Prediction of Epithelial Ovarian Cancer Outcomes With Integration of Genomic Data

Newtson, A.M.; Devor, Eric J.; Bosquet, Jesús González

doi:10.1097/grf.0000000000000493

Cited by 6 publications

(9 citation statements)

References 40 publications

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“…Clinical and pathological data were collected from the electronic medical record. Clinical variables that were previously observed to be associated with chemo-response were included in the data collection [ 26 ]. Only baseline clinical and pathological characteristics that can be obtained before starting initial chemotherapy were included.…”

Section: Methodsmentioning

confidence: 99%

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Cardillo

Russo

Newtson

et al. 2021

IJMS

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Long non-coding RNA’s (lncRNA) are RNA sequences that do not encode proteins and are greater than 200 nucleotides in length. They regulate complex cellular mechanisms and have been associated with prognosis in various types of cancer. We aimed to identify lncRNA sequences that are associated with high grade serous ovarian cancer (HGSC) and assess their impact on overall survival. RNA was extracted from 112 HGSC patients and 12 normal fallopian tube samples from our Biobank tissue repository. RNA was sequenced and the Ultrafast and Comprehensive lncRNA detection and quantification pipeline (UClncR) was used for the identification of lncRNA sequences. Univariate logistic and multivariate lasso regression analyses identified lncRNA that was associated with HGSC. Univariate and multivariate Cox proportional hazard ratios were used to evaluate independent predictors of survival. 1943 of 16,325 investigated lncRNA’s were differentially expressed in HGSC as compared to controls (p < 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA’s associated with survival in HGSC. Three out of these four were found to be independently significant after accounting for all clinical covariates. Lastly, seven lncRNAs were independently associated with initial response to chemotherapy; four portended a worse response, while three were associated with improved response. More research is needed, but there is potential for these lncRNAs to be used as biomarkers of HGSC or predictors of treatment outcome in the future.

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Section: Methodsmentioning

confidence: 99%

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Cardillo

Russo

Newtson

et al. 2021

IJMS

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“…A strength of this study is that we used diverse databases of genomic and clinical variables to build prediction models of response. We postulated that a complete database containing all variables involved in malignant cell functions would make prediction models more accurate 22,27,31,65 . Therefore, we extracted as much information from the HGSC specimens as possible to improve our models.…”

Section: Discussionmentioning

confidence: 99%

“…Clinical and pathological data were collected from the electronic medical record. Clinical variables previously observed to be associated with treatment response were included in the data collection 27 . Only baseline clinical and pathological characteristics that can be obtained before starting initial chemotherapy were collected.…”

Section: Patient Inclusion Criteria Ovarian Cancer Patients With Higmentioning

confidence: 99%

“…Not all clinical data used in the UI cohort was available in TCGA. www.nature.com/scientificreports/ This is a limitation of using TCGA data for model building 27 . Based on these inclusion criteria, there were a total of 189 patients classified as responders and 149 as non-responders in TCGA for validation of prediction models ( Supplementary Table S2).…”

Section: Model Validationmentioning

confidence: 99%

“…Using publicly available multi-dimensional datasets with clinical data, like TCGA, we previously built prediction models that distinguish between different outcomes in ovarian and endometrial cancer; we validated these models in independent datasets [22][23][24][25][26][27] . However, these models had some limitations due to suboptimal clinical data: many patients were lost to follow-up, and others had little information about clinical variables that influenced treatment response, such as stage of disease or number of cycles of chemotherapy.…”

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confidence: 99%

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

Bosquet

Devor

Newtson

et al. 2021

Sci Rep

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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.

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A novel extrachromosomal circular DNA related genes signature for overall survival prediction in patients with ovarian cancer

et al. 2023

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Objective Ovarian cancer (OV) has a high mortality rate all over the world, and extrachromosomal circular DNA (eccDNA) plays a key role in carcinogenesis. We wish to study more about the molecular structure of eccDNA in the UACC-1598–4 cell line and how its genes are associated with ovarian cancer prognosis. Methods We sequenced and annotated the eccDNA by Circle_seq of the OV cell line UACC-1598–4. To acquire the amplified genes of OV on eccDNA, the annotated eccDNA genes were intersected with the overexpression genes of OV in TCGA. Univariate Cox regression was used to find the genes on eccDNA that were linked to OV prognosis. The least absolute shrinkage and selection operator (LASSO) and cox regression models were used to create the OV prognostic model, as well as the receiver operating characteristic curve (ROC) curve and nomogram of the prediction model. By applying the median value of the risk score, the samples were separated into high-risk and low-risk groups, and the differences in immune infiltration between the two groups were examined using ssGSEA. Results EccDNA in UACC-1598–4 has a length of 0-2000 bp, and some of them include the whole genes or gene fragments. These eccDNA originated from various parts of chromosomes, especially enriched in repeatmasker, introns, and coding regions. They were annotated with 2188 genes by Circle_seq. Notably, the TCGA database revealed that a total of 198 of these eccDNA genes were overexpressed in OV (p < 0.05). They were mostly enriched in pathways associated with cell adhesion, ECM receptors, and actin cytoskeleton. Univariate Cox analysis showed 13 genes associated with OV prognosis. LASSO and Cox regression analysis were used to create a risk model based on remained 9 genes. In both the training (TCGA database) and validation (International Cancer Genome Consortium, ICGC) cohorts, a 9-gene signature could successfully discriminate high-risk individuals (all p < 0.01). Immune infiltration differed significantly between the high-risk and low-risk groups. The model’s area under the ROC curve was 0.67, and a nomograph was created to assist clinician. Conclusion EccDNA is found in UACC-1598–4, and part of its genes linked to OV prognosis. Patients with OV may be efficiently evaluated using a prognostic model based on eccDNA genes, including SLC7A1, NTN1, ADORA1, PADI2, SULT2B1, LINC00665, CILP2, EFNA5, TOMM.

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Prediction of Epithelial Ovarian Cancer Outcomes With Integration of Genomic Data

Cited by 6 publications

References 40 publications

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

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

A novel extrachromosomal circular DNA related genes signature for overall survival prediction in patients with ovarian cancer

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