimed to illustrate the incidence of CVD in DLBCL patients treated with DXR and to establish whether there are plasma proteins associated with pre-existing or emerging CVD.

Methods

In 95 patients, 182 different proteins from OLINK panels, NTproBNP, Troponin I and CRP were assessed prior to, during and after treatment. For comparison, samples from controls were analyzed.

Results

In the DLBCL cohort, 33.3% had pre-treatment CVD compared to 5.0% in the controls and 23.2% developed new CVD. Of the 32.6% who died during follow up, CVD was the cause in 4 patients. Spondin-1 (SPON-1) correlated to pre-treatment CVD (1.22 fold change, 95% CI 1.10-1.35, p?=?0.00025, q?=?0.045). Interleukin-1 receptor type 1 (IL-1RT1) was associated to emerging CVD (1.24 fold change, 95% CI 1.10-1.39, p?=?0.00044, q?=?0.082).

Conclusion

We observed a higher prevalence of CVD in DLBCL patients compared to controls prior to DXR therapy. Two proteins, SPON-1 and IL-1RT1, were related to pre-existing and emerging CVD in DXR treated patients. If confirmed in larger cohorts, IL-1RT1 may emerge as a reliable biomarker for unfolding CVD in DLBCL.

SUBMITTER: Morth C

PROVIDER: S-EPMC7856776 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Plasma proteome profiling of cardiotoxicity in patients with diffuse large B-cell lymphoma.

Mörth Charlott C Sabaa Amal Abu AA Freyhult Eva E Christersson Christina C Hashemi Jamileh J Hashemi Nashmil N Kamali-Moghaddam Masood M Molin Daniel D Höglund Martin M Eriksson Anna A Enblad Gunilla G

Cardio-oncology (London, England) 20210203 1

<h4>Background</h4>Cardiovascular toxicity is a notorious complication of doxorubicin (DXR) therapy for diffuse large B-cell lymphoma (DLBCL). Although surveillance of well-known biological markers for cardiovascular disease (CVD) as NTproBNP and Troponins may be helpful, there are no established markers to monitor for evolving CVD during treatment. New possibilities have arisen with the emergence of newer techniques allowing for analysis of plasma proteins that can be associated with cardiovasc ...[more]

PMID: 33536059

Similar Datasets

Project description:BackgroundThirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data.MethodsGene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set.ResultsTwo hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942).ConclusionOur results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

Dataset Information

Plasma proteome profiling of cardiotoxicity in patients with diffuse large B-cell lymphoma.

Background

Objectives

Methods

Results

Conclusion

Publications

Plasma proteome profiling of cardiotoxicity in patients with diffuse large B-cell lymphoma.

Similar Datasets

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