Revealing the heterogeneity of treatment resistance in less-defined subtype diffuse large B cell lymphoma patients by integrating programmed cell death patterns and liquid biopsy

IF 7.9 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Clinical and Translational Medicine Pub Date : 2024-12-27 DOI:10.1002/ctm2.70150

Wei Hua, Jie Liu, Yue Li, Hua Yin, Hao-Rui Shen, Jia-Zhu Wu, Yi-Lin Kong, Bi-Hui Pan, Jun-Heng Liang, Li Wang, Jian-Yong Li, Rui Gao, Jin-Hua Liang, Wei Xu

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引用次数: 0

Abstract

Precision medicine in less-defined subtype diffuse large B-cell lymphoma (DLBCL) remains a challenge due to the heterogeneous nature of the disease. Programmed cell death (PCD) pathways are crucial in the advancement of lymphoma and serve as significant prognostic markers for individuals afflicted with lymphoid cancers. To identify robust prognostic biomarkers that can guide personalized management for less-defined subtype DLBCL patients, we integrated multi-omics data derived from 339 standard R-CHOP-treated patients diagnosed with less-defined subtype DLBCL from three independent cohorts. By employing various machine learning algorithms, we pinpointed eight pivotal genes linked to PCD, specifically FLT3, SORL1, CD8A, BCL2L1, COL13A1, MPG, DYRK2 and CAMK2B. Following this, we established a Programmed Cell Death Index (PCDI) utilizing the aforementioned genes and amalgamated it with pertinent clinical characteristics to formulate a predictive nomogram model for prognosis. We observed a significant correlation between the PCDI, pre-treatment circulating tumour DNA (ctDNA) burden, minimal residual disease (MRD) status and immune features. Furthermore, our research indicated that patients with elevated PCDI scores could potentially show resistance to conventional chemotherapy treatments, yet they might derive an advantage from alternative inhibitors targeting specific signalling pathways. Conclusively, leveraging these results, we have created an online analytical tool (https://xulymphoma.shinyapps.io/PCDI_pred/) designed for the prognostic prediction of patients with less-defined subtype DLBCL. This tool facilitates the forecasting of outcomes for these patients, enhancing the precision of their clinical management.

Key points

Developing the Programmed Cell Death Index (PCDI) utilizing multiple machine learning algorithms for patients with less-defined subtype diffuse large B-cell lymphoma.
The difference in clinical characteristics, circulating tumour DNA burden and immune profiling between patients with distinct PCDI groups.
A potentially effective regimen was speculated for patients with high PCDI scores who tend to exhibit worse progression-free survival.

Abstract Image

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通过整合程序性细胞死亡模式和液体活检，揭示不明确亚型弥漫性大B细胞淋巴瘤患者治疗耐药性的异质性。

由于弥漫性大b细胞淋巴瘤（DLBCL）的异质性，对不明确亚型的精准治疗仍然是一个挑战。程序性细胞死亡（PCD）途径在淋巴瘤的进展中至关重要，并作为淋巴样癌患者的重要预后标志物。为了确定可靠的预后生物标志物，以指导对不明确亚型DLBCL患者的个性化管理，我们整合了来自三个独立队列的339名经r - chop治疗的诊断为不明确亚型DLBCL的标准患者的多组学数据。通过使用各种机器学习算法，我们确定了与PCD相关的8个关键基因，特别是FLT3、SORL1、CD8A、BCL2L1、COL13A1、MPG、DYRK2和CAMK2B。随后，我们利用上述基因建立了程序性细胞死亡指数（PCDI），并将其与相关的临床特征相结合，形成了预测预后的nomogram模型。我们观察到PCDI、治疗前循环肿瘤DNA （ctDNA）负担、最小残留病（MRD）状态和免疫特征之间存在显著相关性。此外，我们的研究表明，PCDI评分升高的患者可能对常规化疗产生耐药性，但他们可能从针对特定信号通路的替代抑制剂中获得优势。最后，利用这些结果，我们创建了一个在线分析工具（https://xulymphoma.shinyapps.io/PCDI_pred/），旨在预测定义不明确的DLBCL亚型患者的预后。该工具有助于预测这些患者的预后，提高其临床管理的准确性。重点：利用多种机器学习算法为不明确亚型弥漫性大b细胞淋巴瘤患者开发程序性细胞死亡指数（PCDI）。不同PCDI组患者临床特征、循环肿瘤DNA负荷和免疫谱的差异对于PCDI评分较高且无进展生存期较差的患者，推测可能有效的治疗方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical and Translational Medicine Multiple-

CiteScore

15.90

自引率

1.90%

发文量

450

审稿时长

4 weeks

期刊介绍： Clinical and Translational Medicine (CTM) is an international, peer-reviewed, open-access journal dedicated to accelerating the translation of preclinical research into clinical applications and fostering communication between basic and clinical scientists. It highlights the clinical potential and application of various fields including biotechnologies, biomaterials, bioengineering, biomarkers, molecular medicine, omics science, bioinformatics, immunology, molecular imaging, drug discovery, regulation, and health policy. With a focus on the bench-to-bedside approach, CTM prioritizes studies and clinical observations that generate hypotheses relevant to patients and diseases, guiding investigations in cellular and molecular medicine. The journal encourages submissions from clinicians, researchers, policymakers, and industry professionals.