Prediction of chemo-response in serous ovarian cancer

Development and Validation of a Predictive Model for Resistance to Platinum-Based Chemotherapy in Patients with Ovarian Cancer through Proteomic Analysis

Platinum resistance in ovarian cancer poses a significant challenge, substantially impacting patient outcomes. Developing an accurate predictive model is crucial for improving clinical decision-making and guiding treatment strategies. Proteomic data from 217 high-grade serous ovarian cancer (HGSOC) biospecimens obtained from JHU, PNNL, and PTRC were used to construct a prediction model for identifying individuals who are resistant to platinum-based chemotherapy. A total of 6437 common proteins were detected across all data sets, with 26 proteins overlapping between the development cohorts JHU and PNNL. Using LASSO and logistic regression analysis, a six-protein model (P31323_PRKAR2B, Q13309_SKP2, Q14997_PSME4, Q6ZRP7_QSOX2, Q7LGA3_HS2ST1, and Q7Z2Z2_EFL1) was developed, which accurately predicted platinum resistance, with an AUC of 0.964 (95% CI, 0.929-0.999). Internal validation by resampling resulted in a C-index of 0.972 (95% CI 0.894-0.988). External validation performed on the PTRC cohort achieved an AUC of 0.855 (95% CI 0.748-0.963). Calibration curves showed good consistency, and DCA indicated superior clinical utility. The model also performed well in predicting PFS and OS at various time points. Based on these proteins, our predictive model can precisely predict platinum response and survival outcomes in HGSOC patients, which can assist clinicians in promptly identifying potentially platinum-resistant individuals.

Enhancing precision medicine: a nomogram for predicting platinum resistance in epithelial ovarian cancer

BACKGROUND

This study aimed to develop a novel nomogram that can accurately estimate platinum resistance to enhance precision medicine in epithelial ovarian cancer(EOC).

METHODS

EOC patients who received primary therapy at the General Hospital of Ningxia Medical University between January 31, 2019, and June 30, 2021 were included. The LASSO analysis was utilized to screen the variables which contained clinical features and platinum-resistance gene immunohistochemistry scores. A nomogram was created after the logistic regression analysis to develop the prediction model. The consistency index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the nomogram's performance.

RESULTS

The logistic regression analysis created a prediction model based on 11 factors filtered down by LASSO regression. As predictors, the immunohistochemical scores of CXLC1, CXCL2, IL6, ABCC1, LRP, BCL2, vascular tumor thrombus, ascites cancer cells, maximum tumor diameter, neoadjuvant chemotherapy, and HE4 were employed. The C-index of the nomogram was found to be 0.975. The nomogram's specificity is 95.35% and its sensitivity, with a cut-off value of 165.6, is 92.59%, as seen by the ROC curve. After the nomogram was externally validated in the test cohort, the coincidence rate was determined to be 84%, and the ROC curve indicated that the nomogram's AUC was 0.949.

CONCLUSION

A nomogram containing clinical characteristics and platinum gene IHC scores was developed and validated to predict the risk of EOC platinum resistance.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.

OBJECTIVE

This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer.

RESULTS

A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort.

CONCLUSIONS

Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

High-expressed ACAT2 predicted the poor prognosis of platinum-resistant epithelial ovarian cancer

BACKGROUND

Acetyl-CoA acetyltransferase 2 (ACAT2) is a lipid metabolism enzyme and rarely was researched in epithelial ovarian cancer (EOC).

METHODS

ACAT2 expressions were confirmed in two pairs of cell lines (A2780 and A2780/DDP, OVCAR8 and OVCAR8/DDP) from Gene Expression Omnibus database by bioinformatics analysis, and in A2780 and A2780/DDP cell lines by quantitative real-time polymerase chain reaction and western blotting. Tissue samples were stained by immunohistochemistry and scored for ACAT2 expression. The relationships between ACAT2 expression and clinicopathological characteristics were analyzed by χ2 test. The prognosis of ACAT2 was analyzed by the log-rank tests and Cox regression models.

