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

Potential utility of pretreatment serum miRNAs for optimal treatment selection in advanced high-grade serous ovarian cancer

OBJECTIVE

The primary treatment of patients with advanced ovarian cancer is selected from whether primary debulking surgery or neoadjuvant chemotherapy. We investigated whether pretreatment serum microRNA profiles are useful for selecting patients with advanced high-grade serous ovarian cancer who obtain better outcomes from undergoing primary debulking surgery or neoadjuvant chemotherapy.

METHODS

Consecutive patients with clinical stage IIIB-IVB and serum microRNA data were selected. Patients who underwent primary debulking surgery or neoadjuvant chemotherapy were subjected to 1:1 propensity score matching before comparing their progression-free survival using Cox modelling. Progression-free probabilities for the selected microRNA profiles were calculated, and the estimated progression-free survival with the recommended primary treatment was determined and compared with the actual progression-free survival of the patients.

RESULTS

Of the 108 patients with stage IIIB-IVB disease, the data of 24 who underwent primary debulking surgery or neoadjuvant chemotherapy were compared. Eleven and three microRNAs were independent predictors of progression-free survival in patients who underwent primary debulking surgery and neoadjuvant chemotherapy, respectively. Two microRNAs correlated significantly with complete resection of the tumours in primary debulking surgery. No differences were found between the actual and estimated progression-free survival in the primary debulking surgery and neoadjuvant chemotherapy groups (P > 0.05). The recommended and actual primary treatments were identical in 27 (56.3%) of the 48 patients. The median improved survival times between recommended and actual treatment were 11.7 and 32.6 months for patients with actual primary debulking surgery and neoadjuvant chemotherapy, respectively.

CONCLUSIONS

Pretreatment microRNA profiles could be used to select subgroups of patients who benefited more from primary debulking surgery or neoadjuvant chemotherapy and might contribute to selecting the optimal primary treatment modality in advanced high-grade serous ovarian cancer patients.

Machine learning for microbiologists

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Genome-wide association analyses of ovarian cancer patients undergoing primary debulking surgery identify candidate genes for residual disease

Survival from ovarian cancer depends on the resection status after primary surgery. We performed genome-wide association analyses for resection status of 7705 ovarian cancer patients, including 4954 with high-grade serous carcinoma (HGSOC), to identify variants associated with residual disease. The most significant association with resection status was observed for rs72845444, upstream of MGMT, in HGSOC (p = 3.9 × 10-8). In gene-based analyses, PPP2R5C was the most strongly associated gene in HGSOC after stage adjustment. In an independent set of 378 ovarian tumours from the AGO-OVAR 11 study, variants near MGMT and PPP2R5C correlated with methylation and transcript levels, and PPP2R5C mRNA levels predicted progression-free survival in patients with residual disease. MGMT encodes a DNA repair enzyme, and PPP2R5C encodes the B56γ subunit of the PP2A tumour suppressor. Our results link heritable variation at these two loci with resection status in HGSOC.

Heterogeneity and treatment landscape of ovarian carcinoma

Ovarian carcinoma is characterized by heterogeneity at the molecular, cellular and anatomical levels, both spatially and temporally. This heterogeneity affects response to surgery and/or systemic therapy, and also facilitates inherent and acquired drug resistance. As a consequence, this tumour type is often aggressive and frequently lethal. Ovarian carcinoma is not a single disease entity and comprises various subtypes, each with distinct complex molecular landscapes that change during progression and therapy. The interactions of cancer and stromal cells within the tumour microenvironment further affects disease evolution and response to therapy. In past decades, researchers have characterized the cellular, molecular, microenvironmental and immunological heterogeneity of ovarian carcinoma. Traditional treatment approaches have considered ovarian carcinoma as a single entity. This landscape is slowly changing with the increasing appreciation of heterogeneity and the recognition that delivering ineffective therapies can delay the development of effective personalized approaches as well as potentially change the molecular and cellular characteristics of the tumour, which might lead to additional resistance to subsequent therapy. In this Review we discuss the heterogeneity of ovarian carcinoma, outline the current treatment landscape for this malignancy and highlight potentially effective therapeutic strategies in development.

VTwins: inferring causative microbial features from metagenomic data of limited samples

It is difficult to infer causality from high-dimension metagenomic data due to interference from numerous confounders. By imitating the twin studies in genetic research, we develop a straightforward method-virtual twins (VTwins)-to eliminate the confounder effects by transforming the original cohort into a paired cohort of "Twin" samples with distinct phenotypes but matched taxonomic profiles. The results show that VTwins outperforms the conventional approach in the sensitivity of identifying causative features and only requires a 10-fold reduced sample size for recalling disease-associated microbes or pathways, as tested by simulated and empirical data. Benchmark test with other 16 kinds of software further validates the power and applicability of VTwins for handling high-dimension compositional datasets and mining causalities in metagenomic research. In conclusion, VTwins is straightforward and effective in handling high-diversity, high-dimension compositional data, promising applications in mining causalities for metagenomic and potentially other omics data. VTwins is open access and available at https://github.com/mengqingren/VTwins.

Tumor biology and impact on timing of surgery in advanced epithelial ovarian cancer

Recent advances in epithelial ovarian cancer research have led to a shift in treatment strategy from the traditional 'organ-centric' to a personalized tumor biology-based approach. Nevertheless, we are still far behind an individualized approach for cytoreductive surgery in advanced ovarian cancer; the gold standard of primary treatment in combination with systemic agents. The impact of tumor biology on treatment sequence is still understudied. It is obvious, that response to platinum-based therapy is crucial for the success of neoadjuvant chemotherapy. While high-grade serous and endometrioid tumors are commonly characterized by an excellent response, other subtypes are considered poor responders or even resistant to platinum. Undoubtedly, neoadjuvant chemotherapy may filter poor responders, but to date, we still do not have appropriate alternatives to platinum-based chemotherapy in the neoadjuvant and first-line setting and 'adjusting' systemic treatment in cases of poor response to neoadjuvant chemotherapy remains elusive. Primary cytoreduction is still considered the gold standard for fit patients with operable tumor dissemination patterns, especially for those ovarian cancer subtypes that show poor response to platinum. Of note, even in high-grade serous ovarian cancer, approximately 20% of tumors are platinum resistant and the benefit of neoadjuvant chemotherapy in this subgroup is limited. Interestingly, these tumors are associated with the mesenchymal molecular subtype, which in turn correlates with high risk for residual disease after cytoreductive surgery and is characterized by the worst survival outcome among high-grade ovarian cancers. This leads to the question, how to best tailor surgical radicality at the onset of patients' presentation to avoid associated morbidity and with a moderate benefit. Here, we give an overview of recent advances of interaction between tumor biology and surgery in ovarian cancer.

