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Updated: Jul 19, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
T-cell priming transcriptomic markers: implications of immunome heterogeneity for precision immunotherapy
Hirotaka Miyashita1, Razelle Kurzrock2,3, Nicholas J Bevins4
1Department of Hematology and Oncology, Dartmouth Cancer Center, Lebanon, NH, USA. miyashita.hirotaka@gmail.com.
Abstract:
Immune checkpoint blockade is effective for only a subset of cancers. Targeting T-cell priming markers (TPMs) may enhance activity, but proper application of these agents in the clinic is challenging due to immune complexity and heterogeneity. We interrogated transcriptomics of 15 TPMs (CD137, CD27, CD28, CD80, CD86, CD40, CD40LG, GITR, ICOS, ICOSLG, OX40, OX40LG, GZMB, IFNG, and TBX21) in a pan-cancer cohort (N = 514 patients, 30 types of cancer). TPM expression was analyzed for correlation with histological type, microsatellite instability high (MSI-H), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) expression. Among 514 patients, the most common histological types were colorectal (27%), pancreatic (11%), and breast cancer (10%). No statistically significant association between histological type and TPM expression was seen. In contrast, expression of GZMB (granzyme B, a serine protease stored in activated T and NK cells that induces cancer cell apoptosis) and IFNG (activates cytotoxic T cells) were significantly higher in tumors with MSI-H, TMB ≥ 10 mutations/mb and PD-L1 ≥ 1%. PD-L1 ≥ 1% was also associated with significantly higher CD137, GITR, and ICOS expression. Patients' tumors were classified into "Hot", "Mixed", or "Cold" clusters based on TPM expression using hierarchical clustering. The cold cluster showed a significantly lower proportion of tumors with PD-L1 ≥ 1%. Overall, 502 patients (98%) had individually distinct patterns of TPM expression. Diverse expression patterns of TPMs independent of histological type but correlating with other immunotherapy biomarkers (PD-L1 ≥ 1%, MSI-H and TMB ≥ 10 mutations/mb) were observed. Individualized selection of patients based on TPM immunomic profiles may potentially help with immunotherapy optimization.
Insights
Targeting T-cell priming markers (TPMs) may enhance cancer immunotherapy. Diverse TPM expression patterns correlate with biomarkers like PD-L1, MSI-H, and TMB, suggesting individualized patient selection for optimized treatment.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Immune checkpoint blockade (ICB) benefits only a subset of cancer patients.
- Targeting T-cell priming markers (TPMs) shows potential to improve ICB efficacy.
- Clinical application of TPMs is complex due to tumor immune heterogeneity.
Purpose of the Study:
- To analyze the expression of 15 TPMs across a pan-cancer cohort.
- To investigate correlations between TPM expression and key cancer biomarkers.
- To explore the potential of TPM immunomic profiles for optimizing cancer immunotherapy.
Main Methods:
- Transcriptomic analysis of 15 TPMs in 514 patients across 30 cancer types.
- Correlation analysis with histological type, microsatellite instability-high (MSI-H), tumor mutational burden (TMB), and PD-L1 expression.
- Hierarchical clustering to classify tumors into 'Hot', 'Mixed', or 'Cold' based on TPM expression.
Main Results:
- TPM expression showed no significant association with histological type.
- GZMB and IFNG expression were higher in tumors with MSI-H, high TMB (≥10 mutations/mb), and PD-L1 expression (≥1%).
- PD-L1 expression (≥1%) correlated with higher CD137, GITR, and ICOS expression. Tumors were classified into distinct immunomic clusters, with 'cold' clusters having less PD-L1 expression.
Conclusions:
- Diverse TPM expression patterns exist across cancers, independent of histology.
- TPM profiles correlate with established immunotherapy biomarkers (PD-L1, MSI-H, TMB).
- Individualized patient selection based on TPM immunomic profiles may enhance immunotherapy outcomes.
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