The Impact of Stroma Admixture on Molecular Subtypes and Prognostic Gene Signatures in Serous Ovarian Cancer

AHRR and SFRP2 in primary versus recurrent high-grade serous ovarian carcinoma and their prognostic implication

BACKGROUND

The aim of this study was to analyse transcriptomic differences between primary and recurrent high-grade serous ovarian carcinoma (HGSOC) to identify prognostic biomarkers.

METHODS

We analysed 19 paired primary and recurrent HGSOC samples using targeted RNA sequencing. We selected the best candidates using in silico survival and pathway analysis and validated the biomarkers using immunohistochemistry on a cohort of 44 paired samples, an additional cohort of 504 primary HGSOCs and explored their function.

RESULTS

We identified 233 differential expressed genes. Twenty-three showed a significant prognostic value for PFS and OS in silico. Seven markers (AHRR, COL5A2, FABP4, HMGCS2, ITGA5, SFRP2 and WNT9B) were chosen for validation at the protein level. AHRR expression was higher in primary tumours (p < 0.0001) and correlated with better patient survival (p < 0.05). Stromal SFRP2 expression was higher in recurrent samples (p = 0.009) and protein expression in primary tumours was associated with worse patient survival (p = 0.022). In multivariate analysis, tumour AHRR and SFRP2 remained independent prognostic markers. In vitro studies supported the anti-tumorigenic role of AHRR and the oncogenic function of SFRP2.

CONCLUSIONS

Our results underline the relevance of AHRR and SFRP2 proteins in aryl-hydrocarbon receptor and Wnt-signalling, respectively, and might lead to establishing them as biomarkers in HGSOC.

Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones

High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

Residual ANTXR1+ myofibroblasts after chemotherapy inhibit anti-tumor immunity via YAP1 signaling pathway

Although cancer-associated fibroblast (CAF) heterogeneity is well-established, the impact of chemotherapy on CAF populations remains poorly understood. Here we address this question in high-grade serous ovarian cancer (HGSOC), in which we previously identified 4 CAF populations. While the global content in stroma increases in HGSOC after chemotherapy, the proportion of FAP+ CAF (also called CAF-S1) decreases. Still, maintenance of high residual CAF-S1 content after chemotherapy is associated with reduced CD8+ T lymphocyte density and poor patient prognosis, emphasizing the importance of CAF-S1 reduction upon treatment. Single cell analysis, spatial transcriptomics and immunohistochemistry reveal that the content in the ECM-producing ANTXR1+ CAF-S1 cluster (ECM-myCAF) is the most affected by chemotherapy. Moreover, functional assays demonstrate that ECM-myCAF isolated from HGSOC reduce CD8+ T-cell cytotoxicity through a Yes Associated Protein 1 (YAP1)-dependent mechanism. Thus, efficient inhibition after treatment of YAP1-signaling pathway in the ECM-myCAF cluster could enhance CD8+ T-cell cytotoxicity. Altogether, these data pave the way for therapy targeting YAP1 in ECM-myCAF in HGSOC.

