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

05/30/2025

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

The Journal for ImmunoTherapy of Cancer

Authors Alia D Zander, Rossin Erbe, Yan Liu, Ailin Jin, Seung Won Hyun, Sayantoni Mukhopadhyay, Ben Terdich, Mario G Rosasco, Nirali Patel, Brett M Mahon, A Kate Sasser, Michelle A Ting-Lin, Halla Nimeiri, Justin Guinney, Douglas Adkins, Matthew Zibelman, Kyle A Beauchamp, Chithra Sangli, Michelle M Stein, Timothy Taxter, Timothy Chan, Sandip P Patel and Ezra E W Cohen

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 (90% CI 0.49 to 0.82)). A test of interaction was statistically significant (p<0.01).

Conclusions Our results demonstrate that IPS is a generalizable multiomic biomarker that can be widely used clinically as a prognosticator of ICI-based regimens.

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