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

Real-time functional proteomics enhances therapeutic targeting in precision oncology molecular tumor boards

Collaborative review of molecular profiling data by multidisciplinary molecular tumor boards (MTB) is increasingly important for improving patient management and outcomes, though currently relies nearly exclusively on nucleic acid next-generation sequencing (NGS) and limited panels of immunohistochemistry-based protein abundance data. We examined the feasibility of incorporating real-time laser microdissection (LMD) enrichment of tumor epithelium and commercial CLIA-based reverse phase protein array (RPPA) protein drug target expression/activation profiling into our cancer center's MTB to complement standard clinical NGS-based profiling. The LMD-RPPA workflow was performed within a therapeutically permissive timeframe with a median dwell time of nine days, during which specimens were processed outside of standard clinical workflows. The RPPA-generated data supported additional and/or alternative therapeutic considerations for 54% of profiled patients following review by the MTB. These findings suggest that integrating proteomic/phosphoproteomic and NGS-based genomic data creates opportunities to further personalize clinical decision-making for precision oncology.

A spheroid whole mount drug testing pipeline with machine-learning based image analysis identifies cell-type specific differences in drug efficacy on a single-cell level

BACKGROUND

The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of single-cell procedures with sufficient detail has hampered the automated observation of cell-type-specific effects in three-dimensional stroma-tumor cell co-cultures.

METHODS

Here, we developed a high-content pipeline ranging from the setup of novel tumor-fibroblast spheroid co-cultures over optical tissue clearing, whole mount staining, and 3D confocal microscopy to optimized 3D-image segmentation and a 3D-deep-learning model to automate the analysis of a range of cell-type-specific processes, such as cell proliferation, apoptosis, necrosis, drug susceptibility, nuclear morphology, and cell density.

RESULTS

This demonstrated that co-cultures of KP-4 tumor cells with CCD-1137Sk fibroblasts exhibited a growth advantage compared to tumor cell mono-cultures, resulting in higher cell counts following cytostatic treatments with paclitaxel and doxorubicin. However, cell-type-specific single-cell analysis revealed that this apparent benefit of co-cultures was due to a higher resilience of fibroblasts against the drugs and did not indicate a higher drug resistance of the KP-4 cancer cells during co-culture. Conversely, cancer cells were partially even more susceptible in the presence of fibroblasts than in mono-cultures.

CONCLUSION

In summary, this underlines that a novel cell-type-specific single-cell analysis method can reveal critical insights regarding the mechanism of action of drug substances in three-dimensional cell culture models.

Tumor-associated fibrosis: a unique mechanism promoting ovarian cancer metastasis and peritoneal dissemination

Epithelial ovarian cancer (EOC) is often diagnosed in advanced stage with peritoneal dissemination. Recent studies indicate that aberrant accumulation of collagen fibers in tumor stroma has a variety of effects on tumor progression. We refer to remodeled fibrous stroma with altered expression of collagen molecules, increased stiffness, and highly oriented collagen fibers as tumor-associated fibrosis (TAF). TAF contributes to EOC cell invasion and metastasis in the intraperitoneal cavity. However, an understanding of molecular events involved is only just beginning to emerge. Further development in this field will lead to new strategies to treat EOC. In this review, we focus on the recent findings on how the TAF contributes to EOC malignancy. Furthermore, we will review the recent initiatives and future therapeutic strategies for targeting TAF in EOC.

Quantitative proteomic analysis of HER2 protein expression in PDAC tumors

Metastatic pancreatic adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the United States, with a 5-year survival rate of only 11%, necessitating identification of novel treatment paradigms. Tumor tissue specimens from patients with PDAC, breast cancer, and other solid tumor malignancies were collected and tumor cells were enriched using laser microdissection (LMD). Reverse phase protein array (RPPA) analysis was performed on enriched tumor cell lysates to quantify a 32-protein/phosphoprotein biomarker panel comprising known anticancer drug targets and/or cancer-related total and phosphorylated proteins, including HER2Total, HER2Y1248, and HER3Y1289. RPPA analysis revealed significant levels of HER2Total in PDAC patients at abundances comparable to HER2-positive (IHC 3+) and HER2-low (IHC 1+ /2+ , FISH-) breast cancer tissues, for which HER2 screening is routinely performed. These data support a critical unmet need for routine clinical evaluation of HER2 expression in PDAC patients and examination of the utility of HER2-directed antibody-drug conjugates in these patients.

