Deciphering Tertiary Lymphoid Structure Heterogeneity Reveals Prognostic Signature and Therapeutic Potentials for Colorectal Cancer: A Multicenter Retrospective Cohort Study

Abstract

Background: Tertiary lymphoid structures (TLSs) exert a crucial role in the tumor microenvironment (TME), impacting tumor development, immune escape, and drug resistance. Nonetheless, the heterogeneity of TLSs in colorectal cancer (CRC) and their impact on prognosis and treatment response remain unclear.

Methods: The authors collected genome, transcriptome, clinicopathological information, and digital pathology images from multiple sources. An unsupervised clustering algorithm was implemented to determine diverse TLS patterns in CRC based on the expression levels of 39 TLS signature genes (TSGs). Comprehensive explorations of heterogeneity encompassing mutation landscape, TME, biological characteristics, response to immunotherapy, and drug resistance were conducted using multiomics data. TLSscore was then developed to quantitatively assess TLS patterns of individuals for further clinical applicability.

Results: Three distinct TLS patterns were identified in CRC. Cluster 1 exhibited upregulation of proliferation-related pathways, high metabolic activity, and intermediate prognosis, while Cluster 2 displayed activation of stromal and carcinogenic pathways and a worse prognosis. Both Cluster 1 and Cluster 2 may potentially benefit from adjuvant chemotherapy. Cluster 3, characterized by the activation of immune regulation and activation pathways, demonstrated a favorable prognosis and enhanced responsiveness to immunotherapy. The authors subsequently employed a regularization algorithm to construct the TLSscore based on nine core genes. Patients with lower TLSscore trended to prolonged prognosis and a more prominent presence of TLSs, which may benefit from immunotherapy. Conversely, those with higher TLSscore exhibited increased benefits from adjuvant chemotherapy.

Conclusions: The authors identified distinct TLS patterns in CRC and characterized their heterogeneity through multiomics analyses. The TLSscore held promise for guiding clinical decision-making and further advancing the field of personalized medicine in CRC.

Jia-Xin LEI

Research Assitant

Run-Xian WANG

Research Student

Chu-Ling HU

PhD Student

Min-Yi LV

PhD Student

Cheng-Hang LI

Research Assistant

Bao-Wen GAI

PhD Student

Du CAI

Postdoc

I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.

Feng GAO

Professor

My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.