Cancer Stem Cell Markers-Clinical Relevance and Prognostic Value in High-Grade Serous Ovarian Cancer (HGSOC) Based on The Cancer Genome Atlas Analysis

Cancer stem cells (CSCs) may contribute to an increased risk of recurrence in ovarian cancer (OC). Further research is needed to identify associations between CSC markers and OC patients' clinical outcomes with greater certainty. If they prove to be correct, in the future, the CSC markers can be used to help predict survival and indicate new therapeutic targets. This study aimed to determine the CSC markers at mRNA and protein levels and their association with clinical presentation, outcome, and risk of recurrence in HGSOC (High-Grade Serous Ovarian Cancer). TCGA (The Cancer Genome Atlas) database with 558 ovarian cancer tumor samples was used for the evaluation of 13 CSC markers (ALDH1A1, CD44, EPCAM, KIT, LGR5, NES, NOTCH3, POU5F1, PROM1, PTTG1, ROR1, SOX9, and THY1). Data on mRNA and protein levels assessed by microarray and mass spectrometry were retrieved from TCGA. Models to predict chemotherapy response and survival were built using multiple variables, including epidemiological data, expression levels, and machine learning methodology. ALDH1A1 and LGR5 mRNA expressions indicated a higher platinum sensitivity (p = 3.50 × 10-3; p = 0.01, respectively). POU5F1 mRNA expression marked platinum-resistant tumors (p = 9.43 × 10-3). CD44 and EPCAM mRNA expression correlated with longer overall survival (OS) (p = 0.043; p = 0.039, respectively). THY1 mRNA and protein levels were associated with worse OS (p = 0.019; p = 0.015, respectively). Disease-free survival (DFS) was positively affected by EPCAM (p = 0.004), LGR5 (p = 0.018), and CD44 (p = 0.012). In the multivariate model based on CSC marker expression, the high-risk group had 9.1 months longer median overall survival than the low-risk group (p < 0.001). ALDH1A1, CD44, EPCAM, LGR5, POU5F1, and THY1 levels in OC may be used as prognostic factors for the primary outcome and help predict the treatment response.

Novel proteomic technologies to address gaps in pre-clinical ovarian cancer biomarker discovery efforts

INTRODUCTION

An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

AREAS COVERED

This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

EXPERT OPINION

Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

In Silico Analysis Predicts Nuclear Factors NR2F6 and YAP1 as Mesenchymal Subtype-Specific Therapeutic Targets for Ovarian Cancer Patients

BACKGROUND

Tumour heterogeneity in high-grade serous ovarian cancer (HGSOC) is a proposed cause of acquired resistance to treatment and high rates of relapse. Among the four distinct molecular subtypes of HGSOC, the mesenchymal subtype (MES) has been observed with high frequency in several study cohorts. Moreover, it exhibits aggressive characteristics with poor prognosis. The failure to adequately exploit such subtypes for treatment results in high mortality rates, highlighting the need for effective targeted therapeutic strategies that follow the idea of personalized medicine (PM).

METHODS

As a proof-of-concept, bulk and single-cell RNA data were used to characterize the distinct composition of the tumour microenvironment (TME), as well as the cell-cell communication and its effects on downstream transcription of MES. Moreover, transcription factor activity contextualized with causal inference analysis identified novel therapeutic targets with potential causal impact on transcription factor dysregulation promoting the malignant phenotype.

FINDINGS

Fibroblast and macrophage phenotypes are of utmost importance for the complex intercellular crosstalk of MES. Specifically, tumour-associated macrophages were identified as the source of interleukin 1 beta (IL1B), a signalling molecule with significant impact on downstream transcription in tumour cells. Likewise, signalling molecules tumour necrosis factor (TNF), transforming growth factor beta (TGFB1), and C-X-C motif chemokine 12 (CXCL12) were prominent drivers of downstream gene expression associated with multiple cancer hallmarks. Furthermore, several consistently hyperactivated transcription factors were identified as potential sources for treatment opportunities. Finally, causal inference analysis identified Yes-associated protein 1 (YAP1) and Nuclear Receptor Subfamily 2 Group F Member 6 (NR2F6) as novel therapeutic targets in MES, verified in an independent dataset.

INTERPRETATION

By utilizing a sophisticated bioinformatics approach, several candidates for treatment opportunities, including YAP1 and NR2F6 were identified. These candidates represent signalling regulators within the cellular network of the MES. Hence, further studies to confirm these candidates as potential targeted therapies in PM are warranted.

Molecular and phenotypic characteristics influencing the degree of cytoreduction in high-grade serous ovarian carcinomas

BACKGROUND

High-grade serous ovarian carcinoma (HGSOC) is the deadliest ovarian cancer subtype, and survival relates to initial cytoreductive surgical treatment. The existing tools for surgical outcome prediction remain inadequate for anticipating the outcomes of the complex relationship between tumour biology, clinical phenotypes, co-morbidity and surgical skills. In this genotype-phenotype association study, we combine phenotypic markers with targeted DNA sequencing to discover novel biomarkers to guide the surgical management of primary HGSOC.

METHODS

Primary tumour tissue samples (n = 97) and matched blood from a phenotypically well-characterised treatment-naïve HGSOC patient cohort were analysed by targeted massive parallel DNA sequencing (next generation sequencing [NGS]) of a panel of 360 cancer-related genes. Association analyses were performed on phenotypic traits related to complete cytoreductive surgery, while logistic regression analysis was applied for the predictive model.

RESULTS

The positive influence of complete cytoreductive surgery (R0) on overall survival was confirmed (p = 0.003). Before surgery, low volumes of ascitic fluid, lower CA125 levels, higher platelet counts and relatively lower clinical stage at diagnosis were all indicators, alone and combined, for complete cytoreduction (R0). Mutations in either the chromatin remodelling SWI_SNF (p = 0.036) pathway or the histone H3K4 methylation pathway (p = 0.034) correlated with R0. The R0 group also demonstrated higher tumour mutational burden levels (p = 0.028). A predictive model was developed by combining two phenotypes and the mutational status of five genes and one genetic pathway, enabling the prediction of surgical outcomes in 87.6% of the cases in this cohort.

CONCLUSION

Inclusion of molecular biomarkers adds value to the pre-operative stratification of HGSOC patients. A potential preoperative risk stratification model combining phenotypic traits and single-gene mutational status is suggested, but the set-up needs to be validated in larger cohorts.

Molecular characteristics and clinical behaviour of epithelial ovarian cancers

Ovarian carcinoma (OC) is an umbrella term for multiple distinct diseases (histotypes), each with their own developmental origins, clinical behaviour and molecular profile. Accordingly, OC management is progressing away from a one-size-fits all approach, toward more molecularly-driven, histotype-specific management strategies. Our knowledge of driver events in high grade serous OC, the most common histotype, has led to major advances in treatments, including PARP inhibitor use. However, these agents are not suitable for all patients, most notably for many of those with rare OC histotypes. Identification of additional targeted therapeutic strategies will require a detailed understanding of the molecular landscape in each OC histotype. Until recently, tumour profiling studies in rare histotypes were sparse; however, significant advances have been made over the last decade. In particular, reports of genomic characterisation in endometrioid, clear cell, mucinous and low grade serous OC have significantly expanded our understanding of mutational events in these tumour types. Nonetheless, substantial knowledge gaps remain. This review summarises our current understanding of each histotype, highlighting recent advances in these unique diseases and outlining immediate research priorities for accelerating progress toward improving patient outcomes.

Histopathological subtyping of high-grade serous ovarian cancer using whole slide imaging

OBJECTIVE

We have established 4 histopathologic subtyping of high-grade serous ovarian cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse prognosis than the other subtypes. In this study, we modified the histopathologic subtyping algorithm to achieve high interobserver agreement in whole slide imaging (WSI) and to characterize the tumor biology of MT type for treatment individualization.

METHODS

Four observers performed histopathological subtyping using WSI of HGSOC in The Cancer Genome Atlas data. As a validation set, cases from Kindai and Kyoto Universities were independently evaluated by the 4 observers to determine concordance rates. In addition, genes highly expressed in MT type were examined by gene ontology term analysis. Immunohistochemistry was also performed to validate the pathway analysis.

