Global expression analysis of endometrial cancer cells in response to progesterone identifies new therapeutic targets

Progesterone prevents development of endometrial cancers through its receptor (PR) although the molecular mechanisms have yet to be fully characterized. In this study, we performed a global analysis of gene regulation by progesterone using human endometrial cancer cells that expressed PR endogenously or exogenously. We found progesterone strongly inhibits multiple components of the platelet derived growth factor receptor (PDGFR), Janus kinase (JAK), signal transducer and activator of transcription (STAT) pathway through PR. The PDGFR/JAK/STAT pathway signals to control numerous downstream targets including AP-1 transcription factors Fos and Jun. Treatment with inhibitors of the PDGFR/JAK/STAT pathway significantly blocked proliferation in multiple novel patient-derived organoid models of endometrial cancer, and activation of this pathway was found to be a poor prognostic signal for the survival of patients with endometrial cancer from The Cancer Genome Atlas. Our study identifies this pathway as central to the growth-limiting effects of progesterone in endometrial cancer and suggests that inhibitors of PDGFR/JAK/STAT should be considered for future therapeutic interventions.

Successful Patient-Derived Organoid Culture of Gynecologic Cancers for Disease Modeling and Drug Sensitivity Testing

Developing reliable experimental models that can predict clinical response before treating the patient is a high priority in gynecologic cancer research, especially in advanced or recurrent endometrial and ovarian cancers. Patient-derived organoids (PDOs) represent such an opportunity. Herein, we describe our successful creation of 43 tumor organoid cultures and nine adjacent normal tissue organoid cultures derived from patients with endometrial or ovarian cancer. From an initial set of 45 tumor tissues and seven ascites fluid samples harvested at surgery, 83% grew as organoids. Drug sensitivity testing and organoid cell viability assays were performed in 19 PDOs, a process that was accomplished within seven days of obtaining the initial surgical tumor sample. Sufficient numbers of cells were obtained to facilitate testing of the most commonly used agents for ovarian and endometrial cancer. The models reflected a range of sensitivity to platinum-containing chemotherapy as well as other relevant agents. One PDO from a patient treated prior to surgery with neoadjuvant trastuzumab successfully predicted the patient's postoperative chemotherapy and trastuzumab resistance. In addition, the PDO drug sensitivity assay identified alternative treatment options that are currently used in the second-line setting. Our findings suggest that PDOs could be used as a preclinical platform for personalized cancer therapy for gynecologic cancer patients.

Creation and validation of models to predict response to primary treatment in serous ovarian cancer

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case–control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.

Prediction of Epithelial Ovarian Cancer Outcomes With Integration of Genomic Data

Some of the patients with epithelial ovarian cancer will not respond to initial therapy. These patients have a poor prognosis. Our aim was to identify patients with a worse prognosis by integrating clinical, pathologic, and genomic data. Using publicly available genomic data and integrating it with clinical data, we significantly improved the prediction of patients with worse surgical outcomes and those who do not respond to initial chemotherapy. We further improved these models with more precise data collection and better understanding of the genetic background of the studied population. Better prediction will lead to better patient classification and opportunities for individualized treatment.

Abstract PO039: Combined histone deacetylase and proteasome inhibition in patient-derived endometrial cancer organoid models promotes massive cell death