RESULTS

ACAT2 was remarkably upregulated in the above drug-resistant cell lines by mRNA (all P < 0.05) and protein expression (P = 0.026) than those in sensitive ones. Patients were classified as ACAT2-high (n = 51) and ACAT2-low (n = 26) according to immunohistochemical score. ACAT2 expression had a significantly inverse correlation with FIGO stage (P = 0.030) and chemo-response (P = 0.041). A marginal statistical significance existed in ACAT2 expression and ascites volume (P = 0.092). Univariate analysis suggested that high-expressed ACAT2 was associated with decreased platinum-free interval (PFI) (8.57 vs. 14.13 months, P = 0.044), progression-free survival (PFS) (14.12 vs. 19.79 months, P = 0.039) and overall survival (OS) (36.89 vs. 52.40 months, P = 0.044). Multivariate analysis demonstrated that ACAT2 expression (hazard ratio = 2.18, 95% confidence interval: 1.15-4.11, P = 0.017) affected OS independently, rather than PFI and PFS.

CONCLUSION

The expression of ACAT2 in A2780/DDP and OVCAR8/DDP was higher than the corresponding A2780 and OVCAR8. High-expressed ACAT2 was associated with advanced FIGO stage, chemo-resistance, and decreased PFI, PFS and OS. It was an independent prognostic factor of OS in EOC.

A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC

Background

Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.

Methods

Two HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).

Results

By combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.

Conclusions

The developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.

Characterizing morphologic subtypes of high-grade serous ovarian cancer by CT: a retrospective cohort study

OBJECTIVE

A novel classification system of high-grade serous ovarian carcinoma based on gross morphology observed at pre-treatment laparoscopy was recently defined. The purpose of this study was to identify radiographic features unique to each morphologic subtype.

METHODS

This retrospective study included 109 patients with high-grade serous ovarian cancer who underwent pre-operative computed tomography (CT) scanning and laparoscopic assessment of disease burden between 1 April 2013 and 5 August 2015. Gross morphologic subtype had been previously assigned by laparoscopy. Two radiologists independently reviewed CT images for each patient, categorized disease at eight anatomic sites, and assessed for radiographic characteristics of interest: large infiltrative plaques, mass-like metastases, enhancing peritoneal lining, architectural distortion, fat stranding, calcifications, and lymph node involvement. Demographic and clinical information was summarized with descriptive statistics and compared using Student's t-tests, χ² tests, or Fisher exact tests as appropriate; kappa statistics were used to assess inter-reader agreement.

RESULTS

Certain radiographic features were found to be associated with gross morphologic subtype. Large infiltrative plaques were more common in type 1 disease (88.7% (47/53) vs 71.4% (25/35), p=0.04), while mass-like metastases were more often present in type 2 disease (48.6% (17/35) vs 22.6% (12/53), p=0.01). Additionally, radiographic presence of disease at the falciform ligament was more common in type 1 morphology (33.9% (19/56) vs 13.2% (5/38), p=0.02).

CONCLUSION

Morphologic subtypes of high-grade serous ovarian cancer were associated with specific CT findings, including the presence of large infiltrative plaques, mass-like metastases, and falciform ligament involvement.

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Interpatient Heterogeneity in Drug Response and Protein Biomarker Expression of Recurrent Ovarian Cancer

Recurrent ovarian-cancer patients face low 5-year survival rates despite chemotherapy. A variety of guideline-recommended second-line therapies are available, but they frequently result in trial-and-error treatment. Alterations and adjustments are common in the treatment of recurrent ovarian cancer. The drug response of 30 lesions obtained from 22 relapsed ovarian cancer patients to different chemotherapeutic and molecular agents was analyzed with the patient-derived ovarian-cancer spheroid model. The profile of druggable biomarkers was immunohistochemically assessed. The second-line combination therapy of carboplatin with gemcitabine was significantly superior to the combination of carboplatin with PEGylated liposomal doxorubicin (p < 0.0001) or paclitaxel (p = 0.0007). Except for treosulfan, all nonplatinum treatments tested showed a lesser effect on tumor spheroids compared to that of platinum-based therapies. Treosulfan showed the highest efficacy of all nonplatinum agents, with significant advantage over vinorelbine (p < 0.0001) and topotecan (p < 0.0001), the next best agents. The comparative testing of a variety of treatment options in the ovarian-cancer spheroid model resulted in the identification of more effective regimens for 30% of patients compared to guideline-recommended therapies. Recurrent cancers obtained from different patients revealed profound interpatient heterogeneity in the expression pattern of druggable protein biomarkers. In contrast, different lesions obtained from the same patient revealed a similar drug response and biomarker expression profile. Biological heterogeneity observed in recurrent ovarian cancers might explain the strong differences in the clinical drug response of these patients. Preclinical drug testing and biomarker profiling in the ovarian-cancer spheroid model might help in optimizing treatment management for individual patients.