RESULTS

After algorithm modification, the kappa coefficient, which indicates interobserver agreement, was greater than 0.5 (moderate agreement) for the 4 classifications and greater than 0.7 (substantial agreement) for the 2 classifications (MT vs. non-MT). Gene expression analysis showed that gene ontology terms related to angiogenesis and immune response were enriched in the genes highly expressed in the MT type. CD31 positive microvessel density was higher in the MT type compared to the non-MT type, and tumor groups with high infiltration of CD8/CD103 positive immune cells were observed in the MT type.

CONCLUSION

We developed an algorithm for reproducible histopathologic subtyping classification of HGSOC using WSI. The results of this study may be useful for treatment individualization of HGSOC, including angiogenesis inhibitors and immunotherapy.

Gene signature of m6A-related targets to predict prognosis and immunotherapy response in ovarian cancer

PURPOSE

The aim of the study was to construct a risk score model based on m6A-related targets to predict overall survival and immunotherapy response in ovarian cancer.

METHODS

The gene expression profiles of 24 m6A regulators were extracted. Survival analysis screened 9 prognostic m6A regulators. Next, consensus clustering analysis was applied to identify clusters of ovarian cancer patients. Furthermore, 47 phenotype-related differentially expressed genes, strongly correlated with 9 prognostic m6A regulators, were screened and subjected to univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression. Ultimately, a nomogram was constructed which presented a strong ability to predict overall survival in ovarian cancer.

RESULTS

CBLL1, FTO, HNRNPC, METTL3, METTL14, WTAP, ZC3H13, RBM15B and YTHDC2 were associated with worse overall survival (OS) in ovarian cancer. Three m6A clusters were identified, which were highly consistent with the three immune phenotypes. What is more, a risk model based on seven m6A-related targets was constructed with distinct prognosis. In addition, the low-risk group is the best candidate population for immunotherapy.

CONCLUSION

We comprehensively analyzed the m6A modification landscape of ovarian cancer and detected seven m6A-related targets as an independent prognostic biomarker for predicting survival. Furthermore, we divided patients into high- and low-risk groups with distinct prognosis and select the optimum population which may benefit from immunotherapy and constructed a nomogram to precisely predict ovarian cancer patients' survival time and visualize the prediction results.

Predictors of residual disease after debulking surgery in advanced stage ovarian cancer

Objective

Optimal debulking with no macroscopic residual disease strongly predicts ovarian cancer survival. The ability to predict likelihood of optimal debulking, which may be partially dependent on tumor biology, could inform clinical decision-making regarding use of neoadjuvant chemotherapy. Thus, we developed a prediction model including epidemiological factors and tumor markers of residual disease after primary debulking surgery.

Methods

Univariate analyses examined associations of 11 pre-diagnosis epidemiologic factors (n=593) and 24 tumor markers (n=204) with debulking status among incident, high-stage, epithelial ovarian cancer cases from the Nurses' Health Studies and New England Case Control study. We used Bayesian model averaging (BMA) to develop prediction models of optimal debulking with 5x5-fold cross-validation and calculated the area under the curve (AUC).

Results

Current aspirin use was associated with lower odds of optimal debulking compared to never use (OR=0.52, 95%CI=0.31-0.86) and two tissue markers, ADRB2 (OR=2.21, 95%CI=1.23-4.41) and FAP (OR=1.91, 95%CI=1.24-3.05) were associated with increased odds of optimal debulking. The BMA selected aspirin, parity, and menopausal status as the epidemiologic/clinical predictors with the posterior effect probability ≥20%. While the prediction model with epidemiologic/clinical predictors had low performance (average AUC=0.49), the model adding tissue biomarkers showed improved, but weak, performance (average AUC=0.62).

Conclusions

Addition of ovarian tumor tissue markers to our multivariable prediction models based on epidemiologic/clinical data slightly improved the model performance, suggesting debulking status may be in part driven by tumor characteristics. Larger studies are warranted to identify those at high risk of poor surgical outcomes informing personalized treatment.

Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers

Cross-cohort validation is essential for gut-microbiome-based disease stratification but was only performed for limited diseases. Here, we systematically evaluated the cross-cohort performance of gut microbiome-based machine-learning classifiers for 20 diseases. Using single-cohort classifiers, we obtained high predictive accuracies in intra-cohort validation (~0.77 AUC), but low accuracies in cross-cohort validation, except the intestinal diseases (~0.73 AUC). We then built combined-cohort classifiers trained on samples combined from multiple cohorts to improve the validation of non-intestinal diseases, and estimated the required sample size to achieve validation accuracies of >0.7. In addition, we observed higher validation performance for classifiers using metagenomic data than 16S amplicon data in intestinal diseases. We further quantified the cross-cohort marker consistency using a Marker Similarity Index and observed similar trends. Together, our results supported the gut microbiome as an independent diagnostic tool for intestinal diseases and revealed strategies to improve cross-cohort performance based on identified determinants of consistent cross-cohort gut microbiome alterations.

Cytoreductive Surgery (CRS) and HIPEC for Advanced Ovarian Cancer with Peritoneal Metastases: Italian PSM Oncoteam Evidence and Study Purposes

Ovarian cancer is the eighth most common neoplasm in women with a high mortality rate mainly due to a marked propensity for peritoneal spread directly at diagnosis, as well as tumor recurrence after radical surgical treatment. Treatments for peritoneal metastases have to be designed from a patient's perspective and focus on meaningful measures of benefit. Hyperthermic intraperitoneal chemotherapy (HIPEC), a strategy combining maximal cytoreductive surgery with regional chemotherapy, has been proposed to treat advanced ovarian cancer. Preliminary results to date have shown promising results, with improved survival outcomes and tumor regression. As knowledge about the disease process increases, practice guidelines will continue to evolve. In this review, we have reported a broad overview of advanced ovarian cancer management, and an update of the current evidence. The future perspectives of the Italian Society of Surgical Oncology (SICO) are discussed conclusively.

Search for New Participants in the Pathogenesis of High-Grade Serous Ovarian Cancer with the Potential to Be Used as Diagnostic Molecules

Recent studies have attempted to develop molecular signatures of epithelial ovarian cancer (EOC) based on the quantitation of protein-coding and non-coding RNAs to predict disease prognosis. Due to the heterogeneity of EOC, none of the developed prognostic signatures were directly applied in clinical practice. Our work focuses on high-grade serous ovarian carcinoma (HGSOC) due to the highest mortality rate relative to other types of EOC. Using deep sequencing of small non-coding RNAs in combination with quantitative real-time PCR, we confirm the dualistic classification of epithelial ovarian cancers based on the miRNA signature of HGSOC (type 2), which differs from benign cystadenoma and borderline cystadenoma-precursors of low-grade serous ovarian carcinoma (type 1)-and identified two subtypes of HGSOC, which significantly differ in the level of expression of the progesterone receptor in the tumor tissue, the secretion of miR-16-5p, miR-17-5p, miR-93-5p, miR-20a-5p, the level of serum CA125, tumor size, surgical outcome (optimal or suboptimal cytoreduction), and response to chemotherapy. It was found that the combined determination of the level of miR-16-5p, miR-17-5p, miR-20a-5p, and miR-93-5p circulating in blood plasma of patients with primary HGSOC tumors makes it possible to predict optimal cytoreduction with 80.1% sensitivity and 70% specificity (p = 0.022, TPR = 0.8, FPR = 0.3), as well as complete response to adjuvant chemotherapy with 77.8% sensitivity and 90.9% specificity (p = 0.001, TPR = 0.78, FPR = 0.09). After the additional verification of the obtained data in a larger HGSOC patient cohort, the combined quantification of these four miRNAs is proposed to be used as a criterion for selecting patients either for primary cytoreduction or neoadjuvant chemotherapy followed by interval cytoreduction.

