Systematic pan-cancer analysis of tumour purity

Aran, Dvir; Sirota, Marina; Butte, Atul J.

doi:10.1038/ncomms9971

Cited by 949 publications

(1,066 citation statements)

References 44 publications

(49 reference statements)

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“…Distinction has historically been made histologically by morphologic features, such as nuclear atypia. However, a systematic pan-cancer comparison found poor correlation of a hematoxylin-eosin (H&E) stain-based tumor purity measure with gold standard genome-wide approaches (3).…”

Section: Defining Tumor Puritymentioning

confidence: 99%

Prognostic Relevance of Tumor Purity and Interaction with MGMT Methylation in Glioblastoma

Heuling

Knab

Radke

et al. 2017

Molecular Cancer Research

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Promoter methylation status of O-6-methylguanine-DNA methyltransferase (MGMT), a DNA repair enzyme, is a critical biomarker in glioblastoma (GBM), as treatment decisions and clinical trial inclusion rely on its accurate assessment. However, interpretation of results is complicated by poor interassay reproducibility as well as a weak correlation between methylation status and expression levels of MGMT. This study systematically investigates the influence of tumor purity on tissue subjected to MGMT analysis. A quantitative, allele-specific realtime PCR (qAS-PCR) assay was developed to determine genotype and mutant allele frequency of telomerase promoter (pTERT) mutations as a direct measure of tumor purity. We studied tumor purity, pTERT mutation by Sanger sequencing, MGMT methylation by pyrosequencing, IDH1 mutation status, and clinical parameters in a cohort of high-grade gliomas (n ¼ 97). The qAS-PCR reliably predicted pTERT genotype and tumor purity compared with independent methods. Tumor purity positively and significantly correlated with the extent of methylation in MGMT methylated GBMs. Extent of MGMT methylation differed significantly with respect to pTERT mutation hotspot (C228T vs. C250T). Interestingly, frontal lobe tumors showed greater tumor purity than those in other locations. Above all, tumor purity was identified as an independent prognostic factor in GBM. In conclusion, we determined mutual associations of tumor purity with MGMT methylation and pTERT mutations and found that the extent of MGMT methylation reflects tumor purity. In turn, tumor purity is prognostic in IDH1 wild-type GBM.Implications: Tumor purity is an independent prognostic marker in glioblastoma and is associated with the extent of MGMT methylation. Mol Cancer Res; 15(5); 532-40. Ó2017 AACR.

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Section: Defining Tumor Puritymentioning

confidence: 99%

Prognostic Relevance of Tumor Purity and Interaction with MGMT Methylation in Glioblastoma

Heuling

Knab

Radke

et al. 2017

Molecular Cancer Research

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“…Early work has also demonstrated potential to predict metastatic relapse in early breast cancer and to identify treatment resistance patterns by detection of ESR1 mutations (20)(21)(22). Potential limitations of ctDNA are based on the quantity (e.g., amount that is accessible in the peripheral blood) or quality (e.g., tumor purity of noncancer cells in the tumor microenvironment) (23).…”

Section: Introductionmentioning

confidence: 99%

Concordance of Genomic Alterations by Next-Generation Sequencing in Tumor Tissue versus Circulating Tumor DNA in Breast Cancer

Chae

Davis

Jain

et al. 2017

Molecular Cancer Therapeutics

115

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While identifying genomic alterations in tumor tissue is the current gold-standard technique for molecular profiling, circulating tumor DNA (ctDNA) represents a noninvasive method of assessing genomic alterations using peripheral blood. The concordance of genomic alterations between two commercially available ctDNA and tissue biopsies was compared in 45 patients with breast cancer using paired next-generation sequencing tissue and ctDNA biopsies. Across all genes, concordance between the two platforms was 91.0% to 94.2%. When only considering genomic alterations in either assay (e.g., excluding wild type/wild type genes), concordance was 10.8% to 15.1% with full plus partial concordance of 13.8% to 19.3%. Concordant mutations were associated with significantly higher variant allele frequency. Over half of mutations detected in either technique were not detected using the other biopsy technique. Including variants of unknown significance, the average number of alterations per patient was significantly higher for tissue (4.56) compared with ctDNA (2.16). When eliminating alterations not detectable in the ctDNA assay, mean number of alterations for tissue and ctDNA was similar (2.67 for tissue, 2.16 for ctDNA). Across five representative genes (TP53, PIK3CA, ERBB2, BRCA1, and BRCA2), sensitivity and specificity were 35.7% and 95.0%, respectively. Concordance when genomic alterations was detected in either tissue or ctDNA was low with each technique detecting a significant amount of nonoverlapping mutations. Potential explanations for the lack of concordance include tumor heterogeneity, different sequencing techniques, spatial and temporal factors, and potential germline DNA contamination. The study indicates that both tissue and blood-based NGS may be necessary to describe the complex biology of breast cancer.

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“…The read depth signals at both flanks can be used to normalize local read counts and eliminate GC bias and large scale copy number change, making the read counts comparable across all mutation regions. To remove normal cell contamination, the discordant and concordant read counts in the tumor sample are normalized using the estimated tumor sample impurity (Aran et al, 2015;Oesper et al, 2013). A flow chart of WGS data preprocessing for paired tumor-normal samples can be found in Supplementary Figure S2.…”

Section: Candidate Structural Variation Detectionmentioning

confidence: 99%

“…The contamination of normal cells should be removed before somatic region calling; otherwise, read counts calculated in tumor samples are not accurate. The proportion of normal cells can be estimated by THetA (Oesper et al, 2013) using WGS data or tools using other types of data (Aran et al, 2015). Finally, the mutation status on two copies of each chromosome is neglected by most SV detection methods (except for GASVPro and Delly).…”

Section: Introductionmentioning

confidence: 99%

PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification

et al. 2016

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Motivation: Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs.Results: We present a novel pattern-based probabilistic approach, PSSV, to identify somatic structural variations from WGS data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with heterozygous mutations in normal samples and homozygous mutations in tumor samples. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development. Availability and Implementation: An R package of PSSV is available at

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Systematic pan-cancer analysis of tumour purity

Cited by 949 publications

References 44 publications

Prognostic Relevance of Tumor Purity and Interaction with MGMT Methylation in Glioblastoma

Prognostic Relevance of Tumor Purity and Interaction with MGMT Methylation in Glioblastoma

Concordance of Genomic Alterations by Next-Generation Sequencing in Tumor Tissue versus Circulating Tumor DNA in Breast Cancer

PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification

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