Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples

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.

CD24-targeted intraoperative fluorescence image-guided surgery leads to improved cytoreduction of ovarian cancer in a preclinical orthotopic surgical model

BACKGROUND

The completeness of resection is a key prognostic indicator in patients with ovarian cancer, and the application of tumour-targeted fluorescence image-guided surgery (FIGS) has led to improved detection of peritoneal metastases during cytoreductive surgery. CD24 is highly expressed in ovarian cancer and has been shown to be a suitable biomarker for tumour-targeted imaging.

METHODS

CD24 expression was investigated in cell lines and heterogenous patient-derived xenograft (PDX) tumour samples of high-grade serous ovarian carcinoma (HGSOC). After conjugation of the monoclonal antibody CD24 to the NIR dye Alexa Fluor 750 and the evaluation of the optimal pharmacological parameters (OV-90, n = 21), orthotopic HGSOC metastatic xenografts (OV-90, n = 16) underwent cytoreductive surgery with real-time feedback. The impact of intraoperative CD24-targeted fluorescence guidance was compared to white light and palpation alone, and the recurrence of disease was monitored post-operatively (OV-90, n = 12). CD24-AF750 was further evaluated in four clinically annotated orthotopic PDX models of metastatic HGSOC, to validate the translational potential for intraoperative guidance.

FINDINGS

CD24-targeted intraoperative NIR FIGS significantly (47•3%) improved tumour detection and resection, and reduced the post-operative tumour burden compared to standard white-light surgery in orthotopic HGSOC xenografts. CD24-AF750 allowed identification of minuscule tumour lesions which were undetectable with the naked eye in four HGSOC PDX.

INTERPRETATION

CD24-targeted FIGS has translational potential as an aid to improve debulking surgery of ovarian cancer.

FUNDING

This study was supported by the H2020 program MSCA-ITN [675743], Helse Vest RHF, and Helse Bergen HF [911809, 911852, 912171, 240222, 911974, HV1269], as well as by The Norwegian Cancer Society [182735], and The Research Council of Norway through its Centres of excellence funding scheme [223250, 262652].

Can we predict who lives long with ovarian cancer?

Women with ovarian cancer benefit from individualized management that incorporates advanced imaging technologies, sophisticated cytoreductive surgery integrated with combination chemotherapy, genetic risk assessment, and tumor molecular profiling. However, advanced ovarian cancer remains a highly lethal disease because of early peritoneal dissemination, rapid development of resistance to key therapeutic agents, and evasion of the host immune response. Over the last 15 years, several models and nomograms have been developed to predict surgical outcomes, progression-free survival, or overall survival on the basis of clinical and pathologic data available at the primary diagnosis and recurrence. Each of these models has its strengths and limitations, and they provide a basis for future models that will incorporate functional imaging and molecular characteristics.

Biomarkers in ovarian cancer: To be or not to be

Biomarkers are becoming increasingly important in the treatment of epithelial ovarian cancer. Recent work from many laboratories has begun to provide clinically meaningful biomarkers. This review summarizes the state of the science regarding biomarkers for stratifying early-stage patients into those who benefit from adjuvant treatment, primary debulking versus interval debulking, and specific targeted therapy. In addition, new molecular imaging technologies have been developed to allow the surgeon to resect subvisible tumor deposits. These efforts should increase clinical effectiveness while minimizing toxicities for patients.

Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

Background

Compromised intestinal barrier (CIB) has been associated with many enteropathies, including colorectal cancer (CRC) and inflammatory bowel disease (IBD). We hypothesized that CIB could lead to increased host-derived contents including epithelial cells into the gut, change its physio-metabolic properties, and globally alter microbial community and metabolic capacities.

Results

Consistently, we found host DNA contents (HDCs), calculated as the percentage of metagenomic sequencing reads mapped to the host genome, were significantly elevated in patients of CRC and Crohn’s disease (CD). Consistent with our hypothesis, we found that HDC correlated with microbial- and metabolic-biomarkers of these diseases, contributed significantly to machine-learning models for patient stratification and was consequently ranked as a top contributor. CD patients with treatment could partially reverse the changes of many CD-signature species over time, with reduced HDC and fecal calprotectin (FCP) levels. Strikingly, HDC showed stronger correlations with the reversing changes of the CD-related species than FCP, and contributed greatly in classifying treatment responses, suggesting that it was also a biomarker for effective treatment.

Conclusions

Together, we revealed that association between HDCs and gut dysbiosis, and identified HDC as a novel biomarker from fecal metagenomics for diagnosis and effective treatment of intestinal diseases; our results also suggested that host-derived contents may have greater impact on gut microbiota than previously anticipated.

A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors

PROBLEM

The current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome.

METHOD OF STUDY

We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real-world patients of a known tumor subtype.

RESULTS

Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two-step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression-based subtyping algorithm in a real-world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer.

CONCLUSION

Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring.

