Search

Epithelial ovarian cancer (EOC) is the most common gynecological cancer and the ninth most common cancer overall. Major problems associated with EOC include poorly characterized disease progression, disease heterogeneity, lack of early detection markers and the development of chemoresistance. Early detection and treatment of EOC would significantly benefit from routine screening tests on available biofluids. We built on our experience in analyzing ovarian cancer ascites and present an analysis pipeline that combines discovery-based proteomics, bioinformatics prioritization and targeted proteomics quantification using Selected Reaction Monitoring Mass Spectrometry (SRM-MS). Ascitic fluids from patients with serous-type epithelial ovarian cancer were analyzed using comprehensive shotgun proteomics and compared to noncancerous ascitic fluids from patients with benign ovarian tumors. Integration of our data with published mRNA transcriptomic and proteomic data sets led to a panel of 51 candidate proteins. Systematic SRM-MS assay development was performed for a subset of these proteins using both synthetic peptides (13 proteins) and stable isotope labeled standards (4 proteins). Subsequently, precise relative quantification by stable isotope dilution-SRM (SID-SRM) in independent ascites and serum samples was performed as a proof-of-concept validation. The analysis strategy outlined here lays the foundation for future experiments using both larger numbers of patient samples and additional candidate proteins, and provides a template for the proteomics-based discovery of cancer biomarkers.

Biobanking efforts, to establish and grow the pool of available tissue from which evidence on aetiology, therapeutic susceptibility and prognosis of various diseases, have been underway for decades. This is illustrated nowhere better than in cancer. High incidence cancers such as breast, colorectal and lung have seen massive increases in their requisite formularies that have yielded improved prognoses. These discoveries, on a very fundamental level, were made by scientists who had access to tumour tissue and associated clinical data from patient donors. As the research space for higher incidence malignancies became increasingly crowded, attention has turned towards those malignancies with lower incidence. In the same time span, technology has continued to evolve, allowing the next generation of scientists and clinicians to ask more nuanced questions. Inquiries are no longer limited to the -omics of tumour tissue but also include biomarkers of blood and excretory products, concurrent disease status and composition of the gut microbiome. The impact of these new technologies and the questions now facing researchers in low-incidence cancers will be summarized and discussed. Our experience with pancreatic ductal adenocarcinoma will be used as a model for this review. [ABSTRACT FROM AUTHOR]

Background: RNA‐sequencing‐based classifiers can stratify pancreatic ductal adenocarcinoma (PDAC) into prognostically significant subgroups but are not practical for use in clinical workflows. Here, we assess whether histomorphological features may be used as surrogate markers for predicting molecular subgroup and overall survival in PDAC. Methods: Ninety‐six tissue samples from 50 patients with non‐resectable PDAC were scored for gland formation, stromal maturity, mucin, necrosis, and neutrophil infiltration. Prognostic PDAC gene expression classifiers were run on all tumors using whole transcriptome sequencing data from the POG trial (NCT02155621). Findings were validated using digital TCGA slides (n = 50). Survival analysis used multivariate Cox proportional‐hazards tests and log‐rank tests. Results: The combination of low gland formation and low neutrophil infiltration was significantly associated with the poor prognosis PDAC molecular subgroup (basal‐like or squamous) and was an independent predictor of shorter overall survival, in both frozen section (n = 47) and formalin‐fixed paraffin‐embedded (n = 49) tissue samples from POG patients, and in the TCGA samples. This finding held true in the subgroup analysis of primary (n = 17) and metastatic samples (n = 79). The combination of high gland formation and high neutrophils had low sensitivity but high specificity for favorable prognosis subgroups. Conclusions: The assessment of gland formation and neutrophil infiltration on routine histological sections can aid in prognostication and allow inferences to be made about molecular subtype, which may help guide patient management decisions and contribute to our understanding of heterogeneity in treatment response. [ABSTRACT FROM AUTHOR]

