Cited by 105Cell death mechanisms and regulationCancer-related Molecular PathwaysUbiquitin and proteasome pathways

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Systems Analysis of BCL2 Protein Family Interactions Establishes a Model to Predict Responses to Chemotherapy

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Lindner, Andreas U., Concannon, Caoimhín G., Boukes, Gerhardt J., Cannon, Mary D., Llambi, Fabien, Ryan, Deborah, Boland, Karen, Kehoe, Joan, McNamara, Deborah A., Murray, Frank, Kay, Elaine W., Hector, Suzanne, Green, Douglas R., Huber, Heinrich J., Prehn, Jochen H.M.·2013·Cancer Research

DOI 10.1158/0008-5472.can-12-2269

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Abstract

Apoptotic desensitization is a hallmark of cancer cells, but present knowledge of molecular systems controlling apoptosis has yet to provide significant prognostic insights. Here, we report findings from a systems study of the intrinsic pathway of apoptosis by BCL2 family proteins and clinical translation of its findings into a model with applications in colorectal cancer (CRC). By determining absolute protein quantifications in CRC cells and patient tumor samples, we found that BAK and BAX were expressed more highly than their antiapoptotic inhibitors. This counterintuitive finding suggested that sole inhibition of effector BAX and BAK could not be sufficient for systems stability in nonstressed cells. Assuming a model of direct effector activation by BH3-only proteins, we calculated that the amount of stress-induced BH3-only proteins required to activate mitochondrial apoptosis could predict individual death responses of CRC cells to 5-fluorouracil/oxaliplatin. Applying this model predictor to protein profiles in tumor and matched normal tissue samples from 26 patients with CRCs, we found that differences in protein quantities were sufficient to model the increased tumor sensitivity to chemotherapy compared with normal tissue. In addition, these differences were sufficient to differentiate clinical responders from nonresponders with high confidence. Applications of our model, termed DR_MOMP, were used to assess the impact of apoptosis-sensitizing dugs in lowering the necessary dose of state-of-the-art chemotherapy in individual patients. Together, our findings offer a ready clinical tool with the potential to tailor chemotherapy to individual patients. Cancer Res; 73(2); 519–28. ©2012 AACR.

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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
The biochemistry of apoptosis
Hengartner, M. O. (2000) · Nature · doi:10.1038/35037710 · Cited by 5,779
Akt phosphorylation of BAD couples survival signals to the cell-intrinsic death machinery
Datta, S. R., Dudek, H., Tao, X., Masters, S., Fu, H., Gotoh, Y., & Greenberg, M. E. (1997) · Cell · doi:10.1016/s0092-8674(00)80405-5 · Cited by 5,689
Proapoptotic BAX and BAK: A Requisite Gateway to Mitochondrial Dysfunction and Death
Wei, M. C., Zong, W.-X., Cheng, E. H.-Y., Lindsten, T., Panoutsakopoulou, V., Ross, A. J., Roth, K. A., MacGregor, G. R., Thompson, C. B., & Korsmeyer, S. J. (2001) · Science · doi:10.1126/science.1059108 · Cited by 3,988
PUMA, a Novel Proapoptotic Gene, Is Induced by p53
Nakano, K., & Vousden, K. H. (2001) · Molecular Cell · doi:10.1016/s1097-2765(01)00214-3 · Cited by 2,315
The Bcl-2 apoptotic switch in cancer development and therapy
Adams, J. M., & Cory, S. (2007) · Oncogene · doi:10.1038/sj.onc.1210220 · Cited by 2,256
A Randomized Controlled Trial of Fluorouracil Plus Leucovorin, Irinotecan, and Oxaliplatin Combinations in Patients With Previously Untreated Metastatic Colorectal Cancer
Goldberg, R. M., Sargent, D. J., Morton, R. F., Fuchs, C. S., Ramanathan, R. K., Williamson, S. K., Findlay, B., Pitot, H. C., & Alberts, S. R. (2003) · Journal of Clinical Oncology · doi:10.1200/jco.2004.09.046 · Cited by 2,251
Differential Targeting of Prosurvival Bcl-2 Proteins by Their BH3-Only Ligands Allows Complementary Apoptotic Function
Chen, L., Willis, S. N., Wei, A., Smith, B. J., Fletcher, J. I., Hinds, M. G., Colman, P. M., Day, C. L., Adams, J. M., & Huang, D. C. S. (2005) · Molecular Cell · doi:10.1016/j.molcel.2004.12.030 · Cited by 1,816
p53- and Drug-Induced Apoptotic Responses Mediated by BH3-Only Proteins Puma and Noxa
Villunger, A., Michalak, E. M., Coultas, L., Müllauer, F., Böck, G., Ausserlechner, M. J., Adams, J. M., & Strasser, A. (2003) · Science · doi:10.1126/science.1090072 · Cited by 1,327
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Certo, M. V., Moore, V. D. G., Nishino, M., Wei, G., Korsmeyer, S. J., Armstrong, S. A., & Letai, A. G. (2006) · Cancer Cell · doi:10.1016/j.ccr.2006.03.027 · Cited by 1,278

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Authors: Lindner, A. U., Concannon, C. G., Boukes, G. J., Cannon, M. D., Llambi, F., Ryan, D., Boland, K., Kehoe, J., McNamara, D. A., Murray, F., Kay, E. W., Hector, S., Green, D. R., Huber, H. J., & Prehn, J. H. M.
Affiliations: St. Jude Children's Research Hospital (US)
Corresponding author: Huber, Heinrich J.
Prehn, Jochen H.M.
ORCID: Lindner, Andreas U.
Boukes, Gerhardt J.
Cannon, Mary D.
Llambi, Fabien
Boland, Karen
McNamara, Deborah A.
Murray, Frank
Green, Douglas R.
Huber, Heinrich J.
Prehn, Jochen H.M.
Venue: Cancer Research
Publisher: American Association for Cancer Research (AACR)
Published: 2013-01-15
Volume: 73
Issue: 2
Pages: 519-528
ISSN: 0008-5472 (ISSN-L)
1538-7445
DOI: 10.1158/0008-5472.can-12-2269
OpenAlex: W1977323318
Cited by: 105
References: 48
Citations by year: 2026: 1 · 2025: 3 · 2024: 4 · 2023: 8 · 2022: 7 · 2021: 15 · 2020: 5 · 2019: 8 · 2018: 6 · 2017: 8 · 2016: 15 · 2015: 7 · 2014: 9 · 2013: 8
FWCI: 5.82
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Cancer Research (publishedVersion)
PubMed (publishedVersion)
Lirias (submittedVersion, other-oa)
Funders: Johns Hopkins University
European Commission
Higher Education Authority
Science Foundation Ireland
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Cell death mechanisms and regulation
Health Sciences › Medicine › Oncology › Cancer-related Molecular Pathways
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ubiquitin and proteasome pathways
Keywords: Bcl-2 family proteins, mitochondrial apoptosis, colorectal cancer, chemotherapy response prediction, Bak, Bax, BH3-only proteins, 5-fluorouracil, oxaliplatin, personalized chemotherapy, absolute protein quantification
MeSH terms: Aged, Antineoplastic Agents, Female, Humans, Male, Models, Biological, Systems Analysis, Colorectal Neoplasms, Treatment Outcome, Apoptosis, Proto-Oncogene Proteins c-bcl-2, Computational Biology, Protein Array Analysis, Cell Line, Tumor, bcl-2 Homologous Antagonist-Killer Protein, bcl-2-Associated X Protein

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Systems Analysis of BCL2 Protein Family Interactions Establishes a Model to Predict Responses to Chemotherapy

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