Interval debulking surgery is not worth the wait: a National Cancer Database study comparing primary cytoreductive surgery versus neoadjuvant chemotherapy

Objective

In previous studies, neoadjuvant chemotherapy followed by interval debulking surgery was not inferior to primary cytoreductive surgery as initial treatment for advanced epithelial ovarian cancer. Our study aimed to compare surgical and survival outcomes between the two treatments in a large national database.

Methods

Data were extracted from the National Cancer Database from January 2004 to December 2015. Patients with FIGO (International Federation of Gynecologists and Obstetricians) stage III-IV epithelial ovarian cancer and known sequence of treatment were included: primary cytoreductive (surgery=26 717 and neoadjuvant chemotherapy=9885). Tubal and primary peritoneal cancer diagnostic codes were not included. Residual disease after treatment was defined based on recorded data: R0 defined as microscopic or no residual disease; R1 defined as macroscopic residual disease. Multivariate Cox proportional HR was used for survival analysis. Multivariate logistic regression analysis was utilized to compare mortality between groups. Outcomes were adjusted for significant covariates. Validation was performed using propensity score matching of significant covariates.

Results

A total of 36 602 patients were included in the analysis. Patients who underwent primary cytoreductive surgery had better survival than those treated with neoadjuvant chemotherapy followed by interval surgery, after adjusting for age, co-morbidities, stage, and residual disease (p<0.001). Primary cytoreductive surgery patients with R0 disease had best median survival (62.6 months, 95% CI 60.5–64.5). Neoadjuvant chemotherapy patients with R1 disease had worst median survival (29.5 months, 95% CI 28.4–31.9). There were small survival differences between primary cytoreductive surgery with R1 (38.9 months) and neoadjuvant chemotherapy with R0 (41.8 months) (HR 0.93, 95% CI 0.87 to 1.0), after adjusting for age, co-morbidities, grade, histology, and stage. Neoadjuvant chemotherapy had 3.5 times higher 30-day mortality after surgery than primary cytoreductive surgery (95% CI 2.46 to 5.64). The 90-day mortality was higher for neoadjuvant chemotherapy in multivariate analysis (HR 1.31, 95% CI 1.06 to 1.61) but similar to primary cytoreductive surgery after excluding high-risk patients.

Conclusions

Most patients with advanced epithelial ovarian cancer may benefit from primary cytoreductive surgery. Patients treated with neoadjuvant chemotherapy should be those with co-morbidities unfit for surgery.

A Solution to the Dilution: The Role for Biomarkers in Advanced Ovarian Cancer

Reliable approaches to predict residual disease prior to primary debulking surgery have been sought to further personalize surgical approaches. Reliance on molecular biomarkers alone in a complex clinical environment is challenging and algorithms that incorporate both molecular and clinical features may need to be considered.See related article by Heitz et al., p. 213.

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.

Targeting the Formyl Peptide Receptor type 1 to prevent the adhesion of ovarian cancer cells onto mesothelium and subsequent invasion

Background

The biological behavior of epithelial ovarian cancer (EOC) is unique since EOC cells metastasize early to the peritoneum. Thereby, new anti-target agents designed to block trans-coelomic dissemination of EOC cells may be useful as anti-metastatic drugs. The Urokinase Plasminogen Activator Receptor (uPAR) is overexpressed in EOC tissues, and its truncated forms released in sera and/or ascitic fluid are associated with poor prognosis and unfavorable clinical outcome. We documented that uPAR triggers intra-abdominal dissemination of EOC cells through the interaction of its 84–95 sequence with the Formyl Peptide Receptor type 1 (FPR1), even as short linear peptide Ser-Arg-Ser-Arg-Tyr (SRSRY). While the pro-metastatic role of uPAR is well documented, little information regarding the expression and role of FPR1 in EOC is currently available.

Methods

Expression levels of uPAR and FPR1 in EOC cells and tissues were assessed by immunofluorescence, Western blot, or immunohystochemistry. Cell adhesion to extra-cellular matrix proteins and mesothelium as well as mesothelium invasion kinetics by EOC cells were monitored using the xCELLigence technology or assessed by measuring cell-associated fluorescence. Cell internalization of FPR1 was identified on multiple z-series by confocal microscopy. Data from in vitro assays were analysed by one-way ANOVA and post-hoc Dunnett t-test for multiple comparisons. Tissue microarray data were analyzed with the Pearson’s Chi-square (χ²) test.

Results

Co-expression of uPAR and FPR1 by SKOV-3 and primary EOC cells confers a marked adhesion to vitronectin. The extent of cell adhesion decreases to basal level by pre-exposure to anti-uPAR84–95 Abs, or to the RI-3 peptide, blocking the uPAR84–95/FPR1 interaction. Furthermore, EOC cells exposed to RI-3 or desensitized with an excess of SRSRY, fail to adhere also to mesothelial cell monolayers, losing the ability to cross them. Finally, primary and metastatic EOC tissues express a high level of FPR1.

Conclusions

Our findings identify for the first time FPR1 as a potential biomarker of aggressive EOC and suggests that inhibitors of the uPAR84–95/FPR1 crosstalk may be useful for the treatment of metastatic EOC.

Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets

Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined via calculation of the prognostic index of various gene signatures from different datasets. As evaluation objects, we selected 13 gene signature models (Cox regression model) and 16 OvCa genomic datasets (including gene expression information and follow-up data) from published studies. The results of LCP showed that three models were universal and better than other models. In addition, combining the three models into one model showed the best performance in all datasets by LCP calculation. The combination gene signature model provides a more reliable model and could be validated in various datasets of OvCa. Thus, our method and findings can provide more accurate prognostic biomarkers and effective reference for the precise clinical treatment of OvCa.

Modeling genetic heterogeneity of drug response and resistance in cancer

Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. Here, we describe recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.

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.

Focal Recurrent Copy Number Alterations Characterize Disease Relapse in High Grade Serous Ovarian Cancer Patients with Good Clinical Prognosis: A Pilot Study

High grade serous ovarian cancer (HGSOC) retains high molecular heterogeneity and genomic instability, which currently limit the treatment opportunities. HGSOC patients receiving complete cytoreduction (R0) at primary surgery and platinum-based therapy may unevenly experience early disease relapse, in spite of their clinically favorable prognosis. To identify distinctive traits of the genomic landscape guiding tumor progression, we focused on the R0 patients of The Cancer Genome Atlas (TCGA) ovarian serous cystadenocarcinoma (TCGA-OV) dataset and classified them according to their time to relapse (TTR) from surgery. We included in the study two groups of R0-TCGA patients experiencing substantially different outcome: Resistant (R; TTR ≤ 12 months; n = 11) and frankly Sensitive (fS; TTR ≥ 24 months; n = 16). We performed an integrated clinical, RNA-Sequencing, exome and somatic copy number alteration (sCNA) data analysis. No significant differences in mutational landscape were detected, although the lack of BRCA-related mutational signature characterized the R group. Focal sCNA analysis showed a higher frequency of amplification in R group and deletions in fS group respectively, involving cytobands not commonly detected by recurrent sCNA analysis. Functional analysis of focal sCNA with a concordantly altered gene expression identified in R group a gain in Notch, and interferon signaling and fatty acid metabolism. We are aware of the constraints related to the low number of OC cases analyzed. It is worth noting, however, that the sCNA identified in this exploratory analysis and characterizing Pt-resistance are novel, deserving validation in a wider cohort of patients achieving complete surgical debulking.

Fibroblast growth factor receptor 4 (FGFR4) as detected by immunohistochemistry is associated with postoperative residual disease in ovarian cancer

PURPOSE

Fibroblast Growth Factor Receptor 4 (FGFR4) was proposed to hold prognostic significance in high-grade serous ovarian carcinoma (HGSOC). However, information on this deriving from large, representative patient panels is still missing, though such data would be indispensable to validate suitability of FGFR4 as prognostic marker or even pharmacological target.

METHODS

1063 ovarian cancer cases were included in this study. Immunohistochemistry (IHC) was performed using two different anti-FGFR4 specific antibodies (HPA027273, sc-124) on an automated staining system. IHC data of both FGFR4 antibodies were available from 995 cases. FGFR4 immunostaining was correlated to prognostic factors including survival using uni- and multivariate proportional hazard models.

RESULTS

FGFR4 was positively associated with advanced FIGO stage, high grade and presence of residual disease. When progression free (PFS) of FGFR4 negative vs. positive patients was compared, patients scored as FGFR4 positive had significantly shortened PFS as compared to those that stained negative. All associations of FGFR4 and shortened PFS were lost during multivariate testing. No significant associations were found in terms of OS.

CONCLUSIONS

We were not able to confirm FGFR4 as an independent negative prognosticator as described before. However, FGFR4 was highly prevalent in those cases harboring residual disease after debulking surgery. Since especially patients that could only be debulked sub-optimally may benefit from targeted adjuvant treatment, tyrosine kinase inhibitors targeting FGFRs might turn out to be an interesting future treatment option.

DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer

Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.

Epithelial ovarian cancer: Evolution of management in the era of precision medicine

Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world. The outcomes are complicated, because the disease is often diagnosed late and composed of several subtypes with distinct biological and molecular properties (even within the same histological subtype), and there is inconsistency in availability of and access to treatment. Upfront treatment largely relies on debulking surgery to no residual disease and platinum-based chemotherapy, with the addition of antiangiogenic agents in patients who have suboptimally debulked and stage IV disease. Major improvement in maintenance therapy has been seen by incorporating inhibitors against poly (ADP-ribose) polymerase (PARP) molecules involved in the DNA damage-repair process, which have been approved in a recurrent setting and recently in a first-line setting among women with BRCA1/BRCA2 mutations. In recognizing the challenges facing the treatment of ovarian cancer, current investigations are enlaced with deep molecular and cellular profiling. To improve survival in this aggressive disease, access to appropriate evidence-based care is requisite. In concert, realizing individualized precision medicine will require prioritizing clinical trials of innovative treatments and refining predictive biomarkers that will enable selection of patients who would benefit from chemotherapy, targeted agents, or immunotherapy. Together, a coordinated and structured approach will accelerate significant clinical and academic advancements in ovarian cancer and meaningfully change the paradigm of care.

MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer

Background

Epithelial ovarian cancer (EOC) is a heterogeneous disease, which has been recently classified into four molecular subtypes, of which the mesenchymal subtype exhibited the worst prognosis. We aimed to identify a microRNA- (miRNA-) based signature by incorporating the molecular modalities involved in the mesenchymal subtype for risk stratification, which would allow the identification of patients who might benefit from more rigorous treatments.

Method

We characterized the regulatory mechanisms underlying the mesenchymal subtype using network analyses integrating gene and miRNA expression profiles from The Cancer Genome Atlas (TCGA) cohort to identify a miRNA signature for prognosis prediction.

Results

We identified four miRNAs as the master regulators of the mesenchymal subtype and developed a risk score model. The 4-miRNA signature significantly predicted overall survival (OS) and progression-free survival (PFS) in discovery (p=0.004 and p=0.04) and two independent public datasets (GSE73582: OS, HR: 2.26 (1.26-4.05), p=0.005, PFS, HR: 2.03 (1.34-3.09), p<0.001; GSE25204: OS, HR: 3.07 (1.73-5.46), p<0.001, PFS, HR: 2.59 (1.72-3.88), p<0.001). Moreover, in multivariate analyses, the miRNA signature maintained as an independent prognostic predictor and achieved superior efficiency compared to the currently used clinical factors.

Conclusions

In conclusion, our network analysis identified a 4-miRNA signature which has prognostic value superior to currently reported clinical covariates. This signature warrants further testing and validation for use in clinical practice.

Review: Targeting the Transforming Growth Factor-Beta Pathway in Ovarian Cancer

Despite extensive efforts, there has been limited progress in optimizing treatment of ovarian cancer patients. The vast majority of patients experience recurrence within a few years despite a high response rate to upfront therapy. The minimal improvement in overall survival of ovarian cancer patients in recent decades has directed research towards identifying specific biomarkers that serve both as prognostic factors and targets for therapy. Transforming Growth Factor-β (TGF-β) is a superfamily of proteins that have been well studied and implicated in a wide variety of cellular processes, both in normal physiologic development and malignant cellular growth. Hypersignaling via the TGF-β pathway is associated with increased tumor dissemination through various processes including immune evasion, promotion of angiogenesis, and increased epithelial to mesenchymal transformation. This pathway has been studied in various malignancies, including ovarian cancer. As targeted therapy has become increasingly prominent in drug development and clinical research, biomarkers such as TGF-β are being studied to improve outcomes in the ovarian cancer patient population. This review article discusses the role of TGF-β in ovarian cancer progression, the mechanisms of TGF-β signaling, and the targeted therapies aimed at the TGF-β pathway that are currently being studied.

Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation

Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.

Multi-study factor analysis

We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate (1) common factors shared across multiple studies, and (2) study-specific factors. We develop an Expectation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. We present simulations for evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both, we clarify the benefits of a joint analysis compared to the standard factor analysis. We have provided a tool to accelerate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross-study reproducibility of signal in multivariate data. An R package (MSFA), is implemented and is available on GitHub.

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

OBJECTIVE

Recently, we established new histopathological subtypes of high-grade serous ovarian cancer (HGSOC) that include the mesenchymal transition (MT) type, the immune reactive (IR) type, the solid and proliferative (SP) type and the papillo-glandular (PG) type. Furthermore, we identified that the mesenchymal transcriptome subtype might be sensitive to taxane. We investigated whether these different histopathological subtypes of HGSOC require individualized chemotherapy for optimal treatment.

METHODS

We conducted the Japanese Gynecologic Oncology Group (JGOG) 3016A1 study, wherein we collected hematoxylin and eosin slides (total n = 201) and performed a histopathological analysis of patients with HGSOC registered in the JGOG3016 study, which compared the efficacy of conventional paclitaxel and carboplatin (TC) and dose-dense TC (ddTC). We analyzed the differences in progression-free survival (PFS) and overall survival (OS) among the four histopathological subtypes. We then compared the PFS between the TC group and the ddTC group for each histopathological subtype.

RESULTS

There were significant differences in both PFS and OS among the four histopathological subtypes (p = 0.001 and p < 0.001, respectively). Overall, the MT subtype had the shortest PFS (median 1.4 y) and OS (median 3.6 y). In addition, the MT subtype had a longer PFS in the ddTC group (median 1.8 y) than in the TC group (median 1.2 y) (p = 0.01). Conversely, the other types had no significant difference in PFS when the two regimens were compared.

CONCLUSIONS

The MT type of HGSOC is sensitive to taxane; therefore, the ddTC regimen is recommended for this histopathological subtype.

