Inducing mismatch repair deficiency sensitizes immune-cold neuroblastoma to anti-CTLA4 and generates broad anti-tumor immune memory
Bibliographic record
Abstract
Immune checkpoint blockade can induce potent and durable responses in patients with highly immunogenic mismatch repair-deficient tumors; however, these drugs are ineffective against immune-cold neuroblastoma tumors. To establish a role for a T cell-based therapy against neuroblastoma, we show that T cell and memory T cell-dependent gene expression are associated with improved survival in high-risk neuroblastoma patients. To stimulate anti-tumor immunity and reproduce this immune phenotype in neuroblastoma tumors, we used CRISPR-Cas9 to knockout MLH1 —a crucial molecule in the DNA mismatch repair pathway—to induce mismatch repair deficiency in a poorly immunogenic murine neuroblastoma model. Induced mismatch repair deficiency increased the expression of proinflammatory genes and stimulated T cell infiltration into neuroblastoma tumors. In contrast to adult cancers with induced mismatch repair deficiency, neuroblastoma tumors remained unresponsive to anti-PD1 treatment. However, anti-CTLA4 therapy was highly effective against these tumors. Anti-CTLA4 therapy promoted immune memory and T cell epitope spreading in cured animals. Mechanistically, the effect of anti-CTLA4 therapy against neuroblastoma tumors with induced mismatch repair deficiency is CD4 + T cell dependent, as depletion of these cells abolished the effect. Therefore, a therapeutic strategy involving mismatch repair deficiency-based T cell infiltration of neuroblastoma tumors combined with anti-CTLA4 can serve as a novel T cell-based treatment strategy for neuroblastoma.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".