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BACKGROUND: Despite treatment advances, there remains a significant risk of recurrence in ovarian cancer, at which stage it is usually incurable. Consequently, there is a clear need for improved patient stratification. However, at present clinical prognosticators remain largely unchanged due to the lack of reproducible methods to identify high-risk patients. METHODS: In high-grade serous ovarian cancer patients with advanced disease, we spatially define a tumour ecological balance of stromal resource and immune hazard using high-throughput image and spatial analysis of routine histology slides. On this basis an EcoScore is developed to classify tumours by a shift in this balance towards cancer-favouring or inhibiting conditions. FINDINGS: The EcoScore provides prognostic value stronger than, and independent of, known risk factors. Crucially, the clinical relevance of mutational burden and genomic instability differ under different stromal resource conditions, suggesting that the selective advantage of these cancer hallmarks is dependent on the context of stromal spatial structure. Under a high resource condition defined by a high level of geographical intermixing of cancer and stromal cells, selection appears to be driven by point mutations; whereas, in low resource tumours featured with high hypoxia and low cancer-immune co-localization, selection is fuelled by aneuploidy. INTERPRETATION: Our study offers empirical evidence that cancer fitness depends on tumour spatial constraints, and presents a biological basis for developing better assessments of tumour adaptive strategies in overcoming ecological constraints including immune surveillance and hypoxia.

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Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment.

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In the title salt, [Ag(CH4N2S)2(PPh3)2]NO3, the Ag(I) atom is coordinated by two thio-urea S atoms and two tri-phenyl-phosphane P atoms in a distorted tetra-hedral geometry, with bond angles in the range 102.90 (4)-123.29 (4)°. The Ag-S=C bond angles are 101.75 (19) and 111.29 (18)°. In the crystal, the component ions are linked by C-H⋯O, C-H⋯S, N-H⋯O and N-H⋯S hydrogen bonds, generating (10-1) sheets.

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The abundance of tumor-infiltrating lymphocytes has been associated with a favorable prognosis in estrogen receptor-negative breast cancer. However, a high degree of spatial heterogeneity in lymphocytic infiltration is often observed and its clinical implication remains unclear. Here we combine automated histological image processing with methods of spatial statistics used in ecological data analysis to quantify spatial heterogeneity in the distribution patterns of tumor-infiltrating lymphocytes. Hematoxylin and eosin-stained sections from two cohorts of estrogen receptor-negative breast cancer patients (discovery: n=120; validation: n=125) were processed with our automated cell classification algorithm to identify the location of lymphocytes and cancer cells. Subsequently, hotspot analysis (Getis-Ord Gi*) was applied to identify statistically significant hotspots of cancer and immune cells, defined as tumor regions with a significantly high number of cancer cells or immune cells, respectively. We found that the amount of co-localized cancer and immune hotspots weighted by tumor area, rather than number of cancer or immune hotspots, correlates with a better prognosis in estrogen receptor-negative breast cancer in univariate and multivariate analysis. Moreover, co-localization of cancer and immune hotspots further stratified patients with immune cell-rich tumors. Our study demonstrates the importance of quantifying not only the abundance of lymphocytes but also their spatial variation in the tumor specimen for which methods from other disciplines such as spatial statistics can be successfully applied.

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The emergent field of digital pathology employing automated image analysis techniques is to revolutionize traditional pathology at the center of clinical diagnostics. Histological images provide important tumor features unavailable in molecular profiling or omics data- the spatial context of tumor and stromal cells at single-cell resolution. Methods to map the spatial and morphological patterns of cancer and normal cells can contribute to a more comprehensive understanding of the highly heterogeneous tumor microenvironment. This review focuses on methods that help expand our knowledge of intra-tumoral spatial heterogeneity of the tumor microenvironment and their potential synergies with molecular profiling technologies.

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In the title compound, [CdBr(2)(C(5)H(12)N(2)S)(2)], the Cd(II) atom lies on a twofold rotation axis. It exhibits a distorted tetra-hedral coordination environment defined by two S atoms of two tetra-methyl-thio-urea (tmtu) ligands and two bromide ions. The crystal structure is consolidated by C-H⋯N and C-H⋯S hydrogen bonds.

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In the title compound, [CdI(2)(C(5)H(12)N(2)S)(2)], the Cd(II) ion is located on a twofold rotation axis and is coordinated in a distorted tetra-hedral mode by two iodide ions and by two tetra-methyl-thio-urea (tmtu) ligands through their S atoms. The crystal structure is stabilized by C-H⋯N and C-H⋯S hydrogen bonds.

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In the title compound, [HgCl(2)(C(5)H(12)N(2)S)(2)], the Hg(II) atom is located on a twofold rotation axis and is bonded in a distorted tetra-hedral coordination mode to two chloride ions and to two tetra-methyl-thio-urea (tmtu) mol-ecules through their S atoms. The crystal structure is stabilized by C-H⋯N and C-H⋯S hydrogen bonds.