Periostin facilitates ovarian cancer recurrence by enhancing cancer stemness

The lethality of epithelial ovarian cancer (OC) is largely due to a high rate of recurrence and development of chemoresistance, which requires synergy between cancer cells and the tumor microenvironment (TME) and is thought to involve cancer stem cells. Our analysis of gene expression microarray data from paired primary and recurrent OC tissues revealed significantly elevated expression of the gene encoding periostin (POSTN) in recurrent OC compared to matched primary tumors (p = 0.015). Secreted POSTN plays a role in the extracellular matrix, facilitating epithelial cell migration and tissue regeneration. We therefore examined how elevated extracellular POSTN, as we found is present in recurrent OC, impacts OC cell functions and phenotypes, including stemness. OC cells cultured with conditioned media with high levels of periostin (CMPOSTNhigh) exhibited faster migration (p = 0.0044), enhanced invasiveness (p = 0.006), increased chemoresistance (p < 0.05), and decreased apoptosis as compared to the same cells cultured with control medium (CMCTL). Further, CMPOSTNhigh-cultured OC cells exhibited an elevated stem cell side population (p = 0.027) along with increased expression of cancer stem cell marker CD133 relative to CMCTL-cultured cells. POSTN-transfected 3T3-L1 cells that were used to generate CMPOSTNhigh had visibly enhanced intracellular and extracellular lipids, which was also linked to increased OC cell expression of fatty acid synthetase (FASN) that functions as a central regulator of lipid metabolism and plays a critical role in the growth and survival of tumors. Additionally, POSTN functions in the TME were linked to AKT pathway activities. The mean tumor volume in mice injected with CMPOSTNhigh-cultured OC cells was larger than that in mice injected with CMCTL-cultured OC cells (p = 0.0023). Taken together, these results show that elevated POSTN in the extracellular environment leads to more aggressive OC cell behavior and an increase in cancer stemness, suggesting that increased levels of stromal POSTN during OC recurrence contribute to more rapid disease progression and may be a novel therapeutic target. Furthermore, they also demonstrate the utility of having matched primary-recurrent OC tissues for analysis and support the need for better understanding of the molecular changes that occur with OC recurrence to develop ways to undermine those processes.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.

Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer

Tertiary lymphoid structure (TLS) is associated with prognosis in copy-number-driven tumors, including high-grade serous ovarian cancer (HGSOC), although the function of TLS and its interaction with copy-number alterations in HGSOC are not fully understood. In the current study, we confirm that TLS-high HGSOC patients show significantly better progression-free survival (PFS). We show that the presence of TLS in HGSOC tumors is associated with B cell maturation and cytotoxic tumor-specific T cell activation and proliferation. In addition, the copy-number loss of IL15 and CXCL10 may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed. Last, a radiomics-based signature is developed to predict the presence of TLS, which independently predicts PFS in both HGSOC patients and immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients. Overall, we reveal that TLS coordinates intratumoral B cell and T cell response to HGSOC tumor, while the cancer genome evolves to counteract TLS formation and function.

In Silico Analysis Predicts Nuclear Factors NR2F6 and YAP1 as Mesenchymal Subtype-Specific Therapeutic Targets for Ovarian Cancer Patients

BACKGROUND

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.

Spatial Transcriptomics Depict Ligand-Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors

Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted.

SIGNIFICANCE

Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383.

Targeting receptor tyrosine kinases in ovarian cancer: Genomic dysregulation, clinical evaluation of inhibitors, and potential for combinatorial therapies

Epithelial ovarian cancer (EOC) remains one of the leading causes of cancer-related deaths among women worldwide. Receptor tyrosine kinases (RTKs) have long been sought as therapeutic targets for EOC, as they are frequently hyperactivated in primary tumors and drive disease relapse, progression, and metastasis. More recently, these oncogenic drivers have been implicated in EOC response to poly(ADP-ribose) polymerase (PARP) inhibitors and epigenome-interfering agents. This evidence revives RTKs as promising targets for therapeutic intervention of EOC. This review summarizes recent studies on the role of RTKs in EOC malignancy and the use of their inhibitors for clinical treatment. Our focus is on the ERBB family, c-Met, and VEGFR, as they are linked to drug resistance and targetable using commercially available drugs. The importance of these RTKs and their inhibitors is highlighted by their impact on signal transduction and intratumoral heterogeneity in EOC and successful use as maintenance therapy in the clinic through suppression of the VEGF/VEGFR axis. Finally, the therapeutic potential of RTK inhibitors is discussed in the context of combinatorial targeting via co-inhibiting proliferative and anti-apoptotic pathways, epigenomic/transcriptional programs, and harnessing the efficacy of PARP inhibitors and programmed cell death 1/ligand 1 immune checkpoint therapies.