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

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/.

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.

Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling

BACKGROUND

Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics.

METHODS

Tumor epithelium, tumor-involved stroma, and whole "bulk" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor.

RESULTS

LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ).

CONCLUSIONS

Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.

Evaluating Alkaline Phosphatase-Instructed Self-Assembly of d-Peptides for Selectively Inhibiting Ovarian Cancer Cells

Cancer is a major public health concern requiring novel treatment approaches. Enzyme-instructed self-assembly (EISA) provides a unique approach for selectively inhibiting cancer cells. However, the structure and activity correlation of EISA remains to be explored. This study investigates new EISA substrates of alkaline phosphatase (ALP) to hinder ovarian cancer cells. Analogues 2-8 were synthesized by modifying the amino acid residues of a potent EISA substrate 1 that effectively inhibits the growth of OVSAHO, a high-grade serous ovarian cancer (HGSOC) cell line. The efficacy of 2-8 against OVSAHO was assessed, along with the combination of substrate 1 with clinically used drugs. The results reveal that substrate 1 displays the highest cytotoxicity against OVSAHO cells, with an IC50 of around 8 μM. However, there was limited synergism observed between substrate 1 and the tested clinically used drugs. These findings indicate that EISA likely operates through a distinct mechanism that necessitates further elucidation.

A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer

Background

Ovarian cancer (OC) is a highly lethal and aggressive gynecologic cancer, with an overall survival rate that has shown little improvement over the decades. Robust models are urgently needed to distinguish high-risk cases and predict reliable treatment options for OC. Although anoikis-related genes (ARGs) have been reported to contribute to tumor growth and metastasis, their prognostic value in OC remains unknown. The purpose of this study was to construct an ARG pair (ARGP)-based prognostic signature for patients with OC and elucidate the potential mechanism underlying the involvement of ARGs in OC progression.

Methods

The RNA-sequencing and clinical information data of OC patients were obtained from The Center Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A novel algorithm based on pairwise comparison was utilized to select ARGPs, followed by the Least Absolute Shrinkage and Selection Operator Cox analysis to construct a prognostic signature. The predictive ability of the model was validated using an external dataset, a receiver operating characteristic curve, and stratification analysis. The immune microenvironment and the proportion of immune cells were analyzed in high- and low-risk OC cases using seven algorithms. Gene set enrichment analysis and weighted gene co-expression network analysis were performed to investigate the potential mechanisms of ARGs in OC occurrence and prognosis.

Results

The 19-ARGP signature was identified as an important prognostic predictor for 1-, 2-, and 3-year overall survival of patients with OC. Gene function enrichment analysis showed that the high-risk group was characterized by the infiltration of immunosuppressive cells and the enrichment of adherence-related signaling pathway, suggesting that ARGs were involved in OC progression by mediating immune escape and tumor metastasis.

Conclusion

We constructed a reliable ARGP prognostic signature of OC, and our findings suggested that ARGs exerted a vital interplay in OC immune microenvironment and therapeutic response. These insights provided valuable information regarding the molecular mechanisms underlying this disease and potential targeted therapies.