High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints

Among a litany of malignancies affecting the female reproductive tract, that of the ovary is the most frequently fatal. Moreover, while the steady pace of scientific discovery has fuelled recent ameliorations in the outcomes of many other cancers, the rates of mortality for ovarian cancer have been stagnant since around 1980. Yet despite the grim outlook, progress is being made towards better understanding the fundamental biology of this disease and how its biology in turn influences clinical behaviour. It has long been evident that ovarian cancer is not a unitary disease but rather a multiplicity of distinct malignancies that share a common anatomical site upon presentation. Of these, the high-grade serous subtype predominates in the clinical setting and is responsible for a disproportionate share of the fatalities from all forms of ovarian cancer. This review aims to provide a detailed overview of the clinical-pathological features of ovarian cancer with a particular focus on the high-grade serous subtype. Along with a description of the relevant clinical aspects of this disease, including novel trends in treatment strategies, this text will inform the reader of recent updates to the scientific literature regarding the origin, aetiology and molecular-genetic basis of high-grade serous ovarian cancer (HGSOC).

High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints

Among a litany of malignancies affecting the female reproductive tract, that of the ovary is the most frequently fatal. Moreover, while the steady pace of scientific discovery has fuelled recent ameliorations in the outcomes of many other cancers, the rates of mortality for ovarian cancer have been stagnant since around 1980. Yet despite the grim outlook, progress is being made towards better understanding the fundamental biology of this disease and how its biology in turn influences clinical behaviour. It has long been evident that ovarian cancer is not a unitary disease but rather a multiplicity of distinct malignancies that share a common anatomical site upon presentation. Of these, the high-grade serous subtype predominates in the clinical setting and is responsible for a disproportionate share of the fatalities from all forms of ovarian cancer. This review aims to provide a detailed overview of the clinical-pathological features of ovarian cancer with a particular focus on the high-grade serous subtype. Along with a description of the relevant clinical aspects of this disease, including novel trends in treatment strategies, this text will inform the reader of recent updates to the scientific literature regarding the origin, aetiology and molecular-genetic basis of high-grade serous ovarian cancer (HGSOC).

High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints

Among a litany of malignancies affecting the female reproductive tract, that of the ovary is the most frequently fatal. Moreover, while the steady pace of scientific discovery has fuelled recent ameliorations in the outcomes of many other cancers, the rates of mortality for ovarian cancer have been stagnant since around 1980. Yet despite the grim outlook, progress is being made towards better understanding the fundamental biology of this disease and how its biology in turn influences clinical behaviour. It has long been evident that ovarian cancer is not a unitary disease but rather a multiplicity of distinct malignancies that share a common anatomical site upon presentation. Of these, the high-grade serous subtype predominates in the clinical setting and is responsible for a disproportionate share of the fatalities from all forms of ovarian cancer. This review aims to provide a detailed overview of the clinical-pathological features of ovarian cancer with a particular focus on the high-grade serous subtype. Along with a description of the relevant clinical aspects of this disease, including novel trends in treatment strategies, this text will inform the reader of recent updates to the scientific literature regarding the origin, aetiology and molecular-genetic basis of high-grade serous ovarian cancer (HGSOC).

Integrated multiomic predictors for ovarian cancer survival

Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.

The road to long-term survival: Surgical approach and longitudinal treatments of long-term survivors of advanced-stage serous ovarian cancer

OBJECTIVE

It is unclear if the types of surgical procedures performed on long-term survivors (LTS) of high-grade serous ovarian carcinoma (HGSOC) contribute to prolonged survival. In this case-control study we review the surgical procedures performed on LTS and describe their individual longitudinal disease courses.

METHODS

Women with FIGO stage III-IV high-grade serous cancer of the ovary, fallopian tube or peritoneum were selected from the University of Chicago ovarian cancer database. LTS were those surviving >7 years and controls were short-term survivors (STS) living 1-2 years. Patients with non-serous histology, low grade, and low malignant potential tumors were excluded.

RESULTS

We identified 450 women with stage III/IV HGSOC including 45 LTS and 78 STS. LTS showed a trend towards lower disease burden, yet underwent more aggressive surgical treatment. Interestingly, only 15 LTS (34%) were debulked to microscopic disease and 9 LTS (21%) underwent suboptimal debulking. Two LTS (5%) recurred within 12 months. LTS had heterogeneous clinical courses with 13 (29%) never experiencing a recurrence with 143 months median follow-up and 32 (71%) experiencing a recurrence with 115 months median follow-up. Of the women who recurred, 19 (59%) underwent at least one surgery for recurrence.

CONCLUSIONS

Aggressive surgical treatment intended to achieve microscopic disease, primary debulking surgery, preservation of sensitivity to chemotherapy, and recurrence amenable to secondary debulking are associated with long-term survival. However, clinicopathologic data are insufficient to predict long-term survival of HGSOC. Biologic characterization of these patient's tumors likely holds the key to understanding their unusually favorable courses.

Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes.Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%; P < 10^-5) and are associated with overall survival in a meta-analysis across datasets (P < 10^-5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037-47. ©2018 AACR.

Consensus on Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes.Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%; P < 10-5) and are associated with overall survival in a meta-analysis across datasets (P < 10-5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037-47. ©2018 AACR.

