Analysis of chemotherapeutic response in ovarian cancers using publicly available high-throughput data

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models

BACKGROUND

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome.

METHODS

Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm.

RESULTS

The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes.

CONCLUSION

Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Protein stabilization of ITF2 by NF-κB prevents colitis-associated cancer development

Chronic colonic inflammation is a feature of cancer and is strongly associated with tumorigenesis, but its underlying molecular mechanisms remain poorly understood. Inflammatory conditions increased ITF2 and p65 expression both ex vivo and in vivo, and ITF2 and p65 showed positive correlations. p65 overexpression stabilized ITF2 protein levels by interfering with the binding of Parkin to ITF2. More specifically, the C-terminus of p65 binds to the N-terminus of ITF2 and inhibits ubiquitination, thereby promoting ITF2 stabilization. Parkin acts as a E3 ubiquitin ligase for ITF2 ubiquitination. Intestinal epithelial-specific deletion of ITF2 facilitated nuclear translocation of p65 and thus increased colitis-associated cancer tumorigenesis, which was mediated by Azoxymethane/Dextran sulfate sodium or dextran sulfate sodium. Upregulated ITF2 expression was lost in carcinoma tissues of colitis-associated cancer patients, whereas p65 expression much more increased in both dysplastic and carcinoma regions. Therefore, these findings indicate a critical role for ITF2 in the repression of colitis-associated cancer progression and ITF2 would be an attractive target against inflammatory diseases including colitis-associated cancer.

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue.

METHODS

In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform.

RESULTS

The prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances.

CONCLUSIONS

Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

Screening for the Key Proteins Associated with Rete Testis Invasion in Clinical Stage I Seminoma via Label-Free Quantitative Mass Spectrometry

Rete testis invasion (RTI) is an unfavourable prognostic factor for the risk of relapse in clinical stage I (CS I) seminoma patients. Notably, no evidence of difference in the proteome of RTI-positive vs. -negative CS I seminomas has been reported yet. Here, a quantitative proteomic approach was used to investigate RTI-associated proteins. 64 proteins were differentially expressed in RTI-positive compared to -negative CS I seminomas. Of them, 14-3-3γ, ezrin, filamin A, Parkinsonism-associated deglycase 7 (PARK7), vimentin and vinculin, were validated in CS I seminoma patient cohort. As shown by multivariate analysis controlling for clinical confounders, PARK7 and filamin A expression lowered the risk of RTI, while 14-3-3γ expression increased it. Therefore, we suggest that in real clinical biopsy specimens, the expression level of these proteins may reflect prognosis in CS I seminoma patients.

PP2A and E3 ubiquitin ligase deficiencies: Seminal biological drivers in endometrial cancer

OBJECTIVE

PI3K-AKT pathway mutations initiate a kinase cascade that characterizes endometrial cancer (EC). As kinases seldom cause oncogenic transformation without dysregulation of antagonistic phosphatases, pivotal interactions governing this pathway were explored and correlated with clinical outcomes.

METHODS

After exclusion of patients with POLE mutations from The Cancer Genome Atlas EC cohort with endometrioid or serous EC, the study population was 209 patients with DNA sequencing, quantitative gene-specific RNA expression, copy number variation (CNV), and surveillance data available. Extracted data were annotated and integrated.

RESULTS

A PIK3CA, PTEN, or PIK3R1 mutant (-mu) was present in 83% of patients; 57% harbored more than 1 mutation without adversely impacting progression-free survival (PFS) (P = .10). PIK3CA CNV of at least 1.1 (CNV high [-H]) was detected in 26% and linked to TP53-mu and CIP2A expression (P < .001) but was not associated with PFS (P = .24). PIK3CA expression was significantly different between those with CIP2A-H and CIP2A low (-L) expression (the endogenous inhibitor of protein phosphatase 2A [PP2A]), when stratified by PIK3CA mutational status or by PIK3CA CNV-H and CNV-L (all P < .01). CIP2A-H or PPP2R1A-mu mitigates PP2A kinase dephosphorylation, and FBXW7-mu nullifies E3 ubiquitin ligase (E3UL) oncoprotein degradation. CIP2A-H and PPP2R1A-mu (PP2A impairment) and FBXW7-mu (E3UL impairment) were associated with compromised PFS (P < .001) and were prognostically discriminatory for PIK3CA-mu and PIK3CA CNV-H tumors (P < .001). Among documented recurrences, 84% were associated with impaired PP2A (75%) and/or E3UL (20%).

