Development and validation of the Immune Profile Score (IPS), a novel multiomic algorithmic assay for stratifying outcomes in a real-world cohort of patients with advanced solid cancer treated with immune checkpoint inhibitors

Background 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. Methods 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. Results 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