Development and validation of the Immune Profile Score (IPS), a novel multi-omic algorithmic assay for stratifying outcomes in a real-world cohort of advanced solid cancer patients treated with immune checkpoint inhibitors
Authors: Zander Alia , Erbe Rossin , Liu Yan , Jin Ailin , Hyun Seung Won , Mukhopadhyay Sayantoni , Terdich Ben , Rosasco Mario , Patel Nirali , Mahon Brett M. , Sasser Kate , Ting-Lin Michelle A. , Nimeiri Halla , Guinney Justin , Adkins Douglas R. , Zibelman Matthew , Beauchamp Kyle A. , Sangli Chithra , Stein Michelle M. , Taxter Timothy , Chan Timothy A. , Patel Sandip Pravin , Cohen Ezra E.W. 
Published: November 2024

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 (RNAseq). The cohort consisted of advanced stage cancer patients treated with any ICI-containing regimen as the first or second line of therapy. The IPS model was developed utilizing 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 to IPS-Low patients (HR=0.45, 90% CI [0.40-0.52]). IPS was consistently prognostic in PD-L1 (positive/negative), TMB (High/Low), microsatellite status (MSS/MSI-H), 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 [0.42-0.56], 0.47 [0.41-0.53], and 0.45 [0.38-0.53] respectively. In an exploratory predictive utility analysis of the subset of patients (n=345) receiving first-line (1L) chemotherapy (CT) and second-line (2L) ICI, there was no significant effect of IPS for time to next treatment on CT in L1 (HR=1.06 [0.85-1.33]). However, there was a significant effect of IPS for OS on ICI in L2 (HR=0.63 [0.46-0.86]). A test of interaction was statistically significant (p<0.01).

Conclusions

Our results demonstrate that IPS is a generalizable multi-omic biomarker that can be widely utilized clinically as a prognosticator of ICI based regimens.

Graphical Abstract

Key Messages

What is already known on this topic– Advancements of multi-omic profiling technology in research settings has demonstrated the potential value of novel immune biomarkers for forecasting response to ICI therapies. However, despite these advances there remains an unmet clinical need for implementation of more sensitive and generalizable biomarkers to better predict patient outcomes to ICI due to limited availability of clinical multi-omic testing and validation cohorts.

What this study adds– Our results demonstrate that IPS is a generalizable multi-omic biomarker that can be widely utilized clinically as a prognosticator of ICI based regimens. Importantly, IPS-High may identify patients within subgroups (TMB-L, MSS, PD-L1 negative) who benefit from ICI beyond what is predicted by existing biomarkers.

How this study might affect research, practice or policy– In the near term IPS results can support patient stratification across pan-solid tumor cohorts to help inform clinicians and researchers which patients are more likely to benefit from ICI based regimens. In the future IPS may support label expansion of ICIs into cancer types without current approvals, and also potentially improve patient selection to minimize over-treatment with ICI in patients unlikely to respond.