Subtypes Of High-grade Serous Ovarian Cancer Research Articles

Prediction of Chemoresistance-How Preclinical Data Could Help to Modify Therapeutic Strategy in High-Grade Serous Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) is one of the most lethal tumors generally and the most fatal cancer of the female genital tract. The approved standard therapy consists of surgical cytoreduction and platinum/taxane-based chemotherapy, and of targeted therapy in selected patients. The main therapeutic problem is chemoresistance of recurrent and metastatic HGSOC tumors which results in low survival in the group of FIGO III/IV. Therefore, the prediction and monitoring of chemoresistance seems to be of utmost importance for the improvement of HGSOC management. This type of cancer has genetic heterogeneity with several subtypes being characterized by diverse gene signatures and disturbed peculiar epigenetic regulation. HGSOC develops and metastasizes preferentially in the specific intraperitoneal environment composed mainly of fibroblasts, adipocytes, and immune cells. Different HGSOC subtypes could be sensitive to distinct sets of drugs. Moreover, primary, metastatic, and recurrent tumors are characterized by an individual biology, and thus diverse drug responsibility. Without a precise identification of the tumor and its microenvironment, effective treatment seems to be elusive. This paper reviews tumor-derived genomic, mutational, cellular, and epigenetic biomarkers of HGSOC drug resistance, as well as tumor microenvironment-derived biomarkers of chemoresistance, and discusses their possible use in the novel complex approach to ovarian cancer therapy and monitoring.

C5aR1 blockade reshapes immunosuppressive tumor microenvironment and synergizes with immune checkpoint blockade therapy in high-grade serous ovarian cancer

ABSTRACT High-grade serous ovarian cancer (HGSC), with a modest response to immune checkpoint blockade (ICB) targeting PD-1/PD-L1 monotherapy, is densely infiltrated by M2-polarized tumor-associated macrophages (TAMs) and regulatory T (Treg) cells. The complement C5a/C5aR1 axis contributes to the programming of the immunosuppressive phenotype of TAMs in solid tumors and represents a promising immunomodulatory target for treating HGSCs. Here, we aimed to identify the relevance of C5aR1 in prognosis, immune microenvironment, and immunotherapy response in HGSCs. The expression and relationship of C5aR1 with tumor-infiltrating immune cells were assessed by immunohistochemistry and flow cytometry in the training cohort (n = 120) and fresh HGSC tissues (n = 36). Transcriptomic analyses of the xenografts delineated the mechanisms driving the immunomodulatory activity of PMX53, an orally bioavailable C5aR1 inhibitor. Therapeutic relevance was confirmed in ex vivo tumor cultures and The Cancer Genome Atlas (TCGA) datasets. C5aR1 expression independently predicted dismal prognosis and was linked to the immunoevasive subtype of HGSC, characterized by increased infiltration of pro-tumor cells (Treg cells, M2-polarized macrophages, and neutrophils) and impaired CD8+T functions. PMX53 antagonized subcutaneous tumor growth, modulated immunosuppressive mechanisms and synergized with aPD-1 in several tumor types. Single-cell RNA-seq analysis revealed predominant C5aR1 expression in TAMs, with an immunosuppressive-related expression signature in C5aR1+TAMs. Furthermore, the combination of C5aR1 and PD-L1 was associated with specific molecular characteristics and matched clinical response annotations. Therefore, the abundance of C5aR1 could predict an inferior prognosis in HGSCs, and incorporating PD-L1 may serve as a novel predictive biomarker to guide therapeutic options.

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.

Multi-omics-based analysis of high grade serous ovarian cancer subtypes reveals distinct molecular processes linked to patient prognosis.

Despite advancements in treatment, high-grade serous ovarian cancer (HGSOC) is still characterized by poor patient outcomes. To understand the molecular heterogeneity of this disease, which underlies the challenge in selecting optimal treatments for HGSOC patients, we have integrated genomic, transcriptomic, and epigenetic information to identify seven new HGSOC subtypes using a multiscale clustering method. These subtypes not only have significantly distinct overall survival, but also exhibit unique patterns of gene expression, microRNA expression, DNA methylation, and copy number alterations. As determined by our analysis, patients with similar clinical outcomes have distinct profiles of activated or repressed cellular processes, including cell cycle, epithelial-to-mesenchymal transition, immune activation, interferon response, and cilium organization. Furthermore, we performed a multiscale gene co-expression network analysis to identify subtype-specific key regulators and predicted optimal targeted therapies based on subtype-specific gene expression. In summary, this study provides new insights into the cellular heterogeneity of the HGSOC genomic, epigenetic, and transcriptomic landscapes and provides a basis for future studies into precision medicine for HGSOC patients.