Integration of survival data from multiple studies

We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. The estimation of the study-specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta-analytic methods.

Systemic benefit of radiation therapy via abscopal effect

Evidence of a systemic response related to localized radiation therapy (RT) in cancer management is rare. However, enhancing the immune response via immunotherapy followed by localized RT has shown evidence of tumor shrinkage to non-irradiated metastatic disease thereby inducing an "abscopal effect." Combined induction of the cGAS-STING pathway and activation of IFN-gamma signaling cascade related to RT within an activated immune environment promotes neoantigen presentation and expansion of cytotoxic effector cells enabling enhancement of systemic immune response. A proposed mechanism, case examples, and clinical trial evidence of "abscopal effect" benefit are reviewed. Results support strategic therapeutic testing to enhance "abscopal effect."

Epithelial ovarian cancer: Review article

Epithelial ovarian cancer is the second commonest cause of death amongst all gynaecological cancers. Treatment is challenging because almost 75% of cases are diagnosed in advanced stages. Front line treatment with aggressive cytoreduction and adjuvant treatment decides the outcome. Despite the complete response to primary treatment majority will relapse with disease. Treatment options of recurrent disease depends on platinum free interval. Systemic therapy is the mainstay of treatment and secondary cytoreduction may be beneficial in selected patients Newer therapeutic agents are being added in the front line and recurrent setting to improve outcome.

Integrated Clinical and Genomic Models to Predict Optimal Cytoreduction in High-Grade Serous Ovarian Cancer

Simple Summary

Approximately 30% of patients with advanced, high-grade serous ovarian cancer who undergo surgery will have a suboptimal result, resulting in decreased overall survival. Improving the ability to predict a successful surgery would improve survival. We aimed to use tumor genomics to create prediction models, which would predict an optimal or complete cytoreduction prior to entering the operating room. We created two sets of models, one for optimal and one for complete cytoreduction. We then validated those models using the TCGA database as well as statistical learning. We developed 21 models for optimal cytoreduction and 37 models for complete cytoreduction, which have the potential to improve our ability to predict these surgical results in patients with ovarian cancer before taking them to the operating room. Improving our pre-operative decision-making will result in more patients having the desired surgical results and, therefore, improved survival.

Abstract

Advanced high-grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal and complete cytoreductive surgery are correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction. With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used statistical learning methods (lasso regression) to integrate these data with pre-operative clinical information to create predictive models to discriminate which patient would have an optimal or complete cytoreductive outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database and using machine learning methods (TensorFlow). Of the 124 models created and validated for optimal cytoreduction, 21 performed at least equal to, if not better than, our historical clinical rate of optimal debulking in advanced-stage HGSC as a control. Of the 89 models created to predict complete cytoreduction, 37 have the potential to outperform clinical decision-making. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

Survivin, Sonic hedgehog, Krüppel-like factors, and p53 pathway in serous ovarian cancer: an immunohistochemical study

OBJECTIVE

Survivin was previously associated with tumor stage and grade in ovarian cancer and interfered with the tumor's drug sensitivity. In addition, Survivin expression was found to be regulated by the Sonic hedgehog (Shh) pathway, Krüppel-like factor (KLF) family proteins, and p53 pathway. The main aim of this study was to assess the prognostic values of immunohistochemical expression of Survivin, Klf5, Klf11, Shh, p53, p21, and Mdm2 in a cohort of high grade ovarian serous cancers. Other aims were comparison between high- and low-grade ovarian serous cancer and between platinum-resistant and the other cases. The last aim was to assess the correlations among the immunohistochemical expression of the studied proteins.

METHODS

Retrospective cohort study to assess immunohistochemical expression of Survivin, Klf5, Klf11, Shh, p53, p21, and Mdm2 in a tissue microarray of primary tumor samples among 73 women affected by high-grade ovarian serous cancer and 9 by low-grade ovarian serous cancer.

RESULTS

Klf5 and Shh cytoplasmic staining were associated to short overall survival (HR 6.38, CI.95 2.25 - 18.01, p<0.05 and 2.25, CI.95 1.19-4.23, p<0.05 respectively). In addition, cytoplasmic Klf5 staining, high Klf11 and p53 nuclear staining were associated with platinum resistance (p<0.05). Cytoplasmic Shh score was significantly correlated to the immunohistochemical expression of Klf5, Klf11, Mdm2, and Survivin.

CONCLUSIONS

Our data highlight the possible role of Klf5 and Shh as prognostic markers, meanwhile confirming the role of the KLF family proteins and p53 in ovarian cancer drug resistance. Moreover, Shh appeared to play an important role in the intracellular network of ovarian neoplasia.

Predictive biomarker for surgical outcome in patients with advanced primary high-grade serous ovarian cancer. Are we there yet? An analysis of the prospective biobank for ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer and is associated with high mortality rates. Surgical outcome is one of the most important prognostic factors. There are no valid biomarkers to identify which patients may benefit from a primary debulking approach.

OBJECTIVE

Our study aimed to discover and validate a predictive panel for surgical outcome of residual tumor mass after first-line debulking surgery.

STUDY DESIGN

Firstly, "In silico" analysis of publicly available datasets identified 200 genes as predictors for surgical outcome. The top selected genes were then validated using the novel Nanostring method, which was applied for the first time for this particular research objective. 225 primary ovarian cancer patients with well annotated clinical data and a complete debulking rate of 60% were compiled for a clinical cohort. The 14 best rated genes were then validated through the cohort, using immunohistochemistry testing. Lastly, we used our biomarker expression data to predict the presence of miliary carcinomatosis patterns.

RESULTS

The Nanostring analysis identified 37 genes differentially expressed between optimal and suboptimal debulked patients (p < 0.05). The immunohistochemistry validated the top 14 genes, reaching an AUC Ø0.650. The analysis for the prediction of miliary carcinomatosis patterns reached an AUC of Ø0.797.

CONCLUSION

The tissue-based biomarkers in our analysis could not reliably predict post-operative residual tumor. Patient and non-patient-associated co-factors, surgical skills, and center experience remain the main determining factors when considering the surgical outcome at primary debulking in high-grade serous ovarian cancer patients.

The use of biomarkers to stratify surgical care in women with ovarian cancer: Scientific Impact Paper No. 69 March 2022: Scientific Impact Paper No. 69 May 2022

Biomarkers may offer unforeseen insights into clinical diagnosis, as well as the likely course and outcome of a condition. In this paper, the focus is on the use of biological molecules found in body fluids or tissues for diagnosis and prediction of outcome in ovarian cancer patients. In cancer care, biomarkers are being used to develop personalised treatment plans for patients based on the unique characteristics of their tumour. This tailoring of care can be used to pursue specific targets identified by biomarkers, or treat the patient according to specific tumour characteristics. Surgery is one of the core treatments for ovarian cancer, whether it is offered in primary surgery or following chemotherapy in delayed surgery. Biomarkers already exist to guide the treatment of tumours with chemotherapy, but very little research has determined the value of biomarkers in tailoring surgical care for ovarian cancer. Such research is required to identify new biomarkers and assess their effectiveness in a clinical setting as well as to help identify specific tumour types to guide surgery. Biomarkers could help to determine the success of removing the disease surgically, or help to identify tumour deposits that persist after chemotherapy. All of these aspects would improve current practice. This Scientific Impact Paper highlights research that may pave the way towards bespoke surgery according to the biological characteristics of a tumour and aid gynaecological oncologists to provide surgical treatment according to individual need, rather than a blanket approach for all.