Targeting LRRC15 Inhibits Metastatic Dissemination of Ovarian Cancer

Dissemination of ovarian cancer cells can lead to inoperable metastatic lesions in the bowel and omentum that cause patient death. Here we show that LRRC15, a type-I 15-leucine-rich repeat-containing membrane protein, highly overexpressed in ovarian cancer bowel metastases compared with matched primary tumors and acts as a potent promoter of omental metastasis. Complementary models of ovarian cancer demonstrated that LRRC15 expression leads to inhibition of anoikis-induced cell death and promotes adhesion and invasion through matrices that mimic omentum. Mechanistically, LRRC15 interacted with β1-integrin to stimulate activation of focal adhesion kinase (FAK) signaling. As a therapeutic proof of concept, targeting LRRC15 with the specific antibody-drug conjugate ABBV-085 in both early and late metastatic ovarian cancer cell line xenograft models prevented metastatic dissemination, and these results were corroborated in metastatic patient-derived ovarian cancer xenograft models. Furthermore, treatment of 3D-spheroid cultures of LRRC15-positive patient-derived ascites with ABBV-085 reduced cell viability. Overall, these data uncover a role for LRRC15 in promoting ovarian cancer metastasis and suggest a novel and promising therapy to target ovarian cancer metastases.

Significance: This study identifies that LRRC15 activates β1-integrin/FAK signaling to promote ovarian cancer metastasis and shows that the LRRC15-targeted antibody-drug conjugate ABBV-085 suppresses ovarian cancer metastasis in preclinical models.

CAF-Associated Paracrine Signaling Worsens Outcome and Potentially Contributes to Chemoresistance in Epithelial Ovarian Cancer

Background

High-grade serous ovarian cancer (HGSOC) is the predominant and deadliest form of ovarian cancer. Some of its histological subtypes can be distinguished by frequent occurrence of cancer-associated myofibroblasts (CAFs) and desmoplastic stroma reaction (DSR). In this study, we want to explore the relationship between therapy outcome and the activity of CAF-associated signaling pathways in a homogeneous HGSOC patient collective. Furthermore, we want to validate these findings in a general Epithelial ovarian cancer (EOC) cohort.

Methods

The investigation cohort consists of 24 HGSOC patients. All of them were treated with platinum-based components and clinical follow-up was available. The validation cohort was comprised of 303 patients. Sequencing data (whole transcriptome) and clinical data were extracted from The Cancer Genome Atlas (TCGA). RNA of HGSOC patients was isolated using a Maxwell RSC instrument and the appropriate RNA isolation kit. For digital expression analysis a custom-designed gene panel was employed. All genes were linked to various DSR- and CAF- associated pathways. Expression analysis was performed on the NanoString nCounter platform. Finally, data were explored using the R programming environment (v. 4.0.3).

Result

In total, 15 CAF-associated genes were associated with patients’ survival. More specifically, 6 genes (MMP13, CGA, EPHA3, PSMD9, PITX2, PHLPP1) were linked to poor therapy outcome. Though a variety of different pathways appeared to be associated with therapy failure, many were related to CAF paracrine signaling, including MAPK, Ras and TGF-β pathways. Similar results were obtained from the validation cohort.

Discussion

In this study, we could successfully link CAF-associated pathways, as shown by increased Ras, MAPK and PI3K-Akt signaling to therapy failure (chemotherapy) in HGSOC and EOCs in general. As platinum-based chemotherapy has been the state-of-the-art therapy to treat HGSOC for decades, it is necessary to unveil the reasons behind resistance developments and poor outcome. In this work, CAF-associated signaling is shown to compromise therapy response. In the validation cohort, CAF-associated signaling is also associated with therapy failure in general EOC, possibly hinting towards a conserved mechanism. Therefore, it may be helpful to stratify HGSOC patients for CAF activity and consider alternative treatment options.

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.

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.

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.

Comprehensive Analysis Identifies Potential Ferroptosis-Associated mRNA Therapeutic Targets in Ovarian Cancer

Objective: This study aimed to explore ferroptosis-related mRNAs as potential therapeutic targets for ovarian cancer treatment.

