Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variants

Luo, Zhihui; Fan, Xinping; Yao, Su; Huang, Yu S.

doi:10.1093/bioinformatics/bty043

Cited by 23 publications

(23 citation statements)

References 35 publications

(56 reference statements)

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“…For deletion, the integer copy number values corresponding to 5%, 10%, and 15% were 1.47, 1.65, and 1.80 copies, respectively. Although ABSOLUTE is widely used for estimating the ploidy and purity of samples, this algorithm tends to overestimate ploidy [30]. Thus, in this study, we chose 10% values corresponding to 3.07 copies and 1.65 copies for amplification and deletion, respectively, after considering overestimation, although this threshold may be rather strict.…”

Section: Resultsmentioning

confidence: 99%

Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer

Soh

Cho

Choi

et al. 2018

Cancers

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MicroRNAs (miRNAs) are key molecules that regulate biological processes such as cell proliferation, differentiation, and apoptosis in cancer. Somatic copy number alterations (SCNAs) are common genetic mutations that play essential roles in cancer development. Here, we investigated the association between miRNAs and SCNAs in cancer. We collected 2538 tumor samples for seven cancer types from The Cancer Genome Atlas. We found that 32−84% of miRNAs are in SCNA regions, with the rate depending on the cancer type. In these regions, we identified 80 SCNA-miRNAs whose expression was mainly associated with SCNAs in at least one cancer type and showed that these SCNA-miRNAs are related to cancer by survival analysis and literature searching. We also identified 58 SCNA-miRNAs common in the seven cancer types (CC-SCNA-miRNAs) and showed that these CC-SCNA-miRNAs are more likely to be related with protein and gene expression than other miRNAs. Furthermore, we experimentally validated the oncogenic role of miR-589. In conclusion, our results suggest that SCNA-miRNAs significantly alter biological processes related to cancer development, confirming the importance of SCNAs in non-coding regions in cancer.

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Section: Resultsmentioning

confidence: 99%

Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer

Soh

Cho

Choi

et al. 2018

Cancers

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“…11,12 Our study also showed high-purity group had more CNA events among all chromosomal locations. According to a previous study, 13 power to detect CNAs is highly dependent on the tumor purity, because that large fraction of copy-neutral DNA from noncancerous cells in low-purity tumors will significantly decrease the signal/noise ratio of CNAs. The results of our study have validated this point.…”

Section: Discussionmentioning

confidence: 99%

Tumor purity as a prognosis and immunotherapy relevant feature in gastric cancer

Gong

Zhang

2020

Cancer Medicine

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Tumor microenvironment (TME) has been illustrated their clinic pathological significance in predicting outcomes and therapeutic efficacy by more and more studies. Tumor purity, which reflects the features of TME, is defined as the proportion of cancer cell in the tumor tissue. However, the current staging and prognostic prediction system in gastric cancer (GC) paid little attention to TME. Therefore, we carried out the study to explore the role of tumor purity in GC. We retrospectively collected the clinical and transcriptomic data from four public data sets (n = 1340), GSE15459 , GSE26253 , GSE62254 , and The Cancer Genome Atlas (TCGA). About 34 GC patients from Fudan University Shanghai Cancer Center (FUSCC) were assigned as an independent validation group. Tumor purity was measured by a computational method. Low tumor purity was associated with unfavorable prognosis, upregulated EMT and stemness pathways, more infiltrating of Tregs, M1 and M2 macrophages and a higher expression level of various immune checkpoints and chemokines recruiting immune suppressive cells. Our study indicates low tumor purity in GC was associated with unfavorable prognosis and immune‐evasion phenotype. Further investigations toward tumor purity in GC may contribute to prognosis prediction and the decision of therapy strategies.

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“…Many methods have been proposed for tumor purity prediction, such as CONSENSUS [15] and ESTIMATE [14]. Other methods for tumor purity predictions consider methylation data [11–13] and copy number variation [8, 36, 37]. Most non-genomic (e.g., transcriptome- or methylome-based) purity prediction methods consider a subset of preselected stromal-cell-expressed genes or stromal-cell-specific methylation loci as predictors.…”

Section: Discussionmentioning

confidence: 99%

Putative biomarkers for predicting tumor sample purity based on gene expression data

Umbach

Bingham

et al. 2019

BMC Genomics

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BackgroundTumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-cancerous cells in a tumor.MethodsWe applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data.ResultsAcross the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted υσινγ expression data. We further confirmed that expression levels of a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) were predictive of tumor purity regardless of tumor type. We tested whether our set of ten genes could accurately predict tumor purity of a TCGA-independent data set. We showed that expression levels from our set of ten genes were highly correlated (ρ = 0.88) with the actual observed tumor purity.ConclusionsOur analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.

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Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variants

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 23 publications

References 35 publications

Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer

Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer

Tumor purity as a prognosis and immunotherapy relevant feature in gastric cancer

Putative biomarkers for predicting tumor sample purity based on gene expression data

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