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Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy
Open accessAbstract
The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this "proteogenomics" approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Self-citationGene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F.-A., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E.-M., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,845
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
Cited by
Proteogenomic characterization of pancreatic ductal adenocarcinoma
Cao, L., Huang, C., Cui Zhou, D., Hu, Y., Lih, T. M., Savage, S. R., Krug, K., Clark, D. J., Schnaubelt, M., Chen, L., et al. (2021) · Cell · doi:10.1016/j.cell.2021.08.023 · Cited by 578
Self-citationZhong, Q., Xiao, X., Qiu, Y., Xu, Z., Chen, C., Chong, B., Zhao, X., Hai, S., Li, S., An, Z., et al. (2023) · MedComm · doi:10.1002/mco2.261 · Cited by 442
Self-citationIntegrative multi-omics approaches in cancer research: from biological networks to clinical subtypes
Heo, Y. J., Hwa, C., Lee, G.-H., Park, J.-M., & An, J.-Y. (2021) · Molecules and Cells · doi:10.14348/molcells.2021.0042 · Cited by 294
Cancer proteogenomics: current impact and future prospects
Mani, D. R., Krug, K., Zhang, B., Satpathy, S., Clauser, K. R., Ding, L., Ellis, M., Gillette, M. A., & Carr, S. A. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00446-5 · Cited by 229
Self-citationUpadhya, S. R., & Ryan, C. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100288 · Cited by 81
Metadata
- Authors
- Krug, K., Jaehnig, E. J., Satpathy, S., Blumenberg, L., Karpova, A., Anurag, M., Miles, G., Mertins, P., Geffen, Y., Tang, L. C., Heiman, D. I., Cao, S., Maruvka, Y. E., Lei, J. T., Huang, C., Kothadia, R. B., Colaprico, A., Birger, C., Wang, J., Dou, Y., Wen, B., Shi, Z., Liao, Y., Wiznerowicz, M., Wyczalkowski, M. A., Chen, X. S., Kennedy, J. J., Paulovich, A. G., Thiagarajan, M., Kinsinger, C. R., Hiltke, T., Boja, E. S., Mesri, M., Robles, A. I., Rodriguez, H., Westbrook, T. F., Ding, L., Getz, G., Clauser, K. R., Fenyö, D., Ruggles, K. V., Zhang, B., Mani, D. R., Carr, S. A., Ellis, M. J., Gillette, M. A., Avanessian, S. C., Cai, S., Chan, D., Chen, X., Edwards, N. J., Hoofnagle, A. N., Kane, M. H., Ketchum, K. A., Kuhn, E., Levine, D. A., Li, S., Liebler, D. C., Liu, T., Luo, J., Madhavan, S., Maher, C., McDermott, J. E., McGarvey, P. B., Oberti, M., Pandey, A., Payne, S. H., Ransohoff, D. F., Rivers, R. C., Rodland, K. D., Rudnick, P., Sanders, M. E., Shaw, K. M., Shih, I.-M., Slebos, R. J. C., Smith, R. D., Snyder, M., Stein, S. E., Tabb, D. L., Thangudu, R. R., Thomas, S., Wang, Y., White, F. M., Whiteaker, J. R., Whiteley, G. A., Zhang, H., Zhang, Z., Zhao, Y., Zhu, H., & Zimmerman, L. J.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 183
- Issue
- 5
- Pages
- 1436-1456.e31
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W3104755560
- Cited by
- 537
- References
- 150
- FWCI
- 23.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Krug, K., Jaehnig, E. J., Satpathy, S., Blumenberg, L., Karpova, A., Anurag, M., Miles, G., Mertins, P., Geffen, Y., Tang, L. C., Heiman, D. I., Cao, S., Maruvka, Y. E., Lei, J. T., Huang, C., Kothadia, R. B., Colaprico, A., Birger, C., Wang, J., . . . Zimmerman, L. J. (2020). Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy. Cell. https://doi.org/10.1016/j.cell.2020.10.036
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