Publications by authors named "T Hatschek"

Sterile alpha motif and HD domain-containing protein 1 (SAMHD1) is a dNTP hydrolase important for intracellular dNTP homeostasis and serves as tumor suppressor and modulator of antimetabolite efficacy in cancer, though largely unexplored in breast cancer (BC). A cohort of patients with early BC (n = 564) with available gene expression data (GEP) was used. SAMHD1 protein expression was assessed by immunohistochemistry performed on tissue microarrays.

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Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response.

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Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach where DNA methylation profiles are integrated with gene expression and results from proteomic data in breast cancer patients to predict response to therapy and pinpoint response-related epigenetic events. Fresh-frozen tumor biopsies taken before, during, and after treatment from human epidermal growth factor receptor 2 negative non-metastatic patients receiving neoadjuvant chemotherapy with or without bevacizumab were subjected to molecular profiling.

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Background: Thymidine kinase 1 (TK1) plays a pivotal role in DNA synthesis and cellular proliferation. TK1 has been studied as a prognostic marker and as an early indicator of treatment response in human epidermal growth factor 2 (HER2)-negative early and metastatic breast cancer (BC). However, the prognostic and predictive value of serial TK1 activity in HER2-positive BC remains unknown.

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Article Synopsis
  • Digital histopathology image analysis is gaining attention for automating diagnostics and creating new prognostic biomarkers, but current models usually focus on either high or low-resolution features.
  • Most existing models lose important tumor diversity information during training because they are patch-based and weakly supervised.
  • This study introduces a new multiresolution framework that effectively captures both cellular and contextual features, outperforming traditional models in breast cancer grading, with the best performance achieved using a multiplication-based approach (AUC=0.864).
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