CONCLUSION

PP2A and E3UL deficiencies are seminal biological drivers in EC independent of PIK3CA-mu, PTEN-mu, and PIK3R1-mu and PIK3CA CNV.

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

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

Prediction of Epithelial Ovarian Cancer Outcomes With Integration of Genomic Data

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

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Association of a novel endometrial cancer biomarker panel with prognostic risk, platinum insensitivity, and targetable therapeutic options

During the past decade, the age-adjusted mortality rate for endometrial cancer (EC) increased 1.9% annually with TP53 mutant (TP53-mu) EC disproportionally represented in advanced disease and deaths. Therefore, we aimed to assess pivotal molecular parameters that differentiate clinical outcomes in high- and low-risk EC. Using the Cancer Genome Atlas, we analyzed EC specimens with available DNA sequences and quantitative gene-specific RNA expression data. After polymerase ɛ (POLE)-mutant specimens were excluded, differential gene-specific mutations and mRNA expressions were annotated and integrated. Consequent to TP53-mu failure to induce p21, derepression of multiple oncogenes harboring promoter p21 repressive sites was observed, including CCNA2 and FOXM1 (P < .001 compared with TP53 wild type [TP53-wt]). TP53-wt EC with high CCNA2 expression (CCNA2-H) had a targeted transcriptomic profile similar to that of TP53-mu EC, suggesting CCNA2 is a seminal determinant for both TP53-wt and TP53-mu EC. CCNA2 enhances E2F1 function, upregulating FOXM1 and CIP2A, as observed in TP53-mu and CCNA2-H TP53-wt EC (P < .001). CIP2A inhibits protein phosphatase 2A, leading to AKT inactivation of GSK3β and restricted oncoprotein degradation; PPP2R1A and FBXW7 mutations yield similar results. Upregulation of FOXM1 and failed degradation of FOXM1 is evidenced by marked upregulation of multiple homologous recombination genes (P < .001). Integrating these molecular aberrations generated a molecular biomarker panel with significant prognostic discrimination (P = 5.8×10⁻⁷); adjusting for age, histology, grade, myometrial invasion, TP53 status, and stage, only CCNA2-H/E2F1-H (P = .0003), FBXW7-mu/PPP2R1A-mu (P = .0002), and stage (P = .017) were significant. The generated prognostic molecular classification system identifies dissimilar signaling aberrations potentially amenable to targetable therapeutic options.

A Novel Role for the Tumor Suppressor Gene ITF2 in Tumorigenesis and Chemotherapy Response

Despite often leading to platinum resistance, platinum-based chemotherapy continues to be the standard treatment for many epithelial tumors. In this study we analyzed and validated the cytogenetic alterations that arise after treatment in four lung and ovarian paired cisplatin-sensitive/resistant cell lines by 1-million microarray-based comparative genomic hybridization (array-CGH) and qRT-PCR methodologies. RNA-sequencing, functional transfection assays, and gene-pathway activity analysis were used to identify genes with a potential role in the development of this malignancy. The results were further explored in 55 lung and ovarian primary tumors and control samples, and in two extensive in silico databases. Long-term cell exposure to platinum induces the frequent deletion of ITF2 gene. Its expression re-sensitized tumor cells to platinum and recovered the levels of Wnt/β-catenin transcriptional activity. ITF2 expression was also frequently downregulated in epithelial tumors, predicting a worse overall survival. We also identified an inverse correlation between ITF2 and HOXD9 expression, revealing that Non-small cell lung cancer (NSCLC) patients with lower expression of HOXD9 had a better overall survival rate. We defined the implication of ITF2 as a molecular mechanism behind the development of cisplatin resistance probably through the activation of the Wnt-signaling pathway. This data highlights the possible role of ITF2 and HOXD9 as novel therapeutic targets for platinum resistant tumors.

Reannotation and Analysis of Clinical and Chemotherapy Outcomes in the Ovarian Data Set From The Cancer Genome Atlas

PURPOSE

The ovarian cancer data set from The Cancer Genome Atlas integrates genomic and proteomic data with clinical annotations based on chart abstractions. We aimed to develop an algorithm to create a matching, more accessible clinical data set cataloging time to treatment failure (TTF) of sequential lines of treatment in patients with serous ovarian cancers.