Clinicopathologic predictors of early relapse in advanced epithelial ovarian cancer: development of prediction models using nationwide data

OBJECTIVE

To identify clinicopathologic factors predictive of early relapse (platinum-free interval (PFI) of ≤6 months) in advanced epithelial ovarian cancer (EOC) in first-line treatment, and to develop and internally validate risk prediction models for early relapse.

METHODS

All consecutive patients diagnosed with advanced stage EOC between 01-01-2008 and 31-12-2015 were identified from the Netherlands Cancer Registry. Patients who underwent cytoreductive surgery and platinum-based chemotherapy as initial EOC treatment were selected. Two prediction models, i.e. pretreatment and postoperative, were developed. Candidate predictors of early relapse were fitted into multivariable logistic regression models. Model performance was assessed on calibration and discrimination. Internal validation was performed through bootstrapping to correct for model optimism.

RESULTS

A total of 4,557 advanced EOC patients were identified, including 1,302 early relapsers and 3,171 late or non-relapsers. Early relapsers were more likely to have FIGO stage IV, mucinous or clear cell type EOC, ascites, >1 cm residual disease, and to have undergone NACT-ICS. The final pretreatment model demonstrated subpar model performance (AUC = 0.64 [95 %-CI 0.62-0.66]). The final postoperative model based on age, FIGO stage, pretreatment CA-125 level, histologic subtype, presence of ascites, treatment approach, and residual disease after debulking, demonstrated adequate model performance (AUC = 0.72 [95 %-CI 0.71-0.74]). Bootstrap validation revealed minimal optimism of the final postoperative model.

CONCLUSION

A (postoperative) discriminative model has been developed and presented online that predicts the risk of early relapse in advanced EOC patients. Although external validation is still required, this prediction model can support patient counselling in daily clinical practice.

A Fifteen-Gene Classifier to Predict Neoadjuvant Chemotherapy Responses in Patients with Stage IB to IIB Squamous Cervical Cancer

Neoadjuvant chemotherapy (NACT) remains an attractive alternative for controlling locally advanced cervical cancer. However, approximately 15-34% of women do not respond to induction therapy. To develop a risk stratification tool, 56 patients with stage IB-IIB cervical cancer are included in 2 research centers from the discovery cohort. Patient-specific somatic mutations led to NACT non-responsiveness are identified by whole-exome sequencing. Next, CRISPR/Cas9-based library screenings are performed based on these genes to confirm their biological contribution to drug resistance. A 15-gene classifier is developed by generalized linear regression analysis combined with the logistic regression model. In an independent validation cohort of 102 patients, the classifier showed good predictive ability with an area under the curve of 0.80 (95% confidence interval (CI), 0.69-0.91). Furthermore, the 15-gene classifier is significantly associated with patient responsiveness to NACT in both univariate (odds ratio, 10.8; 95% CI, 3.55-32.86; p = 2.8 × 10-5) and multivariate analysis (odds ratio, 17.34; 95% CI, 4.04-74.40; p = 1.23 × 10-4) in the validation set. In conclusion, the 15-gene classifier can accurately predict the clinical response to NACT before treatment, representing a promising approach for guiding the selection of appropriate treatment strategies for locally advanced cervical cancer.

Identification of Novel Fusion Transcripts in High Grade Serous Ovarian Cancer

Fusion genes are structural chromosomal rearrangements resulting in the exchange of DNA sequences between genes. This results in the formation of a new combined gene. They have been implicated in carcinogenesis in a number of different cancers, though they have been understudied in high grade serous ovarian cancer. This study used high throughput tools to compare the transcriptome of high grade serous ovarian cancer and normal fallopian tubes in the interest of identifying unique fusion transcripts within each group. Indeed, we found that there were significantly more fusion transcripts in the cancer samples relative to the normal fallopian tubes. Following this, the role of fusion transcripts in chemo-response and overall survival was investigated. This led to the identification of fusion transcripts significantly associated with overall survival. Validation was performed with different analytical platforms and different algorithms to find fusion transcripts.

