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

Sep 1, 2024·
Jia-Xin LEI
Jia-Xin LEI
1st Author
Run-Xian WANG
Run-Xian WANG
Co-1st Author
Chu-Ling HU
Chu-Ling HU
,
Xiaoying Lou
Min-Yi LV
Min-Yi LV
Cheng-Hang LI
Cheng-Hang LI
Bao-Wen GAI
Bao-Wen GAI
,
Xiao-Jian Wu
Co-corresponding Author
,
Ruoxu Dou
Co-corresponding Author
Du CAI
Du CAI
Co-corresponding Author
Feng GAO
Feng GAO
Corresponding Author
· 0 min read
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.

Type
Publication
International Journal of Surgery
publication
Jia-Xin LEI
Authors
Research Assistant
I am a research assistant interested in deep learning and medical image analysis for clinical applications.
Run-Xian WANG
Authors
Research Student
I am a PhD student focusing on AI-based colorectal cancer research and predictive clinical modeling.
Chu-Ling HU
Authors
PhD Student
I am a PhD student focusing on AI-driven colorectal cancer research and clinically useful model development.
Min-Yi LV
Authors
PhD Student
I am a PhD student at Guangzhou National Laboratory, focusing on colorectal cancer research, biostatistics, and evidence-driven clinical modeling.
Cheng-Hang LI
Authors
Research Assistant
I am a research assistant focusing on deep learning, multimodal feature fusion, and medical AI system development.
Bao-Wen GAI
Authors
PhD Student
I am a PhD student working on AI methods for colorectal cancer diagnosis and prognosis.
Du CAI
Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.
Feng GAO
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.