Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer

Safonov, Anton; Jiang, Tingting; Bianchini, Giampaolo; Győrffy, Balázs; Karn, Thomas; Hatzis, Christos; Pusztai, Lajos

doi:10.1158/0008-5472.can-16-3478

Cited by 127 publications

(126 citation statements)

References 42 publications

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“…However, in the case of the TCGA, not all those variables are measurable, but some might be included in further work. In this line of thought, earlier results exhibited correlation of ITH with other factors like CNA abundance, sample purity, immune infiltration [53,54,68,13]. Our results show that the strength (and even direction in the case of CNA abundance and mutation load) of correlation between those factors and ITH varies between the different tested ITH measures.…”

Section: Association With Survival Link With Other Variablessupporting

confidence: 71%

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Abecassis

Hamy

Laurent

et al. 2018

Preprint

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Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications.In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables.In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

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Section: Association With Survival Link With Other Variablessupporting

confidence: 71%

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Abecassis

Hamy

Laurent

et al. 2018

Preprint

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“…Different types of gene mutations are key reasons for tumor formation. [30][31][32] Among hub genes in this study, the mRNA levels of DSN1, AHCY, and ERCC6L in normal colorectal cells and CRC cells were detected Selective splicing produces multiple transcript variants. DSN1 is necessary for correct kinetochore assembly and cell cycle progression.…”

Section: Discussionmentioning

confidence: 64%

“…CBioPortal is used to show the correlation between the hub genes and genetic changes. Different types of gene mutations are key reasons for tumor formation 30‐32 …”

Section: Discussionmentioning

confidence: 99%

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

Zhou

Yang

Yue

et al. 2020

Molecular Carcinogenesis

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Colorectal cancer (CRC) is a kind of malignant cancer with high morbidity and mortality. The purpose of this study was to explore potential regulated key genes involved in CRC through bioinformatics analysis and experimental verification. The gene expression profile data were downloaded from the Gene Expression Omnibus, and the differential expression genes were detected in cancerous and paracancerous samples of CRC patients, respectively. Then functional enrichment analysis, such as the Kyoto Encyclopedia of Genes and Genomes pathway analysis as well as the protein‐protein interaction network were constructed, and the highly related genes were clustered by Molecular COmplex DEtection algorithm to find out the core interaction in different genes' crosstalk. The genes affecting CRC prognosis were screened by the Human Protein Atlas database. In addition, the expression level of core genes was detected by GEPIA database, and the core genes' changes in large‐scale cancer genome data set were directly analyzed by cBioPortal database. The expression of the predicted hub genes DSN1, AHCY, and ERCC6L was verified by reverse‐transcription quantitative polymerase chain reaction in CRC cells. The gene function of DSN1 was analyzed by wound healing and colony formation assays. The results showed that silencing of DSN1 could significantly reduce the migration and proliferation of CRC cells. Further, BUB1B, the potential interacting protein of DSN1, was also predicted via bioinformatics analysis. Above all, this study shows that bioinformatics analysis combined with experimental method verification provide more potential vital genes for the prevention and therapy of CRC.

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“…We conducted unsupervised consensus clustering of 92 luminal tumors using expression of 130 immune-related genes (within 13 previously reported metagenes) 21 (Supplementary Table 2). The best separation was achieved by dividing the luminal patients into three subtypes (lum1: n=40; lum2: n=36; lum3; n=16; Figure 1a); lum1 and lum3 were enriched with luminal-A tumors and lum2 enriched with luminal-B tumors ( Supplementary Table 3).…”

Section: Immune Gene Expression Stratified Luminal Tumors Into Three mentioning

confidence: 99%

Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Zhu

Tse

Wang

et al. 2019

Preprint

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Disease heterogeneity of immune gene expression patterns of luminal breast cancer (BC) has not been well studied. We performed immune gene expression profiling of tumor and adjacent normal tissue in 92 Asian luminal BC patients and identified three distinct immune subtypes. Tumors in one subtype exhibited signs of T-cell activation, lower ESR1/ESR2 expression ratio and higher expression of immune checkpoint genes, nonsynonymous mutation burden, APOBEC-signature mutations, and increasing body mass index compared to other luminal tumors. Tumors in a second subtype were characterized by increased expression of interferonstimulated genes and enrichment for TP53 somatic mutations. The presence of three immune subtypes within luminal BC was replicated in cases drawn from The Cancer Genome Atlas and a Korean breast cancer study. Our findings suggest that immune gene expression and associated genomic features could be useful to further stratify luminal BC beyond the current luminal A/B classification.Introduction:

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Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer

Cited by 127 publications

References 42 publications

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

Immune gene expression profiling reveals heterogeneity in luminal breast tumors

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