MATERIALS AND METHODS

The master data set of 587 patients with serous ovarian cancer was condensed into a more homogeneous and clinically relevant population comprised of high-risk patients with both grade 3 cancers and stage III or IV disease, resulting in a subgroup of 450 patients. We quantified the TTF of different lines of therapy as well as different therapeutic combinations by extrapolating from the time of starting one therapy to the time of starting a subsequent therapy.

RESULTS

The overall survival (OS) of patients was highly related to platinum sensitivity status, with median OS times of 56.6, 27.0, and 11.6 months in patients who had platinum-sensitive, -resistant, or -refractory disease, respectively. In high-risk patients, the median TTFs were 14.8, 10.2, 5.7, and 4.1 months with the first, second, third, and fourth lines of chemotherapy, respectively. Patients with stable disease after first-line therapy had similar OS outcomes as patients with partial remissions (34.4 v 33.7 months, respectively).

CONCLUSION

This new data set enhances the clinical annotation by providing exploitable chemotherapy benefit data that can now be paired with genomic and proteomic data within The Cancer Genome Atlas data. The major determinant of OS in this study was platinum sensitivity status. TTF decreased with each successive line of therapy. However, patients who achieved only stable disease with first-line therapy had OS similar to those with partial remission.

Molecular Classification of Epithelial Ovarian Cancer Based on Methylation Profiling: Evidence for Survival Heterogeneity

PURPOSE

Ovarian cancer is a heterogeneous disease that can be divided into multiple subtypes with variable etiology, pathogenesis, and prognosis. We analyzed DNA methylation profiling data to identify biologic subgroups of ovarian cancer and study their relationship with histologic subtypes, copy number variation, RNA expression data, and outcomes.

EXPERIMENTAL DESIGN

A total of 162 paraffin-embedded ovarian epithelial tumor tissues, including the five major epithelial ovarian tumor subtypes (high- and low-grade serous, endometrioid, mucinous, and clear cell) and tumors of low malignant potential were selected from two different sources: The Polish Ovarian Cancer study, and the Surveillance, Epidemiology, and End Results Residual Tissue Repository (SEER RTR). Analyses were restricted to Caucasian women. Methylation profiling was conducted using the Illumina 450K methylation array. For 45 tumors array copy number data were available. NanoString gene expression data for 39 genes were available for 61 high-grade serous carcinomas (HGSC).

RESULTS

Consensus nonnegative matrix factorization clustering of the 1,000 most variable CpG sites showed four major clusters among all epithelial ovarian cancers. We observed statistically significant differences in survival (log-rank test, P = 9.1 × 10^-7) and genomic instability across these clusters. Within HGSC, clustering showed three subgroups with survival differences (log-rank test, P = 0.002). Comparing models with and without methylation subgroups in addition to previously identified gene expression subtypes suggested that the methylation subgroups added significant survival information (P = 0.007).

CONCLUSIONS

DNA methylation profiling of ovarian cancer identified novel molecular subgroups that had significant survival difference and provided insights into the molecular underpinnings of ovarian cancer.See related commentary by Ishak et al., p. 5729.

Biological Insights into Chemotherapy Resistance in Ovarian Cancer

The majority of patients with high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy; however, most will develop chemotherapy resistance. Gene signatures may change with the development of chemotherapy resistance in this population, which is important as it may lead to tailored therapies. The objective of this study was to compare tumor gene expression profiles in patients before and after treatment with neoadjuvant chemotherapy (NACT). Tumor samples were collected from six patients diagnosed with HGSOC before and after administration of NACT. RNA extraction and whole transcriptome sequencing was performed. Differential gene expression, hierarchical clustering, gene set enrichment analysis, and pathway analysis were examined in all of the samples. Tumor samples clustered based on exposure to chemotherapy as opposed to patient source. Pre-NACT samples were enriched for multiple pathways involving cell cycle growth. Post-NACT samples were enriched for drug transport and peroxisome pathways. Molecular subtypes based on the pre-NACT sample (differentiated, mesenchymal, proliferative and immunoreactive) changed in four patients after administration of NACT. Multiple changes in tumor gene expression profiles after exposure to NACT were identified from this pilot study and warrant further attention as they may indicate early changes in the development of chemotherapy resistance.

