A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy

An Inflammation-Immunity Classifier of 11 Chemokines for Prediction of Overall Survival in Head and Neck Squamous Cell Carcinoma

BACKGROUND Chemokines are important in inflammation, immunity, tumor progression, and metastasis. The purpose of this research was to find an integrated-RNA signature of chemokine family genes to predict the survival prognosis in head and neck squamous carcinoma (HNSC) patients. MATERIAL AND METHODS Relevant data of 504 HNSC patients were extracted from The Cancer Genome Atlas (TCGA) database. Through analyzing RNA sequencing data, the univariate Cox model was used to identify chemokine family genes associated with survival and then to develop a multiple-RNA signature in the training set. The prediction value of this multiple-RNA signature was further verified in the validation and entire sets. The receiver operating characteristic curves were used to assess the predictive value of this multiple-RNA signature. RESULTS Eleven chemokines were included in this prognostic signature. Based on this 11-chemokine signature, we further categorized patients as high or low risk. Compared with low-risk patients, high-risk patients had shorter overall survival (OS) time in the training set [hazard ratio (HR)=3.497, 95% confidence interval (CI)=2.142-5.711, p<0.001], validation set (HR=3.575, 95% CI=1.988-6.390, p<0.001), and entire set (HR=3.416, 95% CI=2.363-4.939, p<0.001). This 11-chemokine signature was an independent prognostic factor for OS in these datasets (p<0.05). The AUC values for predicting overall survival within 48 months in the training, validation, and entire sets were 0.71, 0.69, and 0.69, respectively. CONCLUSIONS This 11-chemokine signature could serve as a reliable prognostic tool for HNSC patients and might be useful to guide individualized treatment or even gene target therapy for high-risk patients.

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.

Non-classical estrogen signaling in ovarian cancer improves chemo-sensitivity and patients outcome

Deficiency in homologous recombination repair (HRR) is frequently associated with hormone-responsive cancers, especially the epithelial ovarian cancer (EOC) which shows defects of HRR in up to half of cases. However, whether there are molecular connections between estrogen signaling and HRR deficiency in EOC remains unknown. Methods: We analyzed the estrogen receptor α (ERα) binding profile in EOC cell lines and investigated its association with genome instability, HRR deficiency and sensitivity to chemotherapy using extensive public datasets and in vitro/in vivo experiments. Results: We found an inverse correlation between estrogen signaling and HRR activity in EOC, and the genome-wide collaboration between ERα and the co-repressor CtBP. Though the non-classical AP-1-mediated ERα signaling, their targets were highly enriched by HRR genes. We found that depleting ERα in EOC cells up-regulates HRR activity and HRR gene expression. Consequently, estrogen signaling enhances the sensitivity of ovarian cancer cells to chemotherapy agents in vitro and in vivo. Large-scale analyses further indicate that estrogen replacement and ESR1 expression are associated with chemo-sensitivity and the favorable survival of EOC patients. Conclusion: These findings characterize a novel role of ERα in mediating the molecular connection between hormone and HRR in EOC and encourage hormone replacement therapy for EOC patients.

Integrative analysis from multi-centre studies identifies a function-derived personalized multi-gene signature of outcome in colorectal cancer

Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision‐making. The present study combined large‐scale gene expression profiles and clinical data of 1828 patients with CRC from multi‐centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function‐derived personalized gene signature [FunPGS]) from an integrated statistics and function‐derived perspective. In the meta‐training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.

The Fanconi Anemia Pathway in Cancer

Fanconi anemia (FA) is a complex genetic disorder characterized by bone marrow failure (BMF), congenital defects, inability to repair DNA interstrand cross-links (ICLs), and cancer predisposition. FA presents two seemingly opposite characteristics: (a) massive cell death of the hematopoietic stem and progenitor cell (HSPC) compartment due to extensive genomic instability, leading to BMF, and (b) uncontrolled cell proliferation leading to FA-associated malignancies. The canonical function of the FA proteins is to collaborate with several other DNA repair proteins to eliminate clastogenic (chromosome-breaking) effects of DNA ICLs. Recent discoveries reveal that the FA pathway functions in a critical tumor-suppressor network to preserve genomic integrity by stabilizing replication forks, mitigating replication stress, and regulating cytokinesis. Homozygous germline mutations (biallelic) in 22 FANC genes cause FA, whereas heterozygous germline mutations in some of the FANC genes (monoallelic), such as BRCA1 and BRCA2, do not cause FA but significantly increase cancer susceptibility sporadically in the general population. In this review, we discuss our current understanding of the functions of the FA pathway in the maintenance of genomic stability, and we present an overview of the prevalence and clinical relevance of somatic mutations in FA genes.

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).

Network-guided prediction of aromatase inhibitor response in breast cancer

Prediction of response to specific cancer treatments is complicated by significant heterogeneity between tumors in terms of mutational profiles, gene expression, and clinical measures. Here we focus on the response of Estrogen Receptor (ER)+ post-menopausal breast cancer tumors to aromatase inhibitors (AI). We use a network smoothing algorithm to learn novel features that integrate several types of high throughput data and new cell line experiments. These features greatly improve the ability to predict response to AI when compared to prior methods. For a subset of the patients, for which we obtained more detailed clinical information, we can further predict response to a specific AI drug.

Breast cancer is the second most common type of cancer in women, with an incidence rate of over 250,000 cases per year, and breast cancer cases show significant heterogeneity in clinical and omic measures. Estrogen receptor positive (ER+) tumors typically grow in response to estrogen, and in post menopausal women, estrogen is only produced in peripheral tissues via the aromatase enzyme. Inhibition of aromatase is often an effective treatment for ER+ tumors, but aromatase inhibitor therapy is not effective for all tumors, and causes of this heterogeneity in response are largely not known. In this work, we present a feature construction and classification method to predict response to aromatase inhibitor therapy. We use network smoothing techniques to combine tumor omic data into predictive features, which we use as input to standard machine learning algorithms. We train predictive models using clinical data, including high-quality clinical data from UPMC patients, and show that our method outperforms previous approaches in predicting response to aromatase inhibitor therapy.

