Agata Mlynska

Extended passaging of the SKOV3 ovarian cancer cell line leads to two phenotypically different strains

ABSTRACT Continuous passaging of cancer cell lines can drive phenotypic and genotypic divergence, potentially compromising the reliability of such models. In this study, we show that two late-passage strains (S1 and S2) of ovarian cancer cell line SKOV3, although authenticated via short tandem repeat (STR) profiling as identical, exhibit substantial differences in morphology, transcriptomic signatures, ability to form 3D cultures and chemotherapeutic responses. Notably, S1 formed compact 3D spheroids and exhibited enhanced epithelial-mesenchymal transition (EMT) pathway activity, whereas S2 displayed a more proliferative, MYC-driven phenotype with larger spheroid structures requiring higher seeding densities. Transcriptomic analysis revealed pathways associated with hypoxia, EMT and angiogenesis in 3D culture, highlighting the complexity introduced by dimensionality in tumour modelling. Critically, S1 showed higher sensitivity to doxorubicin than S2 (IC50 of 0.12 µM versus 1.28 µM, P=0.0001), indicating how clonal evolution can confound drug-response assays. Ultimately, our findings suggest that although STR profiling remains essential for cell line authentication, functionally distinct subpopulations can arise and coexist within the same culture, and their isolation may reveal divergent phenotypes that compromise reproducibility in preclinical cancer research.

A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors

AbstractProblemThe current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome.Method of studyWe used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real‐world patients of a known tumor subtype.ResultsUsing clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two‐step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression–based subtyping algorithm in a real‐world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer.ConclusionOur results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring.