Cancer Immunotherapy Biomarkers — AI Atlas of Tertiary Structures

Cancer immunotherapy biomarkers and AI-powered atlas of tertiary lymphoid structures in the tumor microenvironment Unique spatial biology insights into TLS maturation, immune response in cancer, and predicting checkpoint inhibitor response Understand how AI in cancer research can improve cancer biomarkers and predicting cancer treatment outcomes across 28 tumor types What You'll Learn: How tertiary lymphoid structures (TLSs) function within the tumor microenvironment and why they matter for cancer immunotherapy What a large-scale AI-powered spatial atlas of TLSs across 6,174 tumors and 28 cancers reveals about the immune response in cancer Why mature TLS located within 500 µm of the tumor margin are associated with a median 18% improvement in 5-year overall survival How TLS maturation state, spatial location, and cellular composition can serve as robust cancer biomarkers for prognosis and therapy response How TLS maturation scores predicted anti-PD-1 response with an AUC of 0.79 in validation melanoma cohorts, outperforming PD-L1 (AUC 0.65) How spatial biology and AI models were combined to quantify TLS features across samples from 12 institutions in a harmonized way Practical implications for using TLS-based biomarkers to guide checkpoint inhibitor selection and personalize immuno-oncology treatment strategies Key limitations, future directions, and how this atlas could integrate with other biomarkers in cancer immunotherapy research