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Updated: Sep 2, 2026

Analysis of Human T Cell Activity in an Allogeneic Co-Culture Setting of Pre-Treated Tumor Cells
Published on: March 7, 2025
Oncogenic Signalling and Immune Checkpoint Crosstalk in Cancer: Emerging Targets and Precision Therapeutic Strategies
Praveen Kumar Chandra Sekar1, Ramakrishnan Veerabathiran1
1Human Cytogenetics and Genomics Laboratory, Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, 603103, India.
Introduction:
Cancer progression is sustained by complex interactions between oncogenic signaling pathways and immune checkpoint networks, yet their mechanistic crosstalk remains poorly integrated in existing reviews. This review synthesises the bidirectional regulatory relationships between oncogenic signaling and immune checkpoints, with emphasis on mechanistic interdependencies, therapeutic resistance, and precision oncology strategies.
Methods:
A narrative review of published literature was conducted covering PI3K/AKT/mTOR, RAS/MAPK, and JAK/STAT oncogenic pathways; immune checkpoints including PD-1/PD-L1, CTLA-4, LAG-3, TIM-3, and TIGIT; tumour microenvironment interactions; multi-omics integration; pharmacogenomics; and AI-driven drug discovery platforms.
Results:
Three core crosstalk axes were identified. First, the PI3K/AKT-PD-L1 axis: AKT-mediated GSK-3β inactivation stabilises PD-L1 protein, and PTEN loss constitutively amplifies surface PD-L1 expression, suppressing T-cell cytotoxicity. Second, the MAPK-immune suppression axis: sustained ERK signalling downregulates MHC class I and TAP1/TAP2 components, while BRAF V600E drives secretion of immunosuppressive cytokines including VEGF and IL-10. Third, the JAK/STAT-PD-L1 axis: IFN-γ-driven JAK1/JAK2 activation induces PD-L1 transcription via STAT1, while JAK loss-of-function mutations confer acquired resistance to anti-PD-1 therapy by abrogating MHC-I re-expression.
Discussion:
These crosstalk mechanisms explain why tumours with high oncogenic signalling burden consistently show attenuated immunotherapy responses and provide the molecular rationale for combination targeting strategies. Multi-omics integration, pharmacogenomic biomarkers including MSI-H status and PIK3CA mutation, and AI-guided modelling frameworks offer structured approaches for translating these findings into clinically actionable patient stratification decisions.
Conclusion:
Rational combination strategies co-targeting oncogenic signalling and immune checkpoints, guided by multi-omics profiling and biomarker-driven patient stratification, represent the most promising approach for achieving durable precision oncology outcomes.
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