Better or worse? The prognostic role of the mesenchymal subtype in patients with high-grade serous ovarian carcinoma: A systematic review and meta-analysis

Background

Tumor characteristics can be prognostically relevant in patients with high‐grade serous ovarian carcinoma (HGSOC). This study aimed to determine whether different subtypes of HGSOC, especially the mesenchymal subtype, are associated with overall survival (OS) or progression‐free survival (PFS) in patients with HGSOC.

Methods

PubMed, Embase, and the Cochrane Library were searched for studies published up to September 2020. The eligibility criteria were (1) population: patients with HGSOG with molecular subtyping of their tumor, (2) exposure: mesenchymal subtype, (3) non‐exposure: differentiated, immunoreactive, proliferative, and other non‐mesenchymal subtypes, (4) outcome: survival, with hazard ratios (HRs), and (5) English language.

Results

The mesenchymal subtype showed no statistically significant difference in OS compared with the immunoreactive subtype (HR = 1.47, 95% CI: 0.78–2.78, p = 0.238; I² = 81.2%, p_{heterogeneity} = 0.005) or all non‐mesenchymal subtypes (HR = 1.65, 95% CI: 0.97–2.80, p = 0.063; I² = 79.4%, p_{heterogeneity} = 0.008). The mesenchymal subtype showed no statistically significant difference in PFS compared with the immunoreactive subtype (HR = 1.19, 95% CI: 0.71–2.00, p = 0.514; I² = 71.6%, p_{heterogeneity} = 0.030) but a significant differences was observed when using all non‐mesenchymal subtypes as reference (HR = 1.51, 95% CI: 1.00–2.28, p = 0.049). The results were robust according to the sensitivity analyses.

Conclusions

There are no statistically significant differences in OS between the mesenchymal subtype of HGSOC and other subtypes of HGSOC. Because of statistical power, this meta‐analysis cannot conclude about non‐inferiority, and the relationship between the molecular subtypes and HGSOC prognosis remains controversial. Based on one study, the mesenchymal subtype could have a poorer PFS than the non‐mesenchymal subtypes of HGSOC, but this conclusion requires further evidence.

Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens

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.

Classification of High-Grade Serous Ovarian Carcinoma by Epithelial-to-Mesenchymal Transition Signature and Homologous Recombination Repair Genes

High-grade serous ovarian cancer (HGSOC) is one of the deadliest cancers that can occur in women. This study aimed to investigate the molecular characteristics of HGSOC through integrative analysis of multi-omics data. We used fresh-frozen, chemotherapy-naïve primary ovarian cancer tissues and matched blood samples of HGSOC patients and conducted next-generation whole-exome sequencing (WES) and RNA sequencing (RNA-seq). Genomic and transcriptomic profiles were comprehensively compared between patients with germline BRCA1/2 mutations and others with wild-type BRCA1/2. HGSOC samples initially divided into two groups by the presence of germline BRCA1/2 mutations showed mutually exclusive somatic mutation patterns, yet the implementation of high-dimensional analysis of RNA-seq and application of epithelial-to-mesenchymal (EMT) index onto the HGSOC samples revealed that they can be divided into two subtypes; homologous recombination repair (HRR)-activated type and mesenchymal type. Patients with mesenchymal HGSOC, characterized by the activation of the EMT transcriptional program, low genomic alteration and diverse cell-type compositions, exhibited significantly worse overall survival than did those with HRR-activated HGSOC (p = 0.002). In validation with The Cancer Genome Atlas (TCGA) HGSOC data, patients with a high EMT index (≥the median) showed significantly worse overall survival than did those with a low EMT index (<the median) (p = 0.030). In conclusion, through a comprehensive multi-omics approach towards our HGSOC cohorts, two distinctive types of HGSOC (HRR-activated and mesenchymal) were identified. Our novel EMT index seems to be a potential prognostic biomarker for HGSOC.

High-grade serous tubo-ovarian cancer refined with single-cell RNA sequencing: specific cell subtypes influence survival and determine molecular subtype classification

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.

