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

2022 ◽  
Author(s):  
Nicholas Cardillo ◽  
Eric Devor ◽  
Silvana Pedra Nobre ◽  
Andreea Newtson ◽  
Kimberly Leslie ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Clinical Assessment ◽  
Genomic Data ◽  
Clinical Information ◽  
Micro Rna ◽  
The Cancer Genome Atlas ◽  
High Grade ◽  
Optimal Cytoreduction ◽  
Optimal Outcome ◽  
Single Nucleotide Variants

Abstract Background: 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 either primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal surgery is 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.Methods: 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 machine learning to incorporate this data with pre-operative clinical information to create predictive models which successfully predicted whether or not a patient’s cytoreductive surgery would have an optimal outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database. Results: Of the 124 models created and validated, 21 performed at least equal if not better than our historical clinical rate of optimal debulking in advanced-stage HGSC as a control, 78%. Conclusions: This is the first time tumor genomic data has been used to predict surgical outcome in ovarian cancer. 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.

scholarly journals Precision Oncology of High-Grade Ovarian Cancer Defined through Targeted Sequencing

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5240
Author(s):  
Sandra Wessman ◽  
Beatriz Bohorquez Fuentes ◽  
Therese Törngren ◽  
Anders Kvist ◽  
Georgia Kokaraki ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Molecular Characterization ◽  
Parp Inhibitor ◽  
Clinical Information ◽  
Undifferentiated Carcinoma ◽  
Serous Carcinoma ◽  
Precision Oncology ◽  
High Grade ◽  
Histologic Diagnosis ◽  
Germline Testing

Background: We examined whether molecular characterization of high-grade epithelial ovarian cancer can inform the diagnosis and/or identify potential actionable targets. Methods: All of the consecutively sequenced high-grade ovarian tumours with consent between 2014 until 2019 were included. A total of 274 tumours underwent next generation sequencing using a targeted panel. Results: Patients with high-grade ovarian epithelial cancer were consented to prospective molecular characterization. Clinical information was extracted from their medical record. Tumour DNA was subjected to sequencing, and selected patients received PARP inhibitor therapy. Conclusions: Tumours from 274 women were sequenced, including high-grade serous carcinoma (n = 252), clear cell carcinoma (n = 4), carcinosarcoma (n = 9), endometrioid carcinoma (n = 3), undifferentiated carcinoma (n = 1), and mixed tumours (n = 5). Genomic profiling did not influence histologic diagnosis. Mutations were identified in TP53, BRCA1, BRCA2, as well as additional homologous recombination repair pathway genes BARD1, ATR, CHEK2, PALB2, RAD51D, RAD50, SLX4, FANCA, RAD51C, and RAD54L. In addition, mutations in PTEN and CDKN2A were identified. Several somatic mutations with implications for germline testing were identified, including RMI1, STK11, and CDH1. Germline testing identified 16 previously unknown BRCA1/2 carriers. Finally, 20 patients were treated with the PARP inhibitor olaparib based on the sequencing results.

scholarly journals Development of a Genomic Signatures-Based Predictor of Initial Platinum-Resistance in Advanced High-Grade Serous Ovarian Cancer Patients

2021 ◽  
Vol 10 ◽  
Author(s):  
Yuan Li ◽  
Xiaolan Zhang ◽  
Yan Gao ◽  
Chunliang Shang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Platinum Resistance ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Single Nucleotide Variants ◽  
Tissue Samples ◽  
Platinum Sensitive ◽  
Platinum Based Chemotherapy

BackgroundHigh grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer. Although platinum-based chemotherapy has been the cornerstone for HGSOC treatment, nearly 25% of patients would have less than 6 months of interval since the last platinum chemotherapy, referred to as platinum-resistance. Currently, no precise tools to predict platinum resistance have been developed yet.MethodsNinety-nine HGSOC patients, who have finished cytoreductive surgery and platinum-based chemotherapy in Peking University Third Hospital from 2018 to 2019, were enrolled. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) were performed on the collected tumor tissue samples to establish a platinum-resistance predictor in a discovery cohort of 57 patients, and further validated in another 42 HGSOC patients.ResultsA high prevalence of alterations in DNA damage repair (DDR) pathway, including BRCA1/2, was identified both in the platinum-sensitive and resistant HGSOC patients. Compared with the resistant subgroup, there was a trend of higher prevalence of homologous recombination deficiency (HRD) in the platinum-sensitive subgroup (78.95% vs. 47.37%, p=0.0646). Based on the HRD score, microhomology insertions and deletions (MHID), copy number changes load, duplication load of 1–100 kb, single nucleotide variants load, and eight other mutational signatures, a combined predictor of platinum-resistance, named as DRDscore, was established. DRDscore outperformed in predicting the platinum-sensitivity than the previously reported biomarkers with a predictive accuracy of 0.860 at a threshold of 0.7584. The predictive performance of DRDscore was validated in an independent cohort of 42 HGSOC patients with a sensitivity of 90.9%.ConclusionsA multi-genomic signature-based analysis enabled the prediction of initial platinum resistance in advanced HGSOC patients, which may serve as a novel assessment of platinum resistance, provide therapeutic guidance, and merit further validation.

