The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Systematic pan-cancer analysis of tumour purity
Open accessAbstract
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.
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Microenvironmental regulation of metastasis
Joyce, J. A., & Pollard, J. W. (2009) · Nature Reviews Cancer · doi:10.1038/nrc2618 · Cited by 3,684
Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas
The Cancer Genome Atlas Research Network (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1402121 · Cited by 3,148
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xCell: digitally portraying the tissue cellular heterogeneity landscape
Aran, D., Hu, Z., & Butte, A. J. (2017) · Genome Biology · doi:10.1186/s13059-017-1349-1 · Cited by 4,397
Self-citationA pathology atlas of the human cancer transcriptome
Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) · Science · doi:10.1126/science.aan2507 · Cited by 3,371
LinkedOmics: analyzing multi-omics data within and across 32 cancer types
Vasaikar, S. V., Straub, P., Wang, J., & Zhang, B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1090 · Cited by 2,312
Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., Krogsdam, A., Loncova, Z., Posch, W., Wilflingseder, D., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0638-6 · Cited by 1,542
Conserved pan-cancer microenvironment subtypes predict response to immunotherapy
Bagaev, A., Kotlov, N., Nomie, K., Svekolkin, V., Gafurov, A., Isaeva, O., Osokin, N., Kozlov, I., Frenkel, F., Gancharova, O., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.04.014 · Cited by 1,302
Metadata
- Authors
- Aran, D., Sirota, M., & Butte, A. J.
- Venue
- Nature Communications
- Published
- 2015-12-04
- Volume
- 6
- Issue
- 1
- Pages
- 8971-8971
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2185207072
- Cited by
- 1,216
- References
- 8
- FWCI
- 17.08
- Type
- journal article
- Language
- en
- Open access
- gold
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