Clinical bottom line
Artificial intelligence (AI) approaches show promise in predicting tumor immune checkpoint ligand expression, particularly programmed death-ligand 1 (PD-L1), from pathological and molecular data. This capability could enhance patient stratification for immune checkpoint inhibitor (ICI) therapies across multiple cancer types, including diffuse large B-cell lymphoma (DLBCL), metastatic urothelial carcinoma, colorectal cancer liver metastases, and head and neck cancers. However, current evidence remains preliminary and heterogeneous, with variable predictive accuracy and limited prospective validation. AI-based prediction of checkpoint ligand expression warrants further validation before routine clinical implementation but may ultimately support more precise immunotherapy selection and monitoring.
What the evidence shows
AI models integrating histopathological, molecular, and circulating tumor cell data have been developed to predict tumor immune checkpoint ligand expression, especially PD-L1, which is a key biomarker for ICI response. Ma et al. (2022) demonstrated that immune checkpoint expression profiles in the tumor microenvironment of DLBCL correlate with clinicopathologic features and prognosis, suggesting that AI-driven profiling could refine risk stratification. Huang et al. (2020) conducted a systematic review and meta-analysis confirming the predictive value of PD-L1 expression for ICI response in metastatic urothelial carcinoma, underscoring the clinical relevance of accurate PD-L1 assessment.
Advanced AI techniques such as surface-enhanced Raman scattering (SERS) multiplex profiling of circulating tumor cells have been applied to melanoma, enabling prediction of response to checkpoint blockade therapy (SERS study, 2022). Similarly, Cao Yingying et al. (2023) used single-cell transcriptomics from blood immune cells to predict tumor immune microenvironment characteristics and checkpoint therapy response in head and neck cancer patients, illustrating the potential of AI to noninvasively infer checkpoint ligand status.
In colorectal cancer liver metastasis, Cao Dandan et al. (2025) reviewed the tumor immune microenvironment and current ICI therapy status, highlighting the complexity of checkpoint ligand expression patterns and the need for integrative predictive models. Emerging evidence in glioblastoma also suggests that mesenchymal tumor features, potentially identifiable by AI, predict response to immune checkpoint blockade (Nature Cancer, 2026).
Collectively, these studies indicate that AI can integrate multi-omic and spatial data to predict checkpoint ligand expression and immunotherapy responsiveness, potentially overcoming limitations of conventional immunohistochemistry and biopsy sampling.
Caveats and uncertainty
Despite encouraging findings, several limitations temper enthusiasm for immediate clinical adoption. The AI models reported are often trained on relatively small, heterogeneous cohorts with variable cancer types and treatment regimens, limiting generalizability. Validation in large, prospective clinical trials remains lacking. The biological heterogeneity of checkpoint ligand expression within tumors and across metastatic sites complicates prediction accuracy.
Technical variability in PD-L1 detection methods and cutoff thresholds also challenges standardization. Many AI approaches rely on complex molecular or imaging data not routinely available in all pathology labs, raising issues of accessibility and cost. Furthermore, the dynamic nature of immune checkpoint expression during treatment and tumor evolution may limit static predictive models.
Finally, while AI can augment pathologist interpretation, it should not replace comprehensive clinical and pathological assessment. The predictive value of AI-derived checkpoint ligand expression must be contextualized within broader clinical decision-making frameworks.
How this may change practice
If validated, AI-based prediction of tumor immune checkpoint ligand expression could enhance patient selection for ICIs by providing rapid, reproducible, and potentially noninvasive assessments. This may reduce reliance on invasive biopsies and overcome sampling bias inherent in heterogeneous tumors. Integration of AI tools into digital pathology workflows could support pathologists in identifying patients most likely to benefit from immunotherapy, improving personalized treatment strategies.
Moreover, AI-driven monitoring of checkpoint ligand dynamics through liquid biopsies or circulating tumor cells might enable real-time assessment of treatment response and resistance mechanisms. This could facilitate adaptive therapy adjustments and better clinical outcomes.
However, widespread clinical implementation requires robust validation studies, standardization of AI algorithms, and integration with existing diagnostic protocols. Training and infrastructure development will be essential to ensure equitable access and appropriate use.