PD-L1 near Infrared Photoimmunotherapy of Ovarian Cancer Model

(1) Background: Despite advances in surgical approaches and drug development, ovarian cancer is still a leading cause of death from gynecological malignancies. Patients diagnosed with late-stage disease are treated with aggressive surgical resection and chemotherapy, but recurrence with resistant disease is often observed following treatment. There is a critical need for effective therapy for late-stage ovarian cancer. Photoimmunotherapy (PIT), using an antibody conjugated to a near infrared (NIR) dye, constitutes an effective theranostic strategy to detect and selectively eliminate targeted cell populations. (2) Methods: Here, we are targeting program death ligand 1 (PD-L1) using NIR-PIT in a syngeneic mouse model of ovarian cancer. PD-L1 PIT-mediated cytotoxicity was quantified in RAW264.7 macrophages and ID8-Defb29-VEGF cells in culture, and in vivo with orthotopic ID8-Defb29-VEGF tumors. (3) Results: Treatment efficacy was observed both in vitro and in vivo. (4) Conclusions: Our data highlight the need for further investigations to assess the potential of using NIR-PIT for ovarian cancer therapy to improve the treatment outcome of ovarian cancer.

Validation of anti-correlated TGFβ signaling and alternative end-joining DNA repair signatures that predict response to genotoxic cancer therapy

PURPOSE

Loss of transforming growth factor β (TGFβ) signaling increases error-prone alternative end-joining (alt-EJ) DNA repair. We previously translated this mechanistic relationship as TGFβ and alt-EJ gene expression signatures, which are anti-correlated across cancer types. A score, βAlt, representing anti-correlation predicts patient outcome in response to genotoxic therapy. Here we sought to verify this biology in live specimens and additional datasets.

EXPERIMENTAL DESIGN

Human head and neck squamous cell (HNSC) carcinoma explants were treated in vitro to test whether the signatures report TGFβ signaling, indicated by SMAD2 phosphorylation, and unrepaired DNA damage, indicated by persistent 53BP1 foci after irradiation or olaparib. A custom NanoString assay was implemented to analyze the signatures' expression in explants. Each signature gene was then weighted by its association with functional responses to define a modified score, βAlt_w, that was retested for association with response to genotoxic therapies in independent datasets.

RESULTS

Most genes in each signature were positively correlated with the expected biological response in tumor explants. Anticorrelation of TGFβ and alt-EJ signatures measured by Nanostring was confirmed in explants. βAlt_w was significantly (P<0.001) better than βAlt in predicting overall survival in response to genotoxic therapy in TCGA pancancer patients and in independent HNSC and ovarian cancer patient datasets.

CONCLUSION

Association of the TGFβ and alt-EJ signatures with their biological response validates TGFβ competency as a key mediator of DNA repair that can be readily assayed by gene expression. The predictive value of βAlt_w supports its development to assist in clinical decision-making.

Effect of 1α,25(OH)2D3-Treated M1 and M2 Macrophages on Cell Proliferation and Migration Ability in Ovarian Cancer

The biological active form of vitamin D3, 1α,25-dehydroxyvitamin D3 [1α,25(OH)₂D₃], exerts pleiotropic effects including bone mineralization, anti-tumor, as well as immunomodulator. This study aimed to explore the potential impact of 1α,25(OH)₂D₃ on tumor-associated macrophages (TAMs) infiltration in ovarian cancer. Firstly, human monocytic THP-1 cells were differentiated into macrophages (M0) in the presence of phorbol 12-myristate 13-acetate (PMA). In Vivo, 1α,25(OH)₂D₃ not only reversed the polarization of M2 macrophages, but also decreased the proliferation and migration abilities of ovarian cancer cells induced by M2 macrophages supernatant. Furthermore, 1α,25(OH)₂D₃ dramatically decreased the secretion of TGF-β1 and MMP-9 in M2 macrophages. However, no significant effect was observed in 1α,25(OH)₂D₃ treated M1 macrophages. In Vivo, vitamin D3 had an inhibitive effect of 1α,25(OH)₂D₃-treated M2 macrophages on tumorigenesis. In addition, we conducted the association of TAMs with the poor prognosis of patients with ovarian cancer by meta-analysis, which suggested the higher proportion of M2 macrophages was related to the poorer prognosis in ovarian cancer. Collectively, these results identified distinct roles of 1α,25(OH)₂D₃ treated M1 and M2 macrophages on cell proliferation and migration abilities in ovarian cancer.

Oncogenic microRNA-181d binding to OGT contributes to resistance of ovarian cancer cells to cisplatin

Ovarian cancer (OC), a common gynecological cancer, is characterized by a high malignant potential. MicroRNAs (miRNAs or miRs) have been associated with the chemo- or radiotherapeutic resistance of human malignancies. Herein, the current study set out to explore the regulatory mechanism of miR-181d involved in the cisplatin (DDP) resistance of OC cells. Firstly, in-situ hybridization method was performed to identify miR-181d expression in ovarian tissues of DDP-resistant or DDP-sensitive patients. In addition, miR-181d expression in A2780 cells and A2780/DDP cell lines was determined by RT-qPCR. Gain- and loss-of-function experiments were then performed to characterize the effect of miR-181d on OC cell behaviors. We probed the miR-181d affinity to OGT, as well as the downstream glycosylation of KEAP1 and ubiquitination of NRF2. Further, in vivo experiments were performed to define the role of miR-181d in tumor resistance to DDP. miR-181d was highly expressed in the ovarian tissues of DDP-resistant patients and the A2780/DDP cell line. Ectopic expression of miR-181d augmented DDP resistance in OC cells. In addition, miR-181d was found to target the 3′UTR of OGT mRNA, and negatively regulate the OGT expression. Mechanistic results indicated that OGT repressed NRF2 expression through glycosylation of KEAP1, thereby inhibiting the DDP resistance of OC cells. Furthermore, miR-181d negatively orchestrated the OGT/KEAP1/NRF2 axis to enhance the OC resistance to DDP in vivo. Overall, these findings suggest that miR-181d-mediated OGT inhibition restricts the glycosylation of KEAP1, and then reduces the ubiquitination and degradation of NRF2, leading to DDP resistance of OC. This study provides new insights for prevention and control of OC.

Gemogenovatucel-T (Vigil) maintenance immunotherapy: 3-year survival benefit in homologous recombination proficient (HRP) ovarian cancer

OBJECTIVE

Previously, Vigil demonstrated clinical benefit to prolong relapse free and overall survival in the BRCA wild-type (BRCA-wt), homologous recombination proficient (HRP) patient population. Here we provide long term follow up of 3 years in the HRP patient population enrolled in the Phase 2b VITAL study.

METHODS

HRP patients treated with Vigil (n = 25) or placebo (n = 20) who were enrolled in the Phase 2b, double-blind, placebo-controlled (VITAL study, NCT02346747) were followed for safety, OS and RFS. OS and RFS from time of randomization (immediately prior to maintenance therapy) and from debulking tissue procurement time points were analyzed by Kaplan-Meier (KM) and restricted mean survival time (RMST) analysis.

