Inferring tumour purity and stromal and immune cell admixture from expression data

Yoshihara, Kosuke; Shahmoradgoli, Maria; Martínez, Emmanuel; Vegesna, Rahulsimham; Kim, Hoon; Torres-García, Wandaliz; Treviño, Víctor; Shen, Hui; Laird, Peter W.; Levine, Douglas A.; Carter, Scott L.; Getz, Gad; Stemke-Hale, Katherine; Mills, Gordon B.; Verhaak, Roel G.W.

doi:10.1038/ncomms3612

Cited by 6,036 publications

(5,277 citation statements)

References 62 publications

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“…S1A) higher than estimated microscopically. This lack of correlation of computational purity and histopathologic estimates is consistent with previous reports (26). The median stromal compartment was higher in EAC samples compared with NDBE and LGD and lowest in NE ( Supplementary Fig.…”

Section: Resultssupporting

confidence: 80%

Novel Aberrations Uncovered in Barrett's Esophagus and Esophageal Adenocarcinoma Using Whole Transcriptome Sequencing

Mååg

Fisher

Levert-Mignon³

et al. 2017

Molecular Cancer Research

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Esophageal adenocarcinoma (EAC) has one of the fastest increases in incidence of any cancer, along with poor five-year survival rates. Barrett's esophagus (BE) is the main risk factor for EAC; however, the mechanisms driving EAC development remain poorly understood. Here, transcriptomic profiling was performed using RNA-sequencing (RNA-seq) on premalignant and malignant Barrett's tissues to better understand this disease. Machine-learning and network analysis methods were applied to discover novel driver genes for EAC development. Identified gene expression signatures for the distinction of EAC from BE were validated in separate datasets. An extensive analysis of the noncoding RNA (ncRNA) landscape was performed to determine the involvement of novel transcriptomic elements in Barrett's disease and EAC. Finally, transcriptomic mutational investigation of genes that are recurrently mutated in EAC was performed. Through these approaches, novel driver genes were discovered for EAC, which involved key cell cycle and DNA repair genes, such as BRCA1 and PRKDC. A novel 4-gene signature (CTSL, COL17A1, KLF4, and E2F3) was identified, externally validated, and shown to provide excellent distinction of EAC from BE. Furthermore, expression changes were observed in 685 long noncoding RNAs (lncRNA) and a systematic dysregulation of repeat elements across different stages of Barrett's disease, with wide-ranging downregulation of Alu elements in EAC. Mutational investigation revealed distinct pathways activated between EAC tissues with or without TP53 mutations compared with Barrett's disease. In summary, transcriptome sequencing revealed altered expression of numerous novel elements, processes, and networks in EAC and premalignant BE.Implications: This study identified opportunities to improve early detection and treatment of patients with BE and esophageal adenocarcinoma. Mol Cancer Res; 15(11); 1558-69. Ó2017 AACR.

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Section: Resultssupporting

confidence: 80%

Novel Aberrations Uncovered in Barrett's Esophagus and Esophageal Adenocarcinoma Using Whole Transcriptome Sequencing

Mååg

Fisher

Levert-Mignon³

et al. 2017

Molecular Cancer Research

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“…The tumor cell percentage in each sample was estimated from the SNP array data using the algorithm ASCAT (Allele‐Specific Copy number Analysis of Tumors) (Van Loo et al ., 2010). Abundance of stroma and immune cells in the samples was inferred from gene expression signatures by ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data; Yoshihara et al ., 2013). The abundance of different subsets of immune cells, including macrophages, was evaluated based on gene expression data using TIMER (Tumor Immune Estimation Resource; Li et al ., 2017).…”

