Precision Reprogramming of Lung Cancer Biomarkers via CRISPR/Cas9: A Paradigm Shift in Personalized Immunotherapy
Bibliographic record
Abstract
Lung cancer persists as the leading cause of cancer-related mortality globally, largely due to late detection, genomic complexity, and limited durability of existing therapeutic interventions. The integration of CRISPR/Cas9 gene-editing systems with biomarker-driven therapeutic strategies represents a transformative advance in precision oncology. Biomarkers—including genetic mutations, epigenetic alterations, protein signatures, and circulating analytes—enable early detection, patient stratification, and dynamic monitoring of therapeutic response. CRISPR/Cas9 offers a unique opportunity to directly reprogram these biomarkers or the pathways regulating them, enhancing tumor immunogenicity, reversing immune evasion mechanisms, and strengthening anti-tumor immune responses. Preclinical models demonstrate that CRISPR-mediated biomarker editing can restore antigen presentation, augment T-cell cytotoxicity, sensitize resistant tumors to immunotherapy, and improve tumor regression. Early clinical trials further validate the feasibility and safety of CRISPR-engineered immune cells in patients. However, major challenges persist, including off-target editing, inefficient delivery to solid tumors, tumor microenvironment–mediated suppression, and ethical considerations linked to genome manipulation. Rapid advancements in editing fidelity, lipid nanoparticle systems, viral vectors, engineered vesicles, high-throughput biomarker discovery, and artificial intelligence–assisted CRISPR design are expected to accelerate clinical translation. This review synthesizes the current landscape, mechanistic underpinnings, emerging applications, and future directions of CRISPR/Cas9-enabled biomarker engineering for lung cancer immunotherapy, positioning this technology as a cornerstone of next-generation personalized oncology.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".