Implementation of endometrial cancer molecular subtyping into a hybrid community-academic practice.

OBJECTIVES: We sought to confirm utility of our institution's modified Proactive Molecular Risk Classifier for Endometrial Cancer protocol in our daily practice, which includes mismatch repair (MMR), p53, and L1 cell adhesion molecule (L1CAM) immunohistochemistry with in-house next-generation sequencing for POLE, TP53, and CTNNB1. METHODS: We conducted a retrospective review of all patients in our institution who underwent primary endometrial carcinoma resection from the year prior to protocol implementation (PRE; October 1, 2020, to September 30, 2021) through first year of implementation (POST; October 1, 2021, to September 30, 2022) to compare the distribution of molecular and traditional staging factors using GOG-249 criteria to assign clinical risk. RESULTS: In total, 136 of 260 PRE patients were classified as clinically low risk (LR), of whom 31 were MMR deficient. Of the 157 LR POST patients with endometrioid-type carcinoma, 45 were MMR deficient, 5 were POLE mutant, 5 were TP53 mutant, 56 were of no specific molecular profile (NSMP), and 46 did not receive full protocol testing. Of all 79 POST NSMP endometrioid-type cases, 18 were CTNNB1 mutated and 8 showed L1CAM expression. CONCLUSIONS: Our protocol identified 22 (14%) of 157 LR tumors that harbored incipient intermediate- to high-risk molecular aberrations in TP53, CTNNB1, or L1CAM. Moving forward, results of ongoing trials assessing adjuvant therapy decisions based on molecular classification are necessary to confirm protocol utility and identify appropriate modifications.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

The transcription factor PAX8 promotes angiogenesis in ovarian cancer through interaction with SOX17.

PAX8 is a master transcription factor that is essential during embryogenesis and promotes neoplastic growth. It is expressed by the secretory cells lining the female reproductive tract, and its deletion during development results in atresia of reproductive tract organs. Nearly all ovarian carcinomas express PAX8, and its knockdown results in apoptosis of ovarian cancer cells. To explore the role of PAX8 in these tissues, we purified the PAX8 protein complex from nonmalignant fallopian tube cells and high-grade serous ovarian carcinoma cell lines. We found that PAX8 was a member of a large chromatin remodeling complex and preferentially interacted with SOX17, another developmental transcription factor. Depleting either PAX8 or SOX17 from cancer cells altered the expression of factors involved in angiogenesis and functionally disrupted tubule and capillary formation in cell culture and mouse models. PAX8 and SOX17 in ovarian cancer cells promoted the secretion of angiogenic factors by suppressing the expression of SERPINE1, which encodes a proteinase inhibitor with antiangiogenic effects. The findings reveal a non-cell-autonomous function of these transcription factors in regulating angiogenesis in ovarian cancer.

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges.

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

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.

Sulfated glycolipid PG545 induces endoplasmic reticulum stress and augments autophagic flux by enhancing anticancer chemotherapy efficacy in endometrial cancer.

The sulfated glycolipid PG545 shows promising antitumor activity in various cancers. This study was conducted to explore the effects and the mechanism of PG545 action in endometrial cancer (EC). PG545 exhibited strong synergy as assessed by the Chou-Talalay-Method in vitro when combined with cisplatin, or paclitaxel in both type I (Hec1B) and type II (ARK2) EC cell lines. While PG545 showed antitumor activity as monotherapy, a combination of PG545 with paclitaxel and cisplatin was highly effective in reducing the tumor burden and significantly prolonged survival of both Hec1B and ARK2 xenograft bearing mice. Mechanistically, PG545 elicits ER stress as an early response with resultant induction of autophagy. Our data demonstrated an increase in pERK, Bip/Grp78, IRE1α, Calnexin and CHOP/GADD153 within 6-24 hrs of PG545 treatment in EC cells. In parallel, PG545 also blocked FGF2 and HB-EGF mediated signaling in EC cells. Moreover, melatonin-mediated ER stress inhibition reduced PG545-mediated autophagy and PG545 in combination with cisplatin further heightened this stress response. Collectively these data indicate that PG545 exhibits strong synergistic effects with chemotherapeutics in vitro and showed promising antitumor activity in vivo. Our preclinical data indicates that in future studies PG545 can be a useful adjunct to chemotherapy in endometrial cancer.

Dilution of Molecular-Pathologic Gene Signatures by Medically Associated Factors Might Prevent Prediction of Resection Status After Debulking Surgery in Patients With Advanced Ovarian Cancer.