Expression level of NUAK1 in human nasopharyngeal carcinoma and its prognostic significance

BACKGROUND

Nasopharyngeal carcinoma (NPC) is notable for its high incidence rates in select geographic and ethnic populations, especially among Chinese and Malay populations in Southeastern Asia. However, relevant biomarkers for the development and prognosis of NPC are not yet clear; therefore, discovering novel biomarkers will facilitate prediction of prognosis and development of targeted therapeutic tactics. This study aims to quest the potential prognostic value of NUAK1 (a downstream member of Akt) in NPC.

METHODS

Immunohistochemistry was performed to measure the expression of NUAK1 in paraffin-embedded NPC samples. Statistical analysis, encompassing chi-square tests and Student's t test, was also employed to evaluate the association between the expression of NUAK1 and clinicopathologic features. In addition, the survival analysis was used to detect the prognostic significance of NUAK1 in NPC.

RESULTS

Excessive expression of NUAK1 was found in NPC tissues at mRNA levels. Statistical analysis revealed a correlation of NUAK1 expression with maximum neck lymph node diameter (p = 0.025) and WHO histological type (p = 0.021). Furthermore, according to survival analysis, there was clinical relevance between the upregulation of NUAK1 in NPC and DFS. Subgroup analysis indicated that the expression of NUAK1 was strongly associated with DFS (p = 0.027) and OS (p = 0.026) duration in the patients of N1-3 tumors, but not in patients with N0 tumors. The expression of NUAK1 was also strongly associated with OS (p = 0.044) and DFS (p = 0.007) in patients of late stage tumour (UICC3-5), but not in patients of early stage tumour (UICC1-2). In addition, COX regression illustrated that N classification and NUAK1 expression were independent prognostic factors for disease-free survival.

CONCLUSION

Our study postulated that NUAK1 is excessively expressed in NPC and may serve as a potential predictor of prognosis for NPC.

Transcriptomic Characterization of Endometrioid, Clear Cell, and High-Grade Serous Epithelial Ovarian Carcinoma

Background: Endometrioid carcinoma (EC) and clear cell carcinoma (CC) histotypes of epithelial ovarian cancer are understudied compared with the more common high-grade serous carcinomas (HGSC). We therefore sought to characterize EC and CC transcriptomes in relation to HGSC.Methods: Following bioinformatics processing and gene abundance normalization, differential expression analysis of RNA sequence data collected on fresh-frozen tumors was completed with nonparametric statistical analysis methods (55 ECs, 19 CCs, 112 HGSCs). Association of gene expression with progression-free survival (PFS) was completed with Cox proportional hazards models. Eight additional multi-histotype expression array datasets (N = 852 patients) were used for replication.Results: In the discovery set, tumors generally clustered together by histotype. Thirty-two protein-coding genes were differentially expressed across histotype (P < 1 × 10^-10) and showed similar associations in replication datasets, including MAP2K6, KIAA1324, CDH1, ENTPD5, LAMB1, and DRAM1 Nine genes associated with PFS (P < 0.0001) showed similar associations in replication datasets. In particular, we observed shorter PFS time for CC and EC patients with high gene expression for CCNB2, CORO2A, CSNK1G1, FRMD8, LIN54, LINC00664, PDK1, and PEX6, whereas, the converse was observed for HGSC patients.Conclusions: The results suggest important histotype differences that may aid in the development of treatment options, particularly those for patients with EC or CC.Impact: We present replicated findings on transcriptomic differences and how they relate to clinical outcome for two of the rarer ovarian cancer histotypes of EC and CC, along with comparison with the common histotype of HGSC. Cancer Epidemiol Biomarkers Prev; 27(9); 1101-9. ©2018 AACR.

How can molecular abnormalities influence our clinical approach

Background

Despite improvements in diagnostics and treatment, the clinical outcome of epithelial ovarian cancer remains poor over the last three decades. Recent high-throughput genomic studies have demonstrated ovarian cancer as a highly heterogeneous entity with distinctive molecular signatures among different or even within the same histotype. In this article, we review the molecular genetics of epithelial ovarian cancer and how they have been translated into modern clinical trials, as well as their implications in patient stratification for more targeted and personalized approaches.

Patients and methods

Multiple genomic studies were collected to summarize the major advances in understanding ovarian cancer-associated molecular abnormalities with emphasis on their potential clinical applicability to rationalize the design of recent clinical trials.

Results

The clinical management of ovarian cancer can significantly benefit from comprehensive molecular profiling studies, which have uncovered the distinctiveness of ovarian cancer subsets bearing characteristic genomic aberrance and consequentially dysregulated genes and pathways underlying the tumor progression and chemoresistance. Genomics studies have demonstrated a powerful tool to delineate the molecular basis responsible for diverse clinical behaviors associated with tumor histology and grade. In addition, molecular signatures obtained by integrated 'omics' analyses have promised opportunities for novel therapeutic or stratification biomarkers to tailor current clinical management as well as novel predictive tools of clinical end points including patient prognosis and therapeutic efficacy.