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

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

Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing

Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.

Tumor xenograft modeling identifies an association between TCF4 loss and breast cancer chemoresistance

Understanding the mechanisms of cancer therapeutic resistance is fundamental to improving cancer care. There is clear benefit from chemotherapy in different breast cancer settings; however, knowledge of the mutations and genes that mediate resistance is incomplete. In this study, by modeling chemoresistance in patient-derived xenografts (PDXs), we show that adaptation to therapy is genetically complex and identify that loss of transcription factor 4 (TCF4; also known as ITF2) is associated with this process. A triple-negative BRCA1-mutated PDX was used to study the genetics of chemoresistance. The PDX was treated in parallel with four chemotherapies for five iterative cycles. Exome sequencing identified few genes with de novo or enriched mutations in common among the different therapies, whereas many common depleted mutations/genes were observed. Analysis of somatic mutations from The Cancer Genome Atlas (TCGA) supported the prognostic relevance of the identified genes. A mutation in TCF4 was found de novo in all treatments, and analysis of drug sensitivity profiles across cancer cell lines supported the link to chemoresistance. Loss of TCF4 conferred chemoresistance in breast cancer cell models, possibly by altering cell cycle regulation. Targeted sequencing in chemoresistant tumors identified an intronic variant of TCF4 that may represent an expression quantitative trait locus associated with relapse outcome in TCGA. Immunohistochemical studies suggest a common loss of nuclear TCF4 expression post-chemotherapy. Together, these results from tumor xenograft modeling depict a link between altered TCF4 expression and breast cancer chemoresistance.

Summary: By modeling chemoresistance in patient-derived breast cancer xenografts, this study shows that adaptation to therapy is genetically complex and that loss of transcription factor 4 (TCF4) is associated with this process.

Crosstalk between the mitochondrial fission protein, Drp1, and the cell cycle is identified across various cancer types and can impact survival of epithelial ovarian cancer patients

Mitochondrial metabolic reprogramming is a hallmark of tumorigenesis. Although mitochondrial function can impact cell cycle regulation it has been an understudied area in cancer research. Our study highlights a specific involvement of mitochondria in cell cycle regulation across cancer types. The mitochondrial fission process, which is regulated at the core by Drp1, impacts various cellular functions. Drp1 has been implicated in various cancer types with no common mechanism reported. Our Drp1-directed large-scale analyses of the publically available cancer genomes reveal a robust correlation of Drp1 with cell-cycle genes in 29 of the 31 cancer types examined. Hypothesis driven investigation on epithelial ovarian cancer (EOC) revealed that Drp1 co-expresses specifically with the cell-cycle module responsible for mitotic transition. Repression of Drp1 in EOC cells can specifically attenuate mitotic transition, establishing a potential casual role of Drp1 in mitotic transition. Interestingly, Drp1-Cell-Cycle co-expression module is specifically detected in primary epithelial ovarian tumors that robustly responded to chemotherapy, suggesting that Drp1 driven mitosis may underlie chemo-sensitivity of the primary tumors. Analyses of matched primary and relapsed EOC samples revealed a Drp1-based-gene-expression-signature that could identify patients with poor survival probabilities from their primary tumors. Our results imply that around 60% of platinum-sensitive EOC patients undergoing relapse show poor survival, potentially due to further activation of a mitochondria driven cell-cycle regime in their recurrent disease. We speculate that this patient group could possibly benefit from mitochondria directed therapies that are being currently evaluated at various levels, thus enabling targeted or personalized therapy based cancer management.