Translational Research Opportunities Regarding Homologous Recombination in Ovarian Cancer

Homologous recombination (HR) is a DNA repair pathway that is deficient in 50% of high-grade serous ovarian carcinomas (HGSOC). Deficient HR (DHR) constitutes a therapeutic opportunity for these patients, thanks to poly (ADP-ribose) polymerases (PARP) inhibitors (PARPi; olaparib, niraparib, and rucaparib are already commercialized). Although initially, PARPi were developed for patients with BRCA1/2 mutations, robust clinical data have shown their benefit in a broader population without DHR. This breakthrough in daily practice has raised several questions that necessitate further research: How can populations that will most benefit from PARPi be selected? At which stage of ovarian cancer should PARPi be used? Which strategies are reasonable to overcome PARPi resistance? In this paper, we present a summary of the literature and discuss the present clinical research involving PARPi (after reviewing ClinicalTrials.gov) from a translational perspective. Research into the functional biomarkers of DHR and clinical trials testing PARPi benefits as first-line setting or rechallenge are currently ongoing. Additionally, in the clinical setting, only secondary restoring mutations of BRCA1/2 have been identified as events inducing resistance to PARPi. The clinical frequency of this and other mechanisms that have been described in preclinics is unknown. It is of great importance to study mechanisms of resistance to PARPi to guide the clinical development of drug combinations.

Development and validation of an immunity-related classifier of nine chemokines for predicting recurrence in stage I-III patients with colorectal cancer after operation

Introduction

Chemokines are closely related with tumor immunity, progression, and metastasis. We aimed to construct a multi-RNA classifier of chemokine family genes for predicting tumor recurrence in stage I-III patients with colorectal cancer (CRC) after operation.

Patients and methods

By analyzing microarray data, the Cox regression analysis was conducted to determine survival-related chemokine family genes and develop a multi-RNA classifier in the training set. The prognostic value of this multi-RNA classifier was further validated in the internal validation and external independent sets. Receiver operating characteristic curves were used to compare the prediction ability of the combined model of this multi-RNA classifier and stage, and this multi-RNA classifier and stage alone.

Results

Nine survival-related chemokines were identified in the training set. We identified a nine-chemokine classifier and classified the patients as high-risk or low-risk. Compared with CRC patients with high-risk scores, CRC patients with low-risk scores had longer disease-free survival in the training (HR=2.353, 95% CI=1.480-3.742, P<0.001), internal validation (HR=2.389, 95% CI=1.428-3.996, P<0.001), and external independent (HR=3.244, 95% CI=1.813-5.807, P<0.001) sets. This nine-chemokine classifier was an independent prognostic factor in these datasets (P<0.05). The combined model of this nine-chemokine classifier and tumor stage may tend to have higher accuracy than stage alone in the training (area under curve 0.727 vs 0.626, P<0.01), internal validation (0.668 vs 0.584, P=0.03), and external independent (0.704 vs 0.678, P>0.05) sets. This nine-chemokine classifier may only be applied in Marisa's C2, C5, and C6 subtypes patients.

Conclusion

Our nine-chemokine classifier is a reliable prognostic tool for some specific biological subtypes of CRC patients. It might contribute to guide the personalized treatment for high-risk patients.

Toward A variable RBE for proton beam therapy

In the clinic, proton beam therapy (PBT) is based on the use of a generic relative biological effectiveness (RBE) of 1.1 compared to photons in human cancers and normal tissues. However, the experimental basis for this RBE lacks any significant number of representative tumor models and clinically relevant endpoints for dose-limiting organs at risk. It is now increasingly appreciated that much of the variations of treatment responses in cancers are due to inter-tumoral genomic heterogeneity. Indeed, recently it has been shown that defects in certain DNA repair pathways, which are found in subsets of many cancers, are associated with a RBE increase in vitro. However, there currently exist little in vivo or clinical data that confirm the existence of similarly increased RBE values in human cancers. Furthermore, evidence for variable RBE values for normal tissue toxicity has been sparse and conflicting to date. If we could predict variable RBE values in patients, we would be able to optimally use and personalize PBT. For example, predictive tumor biomarkers may facilitate selection of patients with proton-sensitive cancers previously ineligible for PBT. Dose de-escalation may be possible to reduce normal tissue toxicity, especially in pediatric patients. Knowledge of increased tumor RBE may allow us to develop biologically optimized therapies to enhance local control while RBE biomarkers for normal tissues could lead to a better understanding and prevention of unusual PBT-associated toxicity. Here, we will review experimental data on the repair of proton damage to DNA that impact both RBE values and biophysical modeling to predict RBE variations. Experimental approaches for studying proton sensitivity in vitro and in vivo will be reviewed as well and recent clinical findings discussed. Ultimately, therapeutically exploiting the understudied biological advantages of protons and developing approaches to limit treatment toxicity should fundamentally impact the clinical use of PBT.

A Functional Homologous Recombination Assay Predicts Primary Chemotherapy Response and Long-Term Survival in Ovarian Cancer Patients

Purpose: Homologous recombination deficiency (HRD) correlates with platinum sensitivity in patients with ovarian cancer, which clinically is the most useful predictor of sensitivity to PARPi. To date, there are no reliable diagnostic tools to anticipate response to platinum-based chemotherapy, thus we aimed to develop an ex vivo functional HRD detection test that could predict both platinum-sensitivity and patient eligibility to targeted drug treatments.Experimental Design: We obtained a functional HR score by quantifying homologous recombination (HR) repair after ionizing radiation-induced DNA damage in primary ovarian cancer samples (n = 32). Samples clustered in 3 categories: HR-deficient, HR-low, and HR-proficient. We analyzed the HR score association with platinum sensitivity and treatment response, platinum-free interval (PFI) and overall survival (OS), and compared it with other clinical parameters. In parallel, we performed DNA-sequencing of HR genes to assess if functional HRD can be predicted by currently offered genetic screening.Results: Low HR scores predicted primary platinum sensitivity with high statistical significance (P = 0.0103), associated with longer PFI (HR-deficient vs. HR-proficient: 531 vs. 53 days), and significantly correlated with improved OS (HR score <35 vs. ≥35, hazard ratio = 0.08, P = 0.0116). At the genomic level, we identified a few unclear mutations in HR genes and the mutational signature associated with HRD, but, overall, genetic screening failed to predict functional HRD.Conclusions: We developed an ex vivo assay that detects tumor functional HRD and an HR score able to predict platinum sensitivity, which holds the clinically relevant potential to become the routine companion diagnostic in the management of patients with ovarian cancer. Clin Cancer Res; 24(18); 4482-93. ©2018 AACR.

Promoter methylation of DNA damage repair (DDR) genes in human tumor entities: RBBP8/CtIP is almost exclusively methylated in bladder cancer

Background

Genome-wide studies identified pan-cancer genes and shared biological networks affected by epigenetic dysregulation among diverse tumor entities. Here, we systematically screened for hypermethylation of DNA damage repair (DDR) genes in a comprehensive candidate-approach and exemplarily identify and validate candidate DDR genes as targets of epigenetic inactivation unique to bladder cancer (BLCA), which may serve as non-invasive biomarkers.

