scholarly journals Low-Coverage Sequencing of Urine Sediment DNA for Detection of Copy Number Aberrations in Bladder Cancer

2021 ◽  
Vol Volume 13 ◽  
pp. 1943-1953
Author(s):  
Yun-xi Cai ◽  
Xu Yang ◽  
Sheng Lin ◽  
Ya-wen Xu ◽  
Shan-wen Zhu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Copy Number ◽  
Urine Sediment ◽  
Copy Number Aberrations ◽  
Low Coverage

Low coverage copy number profiling of urinary sediments DNAs for bladder cancer molecular diagnostics

2019 ◽  
Vol 18 (1) ◽  
pp. e313
Author(s):  
H. Liu ◽  
X. Chen ◽  
M. Huang ◽  
N. Ouyang ◽  
K. Xu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Molecular Diagnostics ◽  
Copy Number ◽  
Low Coverage

Copy number aberrations using multicolour fluorescencein situhybridization (FISH) for prognostication in non-muscle-invasive bladder cancer (NIMBC)

2013 ◽  
Vol 113 (4) ◽  
pp. 662-667 ◽  
Author(s):  
Hideyasu Matsuyama ◽  
Kenzo Ikemoto ◽  
Satoshi Eguchi ◽  
Atsunori Oga ◽  
Shigeto Kawauchi ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Copy Number ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Copy Number Aberrations ◽  
Muscle Invasive

scholarly journals Accucopy: accurate and fast inference of allele-specific copy number alterations from low-coverage low-purity tumor sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinping Fan ◽  
Guanghao Luo ◽  
Yu S. Huang
Keyword(s):  
Copy Number ◽  
Bayesian Learning ◽  
Kernel Smoothing ◽  
Gaussian Mixture ◽  
Copy Number Alterations ◽  
Sequencing Data ◽  
Copy Numbers ◽  
Allele Specific ◽  
Tumor Sequencing ◽  
Low Coverage

Abstract Background Copy number alterations (CNAs), due to their large impact on the genome, have been an important contributing factor to oncogenesis and metastasis. Detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample remains a challenging task. Results We introduce Accucopy, a method to infer total copy numbers (TCNs) and allele-specific copy numbers (ASCNs) from challenging low-purity and low-coverage tumor samples. Accucopy adopts many robust statistical techniques such as kernel smoothing of coverage differentiation information to discern signals from noise and combines ideas from time-series analysis and the signal-processing field to derive a range of estimates for the period in a histogram of coverage differentiation information. Statistical learning models such as the tiered Gaussian mixture model, the expectation–maximization algorithm, and sparse Bayesian learning were customized and built into the model. Accucopy is implemented in C++ /Rust, packaged in a docker image, and supports non-human samples, more at http://www.yfish.org/software/. Conclusions We describe Accucopy, a method that can predict both TCNs and ASCNs from low-coverage low-purity tumor sequencing data. Through comparative analyses in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than Sclust, ABSOLUTE, and Sequenza.

scholarly journals Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations

2018 ◽  
Author(s):  
An-Shun Tai ◽  
Chien-Hua Peng ◽  
Shih-Chi Peng ◽  
Wen-Ping Hsieh
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Tumor Evolution ◽  
Depth Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Copy Number Aberrations

AbstractMultistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

scholarly journals MP51-12 INACTIVATION OF FOXA1 COOPERATES WITH PTEN COPY NUMBER LOSS IN BLADDER CANCER TO PROMOTE TUMOR HETEROGENEITY

2019 ◽  
Vol 201 (Supplement 4) ◽  
Author(s):  
Lauren Shuman* ◽  
Vasty Osei-Amponsa ◽  
Jenna Buckwalter ◽  
Zongyu Zheng ◽  
Hironobu Yamashita ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Copy Number Loss

scholarly journals Clinical significance of recurrent copy number aberrations in B-lineage acute lymphoblastic leukaemia without recurrent fusion genes across age cohorts

2017 ◽  
Vol 178 (4) ◽  
pp. 583-587 ◽  
Author(s):  
Monica Messina ◽  
Sabina Chiaretti ◽  
Anna Lucia Fedullo ◽  
Alfonso Piciocchi ◽  
Maria Cristina Puzzolo ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukaemia ◽  
Clinical Significance ◽  
Lymphoblastic Leukaemia ◽  
Copy Number ◽  
Fusion Genes ◽  
Age Cohorts ◽  
Copy Number Aberrations ◽  
B Lineage
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