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

MOTIVATION

Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor.

RESULTS

Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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.

FOXM1 Inhibition in Ovarian Cancer Tissue Cultures Affects Individual Treatment Susceptibility Ex Vivo

Simple Summary

Late diagnosis of ovarian cancer is a major reason for the high mortality rate of this tumor entity. The time to determine tumor susceptibility to treatment is scarce and resistance to therapy occurs very frequently. Here, we aim for a model system that can determine tumor response to (I) study novel drugs and (II) enhance patient stratification. Tissue specimens (n = 10) were acquired from fresh surgical samples. Tissue cultures were cultivated and treated with clinically relevant therapeutics and an FOXM1 inhibitor for 3–6 days. The transcription factor FOXM1 is a key regulator of tumor survival affecting multiple cancerogenic target genes. Gene expression of FOXM1 and its targets BRCA1/2 and RAD51 were investigated together with tumor susceptibility. Tissue cultures successfully demonstrated the individual benefit of FOXM1 inhibition and revealed the potency of the complex model system for oncological research.

Abstract

Diagnosis in an advanced state is a major hallmark of ovarian cancer and recurrence after first line treatment is common. With upcoming novel therapies, tumor markers that support patient stratification are urgently needed to prevent ineffective therapy. Therefore, the transcription factor FOXM1 is a promising target in ovarian cancer as it is frequently overexpressed and associated with poor prognosis. In this study, fresh tissue specimens of 10 ovarian cancers were collected to investigate tissue cultures in their ability to predict individual treatment susceptibility and to identify the benefit of FOXM1 inhibition. FOXM1 inhibition was induced by thiostrepton (3 µM). Carboplatin (0.2, 2 and 20 µM) and olaparib (10 µM) were applied and tumor susceptibility was analyzed by tumor cell proliferation and apoptosis in immunofluorescence microscopy. Resistance mechanisms were investigated by determining the gene expression of FOXM1 and its targets BRCA1/2 and RAD51. Ovarian cancer tissue was successfully maintained for up to 14 days ex vivo, preserving morphological characteristics of the native specimen. Thiostrepton downregulated FOXM1 expression in tissue culture. Individual responses were observed after combined treatment with carboplatin or olaparib. Thus, we successfully implemented a complex tissue culture model to ovarian cancer and showed potential benefit of combined FOXM1 inhibition.

A polygenic methylation prediction model associated with response to chemotherapy in epithelial ovarian cancer

To identify potential aberrantly differentially methylated genes (DMGs) correlated with chemotherapy response (CR) and establish a polygenic methylation prediction model of CR in epithelial ovarian cancer (EOC), we accessed 177 (47 chemo-sensitive and 130 chemo-resistant) samples corresponding to three DNA-methylation microarray datasets from the Gene Expression Omnibus and 306 (290 chemo-sensitive and 16 chemo-resistant) samples from The Cancer Genome Atlas (TCGA) database. DMGs associated with chemotherapy sensitivity and chemotherapy resistance were identified by several packages of R software. Pathway enrichment and protein-protein interaction (PPI) network analyses were constructed by Metascape software. The key genes containing mRNA expressions associated with methylation levels were validated from the expression dataset by the GEO2R platform. The determination of the prognostic significance of key genes was performed by the Kaplan-Meier plotter database. The key genes-based polygenic methylation prediction model was established by binary logistic regression. Among accessed 483 samples, 457 (182 hypermethylated and 275 hypomethylated) DMGs correlated with chemo resistance. Twenty-nine hub genes were identified and further validated. Three genes, anterior gradient 2 (AGR2), heat shock-related 70-kDa protein 2 (HSPA2), and acetyltransferase 2 (ACAT2), showed a significantly negative correlation between their methylation levels and mRNA expressions, which also corresponded to prognostic significance. A polygenic methylation prediction model (0.5253 cutoff value) was established and validated with 0.659 sensitivity and 0.911 specificity.

