# A multi-omics features-based approach integrating immunogenicity and inflammation enhances immunotherapy benefit in clear cell renal cell carcinoma

## TL;DR

A new machine learning model using immune and inflammation features improves prediction of immunotherapy success in kidney cancer patients.

## Contribution

A novel multi-omics machine learning model integrating immune and inflammatory signatures for predicting immunotherapy response in ccRCC.

## Key findings

- Inflammatory signaling is strongly linked to immunotherapy outcomes in ccRCC patients.
- A TIs-ML model outperformed single biomarkers with an AUC > 0.997 in predicting treatment response.
- The model identified two distinct ccRCC subtypes with different prognoses and treatment responses.

## Abstract

Programmed cell death 1 (PD-1) or PD-ligand 1 (PD-L1) blocker-based strategies have improved the survival outcomes of clear cell renal cell carcinomas (ccRCCs) in recent years, but only a small number of patients have benefited from them.

In this study, we developed a multi-omics machine learning model based on inflammatory and immune signatures (TIs) to predict the response and survival of ccRCC patients to immune checkpoint blockade (ICB) therapy. The research collected RNA-seq and single-cell RNA-seq (scRNA-seq) data from more than 1,900 patients with autoimmune nephropathy and analyzed the genomic and transcriptome profiles of ccRCC patients. The predictive power of the method was validated in more than 1,000 ccRCC patients treated with ICB, and compared to single biomarkers (e.g., PD-L1 expression, TMB).

Inflammatory signaling was found to be strongly associated with ICB outcome, and 716 inflammation-related genes were identified that are enriched in the “lymphocyte activation regulation” pathway. The findings suggested that ccRCC patients can be categorized into two subtypes with different treatment responses and prognosis by these features. In addition, the TIs-ML model exhibited superior predictive capabilities compared to an individual biomarker (AUC > 0.997) across multiple independent datasets. It demonstrated the capacity to accurately differentiate between responders and non-responders. Furthermore, the model performed more effectively than existing genetic models and functional scores in predicting survival.

We propose a TIs-ML prediction model based on multi-omics features that can effectively predict ICB treatment response in ccRCC patients. The model integrates inflammatory and immune features, and its high generalization ability was validated in multiple cohorts. Overall, the TIs-ML approach provides a novel method for guiding precise immunotherapy in ccRCC.