Reading the signs: Radiographic characterization of morphologic subtypes of high-grade serous ovarian cancer (141)

<b>Objectives:</b> Discrete patterns of the gross tumor appearance are seen within the singular histologic diagnosis of high-grade serous ovarian cancer (HGSOC). We aimed to identify radiographic features unique to such morphologic subtypes. <b>Methods:</b> Patients with advanced-stage HGSOC underwent laparoscopy prior to treatment, and surgical videos were reviewed to classify gross tumor appearance as type I (an infiltrative disease that distorts surrounding anatomy) or type II (exophytic with well-defined borders) disease. Two radiologists independently reviewed preoperative CT images of 109 patients from this cohort. They categorized disease at eight anatomic sites and assessed for radiographic characteristics of interest, including large infiltrative plaques, mass-like metastases, enhancing peritoneal lining, architectural distortion, fat stranding, calcifications, and lymph node involvement. Radiographic data were described by frequencies and percentages and compared between type I and type II morphology using Chi-square and Fisher's exact tests. Kappa statistics were calculated to assess inter-rater agreement. <b>Results:</b> Fifty-six patients (51%) had type I disease either uniformly or predominantly across anatomic sites upon laparoscopic assessment; however, 38 (35%) patients had type II uniform or predominant morphology. Large infiltrative plaques were more commonly identified by CT in patients with type I disease (89% vs 71%, p=0.04). Mass-like metastases on imaging were correlated with type II morphology (49% vs 23%, p=0.01). Concordance between readers reporting the presence of these features ranged from 0.12-0.79, indicating slight to substantial agreement. Location of radiographically-identified disease did not portend morphologic type, with the exception of the falciform ligament. Falciform involvement on CT scans was more common in type I disease (35% vs 13%, p=0.02). Concordance between radiologists classifying disease at specified sites was fair (κ=0.21-0.41). <b>Conclusions:</b> We identified several radiographic markers for morphologic subtypes of HGSOC. As our understanding of the morphologic subtypes of high-grade serous ovarian cancer grows, knowledge of the radiographic appearance may aid in treatment planning for this population.Fig. 1

Model Cell Lines and Tissues of Different HGSOC Subtypes Differ in Local Estrogen Biosynthesis.

Simple SummaryOvarian cancer (OC) comprises a heterogeneous group of hormone-dependent diseases with very high mortality. Estrogens have been shown to promote the progression of OC; however, their exact role in OC subtypes remains unknown. Here, we investigated the local estrogen biosynthesis in OC. We performed targeted transcriptomics and estrogen metabolism analyses in high-grade serous OC (HGSOC) cell lines that differed in chemoresistance status and compared these data with publicly available transcriptome and proteome data for HGSOC tissues. In HGSOC cells, estrogen metabolism decreased with increasing chemoresistance. In highly chemoresistant cells and platinum-resistant HGSOC tissues, HSD17B14 expression was increased. Proteome data showed differential levels of HSD17B10, SULT1E1, CYP1B1, and NQO1 between the four HGSOC subtypes. Our results confirm that estrogen biosynthesis differs between different HGSOC cell models and possibly between different HGSOC subtypes. Such differentially expressed enzymes have potential as targets in the search of new treatment options.Ovarian cancer (OC) is highly lethal and heterogeneous. Several hormones are involved in OC etiology including estrogens; however, their role in OC is not completely understood. Here, we performed targeted transcriptomics and estrogen metabolism analyses in high-grade serous OC (HGSOC), OVSAHO, Kuramochi, COV632, and immortalized normal ovarian epithelial HIO-80 cells. We compared these data with public transcriptome and proteome data for the HGSOC tissues. In all model systems, high steroid sulfatase expression and weak/undetected aromatase (CYP19A1) expression indicated the formation of estrogens from the precursor estrone-sulfate (E1-S). In OC cells, the metabolism of E1-S to estradiol was the highest in OVSAHO, followed by Kuramochi and COV362 cells, and decreased with increasing chemoresistance. In addition, higher HSD17B14 and CYP1A2 expressions were observed in highly chemoresistant COV362 cells and platinum-resistant tissues compared to those in HIO-80 cells and platinum-sensitive tissues. The HGSOC cell models differed in HSD17B10, CYP1B1, and NQO1 expression. Proteomic data also showed different levels of HSD17B10, CYP1B1, NQO1, and SULT1E1 between the four HGSOC subtypes. These results suggest that different HGSOC subtypes form different levels of estrogens and their metabolites and that the estrogen-biosynthesis-associated targets should be further studied for the development of personalized treatment.

DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most common type of epigenetically heterogeneous ovarian cancer. Methylation typing has previously been used in many tumour types but not in HGSOC. Methylation typing in HGSOC may promote the development of personalized care. The present study used DNA methylation data from The Cancer Genome Atlas database and identified four unique methylation subtypes of HGSOC. With the poorest prognosis and high frequency of residual tumours, cluster 4 featured hypermethylation of a panel of genes, which indicates that demethylation agents may be tested in this group and that neoadjuvant chemotherapy may be used to reduce the possibility of residual lesions. Cluster 1 and cluster 2 were significantly associated with metastasis genes and metabolic disorders, respectively. Two feature CpG sites, cg24673765 and cg25574024, were obtained through Cox proportional hazards model analysis of the CpG sites. Based on the methylation level of the two CpG sites, the samples were classified into high- and low-risk groups to identify the prognostic information. Similar results were obtained in the validation set. Taken together, these results explain the epigenetic heterogeneity of HGSOC and provide guidance to clinicians for the prognosis of HGSOC based on DNA methylation sites.

Classification of molecular subtypes of high-grade serous ovarian cancer by MALDI-Imaging.

e17544 Background: High-grade serous ovarian cancer (HGSOC) can be separated by gene expression profiling into four molecular subtypes with clear correlation of the clinical outcome. However, these gene signatures have not been implemented in clinical practice to stratify patients for targeted therapy. This is mainly due to a lack of easy, cost-effective and reproducible methods, as well as the high heterogeneity of HGSOC. Hence, we aimed to examine the potential of unsupervised matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients, which might benefit from targeted therapeutic strategies. Methods: Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS, a novel technology to identify distinct mass profiles on the same paraffin-embedded tissue sections paired with machine learning algorithms to identify HGSOC subtypes by proteomic signature. Finally, we devised a novel strategy to annotate spectra of stromal origin. Results: 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). False discovery rates (FDR) were reduced from 10.2% to 8.0%. Finally, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999, FDR &lt; 1.0%). Conclusions: 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 targeted therapies.

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

Simple SummaryHigh-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.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.

Stromal infiltrating mast cells identify immunoevasive subtype high-grade serous ovarian cancer with poor prognosis and inferior immunotherapeutic response

ABSTRACT Tumor infiltrating mast cells (TIMs), with pro- or anti-tumorigenic role in different types of malignancies, have been implicated in resistance to anti-PD1 therapy. Here, we aimed to identify the relevance of TIMs with the prognosis, immune contexture, and immunotherapy in high-grade serous ovarian cancer (HGSOC). Tissue microarrays containing 197 HGSOC patients were assessed by immunohistochemistry (IHC) for detecting the expression of mast cell tryptase and other immune markers. Kaplan-Meier curve, log-rank test, and Cox regression model were applied to perform survival analysis. Single-cell RNA-seq analysis and flow cytometric analysis were selected to characterize TIMs. Furthermore, short-term HGSOC organoids were employed to validate the effect of TIMs on anti-PD1 therapy. Abundance of stromal TIMs (sTIMs) predicted dismal prognosis and linked to immunoevasive subtype of HGSOC, characterized by increased infiltration of pro-tumor cells (Treg cells, M2-polarized macrophages, and neutrophils) and impaired anti-tumor immune functions. Intensive inter-cell interactions between TIMs and other immune cells were identified, suggesting potential cross-talks to foster an immunosuppressive microenvironment. Organoids derived from sTIMs-low patients were associated with increased response to anti-PD-1 treatment other than the presence of high sTIMs infiltration. A nomogram, constructed by combining FIGO stage, sTIMs, and PD-L1, with an area under the curve (AUC) for predicting 5-year overall survival of 0.771 was better than that of FIGO staging system of 0.619. sTIMs/PD-L1-based classifier has potential clinical application in predicting prognosis of patients with HGSOC. sTIMs-high tumors correlate with immunosuppressive tumor microenvironment (TME) and possess potential insensitivity to immunotherapy.

A single-cell landscape of high-grade serous ovarian cancer.

Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis1. To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA sequencing to profile ~11,000 cells from 22 ascites specimens from 11 patients with HGSOC. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We found that the previously described immunoreactive and mesenchymal subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells2. Malignant cell variability was partly explained by heterogeneous copy number alteration patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in single-cell RNA sequencing of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient ascites-derived xenograft models. Inhibition of the JAK/STAT pathway, which was expressed in both malignant cells and cancer-associated fibroblasts, had potent anti-tumor activity in primary short-term cultures and patient-derived xenograft models. Our work contributes to resolving the HSGOC landscape3-5 and provides a resource for the development of novel therapeutic approaches.

Orthogonal Proteomic Platforms and Their Implications for the Stable Classification of High-Grade Serous Ovarian Cancer Subtypes.

SummaryThe National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) established a harmonized method for large-scale clinical proteomic studies. SWATH-MS, an instance of data-independent acquisition (DIA) proteomic methods, is an alternate proteomic approach. In this study, we used SWATH-MS to analyze remnant peptides from the original retrospective TCGA samples generated for the CPTAC ovarian cancer proteogenomic study. The SWATH-MS results recapitulated the confident identification of differentially expressed proteins in enriched pathways associated with the robust Mesenchymal high-grade serous ovarian cancer subtype and the homologous recombination deficient tumors. Hence, SWATH/DIA-MS presents a promising complementary or orthogonal alternative to the CPTAC proteomic workflow, with the advantages of simpler and faster workflows and lower sample consumption, albeit with shallower proteome coverage. In summary, both analytical methods are suitable to characterize clinical samples, providing proteomic workflow alternatives for cancer researchers depending on the context-specific goals of the studies.

Tumour immune cell infiltration and survival after platinum-based chemotherapy in high-grade serous ovarian cancer subtypes: A gene expression-based computational study.

BackgroundIncreasing evidence supports that the immune infiltration of tumours is associated with prognosis. Here, we sought to assess the relevance of the cellular composition of the immune infiltrate to survival after platinum-based chemotherapy amongst patients with high-grade serous ovarian cancer and evaluate these effects by molecular subtype.MethodsWe searched publicly available databases and identified 13 studies with more than 2000 patients. We estimated the proportions of 22 immune cell subsets by using a computational approach (CIBERSORT). Then, we investigated the associations between each immune cell subset and progression-free survival (PFS) and overall survival (OS), with cellular proportions modelled as quartiles.FindingsA high fraction of M1 [hazard ratio (HR) = 0.92, 95% confidence interval (CI) = 0.86–0.99] and M0 (HR = 0.93, 95% CI = 0.87–0.99) macrophages emerged as the most closely associated with favourable OS. Neutrophils were associated with poor OS (HR = 1.06, 95% CI = 1.00–1.13) and PFS (HR = 1.10, 95% CI = 1.02–1.13). Amongst the immunoreactive tumours, the M0 macrophages and the CD8+ T cells were associated with improved OS, whereas the M2 macrophages conferred worse OS. Interestingly, PD-1 was associated with good OS (HR=0.89, 95% CI = 0.80–1.00) and PFS (HR=0.89, 95% CI = 0.79–1.01) in this subtype. Four subgroups of tumours with distinct survival patterns were identified using immune cell proportions with unsupervised clustering.InterpretationFurther investigations of the quantitative cellular immune infiltrations in tumours may contribute to therapeutic advances.

Abstract 5000: Cell cycle checkpoint kinase (CHK)1 inhibition induces immune modulation in recurrent BRCA wild-type high-grade serous ovarian cancer (HGSOC) patients

Abstract Background: Data suggest cell cycle checkpoint inhibition with cyclin-dependent protein kinase 4 and 6 inhibitors may modulate the tumor immune microenvironment by augmenting T cell activation. It is unknown whether CHK1, which regulates the G2/M transition, may similarly engage in immune cross-talk. We hypothesize that CHK1 inhibition may increase tumor and peripheral immunogenicity via increased DNA damage and modulation of the innate and adaptive immune system in recurrent ovarian cancer. Methods: We previously reported clinical efficacy of the CHK1 inhibitor (CHK1i) prexasertib in heavily pretreated BRCA wild-type (BRCAwt) HGSOC patients, yielding a 33% response rate. Blood samples were collected at baseline and 6-24 hours post-second dose on cycle 1 day 15 (C1D15) to assess immune subsets and γ-H2AX using multiparametric flow cytometry. Paired fresh core biopsies were collected at baseline and post-treatment on C1D15 for total RNA-seq. Archival tissue samples were also obtained. Results: 23 of 24 patients had paired blood samples for correlative flow cytometry studies. There was a statistically significant increase in γ-H2AX from baseline to C1D15 in CD3+ and CD19+ lymphocytes (median fluorescence intensity 1.63 vs 1.74 p=0.034; 1.69 vs 1.76 p=0.040, respectively) suggesting increased DNA damage after CHK1i treatment. The percent of total CD14+ monocytes per mononuclear cells increased after treatment (31.6 vs 45.6% p=0.005), and an increase from baseline to C1D15 in HLA-DR on CD14+ monocytes, a marker of immunocompetence, was associated with improved PFS (3.5 months vs 9.25 months if decreased vs increased p=0.019). There was a significant decrease in CD4+ and CD8+ T cells on C1D15 compared to baseline (25 vs 19.6% p=0.008; 9.49 vs 7.63% p=0.005, respectively). However, GITR+ CD4+ and CD8+ T cells, suggesting activated effector T cells, increased on C1D15 (0.32 vs 0.82% p=0.0003; 0.23 vs 0.5% p&amp;lt;0.0001, respectively). Of the four The Cancer Genome Atlas molecular subtypes of HGSOC, immunoreactive was most common (7/18 [39%]) among 18 available pretreatment biopsies. Molecular subtype was not predictive of response and did not impact immunomodulatory effects seen. In 9 paired biopsies, there were no significant differences in expression levels of immunostimulatory (e.g. IFN-γ, IL-16) nor suppressive cytokines (e.g. IL10, TGF-β, CCL22). Immunohistochemical staining for PD-L1, Tregs, TILs, TAMs, and CD8 expression on archival tissue samples will be presented. Conclusions: CHK1i treatment increases γ-H2AX levels in peripheral blood indicating prexasertib-induced DNA damage. The data suggest CHK1 inhibition elicits an anti-tumor innate immune response in the peripheral blood of heavily pretreated BRCAwt HGSOC patients. An increase in immunocompetent monocytes may be associated with clinical benefit and warrants further evaluation. Citation Format: Erika J. Lampert, Min-Jung Lee, Jane B. Trepel, Akira Yuno, Ashley Cimino-Mathews, Joo-Sang Lee, Eytan Ruppin, Jayakumar Nair, Irene Ekwede, Jung-Min Lee. Cell cycle checkpoint kinase (CHK)1 inhibition induces immune modulation in recurrent BRCA wild-type high-grade serous ovarian cancer (HGSOC) patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5000.

Abstract 467: Deep single-cell RNA-seq of the putative cell of origin revealed a novel molecular subtype of high-grade serous ovarian cancer with poor prognosis