Methods

Genome-wide DNA methylation datasets (2755 CpG probes of n = 7819 tumor and n = 659 normal samples) of the TCGA network covering 32 tumor entities were analyzed in silico for 177 DDR genes. Genes of interest were defined as differentially methylated between normal and cancerous tissues proximal to transcription start sites. The lead candidate gene was validated by methylation-specific PCR (MSP) and/or bisulfite-pyrosequencing in different human cell lines (n = 36), in primary BLCA tissues (n = 43), and in voided urine samples (n = 74) of BLCA patients. Urines from healthy donors and patients with urological benign and malignant diseases were included as controls (n = 78). mRNA expression was determined using qRT-PCR in vitro before (n = 5) and after decitabine treatment (n = 2). Protein expression was assessed by immunohistochemistry (n = 42). R 3.2.0. was used for statistical data acquisition and SPSS 21.0 for statistical analysis.

Results

Overall, 39 DDR genes were hypermethylated in human cancers. Most exclusively and frequently methylated (37%) in primary BLCA was RBBP8, encoding endonuclease CtIP. RBBP8 hypermethylation predicted longer overall survival (OS) and was found in 2/4 bladder cancer cell lines but not in any of 33 cancer cell lines from entities with another origin like prostate. RBBP8 methylation was inversely correlated with RBBP8 mRNA and nuclear protein expression while RBBP8 was re-expressed after in vitro demethylation. RBBP8 methylation was associated with histological grade in primary BLCA and urine samples. RBBP8 methylation was detectable in urine samples of bladder cancer patients achieving a sensitivity of 52%, at 91% specificity.

Conclusions

RBBP8 was identified as almost exclusively hypermethylated in BLCA. RBBP8/CtIP has a proven role in homologous recombination-mediated DNA double-strand break repair known to sensitize cancer cells for PARP1 inhibitors. Since RBBP8 methylation was detectable in urines, it may be a complementary marker of high specificity in urine for BLCA detection.

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.

Comprehensive analysis of lncRNA expression profiles reveals a novel lncRNA signature to discriminate nonequivalent outcomes in patients with ovarian cancer

There is growing evidence of dysregulated long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic efforts of searching for an expression-based lncRNA signature for prognosis prediction in ovarian cancer (OvCa) have not been made yet. Here, we performed comprehensive analysis for lncRNA expression profiles and clinical data of 544 OvCa patients from The Cancer Genome Atlas (TCGA), and identified an eight-lncRNA signature with ability to classify patients of the training cohort into high-risk group showing poor outcome and low-risk group showing significantly improved outcome, which was further validated in the validation cohort and entire TCGA cohort. Multivariate Cox regression analysis and stratified analysis demonstrated that the prognostic value of this signature was independent of other clinicopathological factors. Associating the outcome prediction with BRCA1 and/or BRCA2 mutation revealed a superior prognosis performance both in BRCA1/2-mutated and BRCA1/2 wild-type tumors. Finally, a significantly correlation was found between the lncRNA signature and the complete response rate of chemotherapy, suggesting that this eight-lncRNA signature may be a measure to predict chemotherapy response and identify platinum-resistant patients who might benefit from other more efficacious therapies. With further prospective validation, this eight-lncRNA signature may have important implications for outcome prediction and therapy decisions.

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.

CONCORD biomarker prediction for novel drug introduction to different cancer types

Many cancer therapeutic agents have shown to be effective for treating multiple cancer types. Yet major challenges exist toward introducing a novel drug used in one cancer type to different cancer types, especially when a relatively small number of patients with the other cancer type often benefit from anti-cancer therapy with the drug. Recently, many novel agents were introduced to different cancer types together with companion biomarkers which were obtained or biologically assumed from the original cancer type. However, there is no guarantee that biomarkers from one cancer can directly predict a therapeutic response in another. To tackle this challenging question, we have developed a concordant expression biomarker-based technique ("CONCORD") that overcomes these limitations. CONCORD predicts drug responses from one cancer type to another by identifying concordantly co-expressed biomarkers across different cancer systems. Application of CONCORD to three standard chemotherapeutic agents and two targeted agents demonstrated its ability to accurately predict the effectiveness of a drug against new cancer types and predict therapeutic response in patients.

The expression of miRNAs is associated with tumour genome instability and predicts the outcome of ovarian cancer patients treated with platinum agents

miRNAs, a class of short but stable noncoding RNA molecules, have been revealed to play important roles in the DNA damage response (DDR). However, their functions in cancer genome instability and the consequent clinical effect as the response to chemotherapy have not been fully elucidated. In this study, we utilized multidimensional TCGA data and the known miRNAs involved in DDR to identify a miRNA-regulatory network that responds to DNA damage. Additionally, based on the expression of ten miRNAs in this network, we developed a 10-miRNA-score that predicts defects in the homologous recombination (HR) pathway and genome instability in ovarian cancer. Importantly, consistent with the association between HR defects and improved response to chemotherapeutic agents, the 10-miRNA-score predicts the outcome of ovarian cancer patients treated with platinum agents, with a surprisingly better performance than the indexes of DNA damage. Therefore, our study demonstrates the implication of miRNA expression on cancer genome instability and provides an alternative method to identify DDR defects in patients who show the best effect with platinum drug treatment.

Recurrence-associated gene signature optimizes recurrence-free survival prediction of colorectal cancer

High throughput gene expression profiling has showed great promise in providing insight into molecular mechanisms. Metastasis‐related mRNAs may potentially enrich genes with the ability to predict cancer recurrence, therefore we attempted to build a recurrence‐associated gene signature to improve prognostic prediction of colorectal cancer (CRC). We identified 2848 differentially expressed mRNAs by analyzing CRC tissues with or without metastasis. For the selection of prognostic genes, a LASSO Cox regression model (least absolute shrinkage and selection operator method) was employed. Using this method, a 13‐mRNA signature was identified and then validated in two independent Gene Expression Omnibus cohorts. This classifier could successfully discriminate the high‐risk patients in discovery cohort [hazard ratio (HR) = 5.27, 95% confidence interval (CI) 2.30–12.08, P < 0.0001). Analysis in two independent cohorts yielded consistent results (GSE14333: HR = 4.55, 95% CI 2.18–9.508, P < 0.0001; GSE33113: HR = 3.26, 95% CI 2.16–9.16, P = 0.0176). Further analysis revealed that the prognostic value of this signature was independent of tumor stage, postoperative chemotherapy and somatic mutation. Receiver operating characteristic (ROC) analysis showed that the area under ROC curve of this signature was 0.8861 and 0.8157 in the discovery and validation cohort, respectively. A nomogram was constructed for clinicians, and did well in the calibration plots. Furthermore, this 13‐mRNA signature outperformed other known gene signatures, including oncotypeDX colon cancer assay. Single‐sample gene‐set enrichment analysis revealed that a group of pathways related to drug resistance, cancer metastasis and stemness were significantly enriched in the high‐risk patients. In conclusion, this 13‐mRNA signature may be a useful tool for prognostic evaluation and will facilitate personalized management of CRC patients.

