Ovarian cancer (OV) remains the deadliest gynecological malignancy, with non-coding RNA-mediated transcriptomic deregulation significantly influencing its prognosis and heterogeneous progression. In this study, we prioritized miRNA-mediated gene expression profiles by identifying key negative correlations between miRNA-mRNA pairs. We developed a machine learning-based non-coding index (NCI), incorporating a four-gene signature (GAS1, GFPT2, ZFHX4, and KCNA1) to predict patient prognosis and therapeutic response. Validation across multiple datasets revealed that OV patients with higher NCI scores had significantly poorer survival outcomes and resistance to immunotherapy. Additionally, we established a four-class subtyping taxonomy through unsupervised clustering, validated in four independent datasets. The S1 and S3 subtypes were characterized by high NCI scores, abundant stromal and immune infiltration, with the S3 subtype exhibiting the worst survival. Conversely, the S2 subtype showed downregulation of immune response genes, while the S4 subtype displayed epithelial differentiation and favourable prognosis. Integrative analyses of bulk and single-cell transcriptomic data revealed that the S3 subtype had a significantly higher fibroblast proportion compared to other subtypes, whereas the S1 subtype was marked by high T cell content. Through ridge regression-based drug sensitivity analyses, we prioritized candidate therapeutics for each subtype. Notably, the S3 subtype demonstrated sensitivity to dasatinib but resistance to methotrexate. Finally, we developed a user-friendly Shiny-based website to facilitate the application of our prognostic and subtype classification models (https://jli-bioinfo.shinyapps.io/NCI_online/). This study establishes a critical prognostic marker and proposes a novel molecular classification framework grounded in miRNA-regulated gene expression profiles, advancing our understanding of the non-coding mechanisms driving OV heterogeneity.

Targeting oncogenes and their interactive partners is an effective approach to developing novel targeted therapies for cancer and other chronic diseases. We and others have long suggested the MDM2 oncogene being an excellent target for cancer therapy, based on its p53-dependent and -independent oncogenic activities in a variety of cancers. The MYC family proteins are transcription factors that also regulate diverse biological functions. Dysregulation of MYC, such as amplification of MYCN, is associated with tumorigenesis, especially for neuroblastoma. Although the general survival rate of neuroblastoma patients has significantly improved over the past few decades, high-risk neuroblastoma still presents a poor prognosis. Therefore, innovative and more potent therapeutic strategies are needed to eradicate these aggressive neoplasms. This review focuses on the oncogenic properties of MYCN and its molecular regulation and summarizes the major therapeutic strategies being developed based on preclinical findings. We also highlight the potential benefits of targeting both the MYCN and MDM2 oncogenes, providing preclinical evidence of the efficacy and safety of this approach. In conclusion, the development of effective small molecules that inhibit both MYCN and MDM2 represents a promising new strategy for the treatment of neuroblastoma and other cancers.

Head and neck squamous cell carcinomas are the seventh most common cancer accounting for 90 % of malignant neoplasia of the upper respiratory system. KRAS is a very important oncogene, leading to the suppression of apoptosis, and promoting the pathogenesis and development of tumors. MicroRNAs (miRNAs) are highly conserved, small noncoding RNA molecules aberrantly expressed in various pathologies including regulation of tumor and metastasis-associated genes. Variant (rs61764370) of the let-7 miRNA complementary site of KRAS 3'-untranslated region (KRAS-LCS6) has been shown to disrupt the ability of miRNAs to target genes resulting in differential target mRNA and protein expression.

In this study, the effects of variant complementary site LCS6 of the let-7 miRNA in head and neck cancer were investigated in vitro using laryngeal carcinoma HEp-2 carrying G12V and LCS6 alterations in the KRAS gene. Non-cancer HEK-293 cells were also used as control cells.

G12V mutation in the KRAS gene increases invasion capacity and is specifically active on the ERK pathway associated with metastasis. Alteration in the LCS6 region of the KRAS gene did not show additional effects compared to cells only carrying G12V mutation. Our results also showed that the coexistence of G12V and LCS6 alterations is lethal to specific cell types, UM-SCC-17A laryngeal cancer cells in our case.

The LCS6 region alteration of the KRAS may play a key role in further cancer progression, and more research is needed to fully understand the mechanisms by which the LCS6 alterations promote cancer progression.