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.

miRNa signature in small extracellular vesicles and their association with platinum resistance and cancer recurrence in ovarian cancer

Carboplatin, administered as a single drug or in combination with paclitaxel, is the standard chemotherapy treatment for patients with ovarian cancer (OVCA). Recent evidence suggests that miRNAs associated with small extracellular vesicles (sEVs) participate in the development of chemoresistance. We studied the effect of carboplatin in a heterogeneity population of OVCA cells and their derived sEVs to identify mechanisms associated with chemoresistance. sEVs were quantified using an engineered superparamagnetic material, gold-loaded ferric oxide nanotubes and a screen-printed electrode. miR-21-3p, miR-21-5p, and miR-891-5p are enriched in sEVs, and they contribute to carboplatin resistance in OVCA. Using a quantitative MS/MS, miR-21-5p activates glycolysis and increases the expression of ATP-binding cassette family and a detoxification enzyme. miR-21-3p and miR-891-5p increase the expression of proteins involved in DNA repair mechanisms. Interestingly, the levels of miR-891-5p within sEVs are significantly higher in patients at risk of ovarian cancer relapse. Identification of miRNAs in sEVs also provides the opportunity to track them in biological fluids to potentially determine patient response to chemotherapy.

Dynamic analysis of N-glycomic and transcriptomic changes in the development of ovarian cancer cell line A2780 to its three cisplatin-resistant variants

Background

Platinum resistance development is a dynamic process that occurs during continuous chemotherapy and contributes to high mortality in ovarian cancer. Abnormal glycosylation has been reported in platinum resistance. Many studies on platinum resistance have been performed, but few of them have investigated platinum resistance-associated glycans based on N-glycomics. Moreover, glycomic alterations during platinum resistance development in ovarian cancer are rarely reported. Therefore, the objective of this study was to determine platinum resistance-related N-glycans in ovarian cancer cells during continuous exposure to cisplatin. These glycans might be involved in the mechanism of platinum resistance and serve as biomarkers to monitor its development.

Methods

This study mimicked the development of platinum resistance in ovarian cancer by continuously exposing A2780 cells to cisplatin. Cisplatin-resistant variants were confirmed by higher half maximal inhibitory concentration (IC₅₀) values and increased P-glycoprotein (ABCB1, P-gp) expression compared to A2780 cells. Analysis of dynamic N-glycomic changes during the development of platinum resistance in cisplatin-resistant variants was performed with MALDI-time-of-flight (TOF)-MS combined with ethyl esterification derivatization, which were used to discriminate between α2,3- and α2,6-linkage N-acetylneuraminic acid. N-glycan alterations were further validated on a glycotransferase level via transcriptome sequencing and real-time PCR (RT-PCR).

Results

Compared to the A2780 cells, MS analysis indicated that α2,3-linked sialic structures and N-glycan gal-ratios were significantly higher, while fucosylated glycans were lower in three cisplatin-resistant variants. Transcriptome sequencing and RT-PCR showed that gene expression of ST3GAL6 and MGAT4A increased, while gene expression of FUT11, FUT1, GMDS, and B4GALT5 decreased in three cisplatin-resistant variants.

Conclusions

Analysis of N-glycans and glycogene expression showed that α2,3-linked sialic structures might serve as biomarkers to monitor the development of platinum resistance and to guide individualized treatment of ovarian cancer patients.

A Qualitative Transcriptional Signature for Predicting Recurrence Risk for High-Grade Serous Ovarian Cancer Patients Treated With Platinum-Taxane Adjuvant Chemotherapy

Resistance to platinum and taxane adjuvant chemotherapy (ACT) is the main cause of the recurrence and poor prognosis of high-grade serous ovarian cancer (HGS-OvCa) patients receiving platinum-taxane ACT after surgery. However, currently reported quantitative transcriptional signatures, which are commonly based on risk scores summarized from gene expression, are unsuitable for clinical application because of their high sensitivity to experimental batch effects and quality uncertainties of clinical samples. Using 226 samples of HGS-OvCa patients receiving platinum-taxane ACT in TCGA, we developed a qualitative transcriptional signature, consisting of four gene pairs whose within-samples relative expression orderings could robustly predict patient recurrence-free survival (RFS). In two independent test datasets, the predicted non-responders had significantly shorter RFS than the predicted responders (log-rank p < 0.05). In a test dataset containing data for patient pathological response state, the signature reclassified 12 out of 22 pathological complete response patients as non-responders and two out of 16 pathological non-complete response patients as responders. Notably, the 12 predicted non-responders in the pathological complete response group had significantly shorter RFS than the predicted responders (log-rank p = 0.0122). This qualitative transcriptional signature, which is insensitive to experimental batch effects and quality uncertainties of clinical samples, can individually identify HGS-OvCa patients who are more likely to benefit from platinum-taxane adjuvant chemotherapy.

