The DOI matches its Crossref record; 27 of the 31 references it cites were found in a registry or an index.
An ErbB3 Antibody, MM-121, Is Active in Cancers with Ligand-Dependent Activation
Open accessPublished version of a preprint
Abstract
ErbB3 is a critical activator of phosphoinositide 3-kinase (PI3K) signaling in epidermal growth factor receptor (EGFR; ErbB1), ErbB2 [human epidermal growth factor receptor 2 (HER2)], and [hepatocyte growth factor receptor (MET)] addicted cancers, and reactivation of ErbB3 is a prominent method for cancers to become resistant to ErbB inhibitors. In this study, we evaluated the in vivo efficacy of a therapeutic anti-ErbB3 antibody, MM-121. We found that MM-121 effectively blocked ligand-dependent activation of ErbB3 induced by either EGFR, HER2, or MET. Assessment of several cancer cell lines revealed that MM-121 reduced basal ErbB3 phosphorylation most effectively in cancers possessing ligand-dependent activation of ErbB3. In those cancers, MM-121 treatment led to decreased ErbB3 phosphorylation and, in some instances, decreased ErbB3 expression. The efficacy of single-agent MM-121 was also examined in xenograft models. A machine learning algorithm found that MM-121 was most effective against xenografts with evidence of ligand-dependent activation of ErbB3. We subsequently investigated whether MM-121 treatment could abrogate resistance to anti-EGFR therapies by preventing reactivation of ErbB3. We observed that an EGFR mutant lung cancer cell line (HCC827), made resistant to gefitinib by exogenous heregulin, was resensitized by MM-121. In addition, we found that a de novo lung cancer mouse model induced by EGFR T790M-L858R rapidly became resistant to cetuximab. Resistance was associated with an increase in heregulin expression and ErbB3 activation. However, concomitant cetuximab treatment with MM-121 blocked reactivation of ErbB3 and resulted in a sustained and durable response. Thus, these results suggest that targeting ErbB3 with MM-121 can be an effective therapeutic strategy for cancers with ligand-dependent activation of ErbB3.
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
“Consistent with these data, blockade of HER3 with the neutralizing antibody MM-121 increases the efficacy of cetuximab in a mouse model of EGFR mutant lung cancer (Schoeberl et al., 2010).”
From ERBB Receptors: From Oncogene Discovery to Basic Science to Mechanism-Based Cancer Therapeutics (2014) · cites this paper as Schoeberl et al., 2010
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
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,420
MET Amplification Leads to Gefitinib Resistance in Lung Cancer by Activating ERBB3 Signaling
Engelman, J. A., Zejnullahu, K., Mitsudomi, T., Song, Y., Hyland, C., Park, J. O., Lindeman, N., Gale, C.-M., Zhao, X., Christensen, J., et al. (2007) · Science · doi:10.1126/science.1141478 · Cited by 4,496
Self-citationEGFR Mutation and Resistance of Non–Small-Cell Lung Cancer to Gefitinib
Kobayashi, S., Boggon, T. J., Dayaram, T., Jänne, P. A., Kocher, O., Meyerson, M., Johnson, B. E., Eck, M. J., Tenen, D. G., & Halmos, B. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa044238 · Cited by 3,964