High-grade serous carcinomas (HGSCs) are highly heterogeneous tumors, both among patients and within a single tumor. Differences in molecular mechanisms significantly describe this heterogeneity. Four molecular subtypes have been previously described by the Cancer Genome Atlas Consortium: differentiated, immunoreactive, mesenchymal, and proliferative. These subtypes may have varying degrees of progression, relapse-free survival, and overall survival, as well as response to therapy. The precise determination of these subtypes is certainly necessary both for diagnosis and future development of targeted therapies within personalized medicine.

In this study, we analyzed gene expression data based on bulk RNA-seq, scRNA-seq, and spatial transcriptomic data from six cohorts (totaling 535 samples, including 60 single-cell samples). Differential expression analysis was performed using the edgeR package. The KEGG database and GSVA package were used for pathways enrichment analysis. As a predictive model, a deep neural network was created using the keras and tensorflow libraries.

We identified 357 differentially expressed genes among the four subtypes: 96 differentiated, 33 immunoreactive, 91 mesenchymal, and 137 proliferative. Based on these, we created OVsignGenes, a neural network model resistant to the effects of platform (test dataset AUC = 0.969). We then ran data from five more cohorts through our model, including scRNA-seq and spatial transcriptomics.

Because the differentiated subtype is located at the intersection of the other three subtypes based on PCA and does not have a unique profile of differentially expressed genes or enriched pathways, it can be considered an initiating subtype of tumor that will develop into one of the three other subtypes.

Advances in transcriptional profiling methods have enabled the discovery of molecular subtypes within and across traditional tissue-based cancer classifications. Such molecular subgroups hold potential for improving patient outcomes by guiding treatment decisions and revealing physiological distinctions and targetable pathways. Computational methods for stratifying transcriptomic data into molecular subgroups are increasingly abundant. However, assigning samples to these subtypes and other transcriptionally inferred predictions is time-consuming and requires significant bioinformatics expertise. To address this need, we recently reported "ClassifieR," a flexible, interactive cloud application for the functional annotation of colorectal and breast cancer transcriptomes. Here, we report "ClassifieR 2.0" which introduces additional modules for the molecular subtyping of prostate and high-grade serous ovarian cancer (HGSOC).

ClassifieR 2.0 introduces ClassifieRp and ClassifieRov, two specialised modules specifically designed to address the challenges of prostate and HGSOC molecular classification. ClassifieRp includes sigInfer, a method we developed to infer commercial prognostic prostate gene expression signatures from publicly available gene-lists or indeed any user-uploaded gene-list. ClassifieRov utilizes consensus molecular subtyping methods for HGSOC, including tools like consensusOV, for accurate ovarian cancer stratification. Both modules include functionalities present in the original ClassifieR framework for estimating cellular composition, predicting transcription factor (TF) activity and single sample gene set enrichment analysis (ssGSEA).

ClassifieR 2.0 combines molecular subtyping of prostate cancer and HGSOC with commonly used sample annotation tools in a single, user-friendly platform, allowing scientists without bioinformatics training to explore prostate and HGSOC transcriptional data without the need for extensive bioinformatics knowledge or manual data handling to operate various packages. Our sigInfer method within ClassifieRp enables the inference of commercially available gene signatures for prostate cancer, while ClassifieRov incorporates consensus molecular subtyping for HGSOC. Overall, ClassifieR 2.0 aims to make molecular subtyping more accessible to the wider research community. This is crucial for increased understanding of the molecular heterogeneity of these cancers and developing personalised treatment strategies.