Large-scale integrated analysis of ovarian cancer tumors and cell lines identifies an individualized gene expression signature for predicting response to platinum-based chemotherapy

Heterogeneity in chemotherapeutic response is directly associated with prognosis and disease recurrence in patients with ovarian cancer (OvCa). Despite the significant clinical need, a credible gene signature for predicting response to platinum-based chemotherapy and for guiding the selection of personalized chemotherapy regimens has not yet been identified. The present study used an integrated approach involving both OvCa tumors and cell lines to identify an individualized gene expression signature, denoted as IndividCRS, consisting of 16 robust chemotherapy-responsive genes for predicting intrinsic or acquired chemotherapy response in the meta-discovery dataset. The robust performance of this signature was subsequently validated in 25 independent tumor datasets comprising 2215 patients and one independent cell line dataset, across different technical platforms. The IndividCRS was significantly correlated with the response to platinum therapy and predicted the improved outcome. Moreover, the IndividCRS correlated with homologous recombination deficiency (HRD) and was also capable of discriminating HR-deficient tumors with or without platinum-sensitivity for guiding HRD-targeted clinical trials. Our results reveal the universality and simplicity of the IndividCRS as a promising individualized genomic tool to rapidly monitor response to chemotherapy and predict the outcome of patients with OvCa.

Unraveling a difficult diagnosis: the tricks for early recognition of ovarian cancer

Epithelial ovarian cancer (EOC) is the predominant type of ovarian cancer (OC). The 5-year survival of patients has improved over the last three decades, although the overall cure rate of OC if about 30%. Despite high response rates after initial chemotherapy, most patients with advanced ovarian cancer ultimately develop the recurrent disease because of resistance to chemotherapy. A proper early diagnosis and treatment of patients with ovarian cancer are urgently needed. Nowadays the diagnosis is performed by means of clinical symptoms and signs, often indicators of a disease already at an advanced stage, tumor markers (CA125 and HE4), transvaginal ultrasonography and imaging, very useful in distinguishing adnexal masses. Understand the nature of an adnexal mass is the primary point to begin the diagnosis of OC. Validated different model to approach and characterize adnexal pathology preoperatively are described, such as the International Ovarian Tumor Analysis (IOTA) and the Assessment of Different NEoplasias in the AdneXa (ADNEX) model. New tumor markers, such as PRSS8, FOLR1, KLK6/7, GSTT1, and miRNAs, are getting ahead and are worth noting for early detection of ovarian cancer. Despite the development of numerous ultrasound models for the diagnosis of adnexal masses and the analysis of different tumor markers, the early diagnosis of ovarian cancer is still difficult to practice. Moreover, identifying genetic risk alleles, such as germline BRCA1 and BRCA2 mutations, for ovarian cancer has had a significant impact on disease prevention strategies.

Resistance to paclitaxel is associated with a variant of the gene BCL2 in multiple tumor types

Paclitaxel, the most commonly used form of chemotherapy, is utilized in curative protocols in different types of cancer. The response to treatment differs among patients. Biological interpretation of a mechanism to explain this personalized response is still unavailable. Since paclitaxel is known to target BCL2 and TUBB1, we used pan-cancer genomic data from hundreds of patients to show that a single-nucleotide variant in the BCL2 sequence can predict a patient’s response to paclitaxel. Here, we show a connection between this BCL2 genomic variant, its transcript structure, and protein abundance. We demonstrate these findings in silico, in vitro, in formalin-fixed paraffin-embedded (FFPE) tissue, and in patient lymphocytes. We show that tumors with the specific variant are more resistant to paclitaxel. We also show that tumor and normal cells with the variant express higher levels of BCL2 protein, a phenomenon that we validated in an independent cohort of patients. Our results indicate BCL2 sequence variations as determinants of chemotherapy resistance. The knowledge of individual BCL2 genomic sequences prior to the choice of chemotherapy may improve patient survival. The current work also demonstrates the benefit of community-wide, integrative omics data sources combined with in-lab experimentation and validation sets.