Pao, W., Miller, V. A., Politi, K. A., Riely, G. J., Somwar, R., Zakowski, M. F., Kris, M. G., & Varmus, H. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020073 · Cited by 3,527
BIBW2992, an irreversible EGFR/HER2 inhibitor highly effective in preclinical lung cancer models
Li, D., Ambrogio, L., Shimamura, T., Kubo, S., Takahashi, M., Chirieac, L. R., Padera, R. F., Shapiro, G. I., Baum, A., Himmelsbach, F., et al. (2008) · Oncogene · doi:10.1038/onc.2008.109 · Cited by 1,447
Self-citationEscape from HER-family tyrosine kinase inhibitor therapy by the kinase-inactive HER3
Sergina, N. V., Rausch, M., Wang, D., Blair, J. A., Hann, B. C., Shokat, K. M., & Moasser, M. M. (2007) · Nature · doi:10.1038/nature05474 · Cited by 993
Engelman, J. A., Zejnullahu, K., Gale, C.-M., Lifshits, E., Gonzales, A. J., Shimamura, T., Zhao, F., Vincent, P. W., Naumov, G. N., Bradner, J. E., et al. (2007) · Cancer Research · doi:10.1158/0008-5472.can-07-1885 · Cited by 730
Self-citationSupport Vector Machines and Kernels for Computational Biology
Ben‐Hur, A., Ong, C. S., Sonnenburg, S., Schölkopf, B., & Rätsch, G. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000173 · Cited by 726
Research works printed but not linked: 1
- Burnham, 2002, Model Selection and Multimodel Interference
Cited by
Sorted by citations
Items 201–262 of 262
Anti‐tumor efficacy of naked siRNAs for ERBB3 or AKT2 against lung adenocarcinoma cell xenografts
Sithanandam, G., Fornwald, L. W., Fields, J. R., Morris, N. L., & Anderson, L. M. (2011) · International Journal of Cancer · doi:10.1002/ijc.26041 · Cited by 9
Inactivating Amplified HER2: Challenges, Dilemmas, and Future Directions
Moasser, M. M. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-1121 · Cited by 9
Xu, T., Schulga, A. A., Коновалова, Е. В., Rinne, S. S., Zhang, H., Vorontsova, O. V., Orlova, A., Deyev, S. M., Максимилианович, Т. В., & Vorobyeva, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032838 · Cited by 9
Pareja, F. G., Pines, G., & Yarden, Y. (2015) · Springer eBooks · doi:10.1007/978-3-319-11888-8_4 · Cited by 9
Bublil, E. M., Cohen, T., Arnusch, C. J., Peleg, A., Pines, G., Lavi, S., Yarden, Y., & Shai, Y. (2016) · Biochemistry · doi:10.1021/acs.biochem.6b00450 · Cited by 8
Thavaneswaran, S., Chan, W. Y., Asghari, R., Grady, J. P., Deegan, M., Jansen, V. M., & Thomas, D. M. (2022) · JCO Precision Oncology · doi:10.1200/po.22.00263 · Cited by 8
Turowec, J. P., Lau, E. W. T., Wang, X., Brown, K. R., Fellouse, F. A., Jawanda, K. K., Pan, J., Moffat, J., & Sidhu, S. S. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.004420 · Cited by 8
Anti-HER3 Monoclonal Antibody Inhibits Acquired Trastuzumab-Resistant Gynecologic Cancers
Li, X., Duan, Y., Qiao, C., Zhou, T., Yu, M., Geng, J., Feng, J., Shen, B., Lv, M., & Li, Y. (2015) · Technology in Cancer Research & Treatment · doi:10.1177/1533034615588422 · Cited by 8
Biological Complexity and the Need for Computational Approaches
Kitano, H. (2016) · History, philosophy and theory of the life sciences · doi:10.1007/978-3-319-47000-9_16 · Cited by 8
Ruiz-Sáenz, A., Sandhu, M. S., Carrasco, Y. P., Maglathlin, R. L., Taunton, J., & Moasser, M. M. (2016) · European Journal of Cancer · doi:10.1016/s0959-8049(16)61463-6 · Cited by 7
Hall, P. E., Spicer, J. F., & Popat, S. (2015) · Future Oncology · doi:10.2217/fon.15.110 · Cited by 7