BACKGROUNDDespite an overall poor prognosis, about 15% of patients with advanced-stage tubo-ovarian high-grade serous carcinoma (HGSC) survive 10 or more years after standard treatment.METHODSWe evaluated the tumor microenvironment of this exceptional, understudied group using a large international cohort enriched for long-term survivors (LTS; 10+ years; n = 374) compared with mid-term (MTS; 5-7.99 years; n = 433) and short-term survivors (STS; 2-4.99 years; n = 416). Primary tumor samples were immunostained and scored for intraepithelial and intrastromal densities of 10 immune-cell subsets (including T cells, B cells, plasma cells, myeloid cells, PD-1+ cells, and PD-L1+ cells) and epithelial content.RESULTSPositive associations with LTS compared with STS were seen for 9 of 10 immune-cell subsets. In particular, the combination of intraepithelial CD8+ T cells and intrastromal B cells showed near 5-fold increased odds of LTS compared with STS. All of these associations were stronger in tumors with high epithelial content and/or the C4/Differentiated molecular subtype, despite immune-cell densities generally being higher in tumors with low epithelial content and/or the C2/Immunoreactive molecular subtype.CONCLUSIONThe tumor microenvironment of HGSC LTS is distinguished by the intersection of T and B cell coinfiltration, high epithelial content, and C4/differentiated molecular subtype, features which may inspire new approaches to immunotherapy.FUNDINGOvarian Cancer Research Program (OCRP) of the Congressionally Directed Medical Research Program (CDMRP), U.S. Department of Defense (DOD); American Cancer Society; BC Cancer Foundation; Canada's Networks of Centres of Excellence; Canadian Cancer Society; Canadian Institutes of Health Research; Cancer Councils of New South Wales, Victoria, Queensland, South Australia, and Tasmania, Cancer Foundation of Western Australia; Cancer Institute NSW; Cancer Research UK; Deutsche Forschungsgesellschaft; ELAN Funds of the University of Erlangen-Nuremberg; Fred C. and Katherine B. Andersen Foundation; Genome BC; German Cancer Research Center; German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research; Instituto de Salud Carlos III; Mayo Foundation; Minnesota Ovarian Cancer Alliance; Ministerio de Economía y Competitividad; Medical Research Council (MRC); National Center for Advancing Translational Sciences; National Health and Medical Research Council of Australia (NHMRC); Ovarian Cancer Australia; Peter MacCallum Foundation; Sydney West Translational Cancer Research Centre; Terry Fox Research Institute; The Eve Appeal (The Oak Foundation); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; University of Pittsburgh School of Medicine; U.S. National Cancer Institute of the National Institutes of Health; VGH & UBC Hospital Foundation; Victorian Cancer Agency.

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals.

We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%-28%) and the differentiated subtype was less common (7% vs. 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)].

Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Whole genome duplication is frequently observed in cancer, and its prevalence in our prior analysis of end-stage, homologous recombination deficient high grade serous ovarian cancer (almost 80% of samples) supports the notion that whole genome duplication provides a fitness advantage under the selection pressure of therapy. Here, we therefore aim to identify potential therapeutic vulnerabilities in primary high grade serous ovarian cancer with whole genome duplication by assessing differentially expressed genes and pathways in 79 samples. We observe that MHC-II expression is lowest in tumors which have acquired whole genome duplication early in tumor evolution, and further demonstrate that reduced MHC-II expression occurs in subsets of tumor cells rather than in canonical antigen-presenting cells. Early whole genome duplication is also associated with worse patient survival outcomes. Our results suggest an association between the timing of whole genome duplication, MHC-II expression and clinical outcome in high grade serous ovarian cancer that warrants further investigation for therapeutic targeting.

Ovarian cancer is one of the most common cancers with a high mortality rate among females worldwide. The understanding of the pathogenesis of the disease is highly important to provide personalized therapy to the patients. Ovarian cancer is as heterogeneous as colon and breast cancer which makes it difficult to treat. The development of gene signature is the only hope in providing targeted therapy to improve the survival of ovarian cancer patients. Malignant epithelial carcinomas are the most common cancers of the ovary with different histological and molecular subtypes and clinical behavior. The development of precursor lesions of ovarian carcinoma in the tubes and endometrium has provided a new dimension to the origin of ovarian cancers. The clinical utility of various gene signatures may not be logical unless validated. Validated gene signatures can aid the clinician in deciding the appropriate line of treatment.

Many types of gynecological cancer (GC) are often silent until they reach an advanced stage, and are therefore often diagnosed too late for effective treatment. Hence, there is a real need for more efficient diagnosis and treatment for patients with GC. During recent years, researchers have increasingly studied the impact of microRNAs cancer development, leading to a number of applications in detection and treatment. MicroRNAs are a particular group of tiny RNA molecules that regulate regular gene expression by affecting the translation process. The downregulation of numerous miRNAs has been observed in human malignancies. Let-7 is an example of a miRNA that controls cellular processes as well as signaling cascades to affect post-transcriptional gene expression. Recent research supports the hypothesis that enhancing let-7 expression in those cancers where it is downregulated may be a potential treatment option. Exosomes are tiny vesicles that move through body fluids and can include components like miRNAs (including let-7) that are important for communication between cells. Studies proved that exosomes are able to enhance tumor growth, angiogenesis, chemoresistance, metastasis, and immune evasion, thus suggesting their importance in GC management.