RESULTS

OS for Vigil treated patients at 3 years has not yet reached median OS time point (95% CI 41.6 months to not achieved) compared to 26.9 (95% CI 17.4 months to not achieved) in placebo treated patients (HR 0.417 p = 0.020). Three year RFS also showed benefit to Vigil (stratified HR 0.405, p = 0.011) and no long term toxicity to Vigil was observed. Three year OS for Vigil of 70% vs. 40% for placebo from time of randomization was observed (p = 0.019). RMST analysis was also significant for OS (45.7 vs. 32.8 months, p = 0.008) and RFS (p = 0.025).

CONCLUSION

In conclusion, results suggest durable activity of Vigil on RFS and OS and support further evaluation of Vigil in HRP ovarian cancer.

NUAK Kinases: Brain-Ovary Axis

Liver kinase B (LKB1) and adenosine monophosphate (AMP)-activated protein kinase (AMPK) are two major kinases that regulate cellular metabolism by acting as adenosine triphosphate (ATP) sensors. During starvation conditions, LKB1 and AMPK activate different downstream pathways to increase ATP production, while decreasing ATP consumption, which abrogates cellular proliferation and cell death. Initially, LKB1 was considered to be a tumor suppressor due to its loss of expression in various tumor types. Additional studies revealed amplifications in LKB1 and AMPK kinases in several cancers, suggesting a role in tumor progression. The AMPK-related proteins were described almost 20 years ago as a group of key kinases involved in the regulation of cellular metabolism. As LKB1-downstream targets, AMPK-related proteins were also initially considered to function as tumor suppressors. However, further research demonstrated that AMPK-related kinases play a major role not only in cellular physiology but also in tumor development. Furthermore, aside from their role as regulators of metabolism, additional functions have been described for these proteins, including roles in the cell cycle, cell migration, and cell death. In this review, we aim to highlight the major role of AMPK-related proteins beyond their functions in cellular metabolism, focusing on cancer progression based on their role in cell migration, invasion, and cell survival. Additionally, we describe two main AMPK-related kinases, Novel (nua) kinase family 1 (NUAK1) and 2 (NUAK2), which have been understudied, but play a major role in cellular physiology and tumor development.

Preclinical Evaluation of a Fluorescent Probe Targeting Receptor CDCP1 for Identification of Ovarian Cancer

Optimal cytoreduction for ovarian cancer is often challenging because of aggressive tumor biology and advanced stage. It is a critical issue since the extent of residual disease after surgery is the key predictor of ovarian cancer patient survival. For a limited number of cancers, fluorescence-guided surgery has emerged as an effective aid for tumor delineation and effective cytoreduction. The intravenously administered fluorescent agent, most commonly indocyanine green (ICG), accumulates preferentially in tumors, which are visualized under a fluorescent light source to aid surgery. Insufficient tumor specificity has limited the broad application of these agents in surgical oncology including for ovarian cancer. In this study, we developed a novel tumor-selective fluorescent agent by chemically linking ICG to mouse monoclonal antibody 10D7 that specifically recognizes an ovarian cancer-enriched cell surface receptor, CUB-domain-containing protein 1 (CDCP1). 10D7^ICG has high affinity for purified recombinant CDCP1 and CDCP1 that is located on the surface of ovarian cancer cells in vitro and in vivo. Our results show that intravenously administered 10D7^ICG accumulates preferentially in ovarian cancer, permitting visualization of xenograft tumors in mice. The data suggest CDCP1 as a rational target for tumor-specific fluorescence-guided surgery for ovarian cancer.

Robustifying genomic classifiers to batch effects via ensemble learning

MOTIVATION

Genomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across batches. Such 'batch effects' often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here, we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods.

RESULTS

We provide a systematic comparison between these two strategies using studies targeting diverse populations infected with tuberculosis. In one study, we simulated increasing levels of heterogeneity across random subsets of the study, which we treat as simulated batches. We then use the two methods to develop a genomic classifier for the binary indicator of disease status. We evaluate the accuracy of prediction in another independent study targeting a different population cohort. We observed that in independent validation, while merging followed by batch adjustment provides better discrimination at low level of heterogeneity, our ensemble learning strategy achieves more robust performance, especially at high severity of batch effects. These observations provide practical guidelines for handling batch effects in the development and evaluation of genomic classifiers.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the article and in its online supplementary material. Processed data is available in the Github repository with implementation code, at https://github.com/zhangyuqing/bea_ensemble.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Ovarian Cancer Immunotherapy and Personalized Medicine

Ovarian cancer response to immunotherapy is limited; however, the evaluation of sensitive/resistant target treatment subpopulations based on stratification by tumor biomarkers may improve the predictiveness of response to immunotherapy. These markers include tumor mutation burden, PD-L1, tumor-infiltrating lymphocytes, homologous recombination deficiency, and neoantigen intratumoral heterogeneity. Future directions in the treatment of ovarian cancer include the utilization of these biomarkers to select ideal candidates. This paper reviews the role of immunotherapy in ovarian cancer as well as novel therapeutics and study designs involving tumor biomarkers that increase the likelihood of success with immunotherapy in ovarian cancer.

MicroRNA characteristics in epithelial ovarian cancer

The purpose of the current study was to clarify differences in microRNA expression according to clinicopathological characteristics, and to investigate if miRNA profiles could predict cytoreductive outcome in patients with FIGO stage IIIC and IV ovarian cancer. Patients enrolled in the Pelvic Mass study between 2004 and 2010, diagnosed and surgically treated for epithelial ovarian cancer, were used for investigation. MicroRNA was profiled from tumour tissue with global microRNA microarray analysis. Differences in miRNA expression profiles were analysed according to histologic subtype, FIGO stage, tumour grade, type I or II tumours and result of primary cytoreductive surgery. One microRNA, miR-130a, which was found to be associated with serous histology and advanced FIGO stage, was also validated using data from external cohorts. Another seven microRNAs (miR-34a, miR-455-3p, miR-595, miR-1301, miR-146-5p, 193a-5p, miR-939) were found to be significantly associated with the clinicopathological characteristics (p ≤ 0.001), in our data, but mere not similarly significant when tested against external cohorts. Further validation in comparable cohorts, with microRNA profiled using newest and similar methods are warranted.

Association of Copy Number Variation Signature and Survival in Patients With Serous Ovarian Cancer

IMPORTANCE

Tailoring therapeutic regimens to individual patients with ovarian cancer is informed by severity of disease using a variety of clinicopathologic indicators. Although DNA repair variations are increasingly used for therapy selection in ovarian cancer, molecular features are not widely used for general assessment of patient prognosis and disease severity.

OBJECTIVE

To distill a highly dynamic characteristic, signature of copy number variations (CNV), into a risk score that could be easily validated analytically or repurposed for use given existing US Food and Drug Administration (FDA)-approved multigene assays.

DESIGN, SETTING, AND PARTICIPANTS

This genetic association study used the Cancer Genome Atlas Ovarian Cancer database to assess for genome-wide survival associations agnostic to gene function. Regions enriched for significant associations were compared to associations from scrambled data. CNV associations were condensed into a risk score, which was internally validated using bootstrapping. The participants were patients with serous ovarian cancer (stages I-IV) diagnosed from 1992 to 2013. Statistical analysis was performed from April to July 2020.

MAIN OUTCOMES AND MEASURES

Overall survival (OS).