A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.

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.

Urine Biomarkers for the Early Detection of Ovarian Cancer - Are We There Yet?

Ovarian cancer affects around 7500 women in the United Kingdom every year. Despite this, there is no effective screening strategy or standard treatment for ovarian cancer. If diagnosed during stage I, ovarian cancer has a 90% 5-year survival rate; however, there is usually a masking of symptoms which leads to an often late-stage diagnosis and correspondingly poor survival rate. Current diagnostic methods are invasive and consist of a pelvic examination, transvaginal ultrasonography, and blood tests to detect cancer antigen 125 (CA125). Unfortunately, surgery is often still required to make a positive diagnosis. To address the need for accurate, specific, and non-invasive diagnostic methods, there has been an increased interest in biomarkers identified through non-invasive tests as tools for the earlier diagnosis of ovarian cancer. Although most studies have focused on the identification of biomarkers in blood, the ease of availability of urine and the high patient compliance rates suggest that it could provide a promising resource for the screening of patients for ovarian cancer.

Identifying disease-associated copy number variations by a doubly penalized regression model

Copy number variation (CNV) of DNA plays an important role in the development of many diseases. However, due to the irregularity and sparsity of the CNVs, studying the association between CNVs and a disease outcome or a trait can be challenging. Up to now, not many methods have been proposed in the literature for this problem. Most of the current researchers reply on an ad hoc two-stage procedure by first identifying CNVs in each individual genome and then performing an association test using these identified CNVs. This potentially leads to information loss and as a result a lower power to identify disease associated CNVs. In this article, we describe a new method that combines the two steps into a single coherent model to identify the common CNV across patients that are associated with certain diseases. We use a double penalty model to capture CNVs' association with both the intensities and the disease trait. We validate its performance in simulated datasets and a data example on platinum resistance and CNV in ovarian cancer genome.

Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker

This study aimed to investigate the feasibility of integrating image features computed from both spatial and frequency domain to better describe the tumor heterogeneity for precise prediction of tumor response to postsurgical chemotherapy in patients with advanced-stage ovarian cancer. A computer-aided scheme was applied to first compute 133 features from five categories namely, shape and density, fast Fourier transform, discrete cosine transform (DCT), wavelet, and gray level difference method. An optimal feature cluster was then determined by the scheme using the particle swarm optimization algorithm aiming to achieve an enhanced discrimination power that was unattainable with the single features. The scheme was tested using a balanced dataset (responders and non-responders defined using 6 month PFS) retrospectively collected from 120 ovarian cancer patients. By evaluating the performance of the individual features among the five categories, the DCT features achieved the highest predicting accuracy than the features in other groups. By comparison, a quantitative image marker generated from the optimal feature cluster yielded the area under ROC curve (AUC) of 0.86, while the top performing single feature only had an AUC of 0.74. Furthermore, it was observed that the features computed from the frequency domain were as important as those computed from the spatial domain. In conclusion, this study demonstrates the potential of our proposed new quantitative image marker fused with the features computed from both spatial and frequency domain for a reliable prediction of tumor response to postsurgical chemotherapy.

Prediction of therapy response in ovarian cancer: Where are we now?

Therapy resistance is a major challenge in the management of ovarian cancer (OC). Advances in detection and new technology validation have led to the emergence of biomarkers that can predict responses to available therapies. It is important to identify predictive biomarkers to select resistant and sensitive patients in order to reduce important toxicities, to reduce costs and to increase survival. The discovery of predictive and prognostic biomarkers for monitoring therapy is a developing field and provides promising perspectives in the era of personalized medicine. This review article will discuss the biology of OC with a focus on targetable pathways; current therapies; mechanisms of resistance; predictive biomarkers for chemotherapy, antiangiogenic and DNA-targeted therapies, and optimal cytoreductive surgery; and the emergence of liquid biopsy using recent studies from the Medline database and ClinicalTrials.gov.