Patient-derived organoids (PDOs) are a promising model for personalized cancer treatment. Accumulating evidence indicates that PDOs can predict clinical outcomes in patients. A handful studies in several cancer types have established that PDOs can recapitulate both histological and genomic features of the lesion from which they were derived. Additionally, PDOs can grow with high efficiency in a short time, enabling prediction of responsiveness to chemotherapy and development of more targeted therapies for patients. Our long-term goal is to create a PDO biorepository comprising all major histopathological subtypes of endometrial cancer, perform drug screening on these PDOs, and identify novel single agent and combinatorial regimens to advance into clinical trials. Herein we describe progress to date on generation of endometrial PDOs and drug screening to identify potential therapeutic options. First, we created PDO cultures from seven patients with different histological subtypes of endometrial cancer, including early stage/grade endometrioid adenocarcinoma and high-grade serous carcinoma. Next, we exposed each PDO to a panel of 23 chemotherapeutic agents and targeted drugs. Surprisingly, despite a divergent response to standard chemotherapy (platinum and taxane compounds), all PDOs showed high responsiveness to the combination of a histone deacetylase (HDAC) inhibitor and a proteasome inhibitor. Specifically, we achieved nearly complete cell killing within 72 hrs of exposure of organoid cultures to HDAC and proteasome inhibitors, with concentrations in the low nanomolar range. Similar results were achieved with different proteasome inhibitors (bortezomib and ixazomib) and HDAC inhibitors (romidepsin and belinostat). At present, these classes of agents have primarily been studied in multiple myeloma, though multiple clinical trials of combined proteasome and HDAC inhibitor therapy are active in advanced solid tumors and lymphomas. HDAC inhibitors maintain the acetylation of histones and thereby increase the expression of genes associated with apoptosis and cell cycle arrest. Proteasome inhibitors prevent proteasomal degradation, leading to upregulation of proapoptotic protein expression, cell cycle arrest, and ER stress, downregulation of angiogenesis and the pro-inflammatory protein NF-κB, and ROS generation. Mechanistically, HDAC inhibitors and proteasome inhibitors promote dual proteasome and aggresome blockage and induce apoptosis due to the accumulation of misfolded proteins. Our findings of profound tumor cell death in multiple different PDOs serve as the foundation for a future clinical trial using the HDAC inhibitor ixazomib and proteasome inhibitor romidepsin in advanced and recurrent endometrial cancers as well as high-grade serous ovarian cancer, which shares histologic and molecular features. Concomitantly, we are investigating the in vitro and in vivo effects of the combination on the induction of apoptosis and endometrial tumor regression.

Citation Format: Jianling Bi, Kristina W. Thiel, Eric J. Devor, Andreea M. Newtson, Yuping Zhang, Kimberly K. Leslie. Combined histone deacetylase and proteasome inhibition in patient-derived endometrial cancer organoid models promotes massive cell death [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PO039.

Abstract PO046: Anti-angiogenic tyrosine kinase inhibitor cediranib is superior to bevacizumab in endometrial cancer due to differential effects on cell cycle regulation

Angiogenesis plays a crucial role in tumor development and metastasis, and many cancer cells upregulate vascular endothelial growth factor-A (VEGF-A) expression to promote angiogenesis. Several clinical trials of anti-angiogenic agents have been conducted in advanced and recurrent endometrial cancer, but only bevacizumab and cediranib have demonstrated activity as single agents. Bevacizumab is a monoclonal antibody against VEGF-A, whereas cediranib is a small molecule angiokinase inhibitor.

However, subsequent studies of bevacizumab+chemotherapy failed to improve outcomes compared to chemotherapy alone. We recently demonstrated that chemotherapy plus nintedanib, another angiokinase inhibitor, promotes catastrophic cell death in a xenograft model of endometrial cancer (Ebeid, et al., Nat Nanotech 2019). This effect was specific to cells with loss of function, i.e., null mutations in TP53. Here we compared the efficacy of cediranib vs. bevacizumab in p53-null gynecologic cancer models and determined the mechanism for differential sensitivity. We first performed a phosphoproteomic array of 875 phosphoproteins to define the signaling changes related to bevacizumab vs. cediranib in two p53-null cell gynecologic cancer cell lines. Several signaling events were similar between bevacizumab and cediranib, including phosphorylation of IGF1R, Abl, p70S6K and proteins in the ERK/MAPK and Wnt signaling pathways. We next tested the impact of the anti- angiogenic agents on cell viability using cancer cell lines and over 20 patient-derived organoid cultures of endometrial or ovarian cancer. Neither bevacizumab nor cediranib alone had a notable effect on cell viability, even at 1-10 µM concentrations. By contrast, cediranib but not bevacizumab promoted marked cell death when combined with chemotherapy. This effect was most pronounced in p53-null models.