RESULTS

Among 564 patients with serous ovarian cancer, the mean (SD) age was 59.7 (11.5) years; 34 (6%) identified as Black or African American. A total of 13 genome regions, comprising 14 alterations, were identified as significantly risk associated. Composite risk score was independent of total CNV burden, total mutational burden, BRCA status, and open-source genome-wide DNA repair deficiency signatures. Binned terciles yielded high-, standard-, and low-risk groups with respective median OS estimates of 2.9 (95% CI, 2.3-3.2) years, 4.1 (95% CI, 3.7-4.8) years, and 5.7 (95% CI, 4.7-7.4) years, respectively (P < .001). Associated 5-year survival estimates in each tercile were 15% (95% CI, 10%-22%), 36% (95% CI, 29%-46%), and 53% (95% CI, 45%-62%). The risk score had more discriminatory ability to prognosticate OS than age, clinical stage, grade, and race combined, and was strongly additive to significant clinical features (P < .001). Simulated adaptation of FDA-approved assays showed similar performance. Gene ontology analyses of identified regions showed an enrichment for regulatory miRNAs and protein kinase regulators.

CONCLUSIONS AND RELEVANCE

This study found that a CNV-based risk score is independent to and stronger than current or near-future ovarian cancer genomic biomarkers to prognosticate OS. CNV regions identified were not strongly associated with canonical ovarian cancer biological pathways, identifying candidates for future mechanistic investigations. External validation of the CNV risk score, especially in concert with more extensive clinical features, could be pursued via existing FDA-approved assays.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Simple Summary

High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian carcinomas with sobering survival rates. The mechanisms mediating treatment efficacy are still poorly understood with no adequate biomarkers of response to treatment and risk assessment. This variability of treatment response might be due to its molecular heterogeneity. Therefore, identification of biomarkers or molecular signatures to stratify patients and offer personalized treatment is of utmost priority. Currently, comprehensive gene expression profiling is time- and cost-extensive and limited by tissue heterogeneity. Thus, it has not been implemented into clinical practice. This study demonstrates for the first time a spatially resolved, time- and cost-effective approach to stratifying HGSOC patients by combining novel matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) technology with machine-learning algorithms. Eventually, MALDI-derived predictive signatures for treatment efficacy, recurrent risk, or, as demonstrated here, molecular subtypes might be utilized for emerging clinical challenges to ultimately improve patient outcomes.

Abstract

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

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.

Periostin Contributes to Immunoglobulin a Nephropathy by Promoting the Proliferation of Mesangial Cells: A Weighted Gene Correlation Network Analysis

Immunoglobulin A nephropathy (IgAN) is a known cause of end-stage kidney disease, but the pathogenesis and factors affecting prognosis are not fully understood. In the present study, we carried out weighted gene correlation network analysis (WGCNA) to identify hub genes related to the occurrence of IgAN and validated candidate genes in experiments using mouse mesangial cells (MMCs) and clinical specimens (kidney tissue from IgAN patients and healthy controls). We screened the GSE37460 and GSE104948 differentially expressed genes common to both datasets and identified periostin (POSTN) as one of the five key genes using the cytoHubba plugin of Cytoscape software and by receiver-operating characteristic curve analysis. The top 25% of genes in the GSE93798 dataset showing variable expression between IgAN and healthy tissue were assessed by WGCNA. The royalblue module in WGCNA was closely related to creatinine and estimated glomerular filtration rate (eGFR) in IgAN patients. POSTN had very high module membership and gene significance values for creatinine (0.82 and 0.66, respectively) and eGFR (0.82 and -0.67, respectively), indicating that it is a co-hub gene. In MMCs, POSTN was upregulated by transforming growth factor β1, and stimulation of MMCs with recombinant POSTN protein resulted in an increase in the level of proliferating cell nuclear antigen (PCNA) and a decrease in that of B cell lymphoma-associated X protein, which were accompanied by enhanced MMC proliferation. POSTN gene knockdown had the opposite effects. Immunohistochemical analysis of kidney tissue specimens showed that POSTN and PCNA levels were elevated, whereas the rate of apoptosis was reduced in IgAN patients relative to healthy controls. POSTN level in the kidney tissue of IgAN patients was positively correlated with creatinine level and negatively correlated with eGFR. Thus, POSTN promotes the proliferation of MCs to promote renal dysfunction in IgAN.

Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

BACKGROUND

The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC.

METHODS

The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis.

RESULTS

8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK.

CONCLUSION

The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.

The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models

Cross-study validation (CSV) of prediction models is an alternative to traditional cross-validation (CV) in domains where multiple comparable datasets are available. Although many studies have noted potential sources of heterogeneity in genomic studies, to our knowledge none have systematically investigated their intertwined impacts on prediction accuracy across studies. We employ a hybrid parametric/non-parametric bootstrap method to realistically simulate publicly available compendia of microarray, RNA-seq, and whole metagenome shotgun microbiome studies of health outcomes. Three types of heterogeneity between studies are manipulated and studied: (i) imbalances in the prevalence of clinical and pathological covariates, (ii) differences in gene covariance that could be caused by batch, platform, or tumor purity effects, and (iii) differences in the "true" model that associates gene expression and clinical factors to outcome. We assess model accuracy, while altering these factors. Lower accuracy is seen in CSV than in CV. Surprisingly, heterogeneity in known clinical covariates and differences in gene covariance structure have very limited contributions in the loss of accuracy when validating in new studies. However, forcing identical generative models greatly reduces the within/across study difference. These results, observed consistently for multiple disease outcomes and omics platforms, suggest that the most easily identifiable sources of study heterogeneity are not necessarily the primary ones that undermine the ability to accurately replicate the accuracy of omics prediction models in new studies. Unidentified heterogeneity, such as could arise from unmeasured confounding, may be more important.

Development and Therapeutic Potential of NUAKs Inhibitors

NUAK isoforms, NUAK1 (ARK5) and NUAK2 (SNARK), are important members of the AMPK family of protein kinases. They are involved in a broad spectrum of physiological and cellular events, and sometimes their biological roles overlap. NUAK isoform dysregulation is associated with numerous pathological disorders, including neurodegeneration, metastatic cancer, and diabetes. Therefore, they are promising therapeutic targets in metabolic diseases and cancers; consequently, various NUAK-targeted inhibitors have been disclosed. The first part of this review comprises a brief discussion of the homology, expression, structure, and characteristics of NUAK isoforms. The second part focuses on NUAK isoforms' involvement in crucial biological operations, including mechanistic findings, highlighting how their abnormal functioning contributes to disease progression and quality of life. The third part summarizes the key findings and applications of targeting NUAK isoforms for treating multiple cancers and neurodegenerative disorders. The final part systematically presents a critical review and analysis of the literature on NUAK isoform inhibitions through small molecules.

Neutralization of TGFβ Improves Tumor Immunity and Reduces Tumor Progression in Ovarian Carcinoma

The immunosuppressive effects of TGFβ promotes tumor progression and diminishes response to therapy. In this study, we used ID8-p53^-/- tumors as a murine model of high-grade serous ovarian cancer. An mAb targeting all three TGFβ ligands was used to neutralize TGFβ. Ascites and omentum were collected and changes in T-cell response were measured using flow. Treatment with anti-TGFβ therapy every other day following injection of tumor cells resulted in decreased ascites volume (4.1 mL vs. 0.7 mL; P < 0.001) and improved the CD8:Treg ratio (0.37 vs. 2.5; P = 0.02) compared with untreated mice. A single dose of therapy prior to tumor challenge resulted in a similar reduction of ascites volume (2.7 vs. 0.67 mL; P = 0.002) and increased CD8:Tregs ratio (0.36 vs. 1.49; P = 0.007), while also significantly reducing omental weight (114.9 mg vs. 93.4 mg; P = 0.017). Beginning treatment before inoculation with tumor cells and continuing for 6 weeks, we observe similar changes and prolonged overall survival (median 70 days vs. 57.5 days). TGFβ neutralization results in favorable changes to the T-cell response within the tumor microenvironment, leading to decreased tumor progression in ovarian cancer. The utilization of anti-TGFβ therapy may be an option for management in patients with ovarian cancer to improve clinical outcomes and warrants further investigation.

