Ovarian cancer diagnosis and treatment

This paper develops a collagen-based 3D microtissue model of ovarian cancer that preserves structural, molecular, and therapeutic heterogeneity across different histotypes.

The paper identifies a polarization axis in ovarian cancer cells linked to tumor architecture and survival, showing SecB cells correlate with poor prognosis and metastasis.

This study uses spatial transcriptomics to identify interferon pathway enrichment in early relapsing high-grade serous carcinoma, linking it to poor prognosis and suggesting C3 as a biomarker.

Blocking MAT2A-driven SAM synthesis in ovarian cancer cells prevents cisplatin-induced DNA hypermethylation, impairs DNA repair signaling, and blocks stem cell enrichment, sensitizing MTAP-proficient and MTAP-deficient lines.

This study uses spatial transcriptomics to identify tumour-intrinsic and microenvironmental features linked to survival in high-grade serous ovarian carcinoma.

This study develops HGSC models with RS-associated mutations to explore diverse tumour ecosystems and therapeutic responses.

This study shows that oxaliplatin and 5-fluorouracil induce a transient senescence-like phenotype and G0/G1-phase arrest in LGSOC cells via the p53-p21-pRb pathway, without killing the cells.

This study identifies ZNF217 as a driver of chemoresistance in ovarian cancer by promoting AXL-ERK signaling plasticity and resistance to ERBB2 inhibitors.

This study uses spatial transcriptomics to analyze fallopian tube lesions and identifies immune and stromal changes associated with early ovarian cancer development.

This paper develops a multi-omics machine learning framework to estimate ovarian cancer survival, identifying a 20-biomarker signature that improves prognostic accuracy.

This paper develops three topical fluorescence probes for intraoperative detection of peritoneal dissemination in high-grade serous ovarian carcinoma, showing high sensitivity and specificity in clinical and mouse models.

This paper describes two patient-derived organoids from a TP53- and KRAS-mutant ovarian carcinoma, showing differential chemotherapy response and using long-read sequencing to characterize structural variants and methylation differences.

This study uses patient-derived organoids to classify ovarian cancer into chemotherapy response phenotypes, showing they correlate with clinical outcomes and traditional biomarkers.

This study shows aging promotes ovarian cancer growth and immunosuppression, which can be mitigated by inhibiting Hedgehog signaling in mice and correlates with immune checkpoint resistance in humans.

This study uses single-cell transcriptomics on patient-derived organoids to identify AGRIN as a regulator of metastatic competence in high-grade serous ovarian cancer.

This study uses spatial transcriptomics to compare gene expression in ovarian clear cell carcinoma and adjacent endometriosis across UK and Japanese patients, finding distinct transcriptional patterns by tissue compartment rather than geographic origin.

This study investigates the predictability of progression-free survival in ovarian cancer using NanoString gene expression data from pre- and post-chemotherapy samples, finding low predictability and suggesting the need for larger datasets.

This paper identifies CACNA1C as a prognostic biomarker and therapeutic target in high-grade serous ovarian cancer, showing nifedipine suppresses tumor growth through calcium channel blockade.

This study shows HGSC transcriptomic subtypes are driven by tumor cellular composition rather than intrinsic biology, with high predictive accuracy using deconvolution methods.

This paper develops a large biobank of patient-derived MOC organoids and screens them for drug efficacy, finding resistance to standard therapies and effectiveness of less commonly used drugs.

The paper compares single-cell transcriptomes of mouse and human tubal epithelium to show injury-driven regeneration in humans may contribute to ovarian cancer initiation.

This study identifies MRPL47 as a biomarker for ovarian cancer, showing its association with poor survival, metabolic changes, and therapeutic response.

The paper uses single-cell RNA sequencing to identify resistance signatures in ovarian cancer and develops a framework for combination therapy with carboplatin.

This study shows that estrogen receptor signaling in fallopian tube stromal cells maintains epithelial homeostasis by promoting proliferation and ciliated differentiation, but excessive estrogen shifts this niche toward inflammation and stemness, contributing to cancer initiation.

The paper deconvolves adipocyte proportions from bulk RNA-seq data of HGSOC tumors and finds higher adipocyte content is associated with worse survival.

This paper identifies syntabulin as a determinant of chemosensitivity in high-grade serous ovarian cancer by linking mitochondrial metabolism and mitotic fidelity.

This paper describes a protocol to generate a 3D multicellular model of ovarian high-grade serous carcinoma that includes stromal cells and organoids for drug testing and scRNA-seq.

This study shows MEX3A promotes ovarian clear cell carcinoma tumorigenesis and liver metastasis by maintaining mitochondrial fitness independently of p53.