Hayes, D., Kunde, D., Taylor, R. L., Pyecroft, S., Sohal, S. S., & Snow, E. T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177919 · Cited by 7
Systems Pharmacology: An Overview
Birtwistle, M. R., Hansen, J., Gallo, J. M., Muppirisetty, S., Ung, P. M. U., Iyengar, R., & Schlessinger, A. (2016) · AAPS advances in the pharmaceutical sciences series · doi:10.1007/978-3-319-44534-2_4 · Cited by 8
Functional drug–gene interactions in lung cancer
Šmída, M., & Nijman, S. M. B. (2012) · Expert Review of Molecular Diagnostics · doi:10.1586/erm.12.16 · Cited by 7
Krämer-Marek, G., & Oyen, W. J. G. (2016) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.169540 · Cited by 6
Context‐dependent regulation of receptor tyrosine kinases: Insights from systems biology approaches
Lam, I., Pickering, C. M., & Gabhann, F. M. (2018) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1437 · Cited by 6
Liao, H., Zhang, C., Chen, Z., Gao, Y., Li, Z., Wang, L., Li, Y., Shen, L., & Gao, J. (2021) · PubMed Central · Cited by 6
Clinical utility of targeted treatments in the management of epithelial ovarian cancer
Han, E. S., & Twu, C. (2012) · Biologics · doi:10.2147/btt.s29356 · Cited by 6
Karamouzis, M. V., & Papavassiliou, A. G. (2013) · Cancer · doi:10.1002/cncr.28424 · Cited by 5
Pseudokinases: Prospects for expanding the therapeutic targets armamentarium
Devang, N., Pani, A., & Rajanikant, G. K. (2020) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2020.09.004 · Cited by 6
Koziolek, E. J., Donoghue, J. F., Bentley, J. D., Lovrecz, G. O., Dolezal, O., Ward, C. W., Rothacker, J., Nice, E. C., Burgess, A. W., Hafner, M. A., et al. (2012) · Growth Factors · doi:10.3109/08977194.2012.709516 · Cited by 5
Rojas, C. M., McGill, M. P., Salvador, A. C., Bautz, D. J., & Threadgill, D. W. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009931 · Cited by 5
EGFR Inhibitors as Therapeutic Agents in Head and Neck Cancer
Liu, H., Cracchiolo, J. R., Beck, T. N., Serebriiskii, I. G., & Golemis, E. A. (2014) · Springer eBooks · doi:10.1007/978-1-4614-8815-6_4 · Cited by 4
HER3 PET Imaging Identifies Dynamic Changes in HER3 in Response to HER2 Inhibition with Lapatinib
Wehrenberg-Klee, E. P., Sinevici, N., Nesti, S., Kalomeris, T. A., Austin, E. E., Larimer, B. M., & Mahmood, U. (2021) · Molecular Imaging and Biology · doi:10.1007/s11307-021-01619-8 · Cited by 4
Klinke, D. J., & Birtwistle, M. R. (2015) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2015.07.006 · Cited by 4
Tummala, H., Goltsov, A., Khalil, H. S., Sproul, A. M., Scott, F. E., Mitev, V., & Zhelev, N. (2012) · Biodiscovery · doi:10.7750/biodiscovery.2012.6.4 · Cited by 4
Deregulation of osteoblast differentiation in primary bone cancers
Marie, P. J., Fromigué, O., & Modrowski, D. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-416721-6.00004-2 · Cited by 4
Li, Y., Wang, Z., Gu, M., Huang, F., & Chang, P. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07934-4 · Cited by 3
Singh, P., Tiwari, A., Abraham, A. K., & Zutshi, A. (2015) · CRC Press eBooks · doi:10.1201/b19023-7 · Cited by 3
The Role of HGF/c-MET in Head and Neck Squamous Cell Carcinoma
Seiwert, T. Y., Beck, T. N., & Salgia, R. (2014) · Springer eBooks · doi:10.1007/978-1-4614-8815-6_5 · Cited by 3
