The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
The prognostic landscape of genes and infiltrating immune cells across human cancers
Open accessAbstract
Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.
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Cites
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The Immune Landscape of Cancer
Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.023 · Cited by 5,870
Self-citationSignatures of T cell dysfunction and exclusion predict cancer immunotherapy response
Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., Li, Z., Traugh, N., Bu, X., Li, B., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0136-1 · Cited by 5,179
Nieto, M. A., Huang, R. Y.-J., Jackson, R. A., & Thiery, J. P. (2016) · Cell · doi:10.1016/j.cell.2016.06.028 · Cited by 4,492
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
Approaches to treat immune hot, altered and cold tumours with combination immunotherapies
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Metadata
- Authors
- Gentles, A. J., Newman, A. M., Liu, C. L., Bratman, S. V., Feng, W., Kim, D., Nair, V. S., Xu, Y., Khuong, A., Hoang, C. D., Diehn, M., West, R. B., Plevritis, S. K., & Alizadeh, A. A.
- Venue
- Nature Medicine
- Published
- 2015-07-20
- Volume
- 21
- Issue
- 8
- Pages
- 938-945
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W1107917109
- Cited by
- 3,118
- References
- 15
- FWCI
- 61.24
- Type
- journal article
- Language
- en
- Open access
- green
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- Gentles, A. J., Newman, A. M., Liu, C. L., Bratman, S. V., Feng, W., Kim, D., Nair, V. S., Xu, Y., Khuong, A., Hoang, C. D., Diehn, M., West, R. B., Plevritis, S. K., & Alizadeh, A. A. (2015). The prognostic landscape of genes and infiltrating immune cells across human cancers. Nature Medicine. https://doi.org/10.1038/nm.3909
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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