scholarly journals Predicting STAT1 as a prognostic marker in patients with solid cancer

2020 ◽  
Vol 12 ◽  
pp. 175883592091755
Author(s):  
Jinguo Zhang ◽  
Fanchen Wang ◽  
Fangran Liu ◽  
Guoxiong Xu
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Value ◽  
Renal Carcinoma ◽  
The Cancer Genome Atlas ◽  
Solid Cancer ◽  
Lower Grade ◽  
High Grade ◽  
Large Tumor ◽  
Normal Tissues ◽  
Tumor Types

Background: Aberrant activities of signal transducer and activator of transcription 1 (STAT1) have been implicated in cancer development. However, the prognostic value of STAT1 remains unclear. This report identified the role of STAT1 in prognosis in patients with solid cancer through open literature and The Cancer Genome Atlas (TCGA) database. Methods: Published articles were obtained from PubMed, Web of Science, and Embase databases according to a search strategy up to October 2019. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were extracted to assess the prognostic factors of patients. TCGA datasets were used to explore the prognostic value of STAT1 in various cancers. Results: A total of 15 studies incorporating 2839 patients with solid cancers were included. Pooled data showed that overexpressed STAT1 favored long overall survival (OS) (HR = 0.604, 95% CI = 0.431–0.846, p = 0.003) and disease-specific survival (DSS) (HR = 0.650, 95% CI = 0.512–0.825, p = 0.000). In subgroup analyses, highly expressed STAT1 was correlated with long OS of patients with high-grade serous ovarian cancer and oral squamous cell carcinoma. Data extracted from TCGA datasets unveiled that STAT1 expression was significantly higher in 12 cancers (e.g. bladder and breast) than their adjacent normal tissues. Again, highly expressed STAT1 favored long OS of patients with ovarian cancer as well as rectum adenocarcinoma, sarcoma, and skin cutaneous melanoma. However, in renal carcinoma, brain lower grade glioma, lung adenocarcinoma, and pancreatic cancer, highly expressed STAT1 was correlated with poor OS of patients. Particularly in renal carcinoma, increased STAT1 expression was associated with high grade, later stage, large tumor size, and lymph node and distant metastasis. Conclusion: STAT1 has been identified to have prognostic value in patients with solid cancer. Highly expressed STAT1 may predict prognosis in cancer patients based on their tumor types.

scholarly journals Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Nicole E. James ◽  
Katherine Miller ◽  
Natalie LaFranzo ◽  
Erin Lips ◽  
Morgan Woodman ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Immune Cell ◽  
Immune Escape ◽  
Progression Free Survival ◽  
The Cancer Genome Atlas ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Immunological Responses ◽  
Modeling Analysis ◽  
Immune Cell Subsets

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy worldwide, as patients are typically diagnosed at a late stage and eventually develop chemoresistant disease following front-line platinum-taxane based therapy. Only modest results have been achieved with PD-1 based immunotherapy in ovarian cancer patients, despite the fact that immunological responses are observed in EOC patients. Therefore, the goal of this present study was to identify novel immune response genes and cell subsets significantly associated with improved high grade serous ovarian cancer (HGSOC) patient prognosis. A transcriptomic-based immune modeling analysis was employed to determine levels of 8 immune cell subsets, 10 immune escape genes, and 22 co-inhibitory/co-stimulatory molecules in 26 HGSOC tumors. Multidimensional immune profiling analysis revealed CTLA-4, LAG-3, and Tregs as predictive for improved progression-free survival (PFS). Furthermore, the co-stimulatory receptor ICOS was also found to be significantly increased in patients with a longer PFS and positively correlated with levels of CTLA-4, PD-1, and infiltration of immune cell subsets. Both ICOS and LAG-3 were found to be significantly associated with improved overall survival in The Cancer Genome Atlas (TCGA) ovarian cancer cohort. Finally, PVRL2 was identified as the most highly expressed transcript in our analysis, with immunohistochemistry results confirming its overexpression in HGSOC samples compared to normal/benign. Results were corroborated by parallel analyses of TCGA data. Overall, this multidimensional immune modeling analysis uncovers important prognostic immune factors that improve our understanding of the unique immune microenvironment of ovarian cancer.

