Abstract IA10: Activating innate and adaptive immunity to improve outcome for “cold” tumors
Bibliographic record
Abstract
Abstract Unlike some adult cancers, most pediatric cancers are considered immunologically cold and generally less responsive to immunotherapy. Anti-GD2 mAb plus cytokine-based immunotherapy has already been incorporated into standard-of-care treatment for pediatric patients with high-risk neuroblastoma achieving remission. However, overall survival remains poor. We are pursuing clinical and preclinical studies to engage innate and adaptive immune responses against immunologically cold tumors. In a recent trial of adjuvant/neoadjuvant anti-GD2 mAb + IL2 (in the form of the hu14.18-IL2 immunocytokine) for advanced resectable melanoma, we evaluated whole-genome transcriptomics (using RNAseq determined immunologic signatures found to be predictive of outcome for patients with neuroblastoma) on resected tumors. The immunologic signatures were highly correlated with overall survival and relapse-free survival for tumors obtained 2 weeks after starting hu14.18-IL2, but not for tumors obtained 2 weeks before starting hu14.18-IL2. In a “functionally cold” mouse melanoma model, we found that radiation and hu14.18-IL2 immunocytokine generate an in situ vaccination-induced adaptive immune response in syngeneic mice bearing large tumors. In contrast, this same approach was not effective in mice bearing a very cold, low-tumor-mutation burden, MYCN driven syngeneic neuroblastoma (9464D-GD2). The addition of checkpoint blockade (IgG2a anti-CTLA-4) was not able to induce tumor eradication. However, the further addition of CpG (as a “danger signal”) and agonist anti-CD40 mAb (as an activator of effector macrophages and of dendritic cells) resulted in tumor-free mice with immune memory. These results suggest that immune activation, with “off the shelf” reagents, may be applicable to the treatment of cold cancers, via innate/adaptive immune engagement. They also suggest that monitoring of intratumoral immune activation could potentially become a useful predictor of which patients will respond to their therapy and which will require additional or alternate therapy. Citation Format: Paul M. Sondel. Activating innate and adaptive immunity to improve outcome for “cold” tumors [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA10.
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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) | 0.002 | 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".