Differentially Expressed lncRNAs in Gastric Cancer Patients: A Potential Biomarker for Gastric Cancer Prognosis

Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC). We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC. Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated it in another independent GEO dataset. An innovative 12-lncRNA signature was established, and it was significantly associated with the disease free survival (DFS) in the training dataset. By applying the 12-lncRNA signature, the training cohort patients could be categorized into high-risk or low-risk subgroup with significantly different DFS (HR = 4.52, 95%CI= 2.49-8.20, P < 0.0001). Similar results were obtained in another independent GEO dataset (HR=1.58, 95%CI=1.05 - 2.38, P=0.0270). Further analysis showed that the prognostic value of this 12-lncRNA signature was independent of AJCC stage and postoperative chemotherapy. Receiver operating characteristic (ROC) analysis showed that the area under receiver operating characteristic curve (AUC) of combined model reached 0.869. Additionally, a well-performed nomogram was constructed for clinicians. Moreover, single-sample gene-set enrichment analysis (ssGSEA) showed that a group of pathways related to drug resistance and cancer metastasis significantly enriched in the high risk patients. A useful innovative 12-lncRNA signature was established for prognostic evaluation of GC. It might complement clinicopathological features and facilitate personalized management of GC.

A molecular portrait of microsatellite instability across multiple cancers

Microsatellite instability (MSI) refers to the hypermutability of short repetitive sequences in the genome caused by impaired DNA mismatch repair. Although MSI has been studied for decades, large amounts of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyse ∼8,000 exomes and ∼1,000 whole genomes of cancer patients across 23 cancer types. Our analysis reveals that the frequency of MSI events is highly variable within and across tumour types. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI. Finally, we propose a highly accurate exome-based predictive model for the MSI phenotype. These results advance our understanding of the genomic drivers and consequences of MSI, and our comprehensive catalogue of tumour-type-specific MSI loci will enable panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.

Some cancers with DNA mismatch repair deficiency display microsatellite instability. Here the authors analyse twenty three cancer types at the exome and whole-genome level, and identify loci with recurrent microsatellite instability that could be used to identify patients who would benefit from immunotherapy.

Panel of seven long noncoding RNA as a candidate prognostic biomarker for ovarian cancer

Ovarian cancer is one of the most common and lethal gynecological malignancies. The diagnosis of ovarian cancer is often at an advanced stage. Accumulated evidence suggests that long noncoding RNAs (lncRNAs) play important roles during ovarian tumorigenesis. In this study, using the lncRNA-mining approach, we analyzed lncRNA expression profiles of 493 ovarian cancer patients from Gene Expression Omnibus datasets, and identified a signature group of seven lncRNAs (BC037530, AK021924, AK094536, AK094536, BC062365, BC004123 and BC007937) associated with patient survival in the training dataset GSE9891. We also formulated a risk score model to divide patients into low-risk and high-risk groups based on the expression of these seven lncRNAs. We further validated the predictive power of our risk score model in two other datasets, GSE26193 and GSE63885. Our analysis showed that the seven-lncRNA signature can serve as an independent predictor apart from Federation of Gynecology and Obstetrics (FIGO) stage and patient age. Further investigation revealed the seven-lncRNA signature correlated with few critical signaling pathways involved in cancer. Combined, all these findings strongly support that the seven-lncRNA signature can serve as a strong prognosis biomarker.

Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm

Despite rapid advancement in generation of large-scale microarray gene expression datasets, robust multigene expression signatures that are capable of guiding the use of specific therapies have not been routinely implemented into clinical care. We have developed an iterative resampling analysis to predict sensitivity algorithm to generate gene expression sensitivity profiles that predict patient responses to specific therapies. The resultant signatures have a robust capacity to accurately predict drug sensitivity as well as the identification of synergistic combinations. Here, we apply this approach to predict response to PARP inhibitors, and show it can greatly outperforms current clinical biomarkers, including BRCA1/2 mutation status, accurately identifying PARP inhibitor-sensitive cancer cell lines, primary patient-derived tumor cells, and patient-derived xenografts. These signatures were also capable of predicting patient response, as shown by applying a cisplatin sensitivity signature to ovarian cancer patients. We additionally demonstrate how these drug-sensitivity signatures can be applied to identify novel synergizing agents to improve drug efficacy. Tailoring therapeutic interventions to improve patient prognosis is of utmost importance, and our drug sensitivity prediction signatures may prove highly beneficial for patient management.

A chemotherapy response classifier based on support vector machines for high-grade serous ovarian carcinoma

Long-term outcome of high-grade serous epithelial ovarian carcinoma (HGSOC) remains poor as a result of recurrence and the emergence of drug resistance. Almost all the patients were given the same platinum-based chemotherapy after debulking surgery even though some of them are naturally resistant to the first-line chemotherapy. No method could verify this part of patients right after the surgery currently. In this study, we used 156 paraffin-embedded high-grade HGSOC specimens for immunohistochemical analysis with 37 immunology markers, and association between the expression levels of these markers and the chemoresponse were evaluated. A support vector machine (SVM)-based HGSOC prognostic classifier was then established, and was validated by a 95-patient independent cohort. The classifier was strongly predictive of chemotherapy resistance, and divided patients into low- and high-risk groups with significant differences progression-free survival (PFS) and overall survival (OS). This classifier may provide a potential way to predict the chemotherapy resistance of HGSOC right after the surgery, and then allow clinicians to make optimal clinical decision for those potentially chemoresistant patients. The potential clinical application of this classifier will benefit those patients with primary drug resistance.

Exosomes-mediate microRNAs transfer in breast cancer chemoresistance regulation

Breast cancer is the most common and fatal type of cancer in women worldwide due to the metastatic process and resistance to treatment. Despite advances in molecular knowledge, little is known regarding resistance to chemotherapy. One highlighted aspect is the DNA damage response (DDR) pathway that is activated upon genotoxic damage, controlling the cell cycle arrest or DNA repair activation. Recently, studies have showed that cancer stem cells (CSCs) could promote chemoresistance through DDR pathway. Furthermore, it is known that the epithelial-mesenchymal transition (EMT) can generate cells with CSCs characteristics and therefore regulate the chemoresistance process. The exosomes are microvesicles filled with RNAs, proteins and microRNAs (miRNAs) that can be released by many cell types, including tumor cells and CSCs. The exosomes content may be cell-to-cell transferable and it could control a wide range of pathways during tumor development and metastasis. A big challenge for modern medicine is to determine the reasons why patients do not respond to chemotherapy treatments and also guide the most appropriate therapy for each one. Considering that the CSCs are able to stimulate the formation of a more aggressive tumor phenotype with migration and metastasis ability, resistance to treatment and disease recurrence, as well as few studies capable to determine clearly the interaction of breast CSCs with its microenvironment, the present review summarize the possibility that exosomes-mediate miRNAs transfer and regulate chemoresistance in breast tumor cells and CSCs, to clarify the complexity of breast cancer progression and therapy.