STAT1-associated intratumoural TH1 immunity predicts chemotherapy resistance in high-grade serous ovarian cancer

High-grade serous ovarian carcinoma (HGSC) accounts for 70% of all epithelial ovarian cancers but clinical management is challenged by a lack of accurate prognostic and predictive biomarkers of chemotherapy response. This study evaluated the role of Signal Transducer and Activator of Transcription 1 (STAT1) as an independent prognostic and predictive biomarker and its correlation with intratumoural CD8⁺ T cells in a second independent biomarker validation study. Tumour STAT1 expression and intratumoural CD8⁺ T cell infiltration were assessed by immunohistochemistry as a multicentre validation study conducted on 734 chemotherapy-naïve HGSCs. NanoString-based profiling was performed to correlate expression of STAT1 target genes CXCL9, CXCL10 and CXCL11 with CD8A transcript expression in 143 primary tumours. Multiplexed cytokine analysis of pre-treatment plasma from resistant and sensitive patients was performed to assess systemic levels of STAT1-induced cytokines. STAT1 was validated as a prognostic and predictive biomarker in both univariate and multivariate models and its expression correlated significantly with intra-epithelial CD8⁺ T cell infiltration in HGSC. STAT1 levels increased the prognostic and predictive value of intratumoural CD8⁺ T cells, confirming their synergistic role as biomarkers in HGSC. In addition, expression of STAT1 target genes (CXCL9, CXCL10 and CXCL11) correlated significantly with levels of, and CD8A transcripts from intratumoural CD8⁺ T cells within the resistant and sensitive tumours. Our findings provide compelling evidence that high levels of STAT1, STAT1-induced chemokines and CD8⁺ T cells correlate with improved chemotherapy response in HGSC. These results identify STAT1 and its target genes as novel biomarkers of chemosensitivity in HGSC. These findings provide new translational opportunities for patient stratification for immunotherapies based on emerging biomarkers of inflammation in HGSC. An improved understanding of the role of interferon-inducible genes will be foundational for developing immunomodulatory therapies in ovarian cancer.

STAT1-associated intratumoural TH1 immunity predicts chemotherapy resistance in high-grade serous ovarian cancer

High-grade serous ovarian carcinoma (HGSC) accounts for 70% of all epithelial ovarian cancers but clinical management is challenged by a lack of accurate prognostic and predictive biomarkers of chemotherapy response. This study evaluated the role of Signal Transducer and Activator of Transcription 1 (STAT1) as an independent prognostic and predictive biomarker and its correlation with intratumoural CD8⁺ T cells in a second independent biomarker validation study. Tumour STAT1 expression and intratumoural CD8⁺ T cell infiltration were assessed by immunohistochemistry as a multicentre validation study conducted on 734 chemotherapy-naïve HGSCs. NanoString-based profiling was performed to correlate expression of STAT1 target genes CXCL9, CXCL10 and CXCL11 with CD8A transcript expression in 143 primary tumours. Multiplexed cytokine analysis of pre-treatment plasma from resistant and sensitive patients was performed to assess systemic levels of STAT1-induced cytokines. STAT1 was validated as a prognostic and predictive biomarker in both univariate and multivariate models and its expression correlated significantly with intra-epithelial CD8⁺ T cell infiltration in HGSC. STAT1 levels increased the prognostic and predictive value of intratumoural CD8⁺ T cells, confirming their synergistic role as biomarkers in HGSC. In addition, expression of STAT1 target genes (CXCL9, CXCL10 and CXCL11) correlated significantly with levels of, and CD8A transcripts from intratumoural CD8⁺ T cells within the resistant and sensitive tumours. Our findings provide compelling evidence that high levels of STAT1, STAT1-induced chemokines and CD8⁺ T cells correlate with improved chemotherapy response in HGSC. These results identify STAT1 and its target genes as novel biomarkers of chemosensitivity in HGSC. These findings provide new translational opportunities for patient stratification for immunotherapies based on emerging biomarkers of inflammation in HGSC. An improved understanding of the role of interferon-inducible genes will be foundational for developing immunomodulatory therapies in ovarian cancer.

Prediction of chemo-response in serous ovarian cancer

Background

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

Methods

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

Results

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

Conclusions

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

Electronic supplementary material

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

STAT1-associated intratumoural TH1 immunity predicts chemotherapy resistance in high-grade serous ovarian cancer