PURPOSE: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome. EXPERIMENTAL DESIGN: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status. RESULTS: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology. CONCLUSIONS: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker.See related commentary by Handley and Sood, p. 9.

Molecular characterization of endometrial cancer and therapeutic implications.

PURPOSE OF REVIEW: The present article reviews molecular subtyping and genomic characterization of endometrial carcinoma, and the associated therapeutic and prognostic implications. RECENT FINDINGS: Endometrial cancer has historically been classified through histology into endometrioid and nonendometrioid subtypes with poor prognostic predictability. Molecular classification through genomic analysis now allows for a major advance in characterization. Four distinct subgroups have been identified: polymerase (POLE) ultramutated, microsatellite unstable, copy number-low--microsatellite stable, and copy number-high-'serous-like'. These subtypes have prognostic implications and may aid in the identification of early-stage patients who are at high risk for recurrence. Through analysis of surrogate markers (POLE, MSI, and p53) and other validated molecular alterations (L1CAM), it is possible to obtain an integrated molecular risk profile that relates to prognosis. Studies utilizing this risk profile in order to identify patients who may benefit from adjuvant treatment for early-stage disease are on-going. SUMMARY: Molecular characterization of endometrial cancer into subgroups has enhanced prognostic and therapeutic implications, contrary to traditional risk stratification. Further development of an integrated molecular risk profile may identify patients who could most benefit from adjuvant treatment following surgery and tailor treatment decisions in the recurrent setting.

De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments.

Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.

UNC-45A is preferentially expressed in epithelial cells and binds to and co-localizes with interphase MTs.

UNC-45A is an ubiquitously expressed protein highly conserved throughout evolution. Most of what we currently know about UNC-45A pertains to its role as a regulator of the actomyosin system. However, emerging studies from both our and other laboratories support a role of UNC-45A outside of actomyosin regulation. This includes studies showing that UNC-45A: regulates gene transcription, co-localizes and biochemically co-fractionates with gamma tubulin and regulates centrosomal positioning, is found in the same subcellular fractions where MT-associated proteins are, and is a mitotic spindle-associated protein with MT-destabilizing activity in absence of the actomyosin system. Here, we extended our previous findings and show that UNC45A is variably expressed across a spectrum of cell lines with the highest level being found in HeLa cells and in ovarian cancer cells inherently paclitaxel-resistant. Furthermore, we show that UNC-45A is preferentially expressed in epithelial cells, localizes to mitotic spindles in clinical tumor specimens of cancer and co-localizes and co-fractionates with MTs in interphase cells independent of actin or myosin. In sum, we report alteration of UNC45A localization in the setting of chemotherapeutic treatment of cells with paclitaxel, and localization of UNC45A to MTs both in vitro and in vivo. These findings will be important to ongoing and future studies in the field that further identify the important role of UNC45A in cancer and other cellular processes.

A patient-centered mobile health application to motivate use of genetic counseling among women with ovarian cancer: A pilot randomized controlled trial.

OBJECTIVE: Despite current guidelines recommending women with ovarian cancer receive genetic risk evaluation by a genetic counselor, utilization has historically been low. We sought to assess the feasibility and effectiveness of a week-long mobile Application for Genetic Information on Cancer (mAGIC) intervention aimed to persuade women with ovarian cancer to pursue genetic counseling. METHODS: The mobile application intervention was based on the Fogg Behavior Model, and consisted of three parts: (1) identifying barriers, (2) developing motivators, and (3) providing triggers to action. The Health Belief Model was used to guide content development. We conducted a prospective, randomized, controlled pilot trial among 104 untested women with a history of epithelial ovarian, primary peritoneal or fallopian tube cancer with the primary objective of increasing uptake of cancer genetic counseling services. RESULTS: Utilization of cancer genetic counseling services improved in both study arms over historical controls, however there was no statistically significant difference between them (intervention: 54.5% versus control: 38.6%; p = 0.14). However, compared to controls, women randomized to the mAGIC intervention demonstrated greater knowledge of hereditary cancer (0-10 scale; 9.4 ±â€¯1.0 vs. 7.1 ±â€¯1.5; p < 0.0001), which persisted for at least three months. Additionally, 96% of women in the intervention group reported they had talked with their family about genetic counseling compared to 77% in the control group (p = 0.01). CONCLUSIONS: The mAGIC intervention did not result in increased uptake of genetic counseling, however it provided significant secondary benefits, including increased participants' knowledge about hereditary ovarian cancer, self-efficacy, and their reported communication with family members. ClinicalTrials.gov Identifier: NCT02877862.

