Simple SummaryHigh-grade serous ovarian cancer (HGSOC) is a highly lethal form of ovarian carcinoma characterized by molecular heterogeneity. This heterogeneity is a proposed causal factor in treatment response, development of resistance mechanisms, and increased relapse rates. HGSOC exhibits four molecular subtypes with distinct characteristics. The identification of treatment targets with high efficacy in patient subpopulation such as these molecular subtypes is an intriguing challenge across tumour entities. To exemplarily characterize the mesenchymal subtype of HGSOC and elucidate targeted treatment opportunities, we integrated bulk and single-cell RNA data analysis, and investigated the composition of the TME, its cell–cell communication, and transcription factor regulation. Together with causal inference analysis, we identified several treatment opportunities specific to the mesenchymal subtype. Such targeted treatment based on stratified patient groups may benefit patient survival and highlight the advantages of personalized treatment.AbstractBackground: Tumour heterogeneity in high-grade serous ovarian cancer (HGSOC) is a proposed cause of acquired resistance to treatment and high rates of relapse. Among the four distinct molecular subtypes of HGSOC, the mesenchymal subtype (MES) has been observed with high frequency in several study cohorts. Moreover, it exhibits aggressive characteristics with poor prognosis. The failure to adequately exploit such subtypes for treatment results in high mortality rates, highlighting the need for effective targeted therapeutic strategies that follow the idea of personalized medicine (PM). Methods: As a proof-of-concept, bulk and single-cell RNA data were used to characterize the distinct composition of the tumour microenvironment (TME), as well as the cell–cell communication and its effects on downstream transcription of MES. Moreover, transcription factor activity contextualized with causal inference analysis identified novel therapeutic targets with potential causal impact on transcription factor dysregulation promoting the malignant phenotype. Findings: Fibroblast and macrophage phenotypes are of utmost importance for the complex intercellular crosstalk of MES. Specifically, tumour-associated macrophages were identified as the source of interleukin 1 beta (IL1B), a signalling molecule with significant impact on downstream transcription in tumour cells. Likewise, signalling molecules tumour necrosis factor (TNF), transforming growth factor beta (TGFB1), and C-X-C motif chemokine 12 (CXCL12) were prominent drivers of downstream gene expression associated with multiple cancer hallmarks. Furthermore, several consistently hyperactivated transcription factors were identified as potential sources for treatment opportunities. Finally, causal inference analysis identified Yes-associated protein 1 (YAP1) and Nuclear Receptor Subfamily 2 Group F Member 6 (NR2F6) as novel therapeutic targets in MES, verified in an independent dataset. Interpretation: By utilizing a sophisticated bioinformatics approach, several candidates for treatment opportunities, including YAP1 and NR2F6 were identified. These candidates represent signalling regulators within the cellular network of the MES. Hence, further studies to confirm these candidates as potential targeted therapies in PM are warranted.