Cell cycle analysis demonstrated an accumulation in mitosis after treatment with cediranib+chemotherapy, consistent with abrogation of the G2/M checkpoint and subsequent mitotic catastrophe. Molecular analysis of key controllers of the G2/M cell cycle checkpoint confirmed its abrogation. Unexpectedly, the endometrial cancer cell lines were unresponsive to stimulation with VEGF-A, the target of bevacizumab. This was confirmed by the lack of ERK phosphorylation, a downstream signaling event expected after VEGF-A treatment, and was potentially caused low expression of VEGFR1 and 2. Based on these data, we conclude that an anti-angiogenic tyrosine kinase inhibitor such as cediranib has the potential to be superior to bevacizumab in combination with chemotherapy. We hypothesize that the mechanism relates to cediranib inhibition of the tumor vasculature as well as its ability to optimize the impact of chemotherapeutic agents during the cell cycle. Future clinical studies should test this hypothesis by studying the combination of standard chemotherapy with cediranib in advanced and recurrent endometrial cancer.

Citation Format: Jianling Bi, Andreea M. Newtson, Eric J. Devor, Yuping Zhang, Kristina W. Thiel, Kimberly K. Leslie. Anti-angiogenic tyrosine kinase inhibitor cediranib is superior to bevacizumab in endometrial cancer due to differential effects on cell cycle regulation [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PO046.

Abstract PO025: Development of patient derived organoid cultures of two advanced stage serous carcinomas of the uterus

Advanced stage serous carcinoma of the uterus typically portends a poor prognosis. There is an urgent need to augment clinical trial data with different methods of treatment evaluation before a patient is made to try and potentially fail a therapy. Patient derived organoids (PDOs) may represent such an opportunity. We describe the successful culture and drug sensitivity testing of two PDOs from patients with advanced stage serous carcinoma of the uterus who received neoadjuvant chemotherapy. Clinical response to treatment was compared with the organoid response to the same regimens. Drug sensitivity data are reported as percentage of residual cell viability 48 hours after one standard treatment dose, in which 100% viability indicates no response and 0% viability reflects complete cell death. We hypothesized that significant cell killing, i.e. a low cell viability in our assay, would be required to achieve clinical drug sensitivity in patients. Both patients were clinically platinum resistant. Patient 1 had a large burden of disease at the time of presentation, including skeletal and liver metastasis. Though she initially partially responded to triplet therapy of paclitaxel, carboplatin and bevacizumab, she ultimately had persistent unresectable disease after 6 cycles of therapy. The percent residual, post-drug cell viability for PDO-1 was 45% in response to paclitaxel, carboplatin and bevacizumab combinatorial treatment, 35% in response to paclitaxel and carboplatin, and 100% in response to single agent bevacizumab. Patient 2 received paclitaxel, carboplatin and trastuzumab because her tumor was positive for HER2neu. Though she also had an initial response to treatment, she experienced a recurrent malignant pleural effusion 3 months after completing platinum-containing triplet (trastuzumab was continued as a maintenance therapy). The percent residual cell viability for PDO-2 was 51% in response to the combination of paclitaxel and carboplatin. Previous investigators have identified PDOs as a potentially powerful functional model for treatment response. We confirm that it is feasible to establish organoids and to rapidly test agents for cell sensitivity even prior to the first post-surgical chemotherapeutic treatment cycle. For both patients, the PDO models were only moderately sensitive to the agents used for therapy, and the lack of long-term benefit achieved for these patients may have been suggested a priori by the PDO models. However, further studies are necessary to establish a threshold percent value of post-treatment cell viability that is predictive of clinical patient response. We hypothesize that such a threshold appears to be significantly below 50% from these two initial cases.

Citation Format: Andreea M. Newtson, Jianling Bi, Yuping Zhang, Eric Devor, Kristina Thiel, Kimberly K. Leslie. Development of patient derived organoid cultures of two advanced stage serous carcinomas of the uterus [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PO025.