Molecular characterization of endometrial cancer and therapeutic implications.

PURPOSE OF REVIEW: The present article reviews genomic subtyping of endometrial carcinoma and new molecular markers with therapeutic and prognostic implications. RECENT FINDINGS: Endometrial cancer has historically been classified through histology into endometrioid (type 1) and nonendometrioid (type II, mainly serous) subtypes. Molecular classification through genomic analysis now allows for a major advance in characterization; four distinct subgroups have been identified: polymerase ε (POLE) ultramutated, microsatellite unstable, copy number low/microsatellite stable, and copy number high/'serous-like'. These subtypes have prognostic implications and may aid in the identification of early-stage patients who are at high risk for recurrence. Through analysis of surrogate markers (POLE, MSI, and p53) and other validated molecular alterations (L1CAM), it may be possible to obtain an integrated molecular risk profile. Ongoing studies are utilizing this risk profile in order to identify patients who may benefit from additional treatment for early-stage disease. SUMMARY: Molecular characterization of endometrial cancer into subgroups has prognostic and therapeutic implications. Further development of an integrated molecular risk profile may identify patients who could benefit from additional treatment because of a higher risk of recurrence.

Single-cell sequencing in ovarian cancer: a new frontier in precision medicine.

PURPOSE OF REVIEW: This article discusses the advances, applications and challenges of using single-cell RNA sequencing data in guiding treatment decisions for ovarian cancer. RECENT FINDINGS: Genetic heterogeneity is a hallmark of ovarian cancer biology and underlies treatment resistance. Defining the different cell types present within a single ovarian cancer is difficult, but could ultimately lead to improvements in diagnosis and treatment. Next-generation sequencing technologies have rapidly increased our understanding of the molecular landscape of epithelial ovarian cancers, but the majority of these studies are conducted on bulk samples, resulting in data that represents an 'average' of all cells present. Single-cell sequencing provides a means to characterize heterogeneity with a tumor tissue in ovarian cancer patients and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and treatment response. SUMMARY: Single-cell sequencing provides a powerful tool in improving our understanding of tumor cell heterogeneity for the purpose of informing personalized cancer treatment.

Developing a Clinico-Molecular Test for Individualized Treatment of Ovarian Cancer: The interplay of Precision Medicine Informatics with Clinical and Health Economics Dimensions.

We report recent progress in the development of a precision test for individualized use of the VEGF-A targeting drug bevacizumab for treating ovarian cancer. We discuss the discovery model stage (i.e., past feasibility modeling and before conversion to the production test). Main results: (a) Informatics modeling plays a critical role in supporting driving clinical and health economic requirements. (b) The novel computational models support the creation of a precision test with sufficient predictivity to reduce healthcare system costs up to $30 billion over 10 years, and make the use of bevacizumab affordable without loss of length or quality of life.

Opposing effects of cancer-type-specific SPOP mutants on BET protein degradation and sensitivity to BET inhibitors.

It is generally assumed that recurrent mutations within a given cancer driver gene elicit similar drug responses. Cancer genome studies have identified recurrent but divergent missense mutations affecting the substrate-recognition domain of the ubiquitin ligase adaptor SPOP in endometrial and prostate cancers. The therapeutic implications of these mutations remain incompletely understood. Here we analyzed changes in the ubiquitin landscape induced by endometrial cancer-associated SPOP mutations and identified BRD2, BRD3 and BRD4 proteins (BETs) as SPOP-CUL3 substrates that are preferentially degraded by endometrial cancer-associated SPOP mutants. The resulting reduction of BET protein levels sensitized cancer cells to BET inhibitors. Conversely, prostate cancer-specific SPOP mutations resulted in impaired degradation of BETs, promoting their resistance to pharmacologic inhibition. These results uncover an oncogenomics paradox, whereby mutations mapping to the same domain evoke opposing drug susceptibilities. Specifically, we provide a molecular rationale for the use of BET inhibitors to treat patients with endometrial but not prostate cancer who harbor SPOP mutations.

Assessment of the Intraoperative Consultation Service Rendered by General Pathologists in a Scenario Where a Well-Defined Decision Algorithm Is Followed.

