Leveraging liquid biopsy to uncover resistance mechanisms and guide personalized immunotherapy
Bibliographic record
Abstract
Cancer therapy has been revolutionized by immune checkpoint inhibitors (ICIs) that create a new paradigm among cancer immunotherapies. These agents restore the immune system capacity to fight cancer through blocking the action of major immune checkpoint proteins that suppress immune responses such as programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which tumors exploit for immune escape. Another advancement is the absence of known biomarkers involves ICIs, which can further be translated into new liquid tests like liquid biopsies, indicating the immune status of the patient. Liquid biopsy is a minimally invasive method for identifying tumor-derived components such as circulating tumor cells, cell-free DNA and extracellular vesicles (EVs) from body fluids for analysis. Researchers increasingly use liquid biopsy for biomarkers discovery and patient stratification into clinical applications. Even though immunotherapy has great advances, still there are obstacles faced while using ICIs. Many patients fail to respond to the treatment because of the heterogeneous mechanisms of resistance. Immunotherapy resistance is a dynamic interplay between tumors and their surrounding stroma. To understand this variability, humanized mice models are increasingly used for mirroring human immune responses. Such models offer insight into cancer immunotherapy when human immune system is engrafted into mice, and EV and biomarker profiles are established in those models. EVs reflect differences in tumor characteristics and the immune landscape around tumors to develop personalized strategies to enhance ICI efficacy. This may improve patient prognosis, giving reason to hope for better, more effective treatments.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".