Section: Methodsmentioning

confidence: 99%

Basal‐like breast cancer engages tumor‐supportive macrophages via secreted factors induced by extracellular S100A4

et al. 2018

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The tumor microenvironment (TME) may influence both cancer progression and therapeutic response. In breast cancer, particularly in the aggressive triple‐negative/basal‐like subgroup, patient outcome is strongly associated with the tumor's inflammatory profile. Tumor‐associated macrophages (TAMs) are among the most abundant immune cells in the TME, shown to be linked to poor prognosis and therapeutic resistance. In this study, we investigated the effect of the metastasis‐ and inflammation‐associated microenvironmental factor S100A4 on breast cancer cells (BCCs) of different subtypes and explored their further interactions with myeloid cells. We demonstrated that extracellular S100A4 activates BCCs, particularly the basal‐like subtype, to elevate secretion of pro‐inflammatory cytokines. The secreted factors promoted conversion of monocytes to TAM‐like cells that exhibited protumorigenic activities: stimulated epithelial–mesenchymal transition, proliferation, chemoresistance, and motility in cancer cells. In conclusion, we have shown that extracellular S100A4 instigates a tumor‐supportive microenvironment, involving a network of cytokines and TAM‐like cells, which was particularly characteristic for basal‐like BCCs and potentiated their aggressive properties. The S100A4–BCC–TAM interaction cascade could be an important contributor to the aggressive behavior of this subtype and should be further explored for therapeutic targeting.

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“…46 Current ssGSEA methods rely on clinical classification or prior information on sample phenotype, and require either cohorts to z-score each sample 24,25 or the application of statistics involving equiprobable genes as inadequate null hypotheses. [21][22][23]47 Our method avoids such pitfalls and its algorithmic optimization allows for the rapid analysis of large series of data, such as labtop-based scoring within seconds for 1,500 gene sets in 1,446 microarrays. Although RNASeq or proteome datasets are rich sources of material for such studies, nearly two decades of microarray-based research in cancer means that large and precious amounts of this publicly-available resource have accumulated.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, it produces excessive false discovery rates and p values since it inappropriately uses the Fischer exact test. 20 Single sample GSEA (ssGSEA) have also been used although carry the same statistical flaw, 21,22,23 and their long computing time is an issue for large datasets. Another method for ssGSEA is based on z scores per gene set per sample 24,25 and avoids this pitfall but still requires cohorts of samples to compute each sample score.…”

Section: Introductionmentioning

confidence: 99%

Large-scale microarray profiling reveals four stages of immune escape in non-Hodgkin lymphomas

et al. 2016

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Non-Hodgkin B-cell lymphoma (B-NHL) are aggressive lymphoid malignancies that develop in patients due to oncogenic activation, chemo-resistance, and immune evasion. Tumor biopsies show that B-NHL frequently uses several immune escape strategies, which has hindered the development of checkpoint blockade immunotherapies in these diseases. To gain a better understanding of B-NHL immune editing, we hypothesized that the transcriptional hallmarks of immune escape associated with these diseases could be identified from the meta-analysis of large series of microarrays from B-NHL biopsies. Thus, 1446 transcriptome microarrays from seven types of B-NHL were downloaded and assembled from 33 public Gene Expression Omnibus (GEO) datasets, and a method for scoring the transcriptional hallmarks in single samples was developed. This approach was validated by matching scores to phenotypic hallmarks of B-NHL such as proliferation, signaling, metabolic activity, and leucocyte infiltration. Through this method, we observed a significant enrichment of 33 immune escape genes in most diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) samples, with fewer in mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL) samples. Comparing these gene expression patterns with overall survival data evidenced four stages of cancer immune editing in B-NHL: non-immunogenic tumors (stage 1), immunogenic tumors without immune escape (stage 2), immunogenic tumors with immune escape (stage 3), and fully immuno-edited tumors (stage 4). This model complements the standard international prognostic indices for B-NHL and proposes that immune escape stages 3 and 4 (76% of the FL and DLBCL samples in this data set) identify patients relevant for checkpoint blockade immunotherapies.

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Inferring tumour purity and stromal and immune cell admixture from expression data

Cited by 6,036 publications

References 62 publications

Novel Aberrations Uncovered in Barrett's Esophagus and Esophageal Adenocarcinoma Using Whole Transcriptome Sequencing

Novel Aberrations Uncovered in Barrett's Esophagus and Esophageal Adenocarcinoma Using Whole Transcriptome Sequencing

Basal‐like breast cancer engages tumor‐supportive macrophages via secreted factors induced by extracellular S100A4

Large-scale microarray profiling reveals four stages of immune escape in non-Hodgkin lymphomas

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