The mixture of epithelial and stromal components in pancreatic ductal adenocarcinoma (PDAC) may confound sequencing‐based studies of tumor gene expression. Virtual microdissection has been suggested as a bioinformatics approach to segment the aforementioned components, and subsequent prognostic gene sets have emerged from this research. We examined the prognostic signature from the epithelial gene set of one such study using laser capture microdissected (LCM) epithelial samples. We also examined this gene set in matched stromal samples to determine whether prognostic findings were specific to the epithelium. LCM samples from 48 long‐term and 48 short‐term PDAC survivors were obtained. The resultant epithelial and stromal components were subjected to direct mRNA quantification using a 49 gene published PDAC classifier. Component‐specific unsupervised hierarchical clustering was used to derive groups and survival differences were quantified. Immunohistochemical validation of particular genes was performed in an independent cohort. Clustering in the epithelial component yielded prognostic differences in univariable analysis (P =.02), but those differences were not significant when controlled for other clinicopathologic covariates (P =.06). Clustering in the stromal component yielded prognostic differences that persisted in the presence of other clinicopathologic covariates (P =.0005). Validation of selected genes in the epithelium (KRT6A—negative prognostic [P =.004]) and stroma (LY6D—improved prognostic [P =.01] and CTSV—negative prognostic [P =.0002]) demonstrated statistical independence in multivariable analysis. Although the genes used in this study were originally identified as being representative of the epithelial component of PDAC, their expression in the stroma appears to provide additional information that may aid in improved prognostication. What's new? The mixture of epithelial and stromal components in pancreatic ductal adenocarcinoma may confound sequencing‐based studies of tumor gene expression and new approaches have been sought to segregate the two components. This study shows that the stratified examination of biochemical expression in the stroma and epithelial components of pancreatic ductal adenocarcinoma has the potential to identify prognostic information inaccessible by whole tumour assessment. The authors identified proteins that can be easily assessed and input into a predictive model which can be used to prognosticate individual patients. The findings may offer a new approach for pancreatic ductal adenocarcinoma research and clinical practice. [ABSTRACT FROM AUTHOR]

Background: Programmed cell death 1 (PD1) inhibitors have recently shown promising anti-cancer effects in a number of solid tumor types. A predictive biomarker to this class of drugs has not been clearly identified; however, overexpression of the PD1 ligand (PD-L1) has shown particular promise in lung adenocarcinoma. In this study, we explore the staining characteristics, prevalence, and clinico-molecular correlates of PD-L1 overexpression in pancreatic ductal adenocarcinoma (PDAC).Methods: A tissue microarray (TMA) was constructed from cases of resected PDAC. PD-L1 immunohistochemistry (IHC) was performed using the SP142 primary antibody. Immunohistochemical assessment for deficient mismatch repair status (MMRd), CD3 and CD8 were performed. All biomarkers were assessed independently by two anatomical pathologists and consensus achieved on all cases. Survival analysis was performed using three thresholds (> = 1%, >5% and >10%) for tumor cell membrane staining.Results: Two-hundred fifty-two cases were included in the TMA and evaluable by IHC. Thirty-one (12%), 17 (7%), 12(5%) cases were positive at percentage cut offs of >0, >5, and >10% respectively. Increased PD-L1 expression was associated with inferior prognosis (p = 0.0367). No statistically significant association was identified between PD-L1 status and MMR status or tumor infiltrating lymphocytes.Conclusions: This data suggests that there is an inverse relationship between PD-L1 expression and disease specific survival times in resected PDAC. Consequently, this association may represent a phenotype where increased PD-L1 expression has an effect on tumor biology and could therefore identify a subgroup where PD1 blockade could have enhanced effectiveness. [ABSTRACT FROM AUTHOR]