Biomarkers of Response and Resistance to DNA Repair Targeted Therapies

Drugs targeting DNA damage repair (DDR) pathways are exciting new agents in cancer therapy. Many of these drugs exhibit synthetic lethality with defects in DNA repair in cancer cells. For example, ovarian cancers with impaired homologous recombination DNA repair show increased sensitivity to poly(ADP-ribose) polymerase (PARP) inhibitors. Understanding the activity of different DNA repair pathways in individual tumors, and the correlations between DNA repair function and drug response, will be critical to patient selection for DNA repair targeted agents. Genomic and functional assays of DNA repair pathway activity are being investigated as potential biomarkers of response to targeted therapies. Furthermore, alterations in DNA repair function generate resistance to DNA repair targeted agents, and DNA repair states may predict intrinsic or acquired drug resistance. In this review, we provide an overview of DNA repair targeted agents currently in clinical trials and the emerging biomarkers of response and resistance to these agents: genetic and genomic analysis of DDR pathways, genomic signatures of mutational processes, expression of DNA repair proteins, and functional assays for DNA repair capacity. We review biomarkers that may predict response to selected DNA repair targeted agents, including PARP inhibitors, inhibitors of the DNA damage sensors ATM and ATR, and inhibitors of nonhomologous end joining. Finally, we introduce emerging categories of drugs targeting DDR and new strategies for integrating DNA repair targeted therapies into clinical practice, including combination regimens. Generating and validating robust biomarkers will optimize the efficacy of DNA repair targeted therapies and maximize their impact on cancer treatment. Clin Cancer Res; 22(23); 5651-60. ©2016 AACR.

Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer

DNA repair genes and pathways that are transcriptionally dysregulated in cancer provide the first line of evidence for the altered DNA repair status in tumours, and hence have been explored intensively as a source for biomarker discovery. The molecular mechanisms underlying DNA repair dysregulation, however, have not been systematically investigated in any cancer type. In this study, we performed a statistical analysis to dissect the roles of DNA copy number alteration (CNA), DNA methylation (DM) at gene promoter regions and the expression changes of transcription factors (TFs) in the differential expression of individual DNA repair genes in normal versus tumour breast samples. These gene-level results were summarised at pathway level to assess whether different DNA repair pathways are affected in distinct manners. Our results suggest that CNA and expression changes of TFs are major causes of DNA repair dysregulation in breast cancer, and that a subset of the identified TFs may exert global impacts on the dysregulation of multiple repair pathways. Our work hence provides novel insights into DNA repair dysregulation in breast cancer. These insights improve our understanding of the molecular basis of the DNA repair biomarkers identified thus far, and have potential to inform future biomarker discovery.

A long non-coding RNA signature to improve prognosis prediction of gastric cancer

BACKGROUND

Increasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed at developing alncRNA signature to improve prognosis prediction of gastric cancer (GC).

METHODS

Using a lncRNA-mining approach, we performed lncRNA expression profiling in large GC cohorts from Gene Expression Ominus (GEO), including GSE62254 data set (N = 300) and GSE15459 data set (N = 192). We established a set of 24-lncRNAs that were significantly associated with the disease free survival (DFS) in the test series.

RESULTS

Based on this 24-lncRNA signature, the test series patients could be classified into high-risk or low-risk subgroup with significantly different DFS (HR = 1.19, 95 % CI = 1.13-1.25, P < 0.0001). The prognostic value of this 24-lncRNA signature was confirmed in the internal validation series and another external validation series, respectively. Further analysis revealed that the prognostic value of this signature was independent of lymph node ratio (LNR) and postoperative chemotherapy. Gene set enrichment analysis (GSEA) indicated that high risk score group was associated with several cancer recurrence and metastasis associated pathways.

CONCLUSIONS

The identification of the prognostic lncRNAs indicates the potential roles of lncRNAs in GC biogenesis. Our results may provide an efficient classification tool for clinical prognosis evaluation of GC.

Patient-Level DNA Damage and Repair Pathway Profiles and Prognosis After Prostatectomy for High-Risk Prostate Cancer

IMPORTANCE

A substantial number of patients diagnosed with high-risk prostate cancer are at risk for metastatic progression after primary treatment. Better biomarkers are needed to identify patients at the highest risk to guide therapy intensification.

OBJECTIVE

To create a DNA damage and repair (DDR) pathway profiling method for use as a prognostic signature biomarker in high-risk prostate cancer.

DESIGN, SETTING, AND PARTICIPANTS

A cohort of 1090 patients with high-risk prostate cancer who underwent prostatectomy and were treated at 3 different academic institutions were divided into a training cohort (n = 545) and 3 pooled validation cohorts (n = 232, 130, and 183) assembled for case-control or case-cohort studies. Profiling of 9 DDR pathways using 17 gene sets for GSEA (Gene Set Enrichment Analysis) of high-density microarray gene expression data from formalin-fixed paraffin-embedded prostatectomy samples with median 10.3 years follow-up was performed. Prognostic signature development from DDR pathway profiles was studied, and DDR pathway gene mutation in published cohorts was analyzed.

MAIN OUTCOMES AND MEASURES

Biochemical recurrence-free, metastasis-free, and overall survival.

RESULTS

Across the training cohort and pooled validation cohorts, 1090 men were studied; mean (SD) age at diagnosis was 65.3 (6.4) years. We found that there are distinct clusters of DDR pathways within the cohort, and DDR pathway enrichment is only weakly correlated with clinical variables such as age (Spearman ρ [ρ], range, -0.07 to 0.24), Gleason score (ρ, range, 0.03 to 0.20), prostate-specific antigen level (ρ, range, -0.07 to 0.10), while 13 of 17 DDR gene sets are strongly correlated with androgen receptor pathway enrichment (ρ, range, 0.33 to 0.82). In published cohorts, DDR pathway genes are rarely mutated. A DDR pathway profile prognostic signature built in the training cohort was significantly associated with biochemical recurrence-free, metastasis-free, and overall survival in the pooled validation cohorts independent of standard clinicopathological variables. The prognostic performance of the signature for metastasis-free survival appears to be stronger in the younger patients (HR, 1.67; 95% CI, 1.12-2.50) than in the older patients (HR, 0.77; 95% CI, 0.29-2.07) on multivariate Cox analysis.

