High‐grade serous ovarian cancer (HGSOC) likely originates from the fallopian tube (FT) epithelium. Here, we established 15 organoid lines from HGSOC primary tumor deposits that closely match the mutational profile and phenotype of the parental tumor. We found that Wnt pathway activation leads to growth arrest of these cancer organoids. Moreover, active BMP signaling is almost always required for the generation of HGSOC organoids, while healthy fallopian tube organoids depend on BMP suppression by Noggin. Fallopian tube organoids modified by stable shRNA knockdown of p53, PTEN, and retinoblastoma protein (RB) also require a low‐Wnt environment for long‐term growth, while fallopian tube organoid medium triggers growth arrest. Thus, early changes in the stem cell niche environment are needed to support outgrowth of these genetically altered cells. Indeed, comparative analysis of gene expression pattern and phenotypes of normal vs. loss‐of‐function organoids confirmed that depletion of tumor suppressors triggers changes in the regulation of stemness and differentiation.

Purpose Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. Experimental design Formalin‐fixed‐paraffin‐embedded tissue of 20 patients with ovarian clear‐cell, 14 low‐grade serous, 19 high‐grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI‐Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM‐lin) and radial basis function kernels (SVM‐rbf), a neural network (NN), and a convolutional neural network (CNN). Results MALDI‐Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM‐lin, 74% SVM‐rbf, 83% NN, and 85% CNN. Based on sensitivity (69–100%) and specificity (90–99%), CCN and NN are most suited to EOC classification. Conclusion and clinical relevance The pilot study demonstrates the potential of MALDI‐Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.

The myofiber disarray in the inner myometrium, and the nuclear membrane irregularities in adenomyosis, are evidence for ultramicro-trauma in adenomyosis. The migrating nonleukocytic pale cells may be involved in pathogenesis of adenomyosis.