Conclusions

Recent progress in understanding the molecular landscape of ovarian cancer has profoundly shifted the design of clinical trials from empirical, unitary paradigms to more rationalized and personalized regimes. Correspondingly, a promising prospective has emerged for ovarian cancer patients to have considerably improved outcome upon careful alignment of patient characteristics, therapeutic biomarkers and targeting approaches. Nevertheless, extensive validation and inference of potential biomarkers are pressing demands on both bioinformatic and biological levels to warrant sufficient clinical relevance for potential translation, so that the performance of related clinical trial can be well predicted and achieved.

Data and Statistical Methods To Analyze the Human Microbiome

The Waldron lab for computational biostatistics bridges the areas of cancer genomics and microbiome studies for public health, developing methods to exploit publicly available data resources and to integrate -omics studies.

The Waldron lab for computational biostatistics bridges the areas of cancer genomics and microbiome studies for public health, developing methods to exploit publicly available data resources and to integrate -omics studies.

Training replicable predictors in multiple studies

This article considers replicability of the performance of predictors across studies. We suggest a general approach to investigating this issue, based on ensembles of prediction models trained on different studies. We quantify how the common practice of training on a single study accounts in part for the observed challenges in replicability of prediction performance. We also investigate whether ensembles of predictors trained on multiple studies can be combined, using unique criteria, to design robust ensemble learners trained upfront to incorporate replicability into different contexts and populations.

CD103+ intraepithelial T cells in high-grade serous ovarian cancer are phenotypically diverse TCRαβ+ CD8αβ+ T cells that can be targeted for cancer immunotherapy

CD103+ tumor-infiltrating lymphocytes (TIL) have been linked to specific epithelial infiltration and a prolonged survival in high-grade serous epithelial ovarian cancer (HGSC). However, whether these cells are induced as part of an ongoing anti-HGSC immune response or represent non-specifically expanded resident or mucosal lymphocytes remains largely unknown. In this study, we first confirmed that CD103+ TIL from HGSC were predominantly localized in the cancer epithelium and were strongly correlated with an improved prognosis. We further demonstrate that CD103+ TIL were almost exclusively CD3+ TCRαβ+ CD8αβ+ CD4- T cells, but heterogeneously expressed T cell memory and differentiation markers. Activation of peripheral T cells in the presence of HGSC was sufficient to trigger induction of CD103 in over 90% of all CD8+ cells in a T cell receptor (TCR)- and TGFβR1-dependent manner. Finally, CD103+ TIL isolated from primary HGSC showed signs of recent activation and dominantly co-expressed key immunotherapeutic targets PD-1 and CD27. Taken together, our data indicate CD103+ TIL in HGSC are formed as the result of an adaptive anti-tumor immune response that might be reactivated by (dual) checkpoint inhibition.

Calcium signalling links MYC to NUAK1

NUAK1 is a member of the AMPK-related family of kinases. Recent evidence suggests that NUAK1 is an important regulator of cell adhesion and migration, cellular and organismal metabolism, and regulation of TAU stability. As such, NUAK1 may play key roles in multiple diseases ranging from neurodegeneration to diabetes and metastatic cancer. Previous work revealed a crucial role for NUAK1 in supporting viability of tumour cells specifically when MYC is overexpressed. This role is surprising, given that NUAK1 is activated by the tumour suppressor LKB1. Here we show that, in tumour cells lacking LKB1, NUAK1 activity is maintained by an alternative pathway involving calcium-dependent activation of PKCα. Calcium/PKCα-dependent activation of NUAK1 supports engagement of the AMPK-TORC1 metabolic checkpoint, thereby protecting tumour cells from MYC-driven cell death, and indeed, MYC selects for this pathway in part via transcriptional regulation of PKCα and ITPR. Our data point to a novel role for calcium in supporting tumour cell viability and clarify the synthetic lethal interaction between NUAK1 and MYC.

Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry

We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.

Ovarian Cancers: Genetic Abnormalities, Tumor Heterogeneity and Progression, Clonal Evolution and Cancer Stem Cells

Four main histological subtypes of ovarian cancer exist: serous (the most frequent), endometrioid, mucinous and clear cell; in each subtype, low and high grade. The large majority of ovarian cancers are diagnosed as high-grade serous ovarian cancers (HGS-OvCas). TP53 is the most frequently mutated gene in HGS-OvCas; about 50% of these tumors displayed defective homologous recombination due to germline and somatic BRCA mutations, epigenetic inactivation of BRCA and abnormalities of DNA repair genes; somatic copy number alterations are frequent in these tumors and some of them are associated with prognosis; defective NOTCH, RAS/MEK, PI3K and FOXM1 pathway signaling is frequent. Other histological subtypes were characterized by a different mutational spectrum: LGS-OvCas have increased frequency of BRAF and RAS mutations; mucinous cancers have mutation in ARID1A, PIK3CA, PTEN, CTNNB1 and RAS. Intensive research was focused to characterize ovarian cancer stem cells, based on positivity for some markers, including CD133, CD44, CD117, CD24, EpCAM, LY6A, ALDH1. Ovarian cancer cells have an intrinsic plasticity, thus explaining that in a single tumor more than one cell subpopulation, may exhibit tumor-initiating capacity. The improvements in our understanding of the molecular and cellular basis of ovarian cancers should lead to more efficacious treatments.

