Zander Alia D, Erbe Rossin, Liu Yan, Jin Ailin, Hyun Seung Won, Mukhopadhyay Sayantoni, Terdich Ben, Rosasco Mario G, Patel Nirali, Mahon Brett M, Sasser A Kate, Ting-Lin Michelle A, Nimeiri Halla, Guinney Justin, Adkins Douglas, Zibelman Matthew, Beauchamp Kyle A, Sangli Chithra, Stein Michelle M, Taxter Timothy, Chan Timothy, Patel Sandip P, Cohen Ezra E W
Tempus AI Inc, Chicago, Illinois, USA.
Washington Univ, St. Louis, Missouri, USA.
J Immunother Cancer. 2025 May 30;13(5):e011363. doi: 10.1136/jitc-2024-011363.
Immune checkpoint inhibitors (ICIs) have transformed the oncology treatment landscape. Despite substantial improvements for some patients, the majority do not benefit from ICIs, indicating a need for predictive biomarkers to better inform treatment decisions.
A de-identified pan-cancer cohort from the Tempus multimodal real-world database was used for the development and validation of the Immune Profile Score (IPS) algorithm leveraging Tempus xT (648 gene DNA panel) and xR (RNA sequencing) (N=1,707 development cohort; N=1,600 validation cohort). The cohort consisted of patients with advanced stage cancer with solid tumor carcinomas across 16 cancer types treated with any ICI-containing regimen as the first or second line of therapy. The IPS model was developed using a machine learning framework that includes tumor mutational burden (TMB) and 11 RNA-based biomarkers as features.
IPS-High patients demonstrated significantly longer overall survival (OS) compared with IPS-Low patients (HR=0.45, 90% CI (0.40 to 0.52)). IPS was consistently prognostic in programmed death-ligand 1 (PD-L1) (positive/negative), TMB (High/Low), microsatellite status (microsatellite instability (MSI)-High), and regimen (ICI only/ICI+other) subgroups. Additionally, IPS remained significant in multivariable models controlling for TMB, MSI, and PD-L1, with IPS HRs of 0.49 (90% CI 0.42 to 0.56), 0.47 (90% CI 0.41 to 0.53), and 0.45 (90% CI 0.38 to 0.53), respectively. In an exploratory predictive utility analysis of the subset of patients (n=345) receiving first-line chemotherapy (CT) and second-line ICI, there was no significant effect of IPS for time to next treatment on CT in L1 (HR=1.06 (90% CI 0.88 to 1.29)). However, there was a significant effect of IPS for OS on ICI in L2 (HR=0.63 (90% CI 0.49 to 0.82)). A test of interaction was statistically significant (p<0.01).
Our results demonstrate that IPS is a generalizable multiomic biomarker that can be widely used clinically as a prognosticator of ICI-based regimens.
免疫检查点抑制剂（ICI）改变了肿瘤治疗格局。尽管部分患者有显著改善，但大多数患者无法从ICI治疗中获益，这表明需要预测性生物标志物以更好地指导治疗决策。
利用来自Tempus多模式真实世界数据库的去识别泛癌队列，开发并验证免疫谱评分（IPS）算法，该算法利用了Tempus xT（648基因DNA检测板）和xR（RNA测序）（开发队列N = 1707；验证队列N = 1600）。该队列由患有晚期实体肿瘤癌的患者组成，涉及16种癌症类型，接受任何含ICI的方案作为一线或二线治疗。IPS模型使用机器学习框架开发，该框架包括肿瘤突变负荷（TMB）和11种基于RNA的生物标志物作为特征。
与IPS低的患者相比，IPS高的患者总生存期（OS）显著更长（HR = 0.45，90%CI（0.40至0.52））。IPS在程序性死亡配体1（PD-L1）（阳性/阴性）、TMB（高/低）、微卫星状态（微卫星不稳定性（MSI）-高）和治疗方案（仅ICI/ICI+其他）亚组中始终具有预后意义。此外，在控制TMB、MSI和PD-L1的多变量模型中，IPS仍然具有显著意义，IPS的HR分别为0.49（90%CI 0.42至0.56）、0.47（90%CI 0.41至0.53）和0.45（90%CI 0.38至0.53）。在对接受一线化疗（CT）和二线ICI的患者子集（n = 345）进行的探索性预测效用分析中，IPS对L1中CT的下次治疗时间没有显著影响（HR = 1.06（90%CI 0.88至1.29））。然而，IPS对L2中ICI的OS有显著影响（HR = 0.63（90%CI 0.49至0.82））。交互作用检验具有统计学意义（p<0.01）。
我们的结果表明，IPS是一种可推广的多组学生物标志物，可在临床上广泛用作基于ICI方案的预后指标。