scholarly journals Gene networks and expression quantitative trait loci associated with platinum-based chemotherapy response in high-grade serous ovarian cancer

2020 ◽  
Author(s):  
Jihoon Choi ◽  
Anastasiya Tarnouskaya ◽  
Sean Nesdoly ◽  
Danai G. Topouza ◽  
Madhuri Koti ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Gene Networks ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Response ◽  
High Grade ◽  
Expression Quantitative Trait Loci ◽  
Platinum Based Chemotherapy ◽  
Trait Loci

Abstract Background A major impediment in the treatment of ovarian cancer is the relapse of platinum-resistant tumors, which occurs in approximately 25% of patients. A better understanding of the biological mechanisms underlying platinum-based response will improve treatment efficacy through genetic testing and novel therapies.Methods Using data from high-grade serous ovarian carcinoma (HGSOC) patients in the Cancer Genome Atlas (TCGA), we classified those who remained progression-free for 12 months following platinum-based chemotherapy as “chemo-sensitive” (N=160) and those who had recurrence within six months as “chemo-resistant” (N=110). Univariate and multivariate analysis of expression microarrays identified differentially expressed genes and co-expression gene networks associated with chemotherapy response. Moreover, we integrated genomics data to determine expression quantitative trait loci (eQTL).Results Differential expression of the Valosin-containing protein (VCP) gene and five co-expression gene networks were associated with chemotherapy response in HGSOC. VCP and the gene networks contribute to protein processing in the endoplasmic reticulum, which has been implicated in chemotherapy response. These findings were successfully replicated using independent replication cohort. Furthermore, 192 QTLs were associated with these gene networks and BRCA2 expression.Conclusion This study implicates both known and novel genes as well as biological networks underlying response to platinum-based chemotherapy among HGSOC patients.

scholarly journals Gene networks and expression quantitative trait loci associated with platinum-based chemotherapy response in high-grade serous ovarian cancer

2019 ◽  
Author(s):  
Jihoon Choi ◽  
Anastasiya Tarnouskaya ◽  
Sean Nesdoly ◽  
Danai G. Topouza ◽  
Madhuri Koti ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Gene Networks ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Response ◽  
High Grade ◽  
Expression Quantitative Trait Loci ◽  
Platinum Based Chemotherapy ◽  
Trait Loci

AbstractBackgroundA major impediment in the treatment of ovarian cancer is the relapse of platinum-resistant tumors, which occurs in approximately 25% of patients. A better understanding of the biological mechanisms underlying platinum-based response will improve treatment efficacy through genetic testing and novel therapies.MethodsUsing data from high-grade serous ovarian carcinoma (HGSOC) patients in the Cancer Genome Atlas (TCGA), we classified those who remained progression-free for 12 months following platinum-based chemotherapy as “chemo-sensitive” (N=160) and those who had recurrence within six months as “chemo-resistant” (N=110). Univariate and multivariate analysis of expression microarrays identified differentially expressed genes and co-expression gene networks associated with chemotherapy response. Moreover, we integrated genomics data to determine expression quantitative trait loci (eQTL).ResultsDifferential expression of the Valosin-containing protein (VCP) gene and five co-expression gene networks were associated with chemotherapy response in HGSOC. VCP and the gene networks contribute to protein processing in the endoplasmic reticulum, which has been implicated in chemotherapy response. These findings were successfully replicated using independent replication cohort. Furthermore, 192 QTLs were associated with these gene networks and BRCA2 expression.ConclusionThis study implicates both known and novel genes as well as biological networks underlying response to platinum-based chemotherapy among HGSOC patients.

