Somatic copy number alteration burden and TP53 status drive radio-resistance in endometrial cancer

5511 Background: Endometrial cancer is classified by tumor stage, histologic subtype and grade. However, a substantial proportion of presumed non-high risk cases recur, supporting the need for improved tools of prognostication. Methods: Using clinical and Affymetrix SNP 6.0 data from The Cancer Genome Atlas (TCGA) endometrial carcinoma project, we identified 4 somatic copy number alteration (SCNA) subtypes, established their prognostic value and validated them in an independent, population-based cohort from Norway. Patients had endometrioid, uterine papillary serous carcinoma (UPSC) or mixed histology tumors. Progression-free survival (PFS) was defined as time from diagnosis to recurrence or progression, and estimated by the Kaplan-Meier method. Results: Four groups of SCNA patterns were identified using hierarchical clustering: low SCNA, moderate SCNA, SCNA dominated by 1q amplification (1q amplified) and high SCNA level (serous-like). Their prognostic value was assessed in all TCGA patients (N = 292) and in a low risk subset with endometrioid histology, stage 1 disease (N = 210). In the full TCGA cohort, patients with low SCNA (reference group) had excellent 2-year PFS of 94%, while for moderate SCNA it was 84% (hazard ratio[HR] 2.7, p = .08). The 1q amplified and serous-like groups had significantly worse outcomes with 2-year PFS of 74% (HR 5.9, p= .002) and 74% (HR 6.0, p <.001), respectively. On multivariable analysis, adjusting for variables including stage and grade, 1q amplified and serous-like SCNA patterns remained independently prognostic (respectively, adjusted HR 6.2, p = .002 and 4.7, p = .02). Similar results were found in the low risk subset. The prognostic value of the SCNA patterns was validated in an independent group of patients with low risk disease (N = 57). 5-year PFS was 91% for low SCNA, 83% for moderate SCNA (HR 2.0, p = .58), 72% for 1q amplified (HR 3.7, p = .11) and 50% for the serous-like SCNA group (HR 6.7, p = .04). Conclusions: Four subtypes of DNA SCNA patterns in endometrial cancer were identified and validated to be prognostic of outcome. These novel biomarkers may be useful in guiding therapeutic decisions, and shed insight on the biology of more, or less, aggressive endometrial cancer.

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Genome-wide somatic copy number alteration analysis and database construction for cervical cancer

Molecular Genetics and Genomics ◽

10.1007/s00438-019-01636-x ◽

2020 ◽

Vol 295 (3) ◽

pp. 765-773 ◽

Cited By ~ 1

Author(s):

Hong Luo ◽

Xiaohan Xu ◽

Jian Yang ◽

Kun Wang ◽

Chen Wang ◽

...

Keyword(s):

Cervical Cancer ◽

Copy Number ◽

Copy Number Alteration ◽

Database Construction ◽

Genome Wide ◽

Somatic Copy Number Alteration

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Identification of Tumor Mutation Burden, Microsatellite Instability, and Somatic Copy Number Alteration derived 9-gene Signatures to Predict Clinical Outcomes in STAD

10.21203/rs.3.rs-956254/v1 ◽

2021 ◽

Author(s):

Chuanzhi Chen ◽

Yi Chen ◽

Xin Jin ◽

Yongfeng Ding ◽

Junjie Jiang ◽

...

Keyword(s):

Microsatellite Instability ◽

Copy Number ◽

Copy Number Alteration ◽

External Validation ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Predictive Tool ◽

Tumor Mutation Burden ◽

Mutation Burden ◽

Somatic Copy Number Alteration

Abstract Background: Genomic features including tumor mutation burden (TMB), microsatellite instability (MSI) and somatic copy number alteration (SCNA), had been demonstrated to be involved with the tumor microenvironment (TME) and outcome of gastric cancer (GC). Methods: We obtained profiles of TMB, MSI and SCNA by processing 405 GC data from The Cancer Genome Atlas (TCGA), then conducted a comprehensive analysis though “iClusterPlus”. Another independent Gene Expression Omnibus (GEO) contained specimens from 109 GC patients was designed as an external validation. Results: Two subgroups were generated, with distinguished prognosis, somatic mutation burden, copy number changes and immune landscape. We revealed that Cluster1 was marked by a better prognosis, accompanied by higher TMB, MSIsensor score, TMEscore, and lower SCNA burden. Based on these clusters, we screened 196 differentially expressed genes (DEGs), which were subsequently projected into univariate Cox survival analysis. Thus, we constructed a 9-gene immune risk score (IRS) model using lasso penalized logistic regression. Moreover, the prognostic prediction of IRS was verified by receiver operating characteristic (ROC) curve analysis and nomogram plot.Conclusions: Our works suggested that the 9‐gene‐signature prediction model, which derived from TMB, MSI, SCNA was a promising predictive tool for clinical outcome in GC patients. This novel methodology may help clinicians uncover the underlying mechanisms and guide future treatment strategies.

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DEFOR: depth- and frequency-based somatic copy number alteration detector

Bioinformatics ◽

10.1093/bioinformatics/btz170 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3824-3825 ◽

Cited By ~ 1

Author(s):

He Zhang ◽

Xiaowei Zhan ◽

James Brugarolas ◽

Yang Xie

Keyword(s):

Exome Sequencing ◽

Copy Number ◽

Copy Number Alteration ◽

High Throughput Sequencing ◽

Supplementary Information ◽

Copy Number Alterations ◽

Sequencing Data ◽

Sequencing Technology ◽

Somatic Copy Number Alterations ◽

Somatic Copy Number Alteration

Abstract Motivation Detection of somatic copy number alterations (SCNAs) using high-throughput sequencing has become popular because of rapid developments in sequencing technology. Existing methods do not perform well in calling SCNAs for the unstable tumor genomes. Results We developed a new method, DEFOR, to detect SCNAs in tumor samples from exome-sequencing data. The evaluation showed that DEFOR has a higher accuracy for SCNA detection from exome sequencing compared with the five existing tools. This advantage is especially apparent in unstable tumor genomes with a large proportion of SCNAs. Availability and implementation DEFOR is available at https://github.com/drzh/defor. Supplementary information Supplementary data are available at Bioinformatics online.

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