In a new study published today in Science , researchers from The University of Texas MD Anderson Cancer Center developed a spatial atlas of specialized immune structures, called tertiary lymphoid structures (TLSs), across multiple cancer types. This first-of-its-kind atlas revealed that TLS maturation state, spatial location and composition within tumors may provide clinically meaningful information about cancer prognosis and treatment response.
The research was led by Linghua Wang, M.D., Ph.D., professor of Genomic Medicine , executive director and head of the Center for Cellular Language Intelligence , associate member of the James P. Allison Institute ™, and focus area co-lead with the Institute for Data Science in Oncology at UT MD Anderson.
In this study, the research team developed scalable artificial intelligence (AI) frameworks to detect, profile and classify TLSs from spatial omics data and routine pathology slides. They also created a composite scoring system to more effectively stratify patients by prognosis and treatment response across different cancer types and treatment contexts.
"Prior to this study, most of the focus on TLSs as biomarkers was simply on whether or not they were present and, in some cases, whether they were mature," Wang said. "Here, we show that we can go much deeper. TLSs in tumor tissues are much more complex than that. Their maturation state, spatial location and composition within tumors can tell us critical information about the tumor immune microenvironment, treatment response and clinical outcomes."
What are TLSs and why do they matter in cancer?
The immune system's response to a tumor is a highly coordinated effort taking place within the tumor microenvironment. In some tumors, immune cells come together to form organized structures called tertiary lymphoid structures, or TLSs. These structures operate as local immune "hubs", bringing together B cells, T cells, antigen-presenting cells and other supporting cells that help coordinate antitumor immune responses.
Previous studies have shown that TLSs – particularly those that are more mature – are often associated with better patient outcomes and improved responses to immunotherapy across a variety of cancer types. However, the presence of TLSs alone does not tell the whole story.
This study takes that understanding several steps further. Tumors can contain TLSs with very different levels of organization, cellular composition and spatial relationships within tumor cells and the researchers showed that these differences carry important biological and clinical information.
What does this study add to the understanding of TLSs?
While it is well acknowledged that TLSs are important in cancer, our understanding of their cellular and molecular heterogeneity has remained limited, especially in their natural spatial context across large cohorts of human tumor samples.
This study addresses that gap by developing scalable computational frameworks to precisely detect, comprehensively profile and classify TLSs from spatial omics data. Leveraging this framework, the team built a pan-cancer spatial atlas of TLSs across 340 samples from 12 cancer types. This atlas allowed them to examine the TLS landscape in tumor tissues, to define how TLSs vary in key features, and identify transcriptional programs associated with TLS maturation.
The study found that TLSs vary substantially across tissues. As TLSs mature, they become more organized and undergo coordinated changes in immune, stromal and vascular components. Further, their proximity to tumor cells is associated with spatial gradients of tumor signaling.
These findings suggest that TLS maturation and spatial context are linked to distinct tumor signaling environments and may reflect important features of the tumor immune microenvironment.
To make these insights more scalable, the team also developed an AI framework to rapidly identify and classify TLSs from pathology images, which are routinely used in daily clinical care. Training this AI model makes the process of analyzing TLSs more easily translatable to the clinic, while also making the process significantly faster and more scalable. The AI framework enabled the researchers to go one step further, evaluating 25,088 TLSs from more than 3,000 whole-slide images across 10 independent cohorts and developing a TLS "composition score" for a given patient's tumor.
This composition score captures not only the number of TLSs, but also their maturation states within a tumor. This method significantly outperformed conventional TLS measures in stratifying patients by prognosis and treatment response, suggesting that a more detailed view of TLS biology, accounting for maturation state, may provide more clinically meaningful information than TLS presence alone.
What's next for this research?
The TLS composite scoring approach must be validated in prospective clinical trials. If successful, the framework could support broader integration of TLS profiling into routine pathology workflows, since it uses routine pathology images.
The findings also raise important biological and therapeutic questions. One important observation from the study is that many TLSs in tumor tissues remain immature, and some are located away from tumor regions rather than within or adjacent to tumor cells. This suggests that future studies should investigate how to promote TLSs toward more mature and functional states, and how to enhance their spatial interaction with tumor cells and the broader tumor microenvironment. These efforts may help identify therapeutic strategies to promote effective TLS formation and maturation and enhance TLS-associated anti-tumor immune responses.
