Immune Evasion and Resistance in Cancer Progression: Overcoming Checkpoint Inhibition Challenges with Personalized Immunotherapy Guided by PD-L1 Expression
Bibliographic record
Abstract
Immune evasion is a hallmark of cancer development and poses an important impediment to the effectiveness of immune checkpoint inhibitors (ICIs). Cancer cells take advantage of heterogeneous intrinsic and extrinsic pathways to circumvent immune detection, such as metabolic remodeling (e.g., increased glycolysis, activation of IDO1), genomic mutation (e.g., JAK/STAT, β-catenin), and epigenetic suppression of immune-regulatory genes. Concurrently, TME promotes immune suppression through Tregs, MDSCs, TAMs, and fibroblast-mediated extracellular matrix remodeling. Hypoxia and cytokine dysregulation also undermine antigen presentation and T-cell functionality. These immunoevasion strategies form the foundation of both native (innate) and adaptive resistance to ICIs, while recent evidence places emphasis on microbiota composition being able to modify therapeutic response. The PD-1/PD-L1 pathway remains the focus of ICI therapy, but PD-L1 expression is limited by spatial, temporal, and technical heterogeneity. Beyond PD-L1, integrated biomarker approaches including tumor mutational burden (TMB), microsatellite instability (MSI), IFN-γ gene signatures, and circulating tumor DNA (ctDNA) have arisen to further inform patient stratification. Emerging therapeutic technologies—e.g., dual checkpoint blockade, engineered cytokines, personalized neoantigen vaccines, and adoptive T cell therapy (CAR-T, TCR-T)—are designed to overcome resistance and maximize clinical efficacy. Integration of multi-omics and AI-based models provides additional precision in the tailoring of immunotherapy. This review integrates existing knowledge of immune escape and resistance, highlighting dynamic biomarker development and combinatorial approaches for next-generation personalized cancer immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".