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

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 678 ◽  
Author(s):  
Dugo ◽  
Devecchi ◽  
De Cecco ◽  
Cecchin ◽  
Mezzanzanica ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Copy Number ◽  
The Cancer Genome Atlas ◽  
Molecular Heterogeneity ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Disease Relapse ◽  
Clinical Prognosis ◽  
Primary Surgery ◽  
Altered Gene

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

scholarly journals USP19 and RPL23 as Candidate Prognostic Markers for Advanced-Stage High-Grade Serous Ovarian Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 3976
Author(s):  
Haeyoun Kang ◽  
Min Chul Choi ◽  
Sewha Kim ◽  
Ju-Yeon Jeong ◽  
Ah-Young Kwon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Ovarian Carcinoma ◽  
Prognostic Markers ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Mrna Levels ◽  
High Grade ◽  
Advanced Stage ◽  
Serous Ovarian Carcinoma

Ovarian cancer is one of the leading causes of deaths among patients with gynecological malignancies worldwide. In order to identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. In order to predict the prognosis of patients with HGSC, we created machine learning-based models and identified USP19 and RPL23 as candidate prognostic markers. Specifically, patients with lower USP19 mRNA levels and those with higher RPL23 mRNA levels had worse prognoses. This model was then used to analyze the data of patients with HGSC hosted on The Cancer Genome Atlas; this analysis validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of USP19 and RPL23 determined using a machine-learning model could serve as prognostic markers for patients with HGSC receiving conventional therapy.

scholarly journals Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10817
Author(s):  
Huiting Xiao ◽  
Kun Wang ◽  
Dan Li ◽  
Ke Wang ◽  
Min Yu
Keyword(s):  
Ovarian Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Residual Tumor ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Diagnostic Biomarker

Background Malignant ovarian cancer is associated with the highest mortality of all gynecological tumors. Designing therapeutic targets that are specific to OC tissue is important for optimizing OC therapies. This study aims to identify different expression patterns of genes related to FGFR1 and the usefulness of FGFR1 as diagnostic biomarker for OC. Methods We collected data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. In the TCGA cohort we analyzed clinical information according to patient characteristics, including age, stage, grade, longest dimension of the tumor and the presence of a residual tumor. GEO data served as a validation set. We obtained data on differentially expressed genes (DEGs) from the two microarray datasets. We then used gene set enrichment analysis (GSEA) to analyze the DEG data in order to identify enriched pathways related to FGFR1. Results Differential expression analysis revealed that FGFR1 was significantly downregulated in OC specimens. 303 patients were included in the TCGA cohort. The GEO dataset confirmed these findings using information on 75 Asian patients. The GSE105437 and GSE12470 database highlighted the significant diagnostic value of FGFR1 in identifying OC (AUC = 1, p = 0.0009 and AUC = 0.8256, p = 0.0015 respectively). Conclusions Our study examined existing TCGA and GEO datasets for novel factors associated with OC and identified FGFR1 as a potential diagnostic factor. Further investigation is warranted to characterize the role played by FGFR1 in OC.

scholarly journals Construction of a ceRNA network of hub genes affecting immune infiltration in ovarian cancer identified by WGCNA

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rongjia Su ◽  
Chengjuan Jin ◽  
Lina Zhou ◽  
Yannan Cao ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Hub Genes ◽  
The Real ◽  
Cerna Network

Abstract Background Ovarian cancer is the leading cause of death among gynecological malignancies. Immunotherapy has demonstrated potential effects in ovarian cancer. However, few studies on immune-related prognostic signatures in ovarian cancer have been reported. This study aimed to identify hub genes associated with immune infiltrates to provide insight into the immune regulatory mechanisms in ovarian cancer. Methods Raw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and University of California, Santa Cruz (UCSC) Xena websites. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were used to identify hub genes. Kaplan-Meier analysis and differential expression analysis were applied to explore the real hub genes. Results Through ssGSEA and WGCNA, 7 hub genes (LY9, CD5, CXCL9, IL2RG, SLAMF1, SLAMF6, and SLAMF7) were identified. Finally, LY9 and SLAMF1 were recognized as the real hub genes in immune infiltrates of ovarian cancer. LY9 and SLAMF1 are classified as SLAM family receptors involved in the activation of hematopoietic cells and the pathogenesis of multiple malignancies. Furthermore, 12 lncRNAs and 43 miRNAs significantly related to the 2 hub genes were applied to construct a lncRNA-miRNA-mRNA ceRNA network. The lncRNA-miRNA-mRNA ceRNA network shows upstream regulatory sites of the 2 hub genes. Conclusions These findings improve our understanding of the regulatory mechanism of and reveal potential immune checkpoints for immunotherapy for ovarian cancer.

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