Methods: Molecular subtypes were classified based on ferroptosis-related mRNAs via ConsensusClusterPlus package. The differences in prognosis, stromal score, immune score, immune function, and immune checkpoints were assessed between subtypes. Small molecular drugs were predicted via the CMap database. The sensitivity to chemotherapy drugs was estimated through the GDSC. A LASSO Cox regression model was conducted via the glmnet package, followed by a nomogram model.

Results: Based on ferroptosis mRNA expression profile, two molecular subtypes (C1 and C2) were classified, with distinct clinical outcomes. C1 subtype exhibited higher stromal score, immune cell score (T helper, Treg, neutrophil) and immune function (APC co-inhibition, parainflammation and Type II IFN response). Higher mRNA expression levels of immune checkpoints (like PDCD1) were found in C1 than C2. Potential small molecular drugs (PI3K and mTOR inhibitors) were found for treatment of ovarian cancer. C1 was more sensitive to eight chemotherapy drugs (A.443654, AZD.0530, AZD6482, AZD7762, AZD8055, BAY.61.3606, Bicalutamide, and CGP.60474). A 15-ferroptosis-related mRNA signature was developed, which could robustly and independently predict the outcomes. Moreover, a nomogram was established combining the signature and age, which could intuitively and accurately predict the 5-year overall survival probability.

Conclusion: Our study characterized two ferroptosis-related subtypes with distinct prognosis and tumor immune features, which could assist clinicians make decisions and individual therapy. Moreover, 15 ferroptosis-related mRNAs were identified, which could become potential therapeutic targets for ovarian cancer.

Stiffness increases with myofibroblast content and collagen density in mesenchymal high grade serous ovarian cancer

Women diagnosed with high-grade serous ovarian cancers (HGSOC) are still likely to exhibit a bad prognosis, particularly when suffering from HGSOC of the Mesenchymal molecular subtype (50% cases). These tumors show a desmoplastic reaction with accumulation of extracellular matrix proteins and high content of cancer-associated fibroblasts. Using patient-derived xenograft mouse models of Mesenchymal and Non-Mesenchymal HGSOC, we show here that HGSOC exhibit distinct stiffness depending on their molecular subtype. Indeed, tumor stiffness strongly correlates with tumor growth in Mesenchymal HGSOC, while Non-Mesenchymal tumors remain soft. Moreover, we observe that tumor stiffening is associated with high stromal content, collagen network remodeling, and MAPK/MEK pathway activation. Furthermore, tumor stiffness accompanies a glycolytic metabolic switch in the epithelial compartment, as expected based on Warburg's effect, but also in stromal cells. This effect is restricted to the central part of stiff Mesenchymal tumors. Indeed, stiff Mesenchymal tumors remain softer at the periphery than at the core, with stromal cells secreting high levels of collagens and showing an OXPHOS metabolism. Thus, our study suggests that tumor stiffness could be at the crossroad of three major processes, i.e. matrix remodeling, MEK activation and stromal metabolic switch that might explain at least in part Mesenchymal HGSOC aggressiveness.

Stromal POSTN induced by TGF-β1 facilitates the migration and invasion of ovarian cancer

OBJECTIVE

Periostin (POSTN) overexpression observed in various cancer types is correlated with metastasis and tumor progression. However, its effect on the crosstalk between ovarian cancer cells and cancer-associated fibroblasts (CAFs) remains elusive. This study aims to ascertain the role of CAF-derived POSTN in the ovarian cancer microenvironment.

METHODS

POSTN expression in high-grade serous ovarian cancer (HGSC) was detected through immunochemistry. Transwell assay was conducted to determine cell migration and invasion. POSTN was knocked down or overexpressed using lentiviral vectors. The potential downstream effects of POSTN were explored and verified by RNA sequencing and western blotting, respectively. In vitro metastatic capability of ovarian cancer cells regulated by POSTN was determined by indirect co-culture.

