IMMU-14. REVEALING THE MANY MYELOID STATES IN HUMAN BRAIN TUMORS AND WAYS TO PERTURB THEM
Bibliographic record
Abstract
Abstract Recent breakthroughs in immunotherapy have revolutionized treatment for many types of cancer, but unfortunately trials of these therapies have failed to provide meaningful life-prolonging benefit for brain tumor patients, potentially due to abundant immunosuppressive myeloid cells in the tumor. Our ultimate goal is to reprogram immunosuppressive tumor associated myeloid cells to an antitumor state to enable effective immunotherapy. Towards this goal, we have deeply characterized the immune microenvironment of more than 50 primary high and low grade gliomas using high-throughput single-cell RNA-sequencing to reveal recurrent myeloid cell states and immunosuppressive programs across IDH1 wild-type and mutant tumors. We have also established a brain tumor organoid model from primary patient tissue that maintains all of the tumor microenvironment, including myeloid and other immune cells. We utilize the this model to functionally test data-driven reprogramming strategies and understand how they impact the states of tumor and immune cells in the ex vivo human tumor microenvironment.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".