Department of Emergency, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Burn Plastic Surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, China.
Melanoma Res. 2024 Jun 1;34(3):215-224. doi: 10.1097/CMR.0000000000000957. Epub 2024 Feb 16.
Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a tumor. ITH was involved in the cancer progression, aggressiveness, therapy resistance and cancer recurrence. Integrative machine learning procedure including 10 methods was conducted to develop an ITH-related signature (IRS) in The Cancer Genome Atlas (TCGA), GSE54467, GSE59455 and GSE65904 cohort. Several scores, including tumor immune dysfunction and exclusion (TIDE) score, tumor mutation burden (TMB) score and immunophenoscore (IPS), were used to evaluate the role of IRS in predicting immunotherapy benefits. Two immunotherapy datasets (GSE91061 and GSE78220) were utilized to the role of IRS in predicting immunotherapy benefits of skin cutaneous melanoma (SKCM) patients. The optimal prognostic IRS constructed by Lasso method acted as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate in SKCM, with the area under the curve of 2-, 3- and 4-year receiver operating characteristic curve being 0.722, 0.722 and 0.737 in TCGA cohort. We also constructed a nomogram and the actual 1-, 3- and 5-year survival times were highly consistent with the predicted survival times. SKCM patients with low IRS scores had a lower TIDE score, lower immune escape score and higher TMB score, higher PD1&CTLA4 IPS. Moreover, SKCM patients with low IRS scores had a lower gene sets score involved in DNA repair, angiogenesis, glycolysis, hypoxia, IL2-STAT5 signaling, MTORC1 signaling, NOTCH signaling and P53 pathway. The current study constructed a novel IRS in SKCM using 10 machine learning methods. This IRS acted as an indicator for predicting the prognosis and immunotherapy benefits of SKCM patients.
肿瘤内异质性（ITH）是指肿瘤内不同肿瘤细胞和免疫细胞之间在分子和表型特征上的差异。ITH 参与了癌症的进展、侵袭性、治疗耐药性和癌症复发。本研究采用包括 10 种方法的综合机器学习程序，在 The Cancer Genome Atlas（TCGA）、GSE54467、GSE59455 和 GSE65904 队列中开发了一个与 ITH 相关的特征（IRS）。几种评分，包括肿瘤免疫功能障碍和排斥（TIDE）评分、肿瘤突变负担（TMB）评分和免疫表型评分（IPS），被用于评估 IRS 预测免疫治疗获益的作用。利用两个免疫治疗数据集（GSE91061 和 GSE78220）评估 IRS 在预测皮肤黑色素瘤（SKCM）患者免疫治疗获益中的作用。Lasso 方法构建的最佳预后 IRS 作为一个独立的危险因素，在 TCGA 队列中预测 SKCM 患者的总生存率具有稳定而强大的性能，其 2 年、3 年和 4 年的曲线下面积分别为 0.722、0.722 和 0.737。我们还构建了一个列线图，实际的 1 年、3 年和 5 年生存率与预测的生存率高度一致。IRS 评分低的 SKCM 患者 TIDE 评分更低，免疫逃逸评分更低，TMB 评分更高，PD1&CTLA4 IPS 更高。此外，IRS 评分低的 SKCM 患者基因集评分涉及 DNA 修复、血管生成、糖酵解、缺氧、IL2-STAT5 信号、MTORC1 信号、NOTCH 信号和 P53 通路的评分更低。本研究采用 10 种机器学习方法在 SKCM 中构建了一个新的 IRS。该 IRS 可作为预测 SKCM 患者预后和免疫治疗获益的指标。
