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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".