The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Drug-Induced Death Signaling Strategy Rapidly Predicts Cancer Response to Chemotherapy
Open accessAbstract
There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“BCL2-homology domain (BH3) profiling 41 demonstrated that cytochrome c release following mitochondrial exposure to pro-apoptotic BH3-only peptides (e.g. BIM BH3, PUMA BH3) was similar in control and Tbk1-null B16 cells (Extended Data Fig. 9a).”
From Targeting TBK1 to overcome resistance to cancer immunotherapy (2023) · cites this paper as [41]
In the citing paper
“Dynamic BH3 profiling robustly predicted which patients would respond to a given therapy across multiple cancer types.”
From Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate (2016) · cites this paper as [29]
In the citing paper
“Montero et al. recently reported a functional approach called ‘dynamic BH3 profiling’, in which therapeutics are applied to cell lines or clinical tumor isolates, and then the percentage of cells that undergo mitochondrial outer membrane permeabilization (MOMP) is measured after introduction of a pro-apoptotic BH3 peptide29.”
From Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate (2016) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy
Paez, J. G., Jänne, P. A., Lee, J. C., Tracy, S., Greulich, H., Gabriel, S., Herman, P., Kaye, F. J., Lindeman, N., Boggon, T. J., et al. (2004) · Science · doi:10.1126/science.1099314 · Cited by 9,368
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Improved survival with vemurafenib in melanoma with BRAF V600E mutation
Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., Dummer, R., Garbe, C., Testori, A., Maio, M., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103782 · Cited by 7,720
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
The Release of Cytochrome c from Mitochondria: A Primary Site for Bcl-2 Regulation of Apoptosis
Kluck, R. M., Bossy‐Wetzel, E., Green, D. R., & Newmeyer, D. D. (1997) · Science · doi:10.1126/science.275.5303.1132 · Cited by 4,715
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
Joensuu, H., Roberts, P., Sarlomo‐Rikala, M., Andersson, L. C., Tervahartiala, P. O., Tuveson, D. A., Silberman, S., Capdeville, R., Dimitrijević, S., Druker, B. J., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200104053441404 · Cited by 2,030
Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research
Hidalgo, M. M., Amant, F. C., Biankin, A. V., Budínská, E., Byrne, A. T., Caldas, C., Clarke, R. B., de Jong, S., Jonkers, J., Mælandsmo, G. M., et al. (2014) · Cancer Discovery · doi:10.1158/2159-8290.cd-14-0001 · Cited by 1,797
Letai, A., Bassik, M. C., Walensky, L. D., Sorcinelli, M. D., Weiler, S., & Korsmeyer, S. J. (2002) · Cancer Cell · doi:10.1016/s1535-6108(02)00127-7 · Cited by 1,677
Self-citationSawyers, C. L. (1999) · New England Journal of Medicine · doi:10.1056/nejm199904293401706 · Cited by 1,615
Chipuk, J. E., Moldoveanu, T., Llambi, F., Parsons, M. J., & Green, D. R. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.01.025 · Cited by 1,476
Engelman, J. A., Chen, L., Tan, X., Crosby, K., Guimaraes, A. R., Upadhyay, R., Maira, M., McNamara, K., Perera, S. A., Song, Y., et al. (2008) · Nature Medicine · doi:10.1038/nm.1890 · Cited by 1,330
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
Self-citationNovel mutant-selective EGFR kinase inhibitors against EGFR T790M
Zhou, W., Ercan, D., Chen, L., Yun, C.-H., Li, D., Capelletti, M., Cortot, A. B., Chirieac, L., Iacob, R. E., Padera, R., et al. (2009) · Nature · doi:10.1038/nature08622 · Cited by 1,013