An integrated prediction model of recurrence in endometrial endometrioid cancers

Objectives: Endometrial cancer incidence and mortality are rising in the US. Disease recurrence has been shown to have a significant impact on mortality. However, to date, there are no accurate and validated prediction models that would discriminate which individual patients are likely to recur. Reliably predicting recurrence would be of benefit for treatment decisions following surgery. We present an integrated model constructed with comprehensive clinical, pathological and molecular features designed to discriminate risk of recurrence for patients with endometrioid endometrial adenocarcinoma. Subjects and methods: A cohort of endometrioid endometrial cancer patients treated at our institution was assembled. Clinical characteristics were extracted from patient charts. Primary tumors from these patients were obtained and total tissue RNA extracted for RNA sequencing. A prediction model was designed containing both clinical characteristics and molecular profiling of the tumors. The same analysis was carried out with data derived from The Cancer Genome Atlas for replication and external validation. Results: Prediction models derived from our institutional data predicted recurrence with high accuracy as evidenced by areas under the curve approaching 1. Similar trends were observed in the analysis of TCGA data. Further, a scoring system for risk of recurrence was devised that showed specificities as high as 81% and negative predictive value as high as 90%. Lastly, we identify specific molecular characteristics of patient tumors that may contribute to the process of disease recurrence. Conclusion: By constructing a comprehensive model, we are able to reliably predict recurrence in endometrioid endometrial cancer. We devised a clinically useful scoring system and thresholds to discriminate risk of recurrence. Finally, the data presented here open a window to understanding the mechanisms of recurrence in endometrial cancer.

A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.

Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA

In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4⁻6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure.

Molecular Characterization of Non-responders to Chemotherapy in Serous Ovarian Cancer

Nearly one-third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial treatment with platinum-based therapy. Genomic and clinical characterization of these patients may lead to potential alternative therapies. Here, the objective is to classify non-responders into subsets using clinical and molecular features. Using patients from The Cancer Genome Atlas (TCGA) dataset with platinum-resistant or platinum-refractory HGSC, we performed a genome-wide unsupervised cluster analysis that integrated clinical data, gene copy number variations, gene somatic mutations, and DNA promoter methylation. Pathway enrichment analysis was performed for each cluster to identify the targetable processes. Following the unsupervised cluster analysis, three distinct clusters of non-responders emerged. Cluster 1 had overrepresentation of the stage IV disease and suboptimal debulking, under-expression of miRNAs and mRNAs, hypomethylated DNA, “loss of function” TP53 mutations, and the overexpression of genes in the PDGFR pathway. Cluster 2 had low miRNA expression, generalized hypermethylation, MUC17 mutations, and significant activation of the HIF-1 signaling pathway. Cluster 3 had more optimally cytoreduced stage III patients, overexpression of miRNAs, mixed methylation patterns, and “gain of function” TP53 mutations. However, the survival for all clusters was similar. Integration of genomic and clinical data from patients that do not respond to chemotherapy has identified different subgroups or clusters. Pathway analysis further identified the potential alternative therapeutic targets for each cluster.

Dual progestin therapy for fertility-sparing treatment of grade 2 endometrial adenocarcinoma

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A case of grade 2 endometrial adenocarcinoma in a young woman desiring fertility-sparing treatment

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Successful conservative management of refractory endometrial adenocarcinoma with dual progestin therapy

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A brief review of conservative management in endometrial adenocarcinoma

Prediction of chemo-response in serous ovarian cancer

Background

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA.

Methods

We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model’s accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets.

Results

The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets.

Conclusions

These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-016-0548-9) contains supplementary material, which is available to authorized users.

Association analysis of a chemo-response signature identified within The Cancer Genome Atlas aimed at predicting genetic risk for chemo-response in ovarian cancer

BACKGROUND

A gene signature associated with chemo-response in ovarian cancer was created through integration of biological data in The Cancer Genome Atlas (TCGA) and validated in five independent microarray experiments. Our study aimed to determine if single nucleotide polymorphisms (SNPs) within the 422-gene signature were associated with a genetic predisposition to platinum-based chemotherapy response in serous ovarian cancer.

METHODS

An association analysis between SNPs within the 422-gene signature and chemo-response in serous ovarian cancer was performed under the log-additive genetic model using the 'SNPassoc' package within the R environment (p<0.0001). Subsequent validation of statistically significant SNPs was done in the Ovarian Cancer Association Consortium (OCAC) database.

RESULTS

19 SNPs were found to be associated with chemo-response with statistical significance. None of the SNPs found significant in TCGA were validated within OCAC for the outcome of interest, chemo-response.

CONCLUSIONS

SNPs associated with chemo-response in ovarian cancer within TGCA database were not validated in a larger database of patients and controls from OCAC. New strategies integrating somatic and germline information may help to characterize genetic predictors for treatment response in ovarian cancer.