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Abstract Triple-negative breast cancer (TNBC) remains the most lethal breast cancer subtype with poor response rates to the current chemotherapies and a lack of additional effective treatment options. We have identified deoxyuridine 5′-triphosphate nucleotidohydrolase (dUTPase) as a critical gatekeeper that protects tumour DNA from the genotoxic misincorporation of uracil during treatment with standard chemotherapeutic agents commonly used in the FEC regimen. dUTPase catalyses the hydrolytic dephosphorylation of deoxyuridine triphosphate (dUTP) to deoxyuridine monophosphate (dUMP), providing dUMP for thymidylate synthase as part of the thymidylate biosynthesis pathway and maintaining low intracellular dUTP concentrations. This is crucial as DNA polymerase cannot distinguish between dUTP and deoxythymidylate triphosphate (dTTP), leading to dUTP misincorporation into DNA. Targeting dUTPase and inducing uracil misincorporation during the repair of DNA damage induced by fluoropyrimidines or anthracyclines represents an effective strategy to induce cell lethality. dUTPase inhibition significantly sensitised TNBC cell lines to fluoropyrimidines and anthracyclines through imbalanced nucleotide pools and increased DNA damage leading to decreased proliferation and increased cell death. These results suggest that repair of treatment-mediated DNA damage requires dUTPase to prevent uracil misincorporation and that inhibition of dUTPase is a promising strategy to enhance the efficacy of TNBC chemotherapy.

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.

BACKGROUND: Tissue MicroArrays (TMAs) represent a potential high-throughput platform for the analysis and discovery of tissue biomarkers. As TMA slides are produced manually and subject to processing and sectioning artefacts, the layout of TMA cores on the final slide and subsequent digital scan (TMA digital slide) is often disturbed making it difficult to associate cores with their original position in the planned TMA map. Additionally, the individual cores can be greatly altered and contain numerous irregularities such as missing cores, grid rotation and stretching. These factors demand the development of a robust method for de-arraying TMAs which identifies each TMA core, and assigns them to their appropriate coordinates on the constructed TMA slide. METHODOLOGY: This study presents a robust TMA de-arraying method consisting of three functional phases: TMA core segmentation, gridding and mapping. The segmentation of TMA cores uses a set of morphological operations to identify each TMA core. Gridding then utilises a Delaunay Triangulation based method to find the row and column indices of each TMA core. Finally, mapping correlates each TMA core from a high resolution TMA whole slide image with its name within a TMAMap. CONCLUSION: This study describes a genuine robust TMA de-arraying algorithm for the rapid identification of TMA cores from digital slides. The result of this de-arraying algorithm allows the easy partition of each TMA core for further processing. Based on a test group of 19 TMA slides (3129 cores), 99.84% of cores were segmented successfully, 99.81% of cores were gridded correctly and 99.96% of cores were mapped with their correct names via TMAMaps. The gridding of TMA cores were also extensively tested using a set of 113 pseudo slide (13,536 cores) with a variety of irregular grid layouts including missing cores, rotation and stretching. 100% of the cores were gridded correctly.