Ovarian cancer (OC) represents the most dismal gynecological cancer. Pathobiology is poorly understood, mainly due to lack of appropriate study models. Organoids, defined as self-developing three-dimensional in vitro reconstructions of tissues, provide powerful tools to model human diseases. Here, we established organoid cultures from patient-derived OC, in particular from the most prevalent high-grade serous OC (HGSOC). Testing multiple culture medium components identified neuregulin-1 (NRG1) as key factor in maximizing OC organoid development and growth, although overall derivation efficiency remained moderate (36% for HGSOC patients, 44% for all patients together). Established organoid lines showed patient tumor-dependent morphology and disease characteristics, and recapitulated the parent tumor's marker expression and mutational landscape. Moreover, the organoids displayed tumor-specific sensitivity to clinical HGSOC chemotherapeutic drugs. Patient-derived OC organoids provide powerful tools for the study of the cancer's pathobiology (such as importance of the NRG1/ERBB pathway) as well as advanced preclinical tools for (personalized) drug screening and discovery.

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Background High-grade serous tubo-ovarian cancer (HGSTOC) is characterised by extensive inter- and intratumour heterogeneity, resulting in persistent therapeutic resistance and poor disease outcome. Molecular subtype classification based on bulk RNA sequencing facilitates a more accurate characterisation of this heterogeneity, but the lack of strong prognostic or predictive correlations with these subtypes currently hinders their clinical implementation. Stromal admixture profoundly affects the prognostic impact of the molecular subtypes, but the contribution of stromal cells to each subtype has poorly been characterised. Increasing the transcriptomic resolution of the molecular subtypes based on single-cell RNA sequencing (scRNA-seq) may provide insights in the prognostic and predictive relevance of these subtypes. Methods We performed scRNA-seq of 18,403 cells unbiasedly collected from 7 treatment-naive HGSTOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. We explored which phenotypic clusters correlated with overall survival based on expression of these transcriptomic markers in microarray data of 1467 tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype. Results We identified 11 cancer and 32 stromal cell phenotypes in HGSTOC tumours. Of these, the relative frequency of myofibroblasts, TGF-β-driven cancer-associated fibroblasts, mesothelial cells and lymphatic endothelial cells predicted poor outcome, while plasma cells correlated with more favourable outcome. Moreover, we identified a clear cell-like transcriptomic signature in cancer cells, which correlated with worse overall survival in HGSTOC patients. Stromal cell phenotypes differed substantially between molecular subtypes. For instance, the mesenchymal, immunoreactive and differentiated signatures were characterised by specific fibroblast, immune cell and myofibroblast/mesothelial cell phenotypes, respectively. Cell phenotypes correlating with poor outcome were enriched in molecular subtypes associated with poor outcome. Conclusions We used scRNA-seq to identify stromal cell phenotypes predicting overall survival in HGSTOC patients. These stromal features explain the association of the molecular subtypes with outcome but also the latter’s weakness of clinical implementation. Stratifying patients based on marker genes specific for these phenotypes represents a promising approach to predict prognosis or response to therapy.

This%is%the%accepted%author%manuscript%of%the%publication%% % % Longitudinal+follow.up+and+characterization+of+a+robust+rat+ model+for+Parkinson's+disease+based+on+overexpression+of+ alpha.synuclein+with+adeno.associated+viral+vectors.+ % % By%Van%der%Perren%A,%Toelen%J,%Casteels%C,%Macchi%F,%Van%Rompuy%AS,%Sarre%S,% Casadei%N,%Nuber%S,%Himmelreich%U,%Osorio%Garcia%MI,%Michotte%Y,%D'Hooge%R,% Bormans% G,% Van% Laere% K,% Gijsbers% R,% Van% den% Haute% C,% Debyser% Z,% Baekelandt%V.% % % Published%in%Neurobiol%Aging.%2015%Mar;36(3):1543[58.%% doi:%10.1016/j.neurobiolaging.2014.11.015.% % % Direct%link%to%the%final%version%of%the%article:% Abstract Testing of new therapeutic strategies for Parkinson's disease (PD) is currently hampered by the lack of relevant and reproducible animal models. Here, we developed a robust rat model for PD by injection of adeno-associated viral vectors (rAAV2/7) encoding -synuclein into the substantia nigra, resulting in reproducible nigrostriatal pathology and behavioral deficits in a 4 weeks time period. Progressive dopaminergic dysfunction was corroborated by histopathological and biochemical analysis, motor behavior testing and in vivo microdialysis. L-DOPA treatment was found to reverse the behavioral phenotype. Non-invasive PET imaging and MR spectroscopy allowed longitudinal monitoring of neurodegeneration. In addition, insoluble -synuclein aggregates were formed in this model. This -synuclein rat model shows improved face and predictive validity, and therefore offers the possibility toreliably test novel therapeutics. Furthermore, it will be of great value for further research into the molecular pathogenesis of PD and the importance of -synuclein aggregation in the disease process.

Dichotomous assessment of the histopathological features of DCIS resulted in moderate to substantial agreement among pathologists. Future studies on prognostic markers in DCIS should take into account this degree of interobserver variability to define cut-offs for categorically assessed histopathological features, as reproducibility is paramount for robust prognostic markers in daily clinical practice. A new prognostic index for DCIS might be considered, based on two-tier grading of histopathological features. Future research should explore the prognostic potential of such two-tier assessment.