Self-citationSawyers, C. L. (2008) · Nature · doi:10.1038/nature06913 · Cited by 993
Patient-derived models of acquired resistance can identify effective drug combinations for cancer
Crystal, A. S., Shaw, A. T., Sequist, L. V., Friboulet, L., Niederst, M. J., Lockerman, E. L., Frias, R. L., Gainor, J. F., Amzallag, A., Greninger, P., et al. (2014) · Science · doi:10.1126/science.1254721 · Cited by 708
Pretreatment Mitochondrial Priming Correlates with Clinical Response to Cytotoxic Chemotherapy
Chonghaile, T. N., Sarosiek, K. A., Vo, T.-T. T., Ryan, J. A., Tammareddi, A., Moore, V. D. G., Deng, J., Anderson, K. C., Richardson, P. G. G., Tai, Y., et al. (2011) · Science · doi:10.1126/science.1206727 · Cited by 588
Self-citationDeng, J., Carlson, N. E., Takeyama, K., Cin, P. D., Shipp, M. A., & Letai, A. G. (2007) · Cancer Cell · doi:10.1016/j.ccr.2007.07.001 · Cited by 528
Self-citationCircumventing cancer drug resistance in the era of personalized medicine
Garraway, L. A., & Jänne, P. A. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0012 · Cited by 503
Inconsistency in large pharmacogenomic studies
Haibe-Kains, B., El-Hachem, N., Birkbak, N. J., Jin, A. C., Beck, A. H., Aerts, H. J. W. L., & Quackenbush, J. (2013) · Nature · doi:10.1038/nature12831 · Cited by 499
Vo, T.-T. T., Ryan, J. A., Carrasco, R. D., Neuberg, D. S., Rossi, D. J., Stone, R. M., DeAngelo, D. J., Frattini, M. G., & Letai, A. G. (2012) · Cell · doi:10.1016/j.cell.2012.08.038 · Cited by 356
Self-citationDifferential induction of apoptosis in HER2 and EGFR addicted cancers following PI3K inhibition
Faber, A. C., Li, D., Song, Y., Liang, M.-C., Yeap, B. Y., Bronson, R. T., Lifshits, E., Chen, Z., Maira, S.-M., García-Echeverría, C., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905056106 · Cited by 319
Reactivation of ERK signaling causes resistance to EGFR kinase inhibitors.
Ercan, D., Xu, C., Yanagita, M., Monast, C. S., Pratilas, C. A., Montero, J., Butaney, M., Shimamura, T., Sholl, L., Ivanova, E. V., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0103 · Cited by 294
Self-citationBIM expression in treatment-naive cancers predicts responsiveness to kinase inhibitors
Faber, A. C., Corcoran, R. B., Ebi, H., Sequist, L. V., Waltman, B. A., Chung, E., Incio, J., Digumarthy, S. R., Pollack, S. F., Song, Y., et al. (2011) · Cancer Discovery · doi:10.1158/2159-8290.cd-11-0106 · Cited by 283
Functional proteomic profiling of AML predicts response and survival
Kornblau, S. M., Tibes, R., Qiu, Y. H., Chen, W., Kantarjian, H. M., Andreeff, M., Coombes, K. R., & Mills, G. B. (2008) · Blood · doi:10.1182/blood-2007-10-119438 · Cited by 266
Elucidating Distinct Roles for NF1 in Melanomagenesis
Maertens, O., Johnson, B., Hollstein, P., Frederick, D. T., Cooper, Z. A., Messiaen, L., Bronson, R. T., McMahon, M., Granter, S., Flaherty, K., et al. (2013) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0313 · Cited by 258
Self-citationSarosiek, K. A., Chi, X., Bachman, J. A., Sims, J. J., Montero, J., Patel, L., Flanagan, A., Andrews, D. W., Sorger, P. K., & Letai, A. G. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.08.048 · Cited by 252
Self-citationAmplification of EGFR T790M causes resistance to an irreversible EGFR inhibitor
Ercan, D., Zejnullahu, K., Yonesaka, K., Xiao, Y., Capelletti, M., Rogers, A. M., Lifshits, E., Brown, A., Lee, C., Christensen, J. G., et al. (2010) · Oncogene · doi:10.1038/onc.2009.526 · Cited by 235
Self-citationBH3 profiling in whole cells by fluorimeter or FACS
Ryan, J. A., & Letai, A. G. (2013) · Methods · doi:10.1016/j.ymeth.2013.04.006 · Cited by 174