CONCLUSIONS AND RELEVANCE

DNA damage and repair pathway profiling revealed patient-level variations and the DDR pathways are rarely affected by mutation. A DDR pathway signature showed strong prognostic performance with the long-term outcomes of metastasis-free and overall survival that may be useful for risk stratification of high-risk prostate cancer patients.

Prognostic Value of a BCSC-associated MicroRNA Signature in Hormone Receptor-Positive HER2-Negative Breast Cancer

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Biology-driven strategy can be used in development of prognostic model.

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The BCSC-associated miRNA classifier can predict prognosis for HR + HER2 − breast cancer.

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The BCSC-associated miRNA classifier outperforms IHC4 scoring and 21-gene RS.

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Chemotherapy can improve DRFS in patients predicted as high-risk.

Breast cancer patients with high proportion of cancer stem cells (BCSCs) have poor clinical outcomes. MiRNAs regulate key features of BCSCs as oncogenes or tumor suppressors. Although hormone receptor (HR)-positive, HER2-negative breast cancers are the most common subtype, current methods are inadequate to predict its clinical outcome. In this multicenter study, we identified and validated a 10 BCSC-associated-miRNA classifier that can predict survival for HR + HER2 − patients. Retrospective analysis showed that this classifier outperformed IHC4 scoring and 21-gene Recurrence Score (RS), and chemotherapy could improve survival in high-risk patients determined by this classifier. This model may facilitate personalized clinical decision for HR + HER2 − individuals.

Purpose

Breast cancer patients with high proportion of cancer stem cells (BCSCs) have unfavorable clinical outcomes. MicroRNAs (miRNAs) regulate key features of BCSCs. We hypothesized that a biology-driven model based on BCSC-associated miRNAs could predict prognosis for the most common subtype, hormone receptor (HR)-positive, HER2-negative breast cancer patients.

Patients and Methods

After screening candidate miRNAs based on literature review and a pilot study, we built a miRNA-based classifier using LASSO Cox regression method in the training group (n = 202) and validated its prognostic accuracy in an internal (n = 101) and two external validation groups (n = 308).

Results

In this multicenter study, a 10-miRNA classifier incorporating miR-21, miR-30c, miR-181a, miR-181c, miR-125b, miR-7, miR-200a, miR-135b, miR-22 and miR-200c was developed to predict distant relapse free survival (DRFS). With this classifier, HR + HER2 − patients were scored and classified into high-risk and low-risk disease recurrence, which was significantly associated with 5-year DRFS of the patients. Moreover, this classifier outperformed traditional clinicopathological risk factors, IHC4 scoring and 21-gene Recurrence Score (RS). The patients with high-risk recurrence determined by this classifier benefit more from chemotherapy.

Conclusions

Our 10-miRNA-based classifier provides a reliable prognostic model for disease recurrence in HR + HER2 − breast cancer patients. This model may facilitate personalized therapy-decision making for HR + HER2 − individuals.

A VEGF-dependent gene signature enriched in mesenchymal ovarian cancer predicts patient prognosis

We have previously reported surrogate biomarkers of VEGF pathway activities with the potential to provide predictive information for anti-VEGF therapies. The aim of this study was to systematically evaluate a new VEGF-dependent gene signature (VDGs) in relation to molecular subtypes of ovarian cancer and patient prognosis. Using microarray profiling and cross-species analysis, we identified 140-gene mouse VDGs and corresponding 139-gene human VDGs, which displayed enrichment of vasculature and basement membrane genes. In patients who received bevacizumab therapy and showed partial response, the expressions of VDGs (summarized to yield VDGs scores) were markedly decreased in post-treatment biopsies compared with pre-treatment baselines. In contrast, VDGs scores were not significantly altered following bevacizumab treatment in patients with stable or progressive disease. Analysis of VDGs in ovarian cancer showed that VDGs as a prognostic signature was able to predict patient outcome. Correlation estimation of VDGs scores and molecular features revealed that VDGs was overrepresented in mesenchymal subtype and BRCA mutation carriers. These findings highlighted the prognostic role of VEGF-mediated angiogenesis in ovarian cancer, and proposed a VEGF-dependent gene signature as a molecular basis for developing novel diagnostic strategies to aid patient selection for VEGF-targeted agents.

Single-sample expression-based chemo-sensitivity score improves survival associations independently from genomic mutations for ovarian cancer Patients

Platinum-based chemotherapies are first-line treatments for ovarian cancer (OC) patients. Although chemotherapy has a high initial response rate, some patients exhibit inherent chemo-resistance. With advancements of molecular and genomic profiling, it is of high interest to identify molecular and genomic signatures predictive of chemo- sensitivity priori to treatment initiation in order to better personalize care decisions. Previous efforts have made use of mRNA expression levels of selected genes responsible for repairing DNA damage, under the hypothesis that chemo efficacy is associated with their proficiency. However, the resulting scores have been difficult to interpret. In this study, we designed a single-sample based approach known as eCARD to investigate chemo-sensitivity in ovarian cancer patients from The Cancer Genome Atlas. We demonstrated that the proposed single-sample based approach can lead to a molecular-based chemo-sensitivity score predictive of prognosis, which validates in 5 independent cohorts, and associates with increasing mutation burden and likelihood of BRCA1/2 mutation.

Development and validation of a microRNA-based signature (MiROvaR) to predict early relapse or progression of epithelial ovarian cancer: a cohort study

BACKGROUND

Risk of relapse or progression remains high in the treatment of most patients with epithelial ovarian cancer, and development of a molecular predictor could be a valuable tool for stratification of patients by risk. We aimed to develop a microRNA (miRNA)-based molecular classifier that can predict risk of progression or relapse in patients with epithelial ovarian cancer.

METHODS

We analysed miRNA expression profiles in three cohorts of samples collected at diagnosis. We used 179 samples from a Multicenter Italian Trial in Ovarian cancer trial (cohort OC179) to develop the model and 263 samples from two cancer centres (cohort OC263) and 452 samples from The Cancer Genome Atlas epithelial ovarian cancer series (cohort OC452) to validate the model. The primary clinical endpoint was progression-free survival, and we adapted a semi-supervised prediction method to the miRNA expression profile of OC179 to identify miRNAs that predict risk of progression. We assessed the independent prognostic role of the model using multivariable analysis with a Cox regression model.

