scholarly journals 592 INPP4B gene is frequently deregulated via copy number alteration and underexpression in ovarian cancer

2021 ◽  
Author(s):  
IK Rzepecka ◽  
B Konopka ◽  
A Podgorska ◽  
R Lotocka ◽  
EM Cybulska ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Copy Number ◽  
Copy Number Alteration

EP970 PIK3R1mRNA expression is associated with copy number alteration in ovarian cancer

2019 ◽  
Author(s):  
IK Rzepecka ◽  
B Konopka ◽  
A Podgorska ◽  
A Stachurska ◽  
R Lotocka ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Copy Number ◽  
Copy Number Alteration

Outlier reset CUSUM for the exploration of copy number alteration data

2015 ◽  
Vol 14 (4) ◽  
Author(s):  
Yinglei Lai ◽  
Joseph L. Gastwirth
Keyword(s):  
Ovarian Cancer ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Simulated Data ◽  
Threshold Value ◽  
The Cancer Genome Atlas ◽  
Research Network ◽  
Data Set ◽  
Cusum Procedure ◽  
Truncated Normal

AbstractCopy number alteration (CNA) data have been collected to study disease related chromosomal amplifications and deletions. The CUSUM procedure and related plots have been used to explore CNA data. In practice, it is possible to observe outliers. Then, modifications of the CUSUM procedure may be required. An outlier reset modification of the CUSUM (ORCUSUM) procedure is developed in this paper. The threshold value for detecting outliers or significant CUSUMs can be derived using results for sums of independent truncated normal random variables. Bartel’s non-parametric test for autocorrelation is also introduced to the analysis of copy number variation data. Our simulation results indicate that the ORCUSUM procedure can still be used even in the situation where the degree of autocorrelation level is low. Furthermore, the results show the outlier’s impact on the traditional CUSUM’s performance and illustrate the advantage of the ORCUSUM’s outlier reset feature. Additionally, we discuss how the ORCUSUM can be applied to examine CNA data with a simulated data set. To illustrate the procedure, recently collected single nucleotide polymorphism (SNP) based CNA data from The Cancer Genome Atlas (TCGA) Research Network is analyzed. The method is applied to a data set collected in an ovarian cancer study. Three cytogenetic bands (cytobands) are considered to illustrate the method. The cytobands 11q13 and 9p21 have been shown to be related to ovarian cancer. They are presented as positive examples. The cytoband 3q22, which is less likely to be disease related, is presented as a negative example. These results illustrate the usefulness of the ORCUSUM procedure as an exploratory tool for the analysis of SNP based CNA data.

The functional consequences and prognostic value of dosage sensitivity in ovarian cancer

2017 ◽  
Vol 13 (2) ◽  
pp. 380-391 ◽  
Author(s):  
Zichuang Yan ◽  
Yongjing Liu ◽  
Yunzhen Wei ◽  
Ning Zhao ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Tumor Growth ◽  
Copy Number ◽  
Prognostic Value ◽  
Copy Number Alteration ◽  
Genetic Variations ◽  
Metastatic Spread ◽  
Important Class ◽  
Functional Consequences ◽  
Dosage Sensitivity

Copy number alteration (CNA) represents an important class of genetic variations that may contribute to tumorigenesis, tumor growth and metastatic spread.

scholarly journals Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 225
Author(s):  
Claudia Cava ◽  
Soudabeh Sabetian ◽  
Isabella Castiglioni
Keyword(s):  
Breast Cancer ◽  
Cancer Therapy ◽  
Protein Interaction Network ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Copy Number Alteration ◽  
Interaction Network ◽  
Computational Approach ◽  
Patient Specific ◽  
Basal Breast Cancer

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

scholarly journals Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 118
Author(s):  
Louisa Lepkes ◽  
Mohamad Kayali ◽  
Britta Blümcke ◽  
Jonas Weber ◽  
Malwina Suszynska ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
In Silico ◽  
Copy Number ◽  
Gc Content ◽  
Copy Number Variations ◽  
Cancer Predisposition ◽  
Predictive Values ◽  
Prediction Tools ◽  
Predisposition Genes ◽  
Female Index

The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than BRCA1/2, mostly in ATM, CHEK2, and RAD51C and less frequently in BARD1, MLH1, MSH2, PALB2, PMS2, RAD51D, and TP53. The Sophia Genetics DDM software showed the highest sensitivity; six CNVs were missed by at least one of the non-commercial tools. The positive predictive values ranged from 5.9% (74/1249) for panelcn.MOPS to 79.1% (72/91) for ExomeDepth. Verification of in silico predicted CNVs is required due to high frequencies of false positive predictions, particularly affecting target regions at the extremes of the GC content or target length distributions. CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further BC/OC predisposition genes.

scholarly journals Copy Number Alteration Profile Provides Additional Prognostic Value for Acute Lymphoblastic Leukemia Patients Treated on BFM Protocols

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3289
Author(s):  
Μirella Αmpatzidou ◽  
Lina Florentin ◽  
Vassilios Papadakis ◽  
Georgios Paterakis ◽  
Marianna Tzanoudaki ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Lymphoblastic Leukemia ◽  
Risk Index ◽  
Outcome Predictor ◽  
Distinct Subgroup ◽  
Genomic Risk ◽  
Good Risk

We present our data of a novel proposed CNA-profile risk-index, applied on a Greek ALLIC-BFM-treated cohort, aiming at further refining genomic risk-stratification. Eighty-five of 227 consecutively treated ALL patients were analyzed for the copy-number-status of eight genes (IKZF1/CDKN2A/2B/PAR1/BTG1/EBF1/PAX5/ETV6/RB1). Using the MLPA-assay, patients were stratified as: (1) Good-risk(GR)-CNA-profile (n = 51), with no deletion of IKZF1/CDKN2A/B/PAR1/BTG1/EBF1/PAX5/ETV6/RB1 or isolated deletions of ETV6/PAX5/BTG1 or ETV6 deletions with a single additional deletion of BTG1/PAX5/CDKN2A/B. (2) Poor-risk(PR)-CNA-profile (n = 34), with any deletion of ΙΚΖF1/PAR1/EBF1/RB1 or any other CΝΑ. With a median follow-up time of 49.9 months, EFS for GR-CNA-profile and PR-CNA-profile patients was 96.0% vs. 57.6% (p < 0.001). For IR-group and HR-group patients, EFS for the GR-CNA/PR-CNA subgroups was 100.0% vs. 60.0% (p < 0.001) and 88.2% vs. 55.6% (p = 0.047), respectively. Among FC-MRDd15 + patients (MRDd15 ≥ 10−4), EFS rates were 95.3% vs. 51.7% for GR-CNA/PR-CNA subjects (p < 0.001). Similarly, among FC-MRDd33 + patients (MRDd33 ≥ 10−4), EFS was 92.9% vs. 27.3% (p < 0.001) and for patients FC-MRDd33 − (MRDd33 < 10−4), EFS was 97.2% vs. 72.7% (p = 0.004), for GR-CNA/PR-CNA patients, respectively. In a multivariate analysis, the CNA-profile was the most important outcome predictor. In conclusion, the CNA-profile can establish a new genomic risk-index, identifying a distinct subgroup with increased relapse risk among the IR-group, as well as a subgroup of patients with superior prognosis among HR-patients. The CNA-profile is feasible in BFM-based protocols, further refining MRD-based risk-stratification.

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