Gene Regulatory Network Rewiring in the Immune Cells Associated with Cancer
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Bibliographic record
Abstract
The gene regulatory networks (GRNs) of immune cells not only indicate cell identity but also reveal the dynamic changes of immune cells when comparing their GRNs. Cancer immunotherapy has advanced in the past few years. Immune-checkpoint blockades (i.e., blocking PD-1, PD-L1, or CTLA-4) have shown durable clinical effects on some patients with various advanced cancers. However, major gaps in our knowledge of immunotherapy have been recognized. To fill these gaps, we conducted a systematic analysis of the GRNs of key immune cell subsets (i.e., B cell, CD4, CD8, CD8 naïve, CD8 Effector memory, CD8 Central Memory, regulatory T, Thelper1, Thelper2, Thelp17, and NK (Nature killer) and DC (Dendritic cell) cells associated with cancer immunologic therapies. We showed that most of the GRNs of these cells in blood share key important hub regulators, but their subnetworks for controlling cell type-specific receptors are different, suggesting that transformation between these immune cell subsets could be fast so that they can rapidly respond to environmental cues. To understand how cancer cells send molecular signals to immune cells to make them more cancer-cell friendly, we compared the GRNs of the tumor-infiltrating immune T cells and their corresponding immune cells in blood. We showed that the network size of the tumor-infiltrating immune T cells' GRNs was reduced when compared to the GRNs of their corresponding immune cells in blood. These results suggest that the shutting down certain cellular activities of the immune cells by cancer cells is one of the key molecular mechanisms for helping cancer cells to escape the defense of the host immune system. These results highlight the possibility of genetic engineering of T cells for turning on the identified subnetworks that have been shut down by cancer cells to combat tumors.
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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 | 0.000 | 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 it