Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Department of Lymphoma and Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Cancer Res Commun. 2023 Aug 2;3(8):1435-1446. doi: 10.1158/2767-9764.CRC-23-0083. eCollection 2023 Aug.
Patients with mantle cell lymphoma (MCL), an incurable B-cell malignancy, benefit from accurate pretreatment disease stratification. We curated an extensive database of 862 patients diagnosed between 2014 and 2022. A machine learning (ML) gradient-boosted model incorporated baseline features from clinicopathologic, cytogenetic, and genomic data with high predictive power discriminating between patients with indolent or responsive MCL and those with aggressive disease (AUC ROC = 0.83). In addition, we utilized the gradient-boosted framework as a robust feature selection method for multivariate logistic and survival modeling. The best ML models incorporated features from clinical and genomic data types highlighting the need for correlative molecular studies in precision oncology. As proof of concept, we launched our most accurate and practical models using an application interface, which has potential for clinical implementation. We designated the 20-feature ML model-based index the "integrative MIPI" or iMIPI and a similar 10-feature ML index the "integrative simplified MIPI" or iMIPI-s. The top 10 baseline prognostic features represented in the iMIPI-s are: lactase dehydrogenase (LDH), Ki-67%, platelet count, bone marrow involvement percentage, hemoglobin levels, the total number of observed somatic mutations, mutational status, Eastern Cooperative Oncology Group performance level, beta-2 microglobulin, and morphology. Our findings emphasize that prognostic applications and indices should include molecular features, especially mutational status. This work demonstrates the clinical utility of complex ML models and provides further evidence for existing prognostic markers in MCL.
Our model is the first to integrate a dynamic algorithm with multiple clinical and molecular features, allowing for accurate predictions of MCL disease outcomes in a large patient cohort.
套细胞淋巴瘤（MCL）是一种不可治愈的 B 细胞恶性肿瘤，患者在接受治疗前需要进行准确的疾病分层。我们从 2014 年至 2022 年间诊断的 862 名患者中整理了一个广泛的数据库。一个机器学习（ML）梯度提升模型将来自临床病理、细胞遗传学和基因组数据的基线特征纳入其中，具有区分惰性或有反应性 MCL 患者与侵袭性疾病患者的高预测能力（AUC ROC = 0.83）。此外，我们还利用梯度提升框架作为一种强大的特征选择方法，用于多元逻辑回归和生存建模。最好的 ML 模型纳入了来自临床和基因组数据类型的特征，突出了在精准肿瘤学中进行相关分子研究的必要性。作为概念验证，我们使用应用程序接口启动了我们最准确和实用的模型，这些模型具有临床实施的潜力。我们将基于 20 个特征的 ML 模型的指数命名为“综合 MIPI”或 iMIPI，以及类似的 10 个特征 ML 指数“综合简化 MIPI”或 iMIPI-s。iMIPI-s 中代表的前 10 个基线预后特征是：乳糖酶脱氢酶（LDH）、Ki-67%、血小板计数、骨髓受累百分比、血红蛋白水平、观察到的体细胞突变总数、突变状态、东部合作肿瘤学组表现水平、β-2 微球蛋白和形态学。我们的研究结果强调，预后应用和指数应包括分子特征，特别是突变状态。这项工作证明了复杂 ML 模型的临床实用性，并为 MCL 中的现有预后标志物提供了进一步的证据。
我们的模型是第一个将动态算法与多个临床和分子特征相结合的模型，可对大型患者队列中 MCL 疾病结局进行准确预测。