Background: When T cells infiltrate the tumor environment they encounter a myriad of metabolic stressors including hypoxia. Overcoming the limitations imposed by an inadequate tumor vasculature that contributes to these stressors may be a crucial step to immune cells mounting an effective anti-tumor response. We sought to determine whether the functional capacity of tumor infiltrating lymphocytes (TIL) could be influenced by the tumor vasculature and correlated this with survival in patients with ovarian cancer. Methodology and Principal Findings: In 196 high-grade serous ovarian tumors, we confirmed that the tumor vascularity as measured by the marker CD31 was associated with improved patient disease-specific survival. We also found that tumors positive for markers of TIL (CD8, CD4 and forkhead box P3 (FoxP3)) and T cell function (granzyme B and T-cell restricted intracellular antigen-1 (TIA-1)) correlated significantly with elevated vascularity. In vitro, hypoxic CD8 T cells showed reduced cytolytic activity, secreted less effector cytokines and upregulated autophagy. Survival analysis revealed that patients had a significant improvement in disease-specific survival when FoxP3 expressing cells were present in CD31-high tumors compared to patients with FoxP3 expressing cells in CD31-low tumors [HR: 2.314 (95% CI 1.049–5.106); p = 0.0377]. Patients with high vascular endothelial growth factor (VEGF) expressing tumors containing granzyme B positive cells had improved survival compared to patients with granzyme B positive cells in VEGF-low tumors [HR: 2.522 (95% CI 1.097–5.799); p = 0.0294]. Significance: Overall, this data provides a rationale for developing strategies aimed at improving the adaptability and function of TIL to hypoxic tumor conditions. [ABSTRACT FROM AUTHOR]

Aims The recent recognition that ovarian carcinoma is composed of five distinct disease entities has served to increase the value of accurate histotyping. Reliable identification of histotypes is essential for the success of studies testing novel therapies, as well as for biomarker discovery research. The aim of this study was to examine the utility of a nine-marker immunohistochemical ( IHC) panel, designated the Calculator for Ovarian Subtype Prediction ( COSP), to reliably reproduce the consensus diagnosis of two expert gynaecological pathologists. Methods and results A total of 423 cases from the AGO- OVAR11 trial were evaluated using the COSP IHC panel, and compared to original diagnoses from >100 local contributing pathologists and independent expert gynaecopathology review. The overall concordance between COSP and expert review was 89%; in cases where a local pathologist's diagnosis was confirmed by COSP, the expert gynaecopathologist also agreed in 97.5% of cases. Conclusions The incorporation of COSP into a high-throughput diagnostic review algorithm will decrease the need for expert review by identifying a small number of difficult cases that truly require expert review. This modification will serve to increase the efficiency of the diagnostic review process, which will probably serve to reduce operational costs and expedite translational studies on ovarian carcinoma. [ABSTRACT FROM AUTHOR]

Background: Ovarian carcinomas consist of at least five distinct diseases: high-grade serous, low-grade serous, clear cell, endometrioid, and mucinous. Biomarker and molecular characterization may represent a more biologically relevant basis for grouping and treating this family of tumors, rather than site of origin. Molecular characteristics have become the new standard for clinical pathology, however development of tailored type-specific therapies is hampered by a failure of basic research to recognize that model systems used to study these diseases must also be stratified. Unrelated model systems do offer value for study of biochemical processes but specific cellular context needs to be applied to assess relevant therapeutic strategies. Methods: We have focused on the identification of clear cell carcinoma cell line models. A panel of 32 “ovarian cancer” cell lines has been classified into histotypes using a combination of mutation profiles, IHC mutation-surrogates, and a validated immunohistochemical model. All cell lines were identity verified using STR analysis. Results: Many described ovarian clear cell lines have characteristic mutations (including ARID1A and PIK3CA) and an overall molecular/immuno-profile typical of primary tumors. Mutations in TP53 were present in the majority of high-grade serous cell lines. Advanced genomic analysis of bona-fide clear cell carcinoma cell lines also support copy number changes in typical biomarkers such at MET and HNF1B and a lack of any recurrent expressed re-arrangements. Conclusions: As with primary ovarian tumors, mutation status of cancer genes like ARID1A and TP53 and a general immuno-profile serve well for establishing histotype of ovarian cancer cell We describe specific biomarkers and molecular features to re-classify generic “ovarian carcinoma” cell lines into type specific categories. Our data supports the use of prototype clear cell lines, such as TOV21G and JHOC-5, and questions the use of SKOV3 and A2780 as models of high-grade serous carcinoma. [ABSTRACT FROM AUTHOR]

David Huntsman and colleagues describe the associations between biomarker expression patterns and survival in different ovarian cancer subtypes. They suggest that the management of ovarian cancer should reflect differences between these subtypes. [ABSTRACT FROM AUTHOR]