Abstract Accurate molecular classification in cancer is essential for rationalized therapy. However, achieving stable subtyping is highly challenging due to the underlying genomic complexity of tumors. An example of the most successful classification is in breast cancer where it is possible to link individual tumor types, basal vs luminal, to individual cell types of origin. However, the generalization of such an approach has not been easy because of the lack of knowledge about subtypes of the putative cells of origin. For instance, it has been difficult to establish a stable molecular classification of high-grade serous ovarian cancer (HGSOC). More recent evidence strongly supports the notion that HGSOC originates from the secretory cells in fallopian tube epithelium (FTE). However, whether or not there are multiple subtypes of secretory cells and how such subtypes are linked to tumor molecular subtypes have remained elusive. To address this issue, we applied single-cell RNA sequencing (Smart-Seq2) on around 4000 FTE cells from patients with ovarian or endometrial cancers. We developed and employed an in-house differential-expression-based clustering method, termed ClinCluster, for clinical samples that takes into account inter-patient variability. Apart from the established secretory and ciliated cell types, we discovered four novel subtypes of secretory cells and a rare intermediate cell type that co-expresses secretory and ciliated markers. We termed these four novel secretory subtypes as cell cycle, progenitor, epithelial-mesenchymal transition (EMT) and immunoreactive subtypes. Based on fifty-three biomarkers of these subtypes, we designed a panel of transcriptomic signatures and exploited them to deconvolute two bulk expression datasets of ovarian cancer: the RNA-seq dataset of TCGA (The Cancer Genome Atlas) and the microarray dataset of AOCS (Australian Ovarian Cancer Study). The deconvolution analysis revealed a poor-prognostic EMThigh subtype of HGSOC, which was not driven by copy number aberrations. Cox regression analysis indicated that the EMThigh tumors were strongly associated with poor overall survival in the two independent datasets tested (hazard ratio = 2.7, 95% confidence interval = 1.4 - 5.1, P = 0.002, n = 307 for TCGA; hazard ratio = 3.2, 95% confidence interval = 1.7 - 6.1, P = 0.0002, n = 253 for AOCS). Importantly, we found that the PI3K-Akt signaling pathway is upregulated in the EMThigh tumors (FDR &amp;lt; 0.002 for the two independent datasets) giving a strong potential for effective rationalized therapies. In summary, our work portrays a new model for the landscape of fallopian tube epithelium, the putative origin of HGSOC, and illustrates that the cellular subtypes in FTE are related to the HGSOC tumor types with important prognostic and therapeutic implications. Citation Format: Zhiyuan Hu, Abdulkhaliq Alsaadi, Nina Wietek, Laura Santana González, Christopher Yau, Ahmed Ashour Ahmed. Deep single-cell RNA-seq of the putative cell of origin revealed a novel molecular subtype of high-grade serous ovarian cancer with poor prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 467.

Dynamic of molecular subtypes of high‐grade serous ovarian cancer in paired primary and relapsed biopsies.

e17091 Background: Previously four molecular subtypes of high grade serous ovarian cancer (HGSOC) with distinct biological features and clinical outcome have been described: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative). Using Nanostring technique and a minimal signature of 39 classifier genes could reproduce the subtypes identified by microarray gene expression profiling (Leong HS et al. Australian Ovarian Cancer Study. J Pathol. 2015). Methods: We characterized paraffin embedded tissue samples from 279 HGSOC patients for molecular subtypes, utilizing the 39 classifier signature and 9 control genes by Nanostring nCounter Analysis System. From 16 patients paired primary and relapsed samples were available. Only chemonaive primary HGSOC patients were included in the study. FFPEs and clinical data were provided by TOC ( www.toc-network.de ). For each sample, probability scores for the four molecular subtypes (C1, C2, C4, and C5) were calculated. The highest calculated score determined the most likely subtype of the tumor. Results: Of all analyzed primary tumor samples, 88 (31.5%) were classified as C1, 83 (29.8%), 53 (19.0%) and 55 (19.7%) as subtypes C2, C4 and C5, respectively. Our results confirmed data by the AOCS study, which described the distribution of HGSOC with 40.2% (C1), 22.5% (C2), 20.1% (C4) and 17.2% (C5), respectively. Within the paired samples, for 12 of the 16 patients dynamic changes in the molecular subtypes between primary and relapse occurred, while in the remaining 4 patients the phenotype was stable. Conclusions: Molecular subtypes of HGSOC using Nanostring technology with a small panel of classifier genes can be confirmed. Furthermore, the data showed that a change of the established molecular subtype might occur during the evolution of the disease, and therefore translate in a different clinical outcome.

The mesenchymal transition subtype more responsive to dose dense taxane chemotherapy combined with carboplatin than to conventional taxane and carboplatin chemotherapy in high grade serous ovarian carcinoma: A survey of Japanese Gynecologic Oncology Group study (JGOG3016A1)

ObjectiveRecently, we established new histopathological subtypes of high-grade serous ovarian cancer (HGSOC) that include the mesenchymal transition (MT) type, the immune reactive (IR) type, the solid and proliferative (SP) type and the papillo-glandular (PG) type. Furthermore, we identified that the mesenchymal transcriptome subtype might be sensitive to taxane. We investigated whether these different histopathological subtypes of HGSOC require individualized chemotherapy for optimal treatment. MethodsWe conducted the Japanese Gynecologic Oncology Group (JGOG) 3016A1 study, wherein we collected hematoxylin and eosin slides (total n = 201) and performed a histopathological analysis of patients with HGSOC registered in the JGOG3016 study, which compared the efficacy of conventional paclitaxel and carboplatin (TC) and dose-dense TC (ddTC). We analyzed the differences in progression-free survival (PFS) and overall survival (OS) among the four histopathological subtypes. We then compared the PFS between the TC group and the ddTC group for each histopathological subtype. ResultsThere were significant differences in both PFS and OS among the four histopathological subtypes (p = 0.001 and p < 0.001, respectively). Overall, the MT subtype had the shortest PFS (median 1.4 y) and OS (median 3.6 y). In addition, the MT subtype had a longer PFS in the ddTC group (median 1.8 y) than in the TC group (median 1.2 y) (p = 0.01). Conversely, the other types had no significant difference in PFS when the two regimens were compared. ConclusionsThe MT type of HGSOC is sensitive to taxane; therefore, the ddTC regimen is recommended for this histopathological subtype.