Subtype classification and heterogeneous prognosis model construction in precision medicine

Common diseases including cancer are heterogeneous. It is important to discover disease subtypes and identify both shared and unique risk factors for different disease subtypes. The advent of high-throughput technologies enriches the data to achieve this goal, if necessary statistical methods are developed. Existing methods can accommodate both heterogeneity identification and variable selection under parametric models, but for survival analysis, the commonly used Cox model is semiparametric. Although finite-mixture Cox model has been proposed to address heterogeneity in survival analysis, variable selection has not been incorporated into such semiparametric models. Using regularization regression, we propose a variable selection method for the finite-mixture Cox model and select important, subtype-specific risk factors from high-dimensional predictors. Our estimators have oracle properties with proper choices of penalty parameters under the regularization regression. An expectation-maximization algorithm is developed for numerical calculation. Simulations demonstrate that our proposed method performs well in revealing the heterogeneity and selecting important risk factors for each subtype, and its performance is compared to alternatives with other regularizers. Finally, we apply our method to analyze a gene expression dataset for ovarian cancer DNA repair pathways. Based on our selected risk factors, the prognosis model accounting for heterogeneity consistently improves the prediction for the survival probability in both training and test datasets.

Epithelial ovarian cancer: the molecular genetics of epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) remains one of the leading causes of cancer-related deaths among women worldwide, despite gains in diagnostics and treatments made over the last three decades. Existing markers of ovarian cancer possess very limited clinical relevance highlighting the emerging need for identification of novel prognostic biomarkers as well as better predictive factors that might allow the stratification of patients who could benefit from a more targeted approach.

PATIENTS AND METHODS

A summary of molecular genetics of EOC.

RESULTS

Large-scale high-throughput genomic technologies appear to be powerful tools for investigations into the genetic abnormalities in ovarian tumors, including studies on dysregulated genes and aberrantly activated signaling pathways. Such technologies can complement well-established clinical histopathology analysis and tumor grading and will hope to result in better, more tailored treatments in the future. Genomic signatures obtained by gene expression profiling of EOC may be able to predict survival outcomes and other important clinical outcomes, such as the success of surgical treatment. Finally, genomic analyses may allow for the identification of novel predictive biomarkers for purposes of treatment planning. These data combined suggest a pathway to progress in the treatment of advanced ovarian cancer and the promise of fulfilling the objective of providing personalized medicine to women with ovarian cancer.

CONCLUSIONS

The understanding of basic molecular events in the tumorigenesis and chemoresistance of EOC together with discovery of potential biomarkers may be greatly enhanced through large-scale genomic studies. In order to maximize the impact of these technologies, however, extensive validation studies are required.

Identification of prognostic signature in cancer based on DNA methylation interaction network

BACKGROUND

The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.

METHODS

In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.

RESULTS

We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.

CONCLUSIONS

A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data

Background

Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L₁, SCAD and MC+. However, none of the existing algorithms optimizes L₀, which penalizes the number of nonzero features directly.

Results

In this paper, we develop a novel sparse generalized linear model (GLM) with L₀ approximation for feature selection and prediction with big omics data. The proposed approach approximate the L₀ optimization directly. Even though the original L₀ problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms (L₀ADRIDGE) for L₀ penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored.

Conclusions

The developed Software L₀ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-017-0159-z) contains supplementary material, which is available to authorized users.

Challenges and Opportunities in Studying the Epidemiology of Ovarian Cancer Subtypes

PURPOSE OF REVIEW

Only recently has it become clear that epithelial ovarian cancer (EOC) is comprised of such distinct histotypes--with different cells of origin, morphology, molecular features, epidemiologic factors, clinical features, and survival patterns-that they can be thought of as different diseases sharing an anatomical location. Herein, we review opportunities and challenges in studying EOC heterogeneity.

RECENT FINDINGS

The 2014 World Health Organization diagnostic guidelines incorporate accumulated evidence that high- and low-grade serous tumors have different underlying pathogenesis, and that, on the basis of shared molecular features, most high grade tumors, including some previously classified as endometrioid, are now considered to be high-grade serous. At the same time, several studies have reported that high-grade serous EOC, which is the most common histotype, is itself made up of reproducible subtypes discernable by gene expression patterns.

SUMMARY

These major advances in understanding set the stage for a new era of research on EOC risk and clinical outcomes with the potential to reduce morbidity and mortality. We highlight the need for multidisciplinary studies with pathology review using the current guidelines, further molecular characterization of the histotypes and subtypes, inclusion of women of diverse racial/ethnic and socioeconomic backgrounds, and updated epidemiologic and clinical data relevant to current generations of women at risk of EOC.