High-grade serous ovarian cancer (HGSC) is a challenging disease, especially for patients with immunologically "cold" tumors devoid of tumor-infiltrating lymphocytes (TILs). We found that HGSC exhibits among the highest levels of MYCN expression and transcriptional signature across human cancers, which is strongly linked to diminished features of antitumor immunity. N-MYC repressed basal and induced IFN type I signaling in HGSC cell lines, leading to decreased chemokine expression and T cell chemoattraction. N-MYC inhibited the induction of IFN type I by suppressing tumor cell-intrinsic STING signaling via reduced STING oligomerization, and by blunting RIG-I-like receptor signaling through inhibition of MAVS aggregation and localization in the mitochondria. Single-cell RNA sequencing of human clinical HGSC samples revealed a strong negative association between cancer cell-intrinsic MYCN transcriptional program and type I IFN signaling. Thus, N-MYC inhibits tumor cell-intrinsic innate immune signaling in HGSC, making it a compelling target for immunotherapy of cold tumors.

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations, and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or ConsensusTME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by race may contribute to poorer HGSC survival among Black versus non-Hispanic White individuals.

We included newly generated RNA-Seq data from Black and White individuals, and array-based genotyping data from four existing studies of White and Japanese individuals. We assigned subtypes using K-means clustering. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. Following mapping to The Cancer Genome Atlas (TCGA)-derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset.

Cluster-specific gene expression was similar across gene expression platforms. Comparing the Black study population to the White and Japanese study populations, the immunoreactive subtype was more common (39% versus 23%-28%) and the differentiated subtype less common (7% versus 22%-31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA-Seq data; compared to mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases (Black population HR=0.79 [0.55, 1.13], White population HR=0.86 [0.62, 1.19]).

A single, platform-agnostic pipeline can be used to assign HGSC gene expression subtypes. While the observed prevalence of HGSC subtypes varied by race, subtype-specific survival was similar.

Limited evidence exists on the impact of spatial and temporal heterogeneity of high-grade serous ovarian cancer (HGSOC) on tumor evolution, clinical outcomes, and surgical operability. We perform systematic multi-site tumor mapping at presentation and matched relapse from 49 high-tumor-burden patients, operated up front. From SNP array-derived copy-number data, we categorize dendrograms representing tumor clonal evolution as sympodial or dichotomous, noting most chemo-resistant patients favor simpler sympodial evolution. Three distinct tumor evolutionary patterns from primary to relapse are identified, demonstrating recurrent disease may emerge from pre-existing or newly detected clones. Crucially, we identify spatial heterogeneity for clinically actionable homologous recombination deficiency scores and for poor prognosis biomarkers CCNE1 and MYC. Copy-number signature, phenotypic, proteomic, and proliferative-index heterogeneity further highlight HGSOC complexity. This study explores HGSOC evolution and dissemination across space and time, its impact on optimal surgical cytoreductive effort and clinical outcomes, and its consequences for clinical decision-making.

LIN28B is an RNA-binding protein that targets a broad range of microRNAs and modulates their maturation and activity. Under normal conditions, LIN28B is exclusively expressed in embryogenic stem cells, blocking differentiation and promoting proliferation. In addition, it can play a role in epithelial-to-mesenchymal transition by repressing the biogenesis of let-7 microRNAs. In malignancies, LIN28B is frequently overexpressed, which is associated with increased tumor aggressiveness and metastatic properties. In this review, we discuss the molecular mechanisms of LIN28B in promoting tumor progression and metastasis in solid tumor entities and its potential use as a clinical therapeutic target and biomarker.

c-MYC and its paralogues MYCN and MYCL are among the most frequently amplified and/or overexpressed oncoproteins in ovarian cancer. c-MYC plays a key role in promoting ovarian cancer initiation and progression. The polyamine pathway is a bona fide target of c-MYC signaling, and polyamine metabolism is strongly intertwined with ovarian malignancy. Targeting of the polyamine pathway via small molecule inhibitors has garnered considerable attention as a therapeutic strategy for ovarian cancer. Herein, we discuss the involvement of c-MYC signaling and that of its paralogues in promoting ovarian cancer tumorigenesis. We highlight the potential of targeting c-MYC-driven polyamine metabolism for the treatment of ovarian cancers and the utility of polyamine signatures in biofluids for early detection applications.