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

Modelling the Functions of Polo-Like Kinases in Mice and Their Applications as Cancer Targets with a Special Focus on Ovarian Cancer

Polo-like kinases (PLKs) belong to a five-membered family of highly conserved serine/threonine kinases (PLK1-5) that play differentiated and essential roles as key mitotic kinases and cell cycle regulators and with this in proliferation and cellular growth. Besides, evidence is accumulating for complex and vital non-mitotic functions of PLKs. Dysregulation of PLKs is widely associated with tumorigenesis and by this, PLKs have gained increasing significance as attractive targets in cancer with diagnostic, prognostic and therapeutic potential. PLK1 has proved to have strong clinical relevance as it was found to be over-expressed in different cancer types and linked to poor patient prognosis. Targeting the diverse functions of PLKs (tumor suppressor, oncogenic) are currently at the center of numerous investigations in particular with the inhibition of PLK1 and PLK4, respectively in multiple cancer trials. Functions of PLKs and the effects of their inhibition have been extensively studied in cancer cell culture models but information is rare on how these drugs affect benign tissues and organs. As a step further towards clinical application as cancer targets, mouse models therefore play a central role. Modelling PLK function in animal models, e.g., by gene disruption or by treatment with small molecule PLK inhibitors offers promising possibilities to unveil the biological significance of PLKs in cancer maintenance and progression and give important information on PLKs' applicability as cancer targets. In this review we aim at summarizing the approaches of modelling PLK function in mice so far with a special glimpse on the significance of PLKs in ovarian cancer and of orthotopic cancer models used in this fatal malignancy.

Multisite Tumor Sampling Reveals Extensive Heterogeneity of Tumor and Host Immune Response in Ovarian Cancer

BACKGROUND/AIM

Ovarian cancer (OVCA) is characterized by genomic/molecular intra-patient heterogeneity (IPH). Tissue histology and morphological features are surrogates of the underlying genomic/molecular contexture. We assessed the morphological IPH of OVCA tumor compartments and of lymphocytic infiltrates in multiple matched samples per patient.

MATERIALS AND METHODS

We examined 294 hematoxylin & eosin (H&E) OVCA tumor whole sections from 70 treatment-naïve patients who had undergone cytoreductive surgery. We assessed morphological subtypes as immunoreactive (IR), solid - proliferative (SD), papilloglandular (PG), and mesenchymal transition (MT); subtype load per patient; stromal tumor-infiltrating lymphocyte (sTIL) density as average per sample; and, as maximal sTIL values (max-TILs) among all samples per patient, ovaries and implants.

RESULTS

Among all 294 tumor sections, the most frequent primary morphological subtype was PG (n=150, 51.0%), followed by MT (71, 24.1%), SD (48, 16.3%) and IR (15, 5.1%). Subtype combinations were observed in 67/294 sections (22.8%) and IPH in 48/70 patients (68.6%). PG prevailed in ovaries (p<0.001), SD and MT in implants (p=0.023 and p<0.001, respectively). sTILs were higher in SD compared to non-SD (p=0.019) and lower in PG, respectively (p<0.001). sTIL density was higher in implants than in ovaries (p<0.001). Higher max-TILs were associated with stage IV disease (p=0.043), upper abdominal dissemination (p=0.024), endometrioid histology (p=0.013), and grade 3 tumors (p=0.021). Favorable prognosticators were higher max-TILs per patient (PFS, OS) and higher SD-load (PFS).

CONCLUSION

Clinically relevant morphological and host immune-response IPH appear to be the norm in OVCA. This may complicate efforts to decipher sensitivity of the tumor to certain treatment modalities from a single pre-operative biopsy.

