Systematic pan-cancer analysis of tumour purity

By D. Aran, Marina Sirota, Atul J. Butte. Nature Communications, 2015

1,055 citations recorded by the source corpus.

The tumour microenvironment is the non-cancerous cells present in and around a tumour, including mainly immune cells, but also fibroblasts and cells that comprise supporting blood vessels. These non-cancerous components of the tumour may play an important role in cancer biology. They also have a strong influence on the genomic analysis of tumour samples, and may alter the biological interpretation of results. Here we present a systematic analysis using different measurement modalities of tumour purity in >10,000 samples across 21 cancer types from the Cancer Genome Atlas. Patients are stratified according to clinical features in an attempt to detect clinical differences driven by purity levels. We demonstrate the confounding effect of tumour purity on correlating and clustering tumours with transcriptomics data. Finally, using a differential expression method that accounts for tumour purity, we find an immunotherapy gene signature in several cancer types that is not detected by traditional differential expression analyses. The importance of the tumour microenvironment has now been realised, however the presence of non-tumour cells in cancer samples can complicate genomic analyses. Here, the authors estimate tumour purity in 10,000 samples from the TCGA dataset and can detect a signature of T cell activation.

Subfields: Cancer Research, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/d4aca3d351b48d1e1d15df646b5bd1095d128527

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Citations in the corpus

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx (2019, methodology): “Other cancer genomic initiatives have been following the TCGA model, such as Therapeutically Applicable Research to Generate Effective Treatments (TARGET), which is an NCIfunded project conducting a large-scale study that seeks to unravel novel therapeutic targets, biomarkers, and drug targets in ch”
xCell: digitally portraying the tissue cellular heterogeneity landscape (2017, methodology): “We then correlated this microenvironment score with our previously generated purity estimations, which are based on copy number variations, gene expression, DNA methylation, and H&E slides [31].”
A pathology atlas of the human cancer transcriptome (2017, result): “Moreover, the purity of the tumor samples should also affect the survival analysis, as previously reported (41).”
Tumor evolution of glioma intrinsic gene expression subtype associates with immunological changes in the microenvironment (2017, methodology): “7) to equalize the mutation detection sensitivity (Aran et al., 2015).”
Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer (2016, background): “Normal cell contamination is one of the factors complicating the analysis of somatic alterations in solid tumors (48).”