FINDINGS

We identified 35 miRNAs that predicted risk of progression or relapse and used them to create a prognostic model, the 35-miRNA-based predictor of Risk of Ovarian Cancer Relapse or progression (MiROvaR). MiROvaR was able to classify patients in OC179 into a high-risk group (89 patients; median progression-free survival 18 months [95% CI 15-22]) and a low-risk group (90 patients; median progression-free survival 38 months [24-not estimable]; hazard ratio [HR] 1·85 [1·29-2·64], p=0·00082). MiROvaR was a significant predictor of progression in the two validation sets (OC263 HR 3·16, 95% CI 2·33-4·29, p<0·0001; OC452 HR 1·39, 95% CI 1·11-1·74, p=0·0047) and maintained its independent prognostic effect when adjusted for relevant clinical covariates using multivariable analyses (OC179: adjusted HR 1·48, 95% CI 1·03-2·13, p=0·036; OC263: adjusted HR 3·09 [2·24-4·28], p<0·0001; and OC452: HR 1·41 [1·11-1·79], p=0·0047).

INTERPRETATION

MiROvaR is a potential predictor of epithelial ovarian cancer progression and has prognostic value independent of relevant clinical covariates. MiROvaR warrants further investigation for the development of a clinical-grade prognostic assay.

FUNDING

AIRC and CARIPLO Foundation.

Frontline treatment of epithelial ovarian cancer

This is a contemporaneous review of the frontline treatment of epithelial ovarian cancer (EOC), specifically on the importance of optimal surgical cytoreductive surgery, the pivotal role of platinum-based adjuvant chemotherapy (which encompasses intraperitoneal and dose-dense regimens) and the emergence of neo-adjuvant chemotherapy. Additionally, the benefit of concurrent and maintenance bevacizumab in the suboptimally debullked stage III and stage IV EOC setting is also reviewed. The article also discusses the increasing importance of prognostic and predictive molecular biomarkers in the future management of EOC.

An integrated model of clinical information and gene expression for prediction of survival in ovarian cancer patients

Accumulating evidence shows that clinical factors alone are not adequate for predicting the survival of patients with ovarian cancer (OvCa), and many genes have been found to be associated with OvCa prognosis. The objective of this study was to develop a model that integrates clinical information and a gene signature to predict the survival durations of patients diagnosed with OvCa. We constructed mRNA and microRNA expression profiles and gathered the corresponding clinical data of 552 OvCa patients and 8 normal controls from The Cancer Genome Atlas. Using univariate Cox regression followed by a permutation test, elastic net-regulated Cox regression, and ridge regression, we generated a prognosis index consisting of 2 clinical variables, 7 protective mRNAs, 12 risky mRNAs, and 1 protective microRNA. The area under the curve of the receiver operating characteristic of the integrated clinical-and-gene model was 0.756, larger than that of the clinical-alone model (0.686) or the gene-alone model (0.703). OvCa patients in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group (hazard ratio = 8.374, 95% confidence interval = 4.444-15.780, P = 4.90 × 10(-11), by the Wald test). The reliability of the gene signature was confirmed by a public external data set from the Gene Expression Omnibus. Our conclusions that we have identified an integrated clinical-and-gene model superior to the traditional clinical-alone model in ascertaining the survival prognosis of patients with OvCa. Our findings may prove valuable for improving the clinical management of OvCa.

Gene and pathway level analyses of germline DNA-repair gene variants and prostate cancer susceptibility using the iCOGS-genotyping array

BACKGROUND

Germline mutations within DNA-repair genes are implicated in susceptibility to multiple forms of cancer. For prostate cancer (PrCa), rare mutations in BRCA2 and BRCA1 give rise to moderately elevated risk, whereas two of B100 common, low-penetrance PrCa susceptibility variants identified so far by genome-wide association studies implicate RAD51B and RAD23B.

METHODS

Genotype data from the iCOGS array were imputed to the 1000 genomes phase 3 reference panel for 21 780 PrCa cases and 21 727 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. We subsequently performed single variant, gene and pathway-level analyses using 81 303 SNPs within 20 Kb of a panel of 179 DNA-repair genes.

RESULTS

Single SNP analyses identified only the previously reported association with RAD51B. Gene-level analyses using the SKAT-C test from the SNP-set (Sequence) Kernel Association Test (SKAT) identified a significant association with PrCa for MSH5. Pathway-level analyses suggested a possible role for the translesion synthesis pathway in PrCa risk and Homologous recombination/Fanconi Anaemia pathway for PrCa aggressiveness, even though after adjustment for multiple testing these did not remain significant.

CONCLUSIONS

MSH5 is a novel candidate gene warranting additional follow-up as a prospective PrCa-risk locus. MSH5 has previously been reported as a pleiotropic susceptibility locus for lung, colorectal and serous ovarian cancers.

Cisplatin Resistant Spheroids Model Clinically Relevant Survival Mechanisms in Ovarian Tumors

The majority of ovarian tumors eventually recur in a drug resistant form. Using cisplatin sensitive and resistant cell lines assembled into 3D spheroids we profiled gene expression and identified candidate mechanisms and biological pathways associated with cisplatin resistance. OVCAR-8 human ovarian carcinoma cells were exposed to sub-lethal concentrations of cisplatin to create a matched cisplatin-resistant cell line, OVCAR-8R. Genome-wide gene expression profiling of sensitive and resistant ovarian cancer spheroids identified 3,331 significantly differentially expressed probesets coding for 3,139 distinct protein-coding genes (Fc >2, FDR < 0.05) (S2 Table). Despite significant expression changes in some transporters including MDR1, cisplatin resistance was not associated with differences in intracellular cisplatin concentration. Cisplatin resistant cells were significantly enriched for a mesenchymal gene expression signature. OVCAR-8R resistance derived gene sets were significantly more biased to patients with shorter survival. From the most differentially expressed genes, we derived a 17-gene expression signature that identifies ovarian cancer patients with shorter overall survival in three independent datasets. We propose that the use of cisplatin resistant cell lines in 3D spheroid models is a viable approach to gain insight into resistance mechanisms relevant to ovarian tumors in patients. Our data support the emerging concept that ovarian cancers can acquire drug resistance through an epithelial-to-mesenchymal transition.