Characterizing the tumor microenvironment of metastatic ovarian cancer by single-cell transcriptomics

Understanding the cellular composition of the tumor microenvironment and the interactions of the cells is essential to the development of successful immunotherapies in cancer. We perform single-cell RNA sequencing (scRNA-seq) of 9,885 cells isolated from the omentum in 6 patients with ovarian cancer and identify 9 major cell types, including cancer, stromal, and immune cells. Transcriptional analysis of immune cells stratifies our patient samples into 2 groups: (1) high T cell infiltration (high T_inf) and (2) low T cell infiltration (low T_inf). TOX-expressing resident memory CD8⁺ T (CD8⁺ Trm) and granulysin-expressing CD4⁺ T cell clusters are enriched in the high T_inf group. Concurrently, we find unique plasmablast and plasma B cell clusters, and finally, NR1H2⁺IRF8⁺ and CD274⁺ macrophage clusters, suggesting an anti-tumor response in the high T_inf group. Our scRNA-seq study of metastatic tumor samples provides important insights in elucidating the immune response within ovarian tumors.

Long non-coding RNA HAL suppresses the migration and invasion of serous ovarian cancer by inhibiting EMT signaling pathway

OBJECTIVE

To investigate the specific function of long non-coding RNA HAL in serous ovarian cancer (SOC) and to further clarify the regulation of HAL on EMT pathway.

MATERIALS AND METHODS

The expression of HAL and TWIST1 was detected by qRT-PCR. CCK8 assay, wound healing assay, transwell assay and flow cytometry were used to detect the HAL function on proliferation, migration, invasion and apoptosis in SOC cells. Western blot was used to calculate protein level of Vimentin, N-cadherin and E-cadherin. The effect of HAL on tumorigenesis of SOC was confirmed by xenograft nude mice model.

RESULTS

HAL was significantly decreased in SOC tissues and cells. Overexpression of HAL inhibited the proliferation, migration and invasion of SKOV3 cells, but promoted apoptosis. Furthermore, overexpression of HAL decreased the mRNA and protein levels of TWIST1 via a binding between HAL and TWIST1. Forced expression of TWIST1 reversed the inhibitory role of HAL on SOC cells' migration and invasion. The in vivo tumor growth assay showed that HAL suppressed SOC tumorigenesis with inhibiting EMT pathway.

CONCLUSIONS

Our research emphasized HAL acting as a tumor-inhibiting gene by regulating EMT signaling pathway, thus providing some novel experimental basis for clinical treatment of SOC.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Simple Summary

High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian carcinomas with sobering survival rates. The mechanisms mediating treatment efficacy are still poorly understood with no adequate biomarkers of response to treatment and risk assessment. This variability of treatment response might be due to its molecular heterogeneity. Therefore, identification of biomarkers or molecular signatures to stratify patients and offer personalized treatment is of utmost priority. Currently, comprehensive gene expression profiling is time- and cost-extensive and limited by tissue heterogeneity. Thus, it has not been implemented into clinical practice. This study demonstrates for the first time a spatially resolved, time- and cost-effective approach to stratifying HGSOC patients by combining novel matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) technology with machine-learning algorithms. Eventually, MALDI-derived predictive signatures for treatment efficacy, recurrent risk, or, as demonstrated here, molecular subtypes might be utilized for emerging clinical challenges to ultimately improve patient outcomes.

Abstract

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

Gene expression profile association with poor prognosis in epithelial ovarian cancer patients

Ovarian cancer (OC) is the eighth most common type of cancer for women worldwide. The current diagnostic and prognostic routine available for OC management either lack specificity or are very costly. Gene expression profiling has shown to be a very effective tool in exploring new molecular markers for patients with OC, although association of such markers with patient survival and clinical outcome is still elusive. Here, we performed gene expression profiling of different subtypes of OC to evaluate its association with patient overall survival (OS) and aggressive forms of the disease. By global mRNA microarray profiling in a total of 196 epithelial OC patients (161 serous, 15 endometrioid, 11 mucinous, and 9 clear cell carcinomas), we found four candidates-HSPA1A, CD99, RAB3A and POM121L9P, which associated with OS and poor clinicopathological features. The overexpression of all combined was correlated with shorter OS and progression-free survival (PFS). Furthermore, the combination of at least two markers were further associated with advanced grade, chemotherapy resistance, and progressive disease. These results indicate that a panel comprised of a few predictors that associates with a more aggressive form of OC may be clinically relevant, presenting a better performance than one marker alone.

