Investigating nano-sized tumor-derived extracellular vesicles in enhancing anti-PD-1 immunotherapy
Bibliographic record
Abstract
Anti-PD1 immune checkpoint blockade (ICB) has shown promising results for treating several aggressive cancers, enhancing patient survival rates. The variability in clinical response to anti-PD1 ICB is thought to be driven by patient-specific biology and heterogeneity within the tumor microenvironment. Tumor-derived extracellular vesicles (TDEVs), nano-sized particles released from tumor cells, can modulate the tumor microenvironment, leading to immunosuppression and tumor progression. Hence, TDEVs may contribute to the variability in treatment response and play a crucial role in the failure of anti-PD1 immunotherapy. In this study, we develop a systems biology approach to interrogate the role of TDEVs on the response dynamics for anti-PD1 blockade. Our results suggest that the detection and profiling of TDEVs can help screen patients for anti-PD-1 immunotherapy. Moreover, the results in this study suggest that TDEVs and IL-12 can potentially be liquid biopsy biomarkers to profile patient response to anti-PD1 ICB and tailor patient-specific treatment protocols. Importantly, the methodology is generalizable to other types of cancer immunotherapies. Therefore, the collection of patient-specific liquid biopsy data, and the implementation of those data into the systems biology framework, may offer the opportunity to discover new biomarkers for patient drug screening and enable the continuous monitoring of patient response to treatment and adaptation of patient-specific immunotherapy treatment protocols to overcome therapeutic resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".