Ovarian cancer (OV) is the most lethal form of gynecological malignancy worldwide, with limited therapeutic options and high recurrence rates. However, research focusing on prognostic patterns of ferroptosis-related genes (FRGs) in ovarian cancer is still lacking. From the 6,406 differentially expressed genes (DEGs) between TCGA-OV (n = 376) and GTEx cohort (n = 180), we identified 63 potential ferroptosis-related genes. Through the LASSO-penalized Cox analysis, 3 prognostic genes, SLC7A11, ZFP36, and TTBK2, were finally distinguished. The time-dependent ROC curves and K-M survival analysis performed powerful prognostic ability of the 3-gene signature. Stepwise, we constructed and validated the nomogram based on the 3-gene signature and clinical features, with promising prognostic value in both TCGA (p-value < .0001) and ICGC cohort (p-value = .0064). Gene Set Enrichment Analysis elucidated several potential pathways between the groups stratified by 3-gene signature, while the m6A gene analysis implied higher m6A level in the high-risk group. We applied the CIBERSORT algorithm to distinct tumor immune microenvironment between two groups, with less activated dendritic cells (DCs) and plasma cells, more M0 macrophages infiltration, and higher expression of key immune checkpoint molecules (CD274, CTLA4, HAVCR2, and PDCD1LG2) in the high-risk group. In addition, the low-risk group exhibited more favorable immunotherapy and chemotherapy responses. Collectively, our findings provided new prospects in the role of ferroptosis-related genes, as a promising prediction tool for prognosis and immune responses, in order to assist personalized treatment decision-making among ovarian cancer patients.

Ovarian carcinosarcoma (OCS) is an aggressive and rare tumor type with limited treatment options. OCS is hypothesized to develop via the combination theory, with a single progenitor resulting in carcinomatous and sarcomatous components, or alternatively via the conversion theory, with the sarcomatous component developing from the carcinomatous component through epithelial-to-mesenchymal transition (EMT). In this study, we analyzed DNA variants from isolated carcinoma and sarcoma components to show that OCS from 18 women is monoclonal. RNA sequencing indicated that the carcinoma components were more mesenchymal when compared with pure epithelial ovarian carcinomas, supporting the conversion theory and suggesting that EMT is important in the formation of these tumors. Preclinical OCS models were used to test the efficacy of microtubule-targeting drugs, including eribulin, which has previously been shown to reverse EMT characteristics in breast cancers and induce differentiation in sarcomas. Vinorelbine and eribulin more effectively inhibited OCS growth than standard-of-care platinum-based chemotherapy, and treatment with eribulin reduced mesenchymal characteristics and N-MYC expression in OCS patient-derived xenografts. Eribulin treatment resulted in an accumulation of intracellular cholesterol in OCS cells, which triggered a downregulation of the mevalonate pathway and prevented further cholesterol biosynthesis. Finally, eribulin increased expression of genes related to immune activation and increased the intratumoral accumulation of CD8+ T cells, supporting exploration of immunotherapy combinations in the clinic. Together, these data indicate that EMT plays a key role in OCS tumorigenesis and support the conversion theory for OCS histogenesis. Targeting EMT using eribulin could help improve OCS patient outcomes.

Genomic analyses and preclinical models of ovarian carcinosarcoma support the conversion theory for disease development and indicate that microtubule inhibitors could be used to suppress EMT and stimulate antitumor immunity.

