A predictive risk-scoring model for survival prognosis of multiple myeloma based on gain/amplification of 1q21: Experience in a tertiary hospital in South-Western China

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-09-05 DOI:10.1002/cam4.70193

Yanqiu Xiong, Shanshan Liang, Wenjiao Tang, Li Zhang, Yuhuan Zheng, Ling Pan, Ting Niu

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Abstract

Background

Chromosomal 1q gains and amplifications (+1q21) are frequently observed in patients with newly diagnosed multiple myeloma (NDMM). However, the interpretation of the high-risk (HR) prognostic implications stemming from 1q21 abnormalities remain challenging to implement effectively.

Methods

In a comprehensive analysis of 367 consecutive patients with symptomatic MM, we assessed the prognostic significance of +1q21 using FISH with a threshold of 7.4%. The patient cohort was randomly divided into a training set (66.5%, n = 244) and a validation set (33.5%, n = 133). A multivariate Cox regression analysis was conducted to identify significant prognostic factors associated with PFS. Weight scores were assigned to each risk factor based on the β-value of the corresponding regression coefficient. A predictive risk-scoring model involving +1q21 was then developed, utilizing the total score derived from these weight scores. The model's discriminative ability was evaluated using the AUC in both the training and validation sets. Finally, we compared the performance of the +1q21-involved risk with the established R-ISS and R2-ISS models.

Results

Upon initial diagnosis, 159 patients (43.32%) exhibited +1q21, with 94 (59.11%) having three copies, referred to as Gain(1q21), and 65 (40.89%) possessing four or more copies, referred to as Amp (1q21). Both were significantly linked to a reduced PFS in myeloma (p < 0.05), which could be effectively mitigated by ASCT. The +1q21-involved risk model, with an AUC of 0.697 in the training set and 0.725 in the validation set, was constructed including Gain(1q21), Amp(1q21), no-ASCT, and TP53 deletion. This model, termed the ultra-high-risk (UHR) model, demonstrated superior performance in predicting shorter PFS compared to the R-ISS stage 3 and R2-ISS stage 4.

Conclusion

The UHR model, which integrates the presence of +1q21 with no-ASCT and TP53 deletion, is designed to identify the early relapse subgroup among patients with +1q21 in NDMM.

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基于1q21增益/扩增的多发性骨髓瘤生存预后预测风险评分模型：中国西南地区一家三甲医院的经验。

背景：在新诊断的多发性骨髓瘤（NDMM）患者中经常观察到染色体1q增益和扩增（+1q21）。然而，如何有效解释 1q21 异常带来的高风险（HR）预后影响仍是一项挑战：在对连续 367 例有症状 MM 患者的综合分析中，我们使用 FISH 评估了 +1q21 的预后意义，阈值为 7.4%。患者队列被随机分为训练集（66.5%，n = 244）和验证集（33.5%，n = 133）。进行了多变量 Cox 回归分析，以确定与 PFS 相关的重要预后因素。根据相应回归系数的β值对每个风险因素进行加权评分。然后，利用这些权重分数得出的总分，建立了一个涉及 +1q21 的预测性风险评分模型。模型的判别能力通过训练集和验证集的 AUC 进行评估。最后，我们比较了+1q21涉及风险与已建立的R-ISS和R2-ISS模型的性能：初步诊断时，159 名患者（43.32%）表现出 +1q21，其中 94 人（59.11%）有三个拷贝，称为 Gain(1q21)，65 人（40.89%）有四个或更多拷贝，称为 Amp (1q21)。这两种情况都与骨髓瘤患者的生存期缩短密切相关（p 结论）：UHR模型将+1q21的存在与无ASCT和TP53缺失相结合，旨在识别NDMM中+1q21患者的早期复发亚组。

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

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.

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