Cancer-associated stroma significantly contributes to the mesenchymal subtype signature of serous ovarian cancer

ObjectiveMesenchymal (MES) subtype of high-grade serous ovarian cancer (HGSOC) is associated with worse outcomes including survival and resectability compared with other molecular subtypes. Molecular subtypes have historically been derived from ‘tumor’, consisting of both cancer and stromal cells. We sought to determine the origins of multiple MES subtype gene signatures in HGSOC. MethodsFifteen patients with MES subtype of HGSOC diagnosed between 2010 and 2013 were identified. Formalin-fixed paraffin-embedded (FFPE) blocks from primary surgery were sectioned for immunohistochemistry (IHC) staining of relevant proteins. Eight genes (ACTA2, COL5A1, COL11A1, FAP, POSTN, VCAN, ZEB1 and p-SMAD2) were selected for IHC staining based on their differential expression in MES vs. non-MES subtypes of HGSOC. Slides were scored for intensity and localization and simple statistics were used to compare expression results in cancer vs. stroma and between primary and metastatic sites. ResultsCOL5A1, VCAN, FAP, and ZEB1 proteins were almost exclusively expressed by stroma as opposed to cancer cells. In addition, stromal expression was dominant for ACTA2, COL11A1, POSTN and p-SMAD2. In general there were minimal differences in expression of proteins between primary and metastatic sites, exceptions being COL5A1 (reduced in metastases) and COL11A1 (increased in metastases). Nuclear p-SMAD2 expression was more common in metastatic stroma. ConclusionsThe existing molecular classification of HGSOC MES subtype reflects a significant stromal contribution, suggesting an important role in HGSOC behavior and thus stroma may be a relevant therapeutic target. Specific patterns of expression indicate that collagens and TGF-β signaling are involved in the metastatic process.

A Genomically Characterized Collection of High-Grade Serous Ovarian Cancer Xenografts for Preclinical Testing

High-grade serous ovarian cancer (HGSC) is the leading cause of morbidity and mortality from gynecologic malignant tumors. Overall survival remains low because of the nearly ubiquitous emergence of platinum resistance and the paucity of effective next-line treatments. Current cell culture-based models show limited similarity to HGSC and are therefore unreliable predictive models for preclinical evaluation of investigational drugs. This deficiency could help explain the low overall rate of successful drug development and the decades of largely unchanged approaches to HGSC treatment. We used gene expression, copy number variation, and exome sequencing analyses to credential HGSC patient-derived xenografts (PDXs) as effective preclinical models that recapitulate the features of human HGSC. Mice bearing PDXs were also treated with standard-of-care carboplatin therapy. PDXs showed similar sensitivity to carboplatin as the patient's tumor at the time of sampling. PDXs also recapitulated the diversity of genomic alterations (copy number variation and mutation profiles) previously described in large data sets that profiled HGSC. Furthermore, mRNA profiling showed that the PDXs represent all HGSC subtypes with the exception of the immunoreactive group. Credentialing of PDX models of HGSC should aid progress in HGSC research by providing improved preclinical models of HGSC that can be used to test novel targets and more accurately evaluate their likelihood of success.

Pooled Clustering of High-Grade Serous Ovarian Cancer Gene Expression Leads to Novel Consensus Subtypes Associated with Survival and Surgical Outcomes.

Purpose: Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC).Experimental Design: Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information.Results: Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had downregulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (P < 0.001) and with rate of optimal surgical debulking (≤1 cm, P = 1.9E-4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (P = 0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors compared with >28% in other subtypes; P = 0.02).Conclusions: HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC. Clin Cancer Res; 23(15); 4077-85. ©2017 AACR.