Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma

• Type of Material: Article
• Authors: Selin Merdan, Georgia Institute of Technology; Kritika Subramanian, Weill Cornell Medicine; Turgay Ayer, Georgia Institute of Technology; Johan Van Weyenbergh, Katholieke Universiteit Leuven; Andres Chang, Emory University; Jean Koff, Emory University; Christopher Flowers, Emory University
• Language: English
• Date: 2021-01-07
• Publisher: SPRINGERNATURE
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41408-020-00404-0
• Title of Journal or Parent Work: BLOOD CANCER JOURNAL
• Volume: 11
• Issue: 1
• Start Page: 2
• End Page: 2
• Grant/Funding Information: The research reported in this publication was supported in part by a Cancer Prevention & Research Institute of Texas (CPRIT) Established Investigator award to Dr. Flowers who is a CPRIT Scholar, a Burrough Wellcome Fund Innovation in Regulatory Science award, and K24CA208132 from the National Cancer Institute to Dr. Flowers. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
• Keywords: CONTRIBUTES; FOLLICULAR LYMPHOMA; TGF-BETA; Hematology; Science & Technology; Oncology; SELECTION; SURVIVAL; Life Sciences & Biomedicine; TUMOR MICROENVIRONMENT; NON-HODGKINS-LYMPHOMA; INTERNATIONAL PROGNOSTIC INDEX; PACKAGE
• Research Categories: Health Sciences, Oncology; Health Sciences, Medicine and Surgery

Abstract

The clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model’s biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated.