Dataset Information

An integrated prognostic model for diffuse large B-cell lymphoma treated with immunochemotherapy.

ABSTRACT: Diffuse large B-cell lymphoma (DLBCL), the most frequent non-Hodgkin's lymphoma subtype, is characterized by strong biological, morphological, and clinical heterogeneity, but patients are treated with immunochemotherapy in a relatively homogeneous way. Here, we have used a customized NanoString platform to analyze a series of 197 homogeneously treated DLBCL cases. The platform includes the most relevant genes or signatures known to be useful for predicting response to R-CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone) in DLBCL cases. We generated a risk score that combines the International Prognostic Index with cell of origin and double expression of MYC/BCL2, and stratified the series into three groups, yielding hazard ratios from 0.15 to 5.49 for overall survival, and from 0.17 to 5.04 for progression-free survival. Group differences were highly significant (p < 0.0001), and the scoring system was applicable to younger patients (<60 years of age) and patients with advanced or localized stages of the disease. Results were validated in an independent dataset from 166 DLBCL patients treated in two distinct clinical trials. This risk score combines clinical and biological data in a model that can be used to integrate biological variables into the prognostic models for DLBCL cases.

SUBMITTER: Rodriguez M

PROVIDER: S-EPMC9422037 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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An integrated prognostic model for diffuse large B-cell lymphoma treated with immunochemotherapy.

Rodríguez Marta M Alonso-Alonso Ruth R Fernández-Miranda Ismael I Mondéjar Rufino R Cereceda Laura L Tráscasa Álvaro Á Antonio-Da Conceiçao Anabel A Borregón Jennifer J Gato Lucía L Tomás-Roca Laura L Bárcena Carmen C Iglesias Begoña B Climent Fina F González-Barca Eva E Camacho Francisca Inmaculada FI Mayordomo Émpar É Olmedilla Gabriel G Gómez-Prieto Pilar P Castro Yolanda Y Serrano-López Juana J Sánchez-García Joaquín J Montes-Moreno Santiago S García-Cosío Mónica M Martín-Acosta Paloma P García Juan F JF Planelles María M Quero Cristina C Provencio Mariano M Mahíllo-Fernández Ignacio I Rodríguez-Pinilla Socorro M SM Derenzini Enrico E Pileri Stefano S Sánchez-Beato Margarita M Córdoba Raúl R Piris Miguel A MA

EJHaem 20220503 3

Diffuse large B-cell lymphoma (DLBCL), the most frequent non-Hodgkin's lymphoma subtype, is characterized by strong biological, morphological, and clinical heterogeneity, but patients are treated with immunochemotherapy in a relatively homogeneous way. Here, we have used a customized NanoString platform to analyze a series of 197 homogeneously treated DLBCL cases. The platform includes the most relevant genes or signatures known to be useful for predicting response to R-CHOP (Rituximab, Cyclopho ...[more]

PMID: 36051055

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Project description:PurposeWe performed a multistage genome-wide association study to identify inherited genetic variants that predict outcome in diffuse large B-cell lymphoma patients treated with immunochemotherapy.MethodsWe conducted a meta-analysis of two genome-wide association study data sets, one from the LNH2003B trial (N = 540), a prospective clinical trial from the Lymphoma Study Association, and the other from the Molecular Epidemiology Resource study (N = 312), a prospective observational study from the University of Iowa-Mayo Clinic Lymphoma Specialized Program of Research Excellence. Top single nucleotide polymorphisms were then genotyped in independent cohorts of patients from the Specialized Program of Research Excellence (N = 391) and the Groupe Ouest-Est des Leucémies Aiguës et Maladies du Sang (GOELAMS) -075 randomized trial (N = 294). We calculated the hazard ratios (HRs) and 95% CIs for event-free survival (EFS) and overall survival (OS) using a log-additive genetic model with adjustment for age, sex, and age-adjusted International Prognostic Index.ResultsIn a meta-analysis of the four studies, the top loci for EFS were marked by rs7712513 at 5q23.2 (near SNX2 and SNCAIP; HR, 1.39; 95% CI, 1.23 to 1.57; P = 2.08 × 10(-7)), and rs7765004 at 6q21 (near MARCKS and HDAC2; HR, 1.38; 95% CI, 1.22 to 1.57; P = 7.09 × 10(-7)), although they did not reach conventional genome-wide significance (P = 5 × 10(-8)). Both rs7712513 (HR, 1.49; 95% CI, 1.29 to 1.72; P = 3.53 × 10(-8)) and rs7765004 (HR, 1.47; 95% CI, 1.27 to 1.71; P = 5.36 × 10(-7)) were also associated with OS. In exploratory analyses, a two-single nucleotide polymorphism risk score was highly predictive of EFS (P = 1.78 × 10(-12)) and was independent of treatment, IPI, and cell-of-origin classification.ConclusionOur study provides encouraging evidence for associations between loci at 5q23.2 and 6q21 with EFS and OS in patients with diffuse large B-cell lymphoma treated with immunochemotherapy, suggesting novel biology and the potential contribution of host genetics to the prognosis of this aggressive malignancy.

Project description:Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.

Project description:BackgroundDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, the survival of DLBCL patients remains low, emphasizing the need to develop novel prognostic biomarkers for early risk stratification and treatment optimization.MethodIn this study, we screened genes related to the overall survival (OS) of DLBCL patients in datasets GSE117556, GSE10846, and GSE31312 using univariate Cox analysis. Survival-related genes among the three datasets were screened according to the criteria: hazard ratio (HR) >1 or <1 and p-value <0.01. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to optimize and establish the final gene risk prediction model. The TCGA-NCICCR datasets and our clinical cohort were used to validate the performance of the prediction model. CIBERSORT and ssGSEA algorithms were used to estimate immune scores in the high- and low-risk groups.ResultsWe constructed an eight-gene prognostic signature that could reliably predict the clinical outcome in training, testing, and validation cohorts. Our prognostic signature also performed distinguished areas under the ROC curve in each dataset, respectively. After stratification based on clinical characteristics such as cell-of-origin (COO), age, eastern cooperative oncology group (ECOG) performance status, international prognostic index (IPI), stage, and MYC/BCL2 expression, the difference in OS between the high- and low-risk groups was statistically significant. Next, univariate and multivariate analyses revealed that the risk score model had a significant prediction value. Finally, a nomogram was established to visualize the prediction model. Of note, we found that the low-risk group was enriched with immune cells.ConclusionIn summary, we identified an eight-gene prognostic prediction model that can effectively predict survival outcomes of patients with DLBCL and built a nomogram to visualize the perdition model. We also explored immune alterations between high- and low-risk groups.

Project description:IntroductionSoluble MHC class I-related chain A (sMICA) and B (sMICB) play a critical role tumor evolution and poor prognosis through an immune evasion mechanism. Thus, this study determines the interaction between sMICA/sMICB and the tumor immune environment in newly diagnosed diffuse large B-cell lymphoma (ND-DLBCL).MethodsWe analyzed sMICA/sMICB, cytokine in serum, and macrophage polarization analysis in tissue samples before the first chemotherapy administration. This research was performed to investigate the correlation between sMICA/sMICB expression and treatment outcomes as well as their influence on the immune system within ND-DLBCL.ResultsOf the 262 patients, 47.3% (n = 124) presented stage III or IV at diagnosis and 50.8% (n = 133) had a high International Prognostic Index (IPI ≥ 3). The patients with high (p = 0.034 and 0.004), elevated lactate dehydrogenase (p = 0.002 and 0.030), advanced stage (p = 0.003 and 0.012), and higher IPI risk (p = 0.009, and 0.032) correlated with the detection of sMICA or sMICB. The median progression-free survival (PFS) of patients with sMICA (p = 0.006) or sMICB (p =0.032) was inferior. Among the patients with advanced-stage or high IPI, those with sMICA or sMICB presented an inferior PFS and OS compared to those without. TNF-a, a pro-inflammatory cytokine, showed statistical significance with detected sMICA (p = 0.035) or sMICB (p = 0.044). Among anti-inflammatory cytokines, IL-1RA (P-value = 0.013) and IL-10 (p = 0.005) were associated with detecting sMICB, but not sMICA. In tissue samples, sMICA or sMICB detection did not correlate with the CD68/CD163 ratio.DiscussionConclusively, the identification of sMICA/sMICB presented unfavorable immunochemotherapy outcomes, and it was assumed that sMICA or sMICB and various cytokines interact, but the relationship with macrophage differentiation is unclear. Therefore, further research is needed to determine the relationship between sMICA/sMICB and tumor microenvironment in DLBCL.

Dataset Information

An integrated prognostic model for diffuse large B-cell lymphoma treated with immunochemotherapy.

Publications

An integrated prognostic model for diffuse large B-cell lymphoma treated with immunochemotherapy.

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