Creation of a resource for deconvolution of circulating extracellular vesicle (EV) small RNA profiles based on tissue-specific signatures for prediction of response to immunotherapy 2233
Bibliographic record
Abstract
Abstract Description Factors that influence response to cancer immunotherapy (IT) are still poorly understood but will be critical in improving patient outcomes. A priori molecular states of immune cells can be important in determining response to IT (Moshe Sade-Feldman et al. 2018), therefore we hypothesized that since immune cells likely disproportionately contribute to the circulating pool of extracellular vesicles (EVs) in blood, these transcriptional states may be detected in EVs. Small RNA from EVs is packaged from the cell-of-origin through a non-stochastic process that is also poorly understood, thus complicating interpretation of results without high-quality reference data for deconvolution. Our goal was to sequence small RNA in EVs from monocultures of cell lines representing different lineages and activation states to build this resource. THP-1, PLB895, Platelets, NALM-6, RAJI, JURKAT and SupT1 cells were cultured and EVs isolated for sequencing. B-lineage cells were activated with anti-IgM and T cells with anti-CD3/ CD28. Stimulation of B-lineage was confirmed by CD80/CD86 expression, while T-lineage cells were analyzed at 48h with IL-2 ELISA (2-fold increase) and proliferation assay (30-39% increase). Small (<200 nm) and large (<1 µm) EVs were collected by filtration and sequenced. Transcriptome profiles established by this work will be key in reusing public liquid biopsy sequencing data and in determining if predictive IT signals are reflected in EV content from host immune cells. Funding Sources Supported by Research New Brunswick, and funds from the Atlantic Cancer Research Institute. Topic Categories Computational and Systems Immunology (COMP)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".