Abstract IA07: Tumor editing suppresses innate and adaptive anti-tumor immunity and is reversed by inhibiting DNA methylation
Bibliographic record
Abstract
Abstract Cancer cells edit gene expression to survive and evade immunosurveillance. However, genome-wide studies of gene editing during early tumorigenesis are lacking because of difficulty capturing emerging tumors. We used single-cell RNA sequencing in an aggressive breast cancer genetically engineered mouse model (GEMM), driven by an inducible constitutively activate Her2 oncogene co-expressed with eGFP in mammary epithelia, to identify edited genes without bias. Early tumors captured 1 week after Her2 induction were compared to late tumors, analyzed 1 mo after induction. Late tumors epigenetically repressed genes that stimulate immune responses, reducing infiltrating immune cells and tumor-immune cell communications. Changes in tumor gene expression from early to late tumors concentrated on suppression of genes that promote tumor recognition by innate and adaptive immune cells, rather than on changes in the expression of genes that promote tumor cell-intrinsic malignancy. Innate immune genes, especially interferon-stimulated genes, dominated the down-regulated tumor genes. These data captured the rapid evolution of tumor immune evasion as naïve and activated CD8+ T cells in early tumors were replaced with exhausted or precursor-exhausted cells in late tumors. Recent unpublished work in progress uses multiplexed error-robust fluorescence in situ hybridization (MERFISH) to assess the spatial expression of 680 genes in the tumor microenvironment of these GEMM mice that identify cell subsets, functional markers, cytokines/chemokines and their receptors, and pathways involved in tumorigenesis and immune evasion, to compare normal mammary gland to early and late GEMM tumors. Repression of immune stimulating genes was reversed by inhibiting DNA methylation using low-dose decitabine, which suppressed tumor growth and restored immune control, increasing the numbers, functionality and memory of tumor-infiltrating lymphocytes and reducing myeloid suppressor cells. Decitabine induced key interferon, pyroptosis and necroptosis genes and tumor cell inflammatory cell death, all of which contributed to immune control of GEMM and implanted breast and melanoma tumors. Citation Format: Judy Lieberman, Ying Zhang, Pourya Nadheri Yeganeh, Haiwei Zhang, Bowen Gu, Simon Yuan Wang, Zhouyihan Li, Dian-Jang Lee, Zhibin Zhang, Hao Wu, Eric Greer, Jeffrey Moffitt, Winston Hide. Tumor editing suppresses innate and adaptive anti-tumor immunity and is reversed by inhibiting DNA methylation [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 IA07.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".