High‐grade serous ovarian carcinoma (HGSC) accounts for 70% of all epithelial ovarian cancers but clinical management is challenged by a lack of accurate prognostic and predictive biomarkers of chemotherapy response. This study evaluated the role of Signal Transducer and Activator of Transcription 1 (STAT1) as an independent prognostic and predictive biomarker and its correlation with intratumoural CD8⁺ T cells in a second independent biomarker validation study. Tumour STAT1 expression and intratumoural CD8⁺ T cell infiltration were assessed by immunohistochemistry as a multicentre validation study conducted on 734 chemotherapy‐naïve HGSCs. NanoString‐based profiling was performed to correlate expression of STAT1 target genes CXCL9, CXCL10 and CXCL11 with CD8A transcript expression in 143 primary tumours. Multiplexed cytokine analysis of pre‐treatment plasma from resistant and sensitive patients was performed to assess systemic levels of STAT1‐induced cytokines. STAT1 was validated as a prognostic and predictive biomarker in both univariate and multivariate models and its expression correlated significantly with intra‐epithelial CD8⁺ T cell infiltration in HGSC. STAT1 levels increased the prognostic and predictive value of intratumoural CD8⁺ T cells, confirming their synergistic role as biomarkers in HGSC. In addition, expression of STAT1 target genes (CXCL9, CXCL10 and CXCL11) correlated significantly with levels of, and CD8A transcripts from intratumoural CD8⁺ T cells within the resistant and sensitive tumours. Our findings provide compelling evidence that high levels of STAT1, STAT1‐induced chemokines and CD8⁺ T cells correlate with improved chemotherapy response in HGSC. These results identify STAT1 and its target genes as novel biomarkers of chemosensitivity in HGSC. These findings provide new translational opportunities for patient stratification for immunotherapies based on emerging biomarkers of inflammation in HGSC. An improved understanding of the role of interferon‐inducible genes will be foundational for developing immunomodulatory therapies in ovarian cancer.

WWOX CNV-67048 Functions as a Risk Factor for Epithelial Ovarian Cancer in Chinese Women by Negatively Interacting with Oral Contraceptive Use

Copy number variations (CNVs) have attracted increasing evidences to represent their roles as cancer susceptibility regulators. However, little is known about the role of CNV in epithelia ovarian cancer (EOC). Recently, the CNV-67048 of WW domain-containing oxidoreductase (WWOX) was reported to alter cancer risks. Considering that WWOX also plays a role in EOC, we hypothesized that the CNV-67048 was associated with EOC risk. In a case-control study of 549 EOC patients and 571 age (±5 years) matched cancer-free controls, we found that the low copy number of CNV-67048 (1-copy and 0-copy) conferred a significantly increased risk of EOC (OR = 1.346, 95% CI = 1.037–1.747) and it determined the risk by means of copy number-dependent dosage effect (P = 0.009). Data from TCGA also confirmed the abovementioned association as the frequency of low copies in EOC group was 3.68 times more than that in healthy group (P = 0.023). The CNV also negatively interacted with oral contraceptive use on EOC risk (P = 0.042). Functional analyses further showed a lower mRNA level of WWOX in tissues with the 0-copy or 1-copy than that in those with the 2-copy (P = 0.045). Our data suggested the CNV-67048 to be a risk factor of EOC in Chinese women.

Biological Processes Discovered by High-Throughput Sequencing

Advances in DNA and RNA sequencing technologies have completely transformed the field of genomics. High-throughput sequencing (HTS) is now a widely used and accessible technology that allows scientists to sequence an entire transcriptome or genome in a timely and cost-effective manner. Application of HTS techniques has led to many key discoveries, including the identification of long noncoding RNAs, microDNAs, a family of small extrachromosomal circular DNA species, and tRNA-derived fragments, which are a group of small non-miRNAs that are derived from tRNAs. Furthermore, public sequencing repositories provide unique opportunities for laboratories to parse large sequencing databases to identify proteins and noncoding RNAs at a scale that was not possible a decade ago. Herein, we review how HTS has led to the discovery of novel nucleic acid species and uncovered new biological processes during the course.

Current state of biomarkers in ovarian cancer prognosis

High-grade serous ovarian cancer remains one of the most lethal malignancies in women. Despite recent advances in surgical and pharmaceutical therapies, survival rates remain poor. A major impediment in management of this disease, that continues to contribute to poor overall survival rates, is resistance to standard carboplatin-paclitaxel combination chemotherapies. In addition to tumor cell intrinsic mechanisms leading to drug resistance, there is increasing awareness of the crucial role of the tumor microenvironment in mediating natural immune defense mechanisms and selective pressures that appear to facilitate chemotherapy sensitivity. We provide an overview of some of the promising new genetic and immunological biomarkers in ovarian cancer and discuss their biology and their likely clinical utility in future ovarian cancer management.

