Dataset Information

DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information.

ABSTRACT: Identifying differentially expressed genes (DEGs) between tumor and normal samples is critical for studying tumorigenesis, and has been routinely applied to identify diagnostic, prognostic, and therapeutic biomarkers for many cancers. It is well-known that solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. However, the tumor purity information is more or less ignored in traditional differential expression analyses, which might decrease the power of differential gene identification or even bias the results. In this paper, we have developed a novel differential gene calling method called DECtp by integrating tumor purity information into a generalized least square procedure, followed by the Wald test. We compared DECtp with popular methods like t-test and limma on nine simulation datasets with different sample sizes and noise levels. DECtp achieved the highest area under curves (AUCs) for all the comparisons, suggesting that cancer purity information is critical for DEG calling between tumor and normal samples. In addition, we applied DECtp into cancer and normal samples of 14 tumor types collected from The Cancer Genome Atlas (TCGA) and compared the DEGs with those called by limma. As a result, DECtp achieved more sensitive, consistent, and biologically meaningful results and identified a few novel DEGs for further experimental validation.

SUBMITTER: Zhang W

PROVIDER: S-EPMC6121016 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information.

Zhang Weiwei W Long Haixia H He Binsheng B Yang Jialiang J

Frontiers in genetics 20180828

Identifying differentially expressed genes (DEGs) between tumor and normal samples is critical for studying tumorigenesis, and has been routinely applied to identify diagnostic, prognostic, and therapeutic biomarkers for many cancers. It is well-known that solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. However, the tumor purity information is more or less ignored in traditional differential expression analyses, which might decrease the ...[more]

PMID: 30210526

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Project description:Archival tumor samples represent a rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and individual-specific germline variants. Archival sections often contain adjacent normal tissue, but this tissue can include infiltrating tumor cells. As existing comparative somatic variant callers are designed to exclude variants present in the normal sample, a novel approach is required to leverage adjacent normal tissue with infiltrating tumor cells for somatic variant calling. Here we present lumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient, built upon our previous single sample tumor only variant caller lumosVar 1.0. The approach assumes that the allelic fraction of somatic variants and germline variants follow different patterns as tumor content and copy number state change. lumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses a to model to determine expected allelic fractions for somatic and germline variants and to classify variants accordingly. To evaluate the utility of lumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high and low tumor content and germline whole exome sequencing data (for true somatic variants) available for each patient. Both sensitivity and positive predictive value were improved when analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using lumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent tissue as presumed normal reference. Together, these results demonstrate the utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional sample is not available.

Project description:Expression and differential expression analysis of breast cancer patient samples and normal samples from breast reduction operations. Fresh frozen tumor biopsies from early breast cancer cases were collected from 920 patients included in the Oslo Micrometastasis (MicMa) Study -- Oslo I from various hospitals between 1995 and 1998 (Naume et al. "Presence of bone marrow micrometastasis is associated with different recurrence risk within molecular subtypes of breast cancer." Mol Oncol 2007, 1: 160-171; Wiedswang et al. "Detection of isolated tumor cells in bone marrow is an independent prognostic factor in breast cancer." J Clin Oncol 2003, 21: 3469-3478.). Breast tissue samples from breast reduction operations were provided from the Colosseum Clinic, Oslo in co-operation with Akershus University Hospital, Lørenskog and are referred to as normal tissue. Expression and differential expression was assessed by using an Agilent custom microarray (244K, nONCOchip). The custom array contains probes for genomic regions that have been found to be differentially expressed (i) throughout cell cycle progression, (ii) in response to the anti-proliferative and pro-apoptotic p53 pathway, and (iii) the anti-apoptotic and pro-proliferative STAT-3 pathway by employing TAS (Kampa et al. "Novel RNAs identified from an in-depth analysis of the transcriptome of human chromosomes 21 and 22". Genome Research, 14:331-42, 2004). In addition, the Agilent custom array (244K) interrogates probes for genomic regions predicted to contain a conserved secondary structure identified by RNAz (Washietl et al. "Fast and reliable prediction of noncoding RNAs." Proc Natl Acad Sci USA. 102:2454-9, 2005.) or Evofold (Pedersen et al. "Identification and classification of conserved RNA secondary structures in the human genome." PLoS Comput Biol. 2:e33, 2006.), as well as known non-coding RNAs from public databases, and the Agilent mRNA probe set 014850. We analyzed 5 to 6 arrays each for breast cancer patient samples and normal samples.

Dataset Information

DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information.

Publications

DECtp: Calling Differential Gene Expression Between Cancer and Normal Samples by Integrating Tumor Purity Information.

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