Spatiotemporal dynamics of adoptively transferred stem-like CD8 + T cells in the tumor microenvironment following vaccination

2026 · doi:10.64898/2026.05.12.724323
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This study used spatial transcriptomics to show that adoptive transfer of stem-like CD8+ T cells combined with vaccination prevented immune exclusion, increased pro-inflammatory macrophages, and reprogrammed tumor cells, leading to better tumor control.

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⚙ AI-generated deep summary by claude@2026-07, 2026-07-15 · read from full text ⓘ

The paper studied how adoptively transferred tumor-specific CD8+ T cells with a stem-like phenotype versus more differentiated effector phenotypes behave within the tumor microenvironment after vaccination, using spatial transcriptomics to assess spatiotemporal changes. Across the authors’ ACT model, using stem-like CD8+ T cells followed by intravenous vaccination prevented immune exclusion, increased infiltration of pro-inflammatory macrophages, and induced tumor-cell transcriptional programs involving Type I/II interferon signaling and apoptosis. They also report that the protective tumor microenvironment signature overlapped with biomarkers observed in patients who responded to ACT, supporting clinical relevance. A stated caveat is that the work is framed within a specific preclinical ACT/vaccination context aimed at optimizing ACT and does not address other determinants or patient populations beyond the reported comparisons. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Adoptive cell therapy (ACT) of tumor-specific T cells can improve survival in a subset of cancer patients. Current ACT approaches may be limited by using highly differentiated T cells which can be inhibited by an immunosuppressive tumor microenvironment (TME). Here, we developed an approach to optimize ACT and used spatial transcriptomics to show how stem-like and effector CD8 + T cells differentially mediate tumor control following vaccination. Spatial transcriptomic profiling of the TME showed that ACT with stem-like T cells followed by intravenous vaccination prevented immune exclusion, increased infiltration of pro-inflammatory macrophages, and reprogrammed tumor cells to upregulate Type I and Type II IFN signaling and apoptotic gene programs. The protective transcriptomic signature of the TME in this ACT model contained overlapping biomarkers with patients who responded to ACT therapy. This approach demonstrates synergy between transferred stem-like T cells and intravenous vaccination to transcriptionally remodel the TME and enhance tumor control.
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Abstract Adoptive cell therapy (ACT) of tumor-specific T cells can improve survival in a subset of cancer patients. Current ACT approaches may be limited by using highly differentiated T cells which can be inhibited by an immunosuppressive tumor microenvironment (TME). Here, we developed an approach to optimize ACT and used spatial transcriptomics to show how stem-like and effector CD8+ T cells differentially mediate tumor control following vaccination. Spatial transcriptomic profiling of the TME showed that ACT with stem-like T cells followed by intravenous vaccination prevented immune exclusion, increased infiltration of pro-inflammatory macrophages, and reprogrammed tumor cells to upregulate Type I and Type II IFN signaling and apoptotic gene programs. The protective transcriptomic signature of the TME in this ACT model contained overlapping biomarkers with patients who responded to ACT therapy. This approach demonstrates synergy between transferred stem-like T cells and intravenous vaccination to transcriptionally remodel the TME and enhance tumor control. Competing Interest Statement A.R.V., C.M.G., V.L.C., G.M.L., A.S.I., and R.A.S. are listed as inventors on patents describing polymer-based vaccines. G.M.L. is an employee of Barinthus Biotherapeutics North America, which is commercializing polymer-based drug delivery technologies for immunotherapeutic applications. Footnotes ↵* These authors co-supervised this work

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last seen: 2026-05-20T01:45:00.602351+00:00