The Emerging Role of CD24 in Cancer Theranostics-A Novel Target for Fluorescence Image-Guided Surgery in Ovarian Cancer and Beyond

Complete cytoreductive surgery is the cornerstone of the treatment of epithelial ovarian cancer (EOC). The application of fluorescence image-guided surgery (FIGS) allows for the increased intraoperative visualization and delineation of malignant lesions by using fluorescently labeled targeting biomarkers, thereby improving intraoperative guidance. CD24, a small glycophosphatidylinositol-anchored cell surface receptor, is overexpressed in approximately 70% of solid cancers, and has been proposed as a prognostic and therapeutic tumor-specific biomarker for EOC. Recently, preclinical studies have demonstrated the benefit of CD24-targeted contrast agents for non-invasive fluorescence imaging, as well as improved tumor resection by employing CD24-targeted FIGS in orthotopic patient-derived xenograft models of EOC. The successful detection of miniscule metastases denotes CD24 as a promising biomarker for the application of fluorescence-guided surgery in EOC patients. The aim of this review is to present the clinical and preclinically evaluated biomarkers for ovarian cancer FIGS, highlight the strengths of CD24, and propose a future bimodal approach combining CD24-targeted fluorescence imaging with radionuclide detection and targeted therapy.

High-throughput sequencing identification of differentially expressed microRNAs in metastatic ovarian cancer with experimental validations

Background

Ovarian cancer (OC) is a common gynecological cancer and characterized by high metastatic potential. MicroRNAs (miRNAs, miRs) have the promise to be harnessed as prognostic and therapeutic biomarkers for OC. Herein, we sought to identify differentially expressed miRNAs and mRNAs in metastatic OC, and to validate them with functional experiments.

Methods

Differentially expressed miRNAs and mRNAs were screened from six pairs of primary OC tissues and metastatic tissues using a miRStar™ Human Cancer Focus miRNA and Target mRNA PCR Array. Then, gene expression profiling results were verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blot assays. The binding affinity between miR-7-5p and TGFβ2 was validated by dual-luciferase reporter assay. Expression of miR-7-5p and TGFβ2 was manipulated to assess their roles in malignant phenotypes of highly metastatic HO-8910PM cells.

Results

MiRNA profiling and sequencing identified 12 miRNAs and 10 mRNAs that were differentially expressed in metastatic tissues. Gene ontology and Pathway analyses determined that 3 differentially expressed mRNAs (ITGB3, TGFβ2 and TNC) were related to OC metastasis. The results of RT-qPCR confirmed that the decrease of miR-7-5p was most significant in OC metastasis, while TGFβ2 was up-regulated in OC metastasis. Moreover, miR-7-5p targeted and negatively regulated TGFβ2. MiR-7-5p overexpression accelerated HO-8910PM cell viability and invasion, and TGFβ2 overexpression reversed the results. Meanwhile, simultaneous miR-7-5p and TGFβ2 overexpression rescued the cell activities.

Conclusions

This study characterizes differentially expressed miRNAs and mRNAs in metastatic OC, where miR-7-5p and its downstream target were most closely associated with metastatic OC. Overexpression of miR-7-5p targets and inhibits TGFβ2 expression, thereby inhibiting the growth and metastasis of OC.

Integrative network analysis identifies an immune-based prognostic signature as the determinant for the mesenchymal subtype in epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) has been classified into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop an immune-based prognostic signature by incorporating molecular subtypes for EOC patients.

METHODS

The gene expression profiles of EOC samples were collected from seven public datasets as well as an internal retrospective validation cohort, containing 1192 EOC patients. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for EOC (IPSEOC). The signature was trained and validated in eight independent datasets.

RESULTS

Seven immune genes were identified as key regulators of the mesenchymal subtype and were used to construct the IPSEOC. The IPSEOC significantly divided patients into high- and low-risk groups in discovery (OS: P < .0001), 6 independent public validation sets (OS: P = .04 to P = .002), and an internal retrospective validation cohort (OS: P = .025). Furthermore, pathway analysis revealed that differences between risk groups were mainly activation of mesenchymal-related signalling. Moreover, a significant correlation existed between the IPSEOC values versus clinical phenotypes including late tumor stages, drug resistance.

CONCLUSION

We propose an immune-based signature, which is a promising prognostic biomarker in ovarian cancer. Prospective studies are needed to further validate its analytical accuracy and test the clinical utility.

Menopausal hormone therapy prior to the diagnosis of ovarian cancer is associated with improved survival

PURPOSE

Prior studies of menopausal hormone therapy (MHT) and ovarian cancer survival have been limited by lack of hormone regimen detail and insufficient sample sizes. To address these limitations, a comprehensive analysis of 6419 post-menopausal women with pathologically confirmed ovarian carcinoma was conducted to examine the association between MHT use prior to diagnosis and survival.

METHODS

Data from 15 studies in the Ovarian Cancer Association Consortium were included. MHT use was examined by type (estrogen-only (ET) or estrogen+progestin (EPT)), duration, and recency of use relative to diagnosis. Cox proportional hazards models were used to estimate the association between hormone therapy use and survival. Logistic regression and mediation analysis was used to explore the relationship between MHT use and residual disease following debulking surgery.

RESULTS

Use of ET or EPT for at least five years prior to diagnosis was associated with better ovarian cancer survival (hazard ratio, 0.80; 95% CI, 0.74 to 0.87). Among women with advanced stage, high-grade serous carcinoma, those who used MHT were less likely to have any macroscopic residual disease at the time of primary debulking surgery (p for trend <0.01 for duration of MHT use). Residual disease mediated some (17%) of the relationship between MHT and survival.

CONCLUSIONS

Pre-diagnosis MHT use for 5+ years was a favorable prognostic factor for women with ovarian cancer. This large study is consistent with prior smaller studies, and further work is needed to understand the underlying mechanism.

Microbes and complex diseases: from experimental results to computational models

Studies have shown that the number of microbes in humans is almost 10 times that of cells. These microbes have been proven to play an important role in a variety of physiological processes, such as enhancing immunity, improving the digestion of gastrointestinal tract and strengthening metabolic function. In addition, in recent years, more and more research results have indicated that there are close relationships between the emergence of the human noncommunicable diseases and microbes, which provides a novel insight for us to further understand the pathogenesis of the diseases. An in-depth study about the relationships between diseases and microbes will not only contribute to exploring new strategies for the diagnosis and treatment of diseases but also significantly heighten the efficiency of new drugs development. However, applying the methods of biological experimentation to reveal the microbe-disease associations is costly and inefficient. In recent years, more and more researchers have constructed multiple computational models to predict microbes that are potentially associated with diseases. Here, we start with a brief introduction of microbes and databases as well as web servers related to them. Then, we mainly introduce four kinds of computational models, including score function-based models, network algorithm-based models, machine learning-based models and experimental analysis-based models. Finally, we summarize the advantages as well as disadvantages of them and set the direction for the future work of revealing microbe-disease associations based on computational models. We firmly believe that computational models are expected to be important tools in large-scale predictions of disease-related microbes.