Intratumoral IL12 mRNA administration activates innate and adaptive pathways in checkpoint inhibitor-resistant tumors resulting in complete responses
Bibliographic record
Abstract
Abstract Despite the proven clinical activity of checkpoint inhibitors (ICIs) in several cancer indications, frequent occurrence of primary and secondary resistance reduces their overall effectiveness. Development of ICI resistance has been attributed mainly to genetic or epigenic alterations that affect the tumor antigen presentation machinery leading to diminished anti-tumor immune responses. There is an urgent need for new approaches which can either re-sensitize resistant tumors to the ICIs or engage alternate immune pathways to inhibit tumors. Intratumoral delivery of nanoparticle encapsulated murine IL12 (mIL12) mRNA induces powerful anti-tumor immune responses in murine tumor models, and the human version of this drug results in objective responses in patients with advanced disease. Here, we tested the efficacy of mIL12 mRNA as a single agent and in combination with anti-PD-L1 antibodies in ICI-sensitive Yummer 1.7 melanoma and MC38 colorectal murine tumors and in ICI resistant, β2-microglobulin ( B2M ) knockout versions of these models. mIL12 mRNA monotherapy was sufficient to cause complete responses (CRs) in ≥ 60% of both ICI-sensitive or -resistant Yummer 1.7 melanoma and MC38 colorectal carcinoma tumors. The mIL12 mRNA treatment resulted in potent upregulation of T H 1 type cytokines and chemokines. A reduction in number of Tregs, increase in numbers and activation state of both cytotoxic T cells (CTLs) as well as tumor-associated macrophages (TAMs) was observed indicating enhanced anti-tumor, cell-based immune responses in the tumor microenvironment. This mIL12-induced concerted immune activation was associated with a robust killing and phagocytosis of tumor cells resulting in durable CRs. These observations suggest that intratumoral IL12mRNA therapy may benefit patients with ICI-resistant cancers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".