High-grade serous ovarian carcinoma (HGSOC) is a genomically unstable malignancy responsible for over 70% of all deaths due to ovarian cancer. With roughly 50% of all HGSOC harboring defects in the homologous recombination (HR) DNA repair pathway (e.g., BRCA1/2 mutations), the introduction of poly ADP-ribose polymerase inhibitors (PARPi) has dramatically improved outcomes for women with HR defective HGSOC. By blocking the repair of single-stranded DNA damage in cancer cells already lacking high-fidelity HR pathways, PARPi causes the accumulation of double-stranded DNA breaks, leading to cell death. Thus, this synthetic lethality results in PARPi selectively targeting cancer cells, resulting in impressive efficacy. Despite this, resistance to PARPi commonly develops through diverse mechanisms, such as the acquisition of secondary BRCA1/2 mutations. Perhaps less well documented is that PARPi can impact both the tumour microenvironment and the immune response, through upregulation of the stimulator of interferon genes (STING) pathway, upregulation of immune checkpoints such as PD-L1, and by stimulating the production of pro-inflammatory cytokines. Whilst targeted immunotherapies have not yet found their place in the clinic for HGSOC, the evidence above, as well as ongoing studies exploring the synergistic effects of PARPi with immune agents, including immune checkpoint inhibitors, suggests potential for targeting the immune response in HGSOC. Additionally, combining PARPi with epigenetic-modulating drugs may improve PARPi efficacy, by inducing a BRCA-defective phenotype to sensitise resistant cancer cells to PARPi. Finally, invigorating an immune response during PARPi therapy may engage anti-cancer immune responses that potentiate efficacy and mitigate the development of PARPi resistance. Here, we will review the emerging PARPi literature with a focus on PARPi effects on the immune response in HGSOC, as well as the potential of epigenetic combination therapies. We highlight the potential of transforming HGSOC from a lethal to a chronic disease and increasing the likelihood of cure.

Vitamin-D receptor (VDR) mRNA is overexpressed in neuroblastoma and carcinomas of lung, pancreas, and ovaries and predicts poor prognoses. VDR antagonists may be able to inhibit tumors that overexpress VDR. However, the current antagonists are arduous to synthesize and are only partial antagonists, limiting their use. Here, we show that the VDR antagonist MeTC7 (5), which can be synthesized from 7-dehydrocholesterol (6) in two steps, inhibits VDR selectively, suppresses the viability of cancer cell-lines, and reduces the growth of the spontaneous transgenic TH-MYCN neuroblastoma and xenografts in vivo. The VDR selectivity of 5 against RXRα and PPAR-γ was confirmed, and docking studies using VDR-LBD indicated that 5 induces major changes in the binding motifs, which potentially result in VDR antagonistic effects. These data highlight the therapeutic benefits of targeting VDR for the treatment of malignancies and demonstrate the creation of selective VDR antagonists that are easy to synthesize.

Ovarian cancer is one of the leading causes of deaths among patients with gynecological malignancies worldwide. In order to identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. In order to predict the prognosis of patients with HGSC, we created machine learning-based models and identified USP19 and RPL23 as candidate prognostic markers. Specifically, patients with lower USP19 mRNA levels and those with higher RPL23 mRNA levels had worse prognoses. This model was then used to analyze the data of patients with HGSC hosted on The Cancer Genome Atlas; this analysis validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of USP19 and RPL23 determined using a machine-learning model could serve as prognostic markers for patients with HGSC receiving conventional therapy.

Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten high-grade serous ovarian carcinomas (HGSOCs) and analyzed by mass spectrometry, reverse phase protein arrays, and RNA sequencing. Unsupervised analyses of protein abundance data revealed independent clustering of an enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering driven by overall tumor "purity." Comparing these data to previously defined prognostic HGSOC molecular subtypes revealed protein and transcript expression from tumor epithelium correlated with the differentiated subtype, whereas stromal proteins (and transcripts) correlated with the mesenchymal subtype. Protein and transcript abundance in the tumor epithelium and stroma exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial tumor microenvironment protein heterogeneity that directly bears on prognostic signatures, biomarker discovery, and cancer pathophysiology and underscore the need to enrich cellular subpopulations for expression profiling.

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.

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.

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.

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.

Ovarian cancer (OC) represents the most lethal malignancy in gynaecologic oncology practice and shows a high recurrence rate due to its early chemoresistance to first-line chemotherapy. Yet, timely selection of the correct treatment strategy is likely to prolong a patient's survival. MicroRNAs (miRNAs) are a class of short non-coding RNAs responsible for the expression of 30%-60% of human genes. In numerous studies, miRNAs have been used to provide the overall prognosis for patients and analyse the process's prevalence and responses to chemotherapy. In particular, miRNAs as markers for predicting the sensitivity of OC to platinum- and taxane-based chemotherapeutics can significantly improve the treatment efficacy. This article highlights two families of miRNAs: miR-200 and let-7, which are promising for further research on OC and its chemosensitivity.