Radiation induces immunosuppressive myeloid cells that drive therapeutic resistance and metastasis. We identify TET2 as a radiation-induced regulator of myeloid-derived suppressor cells whose expression correlates with poor outcomes in advanced lung cancer patients receiving radio-immunotherapy. Following radiotherapy, myeloid-specific Tet2 deletion suppresses tumor progression and redirects monocyte differentiation toward antigen-presenting myeloid-derived activating cells (MDACs), which augment antitumor T cell responses; spatiotemporal mapping shows that this fate program initiates rapidly in the bone marrow. Mechanistically, a radiation-responsive p53-TET2 axis decreases m5C on chromatin-associated RNAs (caRNAs), enforcing immunosuppressive chromatin compaction and silencing interferon signaling, whereas Tet2 loss restores chromatin accessibility and enhances CD8+ T cell cytotoxicity. Pharmacological TET2 inhibition recapitulates this phenotype and augments radiotherapy and PD-L1 blockade to control primary and metastatic tumors. These findings define MDACs as an immunogenic myeloid subset and establish myeloid reprogramming as a strategy to improve radiotherapy-immunotherapy outcomes.
Fei Ji, Lei Zheng, Da-Peng Chen et al.· Cancer Cell· 0 citations
Background: Accurate glioma subregion delineation is important for radiotherapy planning and longitudinal monitoring, but manual contour correction is time-consuming. Models such as nnU-Net may generalize imperfectly and lack clinician-directed text correction. Purpose: We investigated adapting a three-dimensional (3D) vision-language foundation model for text-guided brain tumor segmentation refinement. Methods: We developed a lightweight VoxTell-based framework. Pretrained VoxTell generated initial masks. Oracle prompts derived from segmentation errors encoded target, action, location, imaging evidence, edit size, and preservation constraints. Frozen Qwen/VoxTell prompt embeddings were injected through trainable projections into its multiscale decoder conditioning; other weights remained frozen. Training, validation, and testing used 901, 100, and 250 BraTS-GLI cases. Cross-dataset transfer was evaluated on 100 meningioma, metastasis, pediatric tumor, and UPENN-GBM cases. Results: On the internal test set using post-contrast T1-weighted input, correct instructions improved subregion Dice similarity coefficient (DSC; enhancing tumor, edema, and necrotic/non-enhancing core) from $0.774\pm0.158$ to $0.796\pm0.137$. They outperformed blank prompts ($0.762\pm0.155$; Holm-adjusted $p<0.001$, $d_z=0.71$) and contradictory prompts ($0.770\pm0.163$; $p<0.001$, $d_z=0.48$). In cross-dataset testing, correct instructions improved DSC from $0.527\pm0.287$ to $0.550\pm0.278$ and outperformed contradictory instructions ($0.504\pm0.275$; $p<0.001$, $d_z=0.43$). Conclusion: A 3D vision-language foundation model can perform instruction-guided refinement of glioma subregion segmentations. Sensitivity to correct, blank, and contradictory prompts suggests text-dependent contour editing rather than nonspecific post-processing, supporting further evaluation as a clinician-in-the-loop tool.
Zach Eidex, Yunyan Lin, M. Safari et al.· 0 citations
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