Abstract A021: Sumoylation blockade exposes the dark epigenome to drive ZBP1 viral mimicry for cancer immunotherapy
Bibliographic record
Abstract
Abstract Viral mimicry strategies are emerging as powerful tools in cancer therapy by leveraging the reactivation of endogenous elements that remain dormant in the 'dark epigenome'. Re-expression of transposable elements (TEs) from these latent chromatin regions can generate potent immunostimulatory RNAs that trigger innate immune responses against tumors. A key effector in this context is the innate immune sensor ZBP1, which triggers necroptotic cell death and immune activation in response to viral or endogenous Z-form nucleic acids. Our data reveal that ZBP1 activity is tightly suppressed in cancer cells through two converging and sequential mechanisms: SUMOylation and inhibitor of apoptosis proteins (IAPs). First, SUMOylation represses TEs, thereby preventing their transcription into immunogenic nucleic acids that can accumulate in the cytoplasm, trigger nucleic acid stress, and activate an interferon response. The resulting IFN-rich environment not only reinforces TE expression and contributes to further RNA buildup, but also upregulates ZBP1, priming cells for ZBP1-mediated necroptosis. Execution of this death pathway is facilitated when Z-RNA species within the TE-derived transcripts engage ZBP1 and trigger downstream necroptotic signaling. At this stage, IAPs impose a secondary brake, restraining necroptotic cell death despite ZBP1 activation. This model was mechanistically dissected using a series of gene-specific knockout models alongside the SUMO inhibitor subasumstat and SMAC mimetics, both of which are currently being evaluated in separate clinical trials. We demonstrate that the proposed regulatory axis operates in murine primary stromal and cancer cell lines and in human cancer cells across distinct tumor types. Based on these findings, we propose a combinatorial treatment strategy that employs these clinically available agents to simultaneously target SUMOylation and IAPs, thereby reactivating ZBP1 and unleashing a powerful, intrinsic viral-mimicry circuit that drives robust antitumor immunity. This dual-targeting approach offers a novel therapeutic avenue - turning epigenetically silenced viral elements into cancer’s Achilles’ heel. Citation Format: Maria Goicoechea, Nathalia M. Vasconcelos, Tencho Tenev, Pascal Meier. Sumoylation blockade exposes the dark epigenome to drive ZBP1 viral mimicry for cancer immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A021.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".