OBJECTIVES: Intraoperative consultation (IOC) remains an area of general practice even within subspecialized pathology departments. This study assesses the IOCs rendered in a general pathology setting where surgeons integrate these results in a well-defined algorithm, developed with the input of specialized pathologists. METHODS: The surgical decisions to perform lymphadenectomy in patients with endometrial adenocarcinoma operated on at our institution between January 2003 and June 2015 as a result of the IOC assessment of tumor size, histologic grade, and depth of invasion in the hysterectomy specimen were analyzed. RESULTS: Frozen section (FS) was examined in 801 cases. In comparison to permanent section analysis, FS International Federation of Gynecology and Obstetrics (FIGO) grade had an overall accuracy of 0.95 (95% confidence interval [CI], 0.93-0.98). The FS depth of invasion had an overall accuracy of 0.92 (95% CI, 0.89-0.94). FIGO grade was not documented in 47.8%, the depth of myometrial invasion in 45.2%, and tumor size in 41.8% of the pathology reports. CONCLUSIONS: The high omission rate of the needed parameters by the general pathologists would question their overall understanding of the paradigm shift intended by this algorithm. Possible explanations of this phenomenon and potential solutions are discussed.

Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes.

Purpose: Recent progress in understanding the molecular biology of epithelial ovarian cancer has not yet translated into individualized treatment for these women or improvements in their disease outcome. Gene expression has been utilized to identify distinct molecular subtypes, but there have been no reports investigating whether or not molecular subtyping is predictive of response to bevacizumab in ovarian cancer.Experimental Design: DASL gene expression arrays were performed on FFPE tissue from patients enrolled on the ICON7 trial. Patients were stratified into four TCGA molecular subtypes. Associations between molecular subtype and the efficacy of randomly assigned therapy with bevacizumab were assessed.Results: Molecular subtypes were assigned as follows: 122 immunoreactive (34%), 96 proliferative (27%), 73 differentiated (20%), and 68 mesenchymal (19%). In univariate analysis patients with tumors of proliferative subtype obtained the greatest benefit from bevacizumab with a median PFS improvement of 10.1 months [HR, 0.55 (95% CI, 0.34-0.90), P = 0.016]. For the mesenchymal subtype, bevacizumab conferred a nonsignificant improvement in PFS of 8.2 months [HR 0.78 (95% CI, 0.44-1.40), P = 0.41]. Bevacizumab conferred modest improvements in PFS for patients with immunoreactive subtype (3.8 months; P = 0.08) or differentiated subtype (3.7 months; P = 0.61). Multivariate analysis demonstrated significant PFS improvement in proliferative subtype patients only [HR, 0.45 (95% CI, 0.27-0.74), P = 0.0015].Conclusions: Ovarian carcinoma molecular subtypes with the poorest survival (proliferative and mesenchymal) derive a comparably greater benefit from treatment that includes bevacizumab. Validation of our findings in an independent cohort could enable the use of bevacizumab for those patients most likely to benefit, thereby reducing side effects and healthcare cost. Clin Cancer Res; 23(14); 3794-801. ©2017 AACR.

Single cell sequencing reveals heterogeneity within ovarian cancer epithelium and cancer associated stromal cells.

OBJECTIVES: The purpose of this study was to determine the level of heterogeneity in high grade serous ovarian cancer (HGSOC) by analyzing RNA expression in single epithelial and cancer associated stromal cells. In addition, we explored the possibility of identifying subgroups based on pathway activation and pre-defined signatures from cancer stem cells and chemo-resistant cells. METHODS: A fresh, HGSOC tumor specimen derived from ovary was enzymatically digested and depleted of immune infiltrating cells. RNA sequencing was performed on 92 single cells and 66 of these single cell datasets passed quality control checks. Sequences were analyzed using multiple bioinformatics tools, including clustering, principle components analysis, and geneset enrichment analysis to identify subgroups and activated pathways. Immunohistochemistry for ovarian cancer, stem cell and stromal markers was performed on adjacent tumor sections. RESULTS: Analysis of the gene expression patterns identified two major subsets of cells characterized by epithelial and stromal gene expression patterns. The epithelial group was characterized by proliferative genes including genes associated with oxidative phosphorylation and MYC activity, while the stromal group was characterized by increased expression of extracellular matrix (ECM) genes and genes associated with epithelial-to-mesenchymal transition (EMT). Neither group expressed a signature correlating with published chemo-resistant gene signatures, but many cells, predominantly in the stromal subgroup, expressed markers associated with cancer stem cells. CONCLUSIONS: Single cell sequencing provides a means of identifying subpopulations of cancer cells within a single patient. Single cell sequence analysis may prove to be critical for understanding the etiology, progression and drug resistance in ovarian cancer.