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@PHDTHESIS{Truxa:306316,
      author       = {S. Truxa$^*$},
      title        = {{S}patial {M}apping of {S}ingle {C}ell {M}etabolic {S}tates
                      {A}cross {H}uman {T}umors and {T}umor {M}odel {S}ystems
                      {U}sing {M}ultiplexed {I}on {B}eam {I}maging},
      school       = {University of Heidelberg},
      type         = {Dissertation},
      reportid     = {DKFZ-2025-02532},
      year         = {2025},
      note         = {Dissertation, University of Heidelberg, 2025},
      abstract     = {Metastatic melanoma is a cancer with poor prognosis and
                      rising global incidence. Although immune checkpoint
                      inhibition (ICI) has revolutionized treatment in advanced
                      disease, most patients fail to achieve durable responses.
                      The biological basis of this variability is insufficiently
                      understood, and the identification of reliable biomarkers
                      remains an unmet clinical need.As metabolic and functional
                      profiles of immune cells are tightly intertwined, I
                      hypothesized that metabolic profiling could complement
                      functional immune cell characterization within the tumor
                      microenvironment (TME). Specifically, I hypothesized that
                      the presence of specific metabolic states at treatment
                      baseline could inform ICI responses.Combining single-cell
                      metabolic regulome profiling (scMEP) and multiplexed ion
                      beam imaging (MIBI), I, for the first time, mapped
                      expression profiles of rate-limiting metabolic enzymes and
                      transporters across twelve cell lineages in tumors from 27
                      metastatic melanoma patients at spatial, single-cell
                      resolution.Alongside a comprehensive structural
                      characterization that identified compositional hallmarks of
                      ICI response, I could identify several metabolic immune cell
                      states associating with clinical prognosis. Hypoxic CD8+ T
                      cell and macrophage states that displayed high levels of
                      oxidative marks characterized future non-responders, while
                      metabolic states with preferential expression of lactate
                      dehydrogenase (LDH) differed in both their
                      functionalphenotypes and their association with response,
                      indicating metabolic flexibility within lineages.
                      Importantly, I could show that metabolic states associate,
                      but not fully recapitulate functional states, suggesting
                      that they represent a complementary layer of immune
                      biology.To leverage spatial context, I developed a
                      computational framework to quantify zonation patterns of
                      metabolic regulator expression, metabolic niches. Metabolic
                      niches were conserved within and across patients, and
                      significantly associated with future ICI response. Using
                      hundreds of image-derived features, I trained machine
                      learning models to accurately predict ICI response.
                      Crucially, the addition of metabolic features significantly
                      improved model performance.Finally, to enable mechanistic
                      follow-up, I established experimental and computational
                      frameworks for multi-modal spatial metabolic profiling of 3D
                      model systems. This bridges the gap between observational
                      studies on clinical material and perturbable model systems,
                      providing a basis for mechanistic studies of metabolic
                      reprogramming in cancer.},
      cin          = {D260},
      cid          = {I:(DE-He78)D260-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)11},
      url          = {https://inrepo02.dkfz.de/record/306316},
}