The NER-related gene GTF2H5 predicts survival in high-grade serous ovarian cancer patients

OBJECTIVE

We aimed to evaluate the prognostic and predictive value of the nucleotide excision repair-related gene GTF2H5, which is localized at the 6q24.2-26 deletion previously reported by our group to predict longer survival of high-grade serous ovarian cancer patients.

METHODS

In order to test if protein levels of GTF2H5 are associated with patients' outcome, we performed GTF2H5 immunohistochemical staining in 139 high-grade serous ovarian carcinomas included in tissue microarrays. Upon stratification of cases into high- and low-GTF2H5 staining categories (> and ≤ median staining, respectively) Kaplan-Meier and log-rank test were used to estimate patients' survival and assess statistical differences. We also evaluated the association of GTF2H5 with survival at the transcriptional level by using the on-line Kaplan-Meier plotter tool, which includes gene expression and survival data of 855 high-grade serous ovarian cancer patients from 13 different datasets. Finally, we determined whether stable short hairpin RNA-mediated GTF2H5 downregulation modulates cisplatin sensitivity in the SKOV3 and COV504 cell lines by using cytotoxicity assays.

RESULTS

Low expression of GTF2H5 was associated with longer 5-year survival of patients at the protein (hazard ratio [HR], 0.52; 95% CI, 0.29 to 0.93; p=0.024) and transcriptional level (HR, 0.80; 95% CI, 0.65 to 0.97; p=0.023) in high-grade serous ovarian cancer patients. We confirmed the association with 5-year overall survival (HR, 0.55; 95% CI, 0.38 to 0.78; p=0.0007) and also found an association with progression-free survival (HR, 0.72; 95% CI, 0.54 to 0.96; p=0.026) in a homogenous group of 388 high-stage (stages III-IV using the International Federation of Gynecology and Obstetrics staging system), optimally debulked high-grade serous ovarian cancer patients. GTF2H5-silencing induced a decrease of the half maximal inhibitory concentration upon cisplatin treatment in GTF2H5-silenced ovarian cancer cells.

CONCLUSION

Low levels of GTF2H5 are associated with enhanced prognosis in high-grade serous ovarian cancer patients and may contribute to cisplatin sensitization.

Deletion at 6q24.2-26 predicts longer survival of high-grade serous epithelial ovarian cancer patients

Standard treatments for advanced high-grade serous ovarian carcinomas (HGSOCs) show significant side-effects and provide only short-term survival benefits due to disease recurrence. Thus, identification of novel prognostic and predictive biomarkers is urgently needed. We have used 42 paraffin-embedded HGSOCs, to evaluate the utility of DNA copy number alterations, as potential predictors of clinical outcome. Copy number-based unsupervised clustering stratified HGSOCs into two clusters of different immunohistopathological features and survival outcome (HR = 0.15, 95%CI = 0.03-0.81; Padj = 0.03). We found that loss at 6q24.2-26 was significantly associated with the cluster of longer survival independently from other confounding factors (HR = 0.06, 95%CI = 0.01-0.43, Padj = 0.005). The prognostic value of this deletion was validated in two independent series, one consisting of 36 HGSOCs analyzed by fluorescent in situ hybridization (P = 0.04) and another comprised of 411 HGSOCs from the Cancer Genome Atlas study (TCGA) (HR = 0.67, 95%CI = 0.48-0.93, Padj = 0.019). In addition, we confirmed the association of low expression of the genes from the region with longer survival in 799 HGSOCs (HR = 0.74, 95%CI = 0.61-0.90, log-rank P = 0.002) and 675 high-FIGO stage HGSOCs (HR = 0.76, 95%CI = 0.61-0.96, log-rank P = 0.02) available from the online tool KM-plotter. Finally, by integrating copy number, RNAseq and survival data of 296 HGSOCs from TCGA we propose a few candidate genes that can potentially explain the association. Altogether our findings indicate that the 6q24.2-26 deletion is an independent marker of favorable outcome in HGSOCs with potential clinical value as it can be analyzed by FISH on tumor sections and guide the selection of patients towards more conservative therapeutic strategies in order to reduce side-effects and improve quality of life.