RESULTS

POSTN was highly enriched in HGSC stromal components, particularly in fibroblasts, while its overexpression was correlated with reduced overall survival (OS). CAF-derived POSTN functioned as a ligand for integrin αvβ3, fueling the migration and invasion of ovarian cancer cells by activating the PI3K/Akt pathway and inducing the epithelial-mesenchymal transition (EMT). Additionally, the pro-metastatic properties and the activation of fibroblasts induced by TGF-β1 partly relied on POSTN.

CONCLUSIONS

Stromal-derived POSTN drives the remodeling of the pro-metastatic microenvironment, which might be as a potential therapeutic target in patients with ovarian cancer.

COL11A1 Was Involved in Cell Proliferation, Apoptosis and Migration in Non-Small Cell Lung Cancer Cells

BACKGROUND

Type XI collagen (COL11A1) was reported to associate with malignancy in several cancer types, whereas its role in lung cancer is not fully understood. Therefore, the present study aimed to explore the expression level and biological role of COL11A1 in lung cancer cells.

METHODS

Gene Expression Omnibus (GEO) database containing 6 lung cancer tissues and normal lung tissues was used to identify potential aberrantly expressed genes. The expression of COL11A1, apoptosis related genes, cell cycle related genes and migration associated genes at the protein level were evaluated by western blot and at the mRNA level was determined by real-time PCR in lung cancer cell lines. Furthermore, the expression of COL11A1 was silenced by siRNA, and cell viability was detected by Cell counting kit-8 (CCK-8) assay. Moreover, cell apoptosis and cell cycle were determined by flow cytometry. In addition, transwell and wound-healing assay were applied to determine cell migration ability.

RESULTS

GEO analysis suggests that COL11A1 was highly expressed in patients with lung cancer, which was consistent with the results in lung cancer cell lines. COL11A1 knockdown in lung cancer cells significantly lowered the colony formation ability, augmented cell apoptosis rate and elevated the expression of cleaved caspase-3, cleaved caspase-9, Bax, P21 and the expression of Bcl-2, CyclinD1, CDK2 and CDK-4 was significantly downregulated (all p < 0.05). Moreover, post-COL11A1 knockdown, the cell wound-healing and migration ability was significantly impaired, which also supported by the upregulation of E-Cadherin and downregulation of N-Cadherin, Vimentin and Snail (all p < 0.05). Furthermore, we also found that COL11A1 knockdown decreased the expression of p-AKT, p-PI3K and p-ERK.

CONCLUSION

The present study revealed that COL11A1 may contribute to the malignancy and involve in the pathogenesis of lung cancer.

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.

Are Epithelial Ovarian Cancers of the Mesenchymal Subtype Actually Intraperitoneal Metastases to the Ovary?

Primary ovarian high-grade serous carcinoma (HGSC) has been classified into 4 molecular subtypes: Immunoreactive, Proliferative, Differentiated, and Mesenchymal (Mes), of which the Mes subtype (Mes-HGSC) is associated with the worst clinical outcomes. We propose that Mes-HGSC comprise clusters of cancer and associated stromal cells that detached from tumors in the upper abdomen/omentum and disseminated in the peritoneal cavity, including to the ovary. Using comparative analyses of multiple transcriptomic data sets, we provide the following evidence that the phenotype of Mes-HGSC matches the phenotype of tumors in the upper abdomen/omentum: (1) irrespective of the primary ovarian HGSC molecular subtype, matched upper abdominal/omental metastases were typically of the Mes subtype, (2) the Mes subtype was present at the ovarian site only in patients with concurrent upper abdominal/omental metastases and not in those with HGSC confined to the ovary, and (3) ovarian Mes-HGSC had an expression profile characteristic of stromal cells in the upper abdominal/omental metastases. We suggest that ovarian Mes-HGSC signifies advanced intraperitoneal tumor dissemination to the ovary rather than a subtype of primary ovarian HGSC. This is consistent with the presence of upper abdominal/omental disease, suboptimal debulking, and worst survival previously reported in patients with ovarian Mes-HGSC compared to other molecular subtypes.