Targeting the tumour microenvironment in platinum-resistant ovarian cancer

Ovarian cancer typically presents at an advanced stage, and although the majority of cases initially respond well to platinum-based therapies, chemoresistance almost always occurs leading to a poor long-term prognosis. While various cellular autonomous mechanisms contribute to intrinsic or acquired platinum resistance, the tumour microenvironment (TME) plays a central role in resistance to therapy and disease progression by providing cancer stem cell niches, promoting tumour cell metabolic reprogramming, reducing chemotherapy drug perfusion and promoting an immunosuppressive environment. As such, the TME is an attractive therapeutic target which has been the focus of intense research in recent years. This review provides an overview of the unique ovarian cancer TME and its role in disease progression and therapy resistance, highlighting some of the latest preclinical and clinical data on TME-targeted therapies. In particular, it focuses on strategies targeting cancer-associated fibroblasts, tumour-associated macrophages, cancer stem cells and cancer cell metabolic vulnerabilities.

Classification of ovarian cancer associated with BRCA1 mutations, immune checkpoints, and tumor microenvironment based on immunogenomic profiling

Background

Ovarian cancer is a highly fatal gynecological malignancy and new, more effective treatments are needed. Immunotherapy is gaining attention from researchers worldwide, although it has not proven to be consistently effective in the treatment of ovarian cancer. We studied the immune landscape of ovarian cancer patients to improve the efficacy of immunotherapy as a treatment option.

Methods

We obtained expression profiles, somatic mutation data, and clinical information from The Cancer Genome Atlas. Ovarian cancer was classified based on 29 immune-associated gene sets, which represented different immune cell types, functions, and pathways. Single-sample gene set enrichment (ssGSEA) was used to quantify the activity or enrichment levels of the gene sets in ovarian cancer, and the unsupervised machine learning method was used sort the classifications. Our classifications were validated using Gene Expression Omnibus datasets.

Results

We divided ovarian cancer into three subtypes according to the ssGSEA score: subtype 1 (low immunity), subtype 2 (median immunity), and subtype 3 (high immunity). Most tumor-infiltrating immune cells and immune checkpoint molecules were upgraded in subtype 3 compared with those in the other subtypes. The tumor mutation burden (TMB) was not significantly different among the three subtypes. However, patients with BRCA1 mutations were consistently detected in subtype 3. Furthermore, most immune signature pathways were hyperactivated in subtype 3, including T and B cell receptor signaling pathways, PD-L1 expression and PD-1 checkpoint pathway the NF-κB signaling pathway, Th17 cell differentiation and interleukin-17 signaling pathways, and the TNF signaling pathway.

Conclusion

Ovarian cancer subtypes that are based on immune biosignatures may contribute to the development of novel therapeutic treatment strategies for ovarian cancer.

Expanding and Remixing the Metadata Landscape

Genomic data sharing accelerates research. Data are most valuable when they are accompanied by detailed metadata. To date, metadata are often human-annotated descriptions of samples and their handling. We discuss how machine learning-derived elements complement such descriptions to enhance the research ecosystem around genomic data.

Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy-number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes. SIGNIFICANCE: This study infers whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are, therefore, appropriate targets for therapy and demonstrates that this is not the case for HGSOC.

Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches

High-grade serous epithelial ovarian cancer (HGSC) is the most aggressive subtype of epithelial ovarian cancer. The identification of germline and somatic mutations along with genomic information unveiled by The Cancer Genome Atlas (TCGA) and other studies has laid the foundation for establishing preclinical models with high fidelity to the molecular features of HGSC. Notwithstanding such progress, the field of HGSC research still lacks a model that is both robust and widely accessible. In this review, we discuss the recent advancements and utility of HGSC genetically engineered mouse models (GEMMs) to date. Further analysis and critique on alternative approaches to modelling HGSC considers technological advancements in somatic gene editing and modelling prototypic organs, capable of tumorigenesis, on a chip.