Self-citationDeng, J., Shimamura, T., Perera, S. A., Carlson, N. E., Cai, D. D., Shapiro, G. I., Wong, K., & Letai, A. G. (2007) · Cancer Research · doi:10.1158/0008-5472.can-07-1961 · Cited by 160
Self-citationBhatt, A. P., Bhende, P. M., Sin, S., Roy, D., Dittmer, D. P., & Damania, B. (2010) · Blood · doi:10.1182/blood-2009-10-251082 · Cited by 147
Burstein, H. J., Mangu, P. B., Somerfield, M. R., Schrag, D., Samson, D. J., Holt, L., Zelman, D., & Ajani, J. A. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2011.36.0354 · Cited by 139
Ryan, J. A., Brunelle, J. K., & Letai, A. G. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914878107 · Cited by 128
Self-citationThe Scientific Drunk and the Lamppost: Massive Sequencing Efforts in Cancer Discovery and Treatment
Yaffe, M. B. (2013) · Science Signaling · doi:10.1126/scisignal.2003684 · Cited by 67
Visualizing the Landscape of Selection Biomarkers in Current Phase III Oncology Clinical Trials
Sikorski, R. S., & Yao, B. (2010) · Science Translational Medicine · doi:10.1126/scitranslmed.3001087 · Cited by 8
Flagged first, then by citations
Cited by
Sorted by citations
Regulation of apoptosis in health and disease: the balancing act of BCL-2 family proteins
Singh, R., Letai, A., & Sarosiek, K. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0089-8 · Cited by 2,111
Self-citationResponse and resistance to BET bromodomain inhibitors in triple-negative breast cancer
Shu, S., Lin, C. Y., He, H. H., Witwicki, R. M., Tabassum, D. P., Roberts, J. M., Janiszewska, M., Jin Huh, S., Liang, Y., Ryan, J., et al. (2016) · Nature · doi:10.1038/nature16508 · Cited by 632
Self-citationBCL-2 proteins and apoptosis: Recent insights and unknowns
Edlich, F. (2017) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2017.06.190 · Cited by 640
The BCL2 Family: Key Mediators of the Apoptotic Response to Targeted Anticancer Therapeutics
Hata, A. N., Engelman, J. A., & Faber, A. C. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0011 · Cited by 619
Precision medicine for cancer with next-generation functional diagnostics
Friedman, A. A., Letai, A., Fisher, D. E., & Flaherty, K. T. (2015) · Nature Reviews Cancer · doi:10.1038/nrc4015 · Cited by 570
Self-citation
Metadata
- Authors
- Montero, J., Sarosiek, K. A., DeAngelo, J. D., Maertens, O., Ryan, J., Ercan, D., Piao, H., Horowitz, N. S., Berkowitz, R. S., Matulonis, U., Jänne, P. A., Amrein, P. C., Cichowski, K., Drapkin, R., & Letai, A.
- Affiliations
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Letai, Anthony
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2015-02
- Volume
- 160
- Issue
- 5
- Pages
- 977-989
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4391197
- OpenAlex
- W2165056381
- Cited by
- 378
- References
- 35
- Citations by year
- 2026: 19 · 2025: 19 · 2024: 19 · 2023: 31 · 2022: 37 · 2021: 36 · 2020: 37 · 2019: 44 · 2018: 40 · 2017: 39 · 2016: 38 · 2015: 16
- FWCI
- 19.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (U01CA152990, K99CA188679, P01CA139980, P01CA068484, R01CA129974, R01CA135257, R21CA188858, R00CA188679)
- 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 › Mitochondrial Function and Pathology
- Keywords
- chemotherapy response prediction, predictive biomarkers, apoptosis signaling, drug-induced cell death, combination chemotherapy, cytotoxicity
- MeSH terms
- Carcinoma, Non-Small-Cell Lung, Cell Line, Female, Humans, Mitochondria, Neoplasms, Ovarian Neoplasms, Signal Transduction, Leukemia, Myelogenous, Chronic, BCR-ABL Positive, Cell Death, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