The BRCA1/2-directed miRNA signature predicts a good prognosis in ovarian cancer patients with wild-type BRCA1/2

Ovarian cancer patients carrying alterations (i.e., germline mutations, somatic mutations, hypermethylations and/or deletions) of BRCA1 or BRCA2 (BRCA1/2) have a better prognosis than BRCA1/2 alteration non-carriers. However, patients with wild-type BRCA1/2 may also have a favorable prognosis as a result of other mechanisms that remain poorly elucidated, such as the deregulation of miRNAs. We therefore sought to identify BRCA1/2-directed miRNA signatures that have prognostic value in ovarian cancer patients with wild-type BRCA1/2 and study how the deregulation of miRNAs impacts the prognosis of patients treated with platinum-based chemotherapy. By analyzing multidimensional datasets of ovarian cancer patients from the TCGA data portal, we identified three miRNAs (hsa-miR-146a, hsa-miR-148a and hsa-miR-545) that target BRCA1/2 and were associated with overall survival and progression-free survival in patients with wild-type BRCA1/2. By analyzing the expression profiles and Gene Ontology functional enrichment, we found that carriers of BRCA1/2 alterations and patients with miRNA deregulation shared a common mechanism, regulation of the DNA repair-related pathways, that affects the prognosis of ovarian cancer patients. Our work highlights that a proportion of patients with wild-type BRCA1/2 ovarian cancers benefit from platinum-based chemotherapy and that the patients who benefit could be predicted from BRCA1/2-directed miRNA signature.

Predicting efficacies of anticancer drugs using single cell HaloChip assay

Single cell HaloChip assay can be used to assess DNA repair ability.

Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer

High-grade serous ovarian carcinoma (HGS-OvCa) has the lowest survival rate among all gynecologic cancers and is hallmarked by a high degree of heterogeneity. The Cancer Genome Atlas network has described a gene expression-based molecular classification of HGS-OvCa into Differentiated, Mesenchymal, Immunoreactive and Proliferative subtypes. However, the biological underpinnings and regulatory mechanisms underlying the distinct molecular subtypes are largely unknown. Here we showed that tumor-infiltrating stromal cells significantly contributed to the assignments of Mesenchymal and Immunoreactive clusters. Using reverse engineering and an unbiased interrogation of subtype regulatory networks, we identified the transcriptional modules containing master regulators that drive gene expression of Mesenchymal and Immunoreactive HGS-OvCa. Mesenchymal master regulators were associated with poor prognosis, while Immunoreactive master regulators positively correlated with overall survival. Meta-analysis of 749 HGS-OvCa expression profiles confirmed that master regulators as a prognostic signature were able to predict patient outcome. Our data unraveled master regulatory programs of HGS-OvCa subtypes with prognostic and potentially therapeutic relevance, and suggested that the unique transcriptional and clinical characteristics of ovarian Mesenchymal and Immunoreactive subtypes could be, at least partially, ascribed to tumor microenvironment.

Homologous Recombination Deficiency: Exploiting the Fundamental Vulnerability of Ovarian Cancer

Approximately 50% of epithelial ovarian cancers (EOC) exhibit defective DNA repair via homologous recombination (HR) due to genetic and epigenetic alterations of HR pathway genes. Defective HR is an important therapeutic target in EOC as exemplified by the efficacy of platinum analogues in this disease, as well as the advent of PARP inhibitors, which exhibit synthetic lethality when applied to HR-deficient cells. Here, we describe the genotypic and phenotypic characteristics of HR-deficient EOCs, discuss current and emerging approaches for targeting these tumors, and present challenges associated with these approaches, focusing on development and overcoming resistance.

SIGNIFICANCE

Defective DNA repair via HR is a pivotal vulnerability of EOC, particularly of the high-grade serous histologic subtype. Targeting defective HR offers the unique opportunity of exploiting molecular differences between tumor and normal cells, thereby inducing cancer-specific synthetic lethality; the promise and challenges of these approaches in ovarian cancer are discussed in this review.

Homologous Recombination Deficiency: Exploiting the Fundamental Vulnerability of Ovarian Cancer

Approximately 50% of epithelial ovarian cancers (EOC) exhibit defective DNA repair via homologous recombination (HR) due to genetic and epigenetic alterations of HR pathway genes. Defective HR is an important therapeutic target in EOC as exemplified by the efficacy of platinum analogues in this disease, as well as the advent of PARP inhibitors, which exhibit synthetic lethality when applied to HR-deficient cells. Here, we describe the genotypic and phenotypic characteristics of HR-deficient EOCs, discuss current and emerging approaches for targeting these tumors, and present challenges associated with these approaches, focusing on development and overcoming resistance.

SIGNIFICANCE

Defective DNA repair via HR is a pivotal vulnerability of EOC, particularly of the high-grade serous histologic subtype. Targeting defective HR offers the unique opportunity of exploiting molecular differences between tumor and normal cells, thereby inducing cancer-specific synthetic lethality; the promise and challenges of these approaches in ovarian cancer are discussed in this review.

Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.

Ovarian cancer: diagnostic, biological and prognostic aspects

Ovarian cancer remains the most lethal gynecologic malignancy, owing to late detection, intrinsic and acquired chemoresistance and remarkable heterogeneity. Despite optimization of surgical and chemotherapy protocols and initiation of clinical trials incorporating targeted therapy, only modest gains have been achieved in prolonging survival in this cancer. This review provides an update of recent developments in our understanding of the etiology, origin, diagnosis, progression and treatment of this malignancy, with emphasis on clinically relevant genetic classification approaches. In the authors' opinion, focused effort directed at understanding the molecular make-up of recurrent and metastatic ovarian cancer, while keeping in mind the unique molecular character of each of its histological types, is central to our effort to improve patient outcome in this cancer.

Epithelial-mesenchymal status renders differential responses to cisplatin in ovarian cancer

Chemoresistance to platinums, such as cisplatin, is of critical concern in the treatment of ovarian cancer. Recent evidence has linked epithelial-mesenchymal transition (EMT) as a contributing mechanism. The current study explored the connection between cellular responses to cisplatin and EMT in ovarian cancer. Expression microarrays were utilized to estimate the EMT status as a binary phenotype, and the transcriptional responses of 46 ovarian cancer cell lines to cisplatin were measured at dosages equivalent to 50% growth inhibition. Phenotypic responses to cisplatin were quantified with respect to cell number, proliferation rate and apoptosis, and then compared with the epithelial or mesenchymal status. Ovarian cancer cell lines with an epithelial status exhibited higher resistance to cisplatin treatment in the MTS assay than those with a mesenchymal status. Pathway analyses revealed the induction of G1/S- and S-phase genes (P=0.001) and the activation of multiple NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) downstream genes (P=0.0016) by cisplatin selectively in epithelial-like cell lines. BrdU incorporation and Caspase-3/7 release assays confirmed impaired apoptosis in epithelial-like ovarian cancer cells. In clinical samples, we observed resistance to single platinum treatment and the selective activation of the NF-κB pathway by platinum in ovarian cancers with an epithelial status. Overall, our results suggest that, in epithelial-like ovarian cancer cells, NF-κB activation by cisplatin may lead to defective apoptosis, preferential proliferation arrest and a consequential decreased sensitivity to cisplatin.