Immunotherapeutic Approaches to Diffuse Intrinsic Pontine Gliomas
Bibliographic record
Abstract
Diffuse intrinsic pontine glioma (DIPG) is a lethal brain tumour for which there are no effective therapies. Recently, immunotherapy has emerged as a promising therapeutic option for solid brain tumours, but its implementation requires an in-depth understanding of the immune landscape and the identification of protein targets. To date, the immune landscape of DIPG has been poorly defined with controversies around cell identities and activation states and various protein targets have been identified however with limited efficacy. Our analysis of RNA sequencing data from 42 tumour bulk revealed increased immune cell abundance and pathways involving regulation of immune cells in tumour samples when compared to normal brain. Complemented by single cell RNA sequencing, our analysis reveals increased immune activity within the tumour core and the invading margin of DIPG tumours. There is limited infiltration of T cells, but high infiltration of tumour associated macrophages (TAMs) specifically bone-marrow derived macrophages and resident microglia in the primary and disseminated locations of the tumour. Using marker discovery algorithms to define TAM specific signatures, we inferred TAM states and found activation states distributed along a scale of M1 (“pro-inflammatory”) to M2 (“oncogenic”) macrophage states. Our results further add to the growing body of evidence that TAMs do not cleanly fit into the dichotomous M1/M2 classification of activation. Our results suggest immune cells in DIPG are lacking inflammation signatures and are promoting an immune suppressed microenvironment thus elucidating a target for therapy. Finally, we identify a cancer-testis antigen that is upregulated in DIPGs and can be exploited as an adoptive T cell immunotherapeutic target.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".