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

The 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.

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SWATH-MS and iTRAQ-DDA are used to classify 103 high-grade serous ovarian cancer

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SWATH-MS re-capitulates differentially expressed proteins in ovarian cancer subtypes

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SWATH-MS is a robust proteomic approach for large-scale clinical proteomic studies

The Repertoire of Serous Ovarian Cancer Non-genetic Heterogeneity Revealed by Single-Cell Sequencing of Normal Fallopian Tube Epithelial Cells

The inter-differentiation between cell states promotes cancer cell survival under stress and fosters non-genetic heterogeneity (NGH). NGH is, therefore, a surrogate of tumor resilience but its quantification is confounded by genetic heterogeneity. Here we show that NGH in serous ovarian cancer (SOC) can be accurately measured when informed by the molecular signatures of the normal fallopian tube epithelium (FTE) cells, the cells of origin of SOC. Surveying the transcriptomes of ∼6,000 FTE cells, predominantly from non-ovarian cancer patients, identified 6 FTE subtypes. We used subtype signatures to deconvolute SOC expression data and found substantial intra-tumor NGH. Importantly, NGH-based stratification of ∼1,700 tumors robustly correlated with survival. Our findings lay the foundation for accurate prognostic and therapeutic stratification of SOC.

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

BACKGROUND

Recent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.

METHODS

Gene signatures of tumor and stroma were developed using paired microdissected tissue from two independent studies. Stromal genes were investigated in two molecular subtype classifications and 61 published gene signatures. Prognostic performance of gene signatures of stromal admixture was evaluated in 2,527 ovarian tumors (16 studies). Computational simulations of increasing stromal cell proportion were performed by mixing gene-expression profiles of paired microdissected ovarian tumor and stroma.

RESULTS

Recently described ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations where the percentage of stromal cells increased. Stromal gene expression in bulk tumors was associated with overall survival (hazard ratio, 1.17; 95% confidence interval, 1.11-1.23), and in one data set, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content.

CONCLUSIONS

Cell admixture affects the interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Elucidating the role of stroma in the tumor microenvironment and in prognosis is important.

IMPACT

Single-cell analyses may be required to refine the molecular subtypes of high-grade serous ovarian cancer.

Genes associated with bowel metastases in ovarian cancer

OBJECTIVE

This study is designed to identify genes and pathways that could promote metastasis to the bowel in high-grade serous ovarian cancer (OC) and evaluate their associations with clinical outcomes.

METHODS

We performed RNA sequencing of OC primary tumors (PTs) and their corresponding bowel metastases (n = 21 discovery set; n = 18 replication set). Differentially expressed genes (DEGs) were those expressed at least 2-fold higher in bowel metastases (BMets) than PTs in at least 30% of patients (P < .05) with no increased expression in paired benign bowel tissue and were validated with quantitative reverse transcription PCR. Using an independent OC cohort (n = 333), associations between DEGs in PTs and surgical and clinical outcomes were performed. Immunohistochemistry and mouse xenograft studies were performed to confirm the role of LRRC15 in promoting metastasis.

RESULTS

Among 27 DEGs in the discovery set, 21 were confirmed in the replication set: SFRP2, Col11A1, LRRC15, ADAM12, ADAMTS12, MFAP5, LUM, PLPP4, FAP, POSTN, GRP, MMP11, MMP13, C1QTNF3, EPYC, DIO2, KCNA1, NETO1, NTM, MYH13, and PVALB. Higher expression of more than half of the genes in the PT was associated with an increased requirement for bowel resection at primary surgery and an inability to achieve complete cytoreduction. Increased expression of LRRC15 in BMets was confirmed by immunohistochemistry and knockdown of LRRC15 significantly inhibited tumor progression in mice.

CONCLUSIONS

We identified 21 genes that are overexpressed in bowel metastases among patients with OC. Our findings will help select potential molecular targets for the prevention and treatment of malignant bowel obstruction in OC.