# Identification of a Tertiary Lymphoid Structure Signature for Predicting Tumor Outcomes Through Transcriptomics Analysis

## TL;DR

This study identifies a new gene signature for predicting cancer outcomes and immunotherapy response based on tertiary lymphoid structures.

## Contribution

A novel de novo TLS-related gene feature set, predictTLS, is developed for pan-cancer prognosis and immunotherapy prediction.

## Key findings

- The predictTLS gene set effectively predicts immunotherapy outcomes and patient prognosis across multiple cancer types.
- Validation using single-cell and spatial transcriptomics confirms the biological relevance of predictTLS.
- TLS gene signatures are associated with key immune-related indicators in tumors.

## Abstract

Background: Tertiary lymphoid structures (TLSs) play a crucial role in regulating tumor invasion and metastasis and serve as a promising prognostic biomarker in immunotherapy, influencing survival and immune response in multiple cancers. However, existing studies rely on limited gene signatures to assess TLSs, and there remains a lack of comprehensive TLS-related features for pan-cancer prognosis or immunotherapy response prediction. Methods: Based on published TLS gene signatures, mutation data, and expression profiles from 33 tumor types in TCGA, along with data from 15 immune checkpoint blockade (ICB) cohorts, we first systematically evaluated six TLS gene signatures in relation to immune-related indicators and assessed their predictive and prognostic performance across tumors and immunotherapy. Subsequently, using meta-analysis, we constructed a de novo TLS-related gene feature set, termed predictTLS, designed to predict ICB efficacy and prognosis. The rationality and effectiveness of predictTLS were validated using internal validation sets, single-cell transcriptomic, and spatial transcriptomic data. Results: The evaluation revealed associations between TLS gene signatures and key immune-related indicators. The newly constructed predictTLS feature set demonstrated effectiveness in predicting both ICB therapy outcomes and patient prognosis across the analyzed cohorts. Validation across internal datasets, single-cell profiles, and spatial transcriptomics supported the robustness and biological relevance of predictTLS. Conclusions: This study provides a systematically validated, de novo TLS-related gene signature that can serve as a clinical biomarker for predicting immunotherapy response and prognosis in pan-cancer settings. These findings offer new tools for risk stratification and potential therapeutic targeting in tumor immunotherapy.