Huang, Y., Burns, D. J., Rich, B. E., MacNeil, I. A., Dandapat, A., Soltani, S. M., Myhre, S., Sullivan, B. F., Furcht, L. T., Lange, C. A., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.12480 · Cited by 3
c-MET in Head and Neck Squamous Cell Carcinoma
Kaczmar, J. M., & Beck, T. N. (2018) · Current cancer research · doi:10.1007/978-3-319-78762-6_3 · Cited by 2
Nonclinical Development of Multi-targeting Biopharmaceuticals
Prell, R. A., Lee, D. W., Halpern, W., & Connor, A. V. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-394810-6.00014-9 · Cited by 2
Ahmed, Y., Mahgoub, T., Alsindi, M., & Berenguer-Pina, J. J. (2022) · MedComm – Oncology · doi:10.1002/mog2.19 · Cited by 2
Current trend in translational research for treatment of ovarian cancer
Ju, W. (2016) · Journal of Korean Medical Association · doi:10.5124/jkma.2016.59.3.189 · Cited by 1
Neuregulin Signaling in the Tumor Microenvironment
Jia, R., Zhao, H., & Wang, S. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-47189-7_1 · Cited by 1
Recent Studies on Aromatase and EGFR Involved in Breast Cancer and their Inhibitors
Takla, F. N., Bayoumi, W. A., El‐Messery, S. M., & Nasr, M. N. A. (2024) · Delta University Scientific Journal · doi:10.21608/dusj.2024.248899.1046 · Cited by 1
Targeting the ErbB Family in Head and Neck Cancer
Kiseleva, A. A., Beck, T. N., Serebriiskii, I. G., Liu, H., Burtness, B. A., & Golemis, E. A. (2018) · Current cancer research · doi:10.1007/978-3-319-78762-6_2 · Cited by 1
Kubo, N., Harada, T., Shiraishi, Y., Nosaki, K., Nakagaki, N., Takeshita, M., Ouchi, H., Iwama, E., Tanaka, K., Okamoto, I., et al. (2019) · Anticancer Research · doi:10.21873/anticanres.13162 · Cited by 1
Telesco, S. (2011) · ScholarlyCommons (University of Pennsylvania) · Cited by 1
Schram, A. M., Iasonos, A. E., & Hyman, D. M. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2016.69.7169 · Cited by 1
Signaling by Non-Canonical Receptor Tyrosine Kinases ERBB3 and RYK
Shi, F. (2012) · ScholarlyCommons (University of Pennsylvania)
Schrock, A. B. (2013) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
Chien, A. J., & Moasser, M. M. (2011) · Breast Diseases A Year Book Quarterly · doi:10.1016/j.breastdis.2011.06.051
Molecular mechanisms controlling endocytic downregulation of EGFR and ErbB3
Sak, M. M. (2013) · NORA - Norwegian Open Research Archives
Tiwary, S. (2015) · UR Research (University of Rochester) · doi:10.13140/rg.2.2.13512.93444
Kaumaya, P. T. P., & Bekaii‐Saab, T. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-409547-2.12422-9
EGF receptor inhibitors: patient selection and clinical outcomes
Warsch, S., Tinoco, G., Glück, S., Avancha, K. K. V. R., & Montero, A. J. (2013) · doi:10.2217/ebo.13.381
Computational Systems Biology Applications
Yachie‐Kinoshita, A., Palaniappan, S. K., & Ghosh, S. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20079-4
Zhu, L., Yuan, P., Zhao, Z., Wang, X., Wang, G., & Liang, Y. (2020) · PubMed Central · doi:10.12122/j.issn.1673-4254.2020.06.06
Rathore, M. G., Wright, M., Bhattacharya, R., Fan, F., Gohar, A. V., Winter, J. M., Wang, Z., Markowitz, S. D., Willis, J. E., Ellis, L. M., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-745208/v1
Mathematical Modeling of the Dimerization of EGFR and ErbB3 in Lung Adenocarcinoma
Ito, T., Suzuki, T., & Murakami, Y. (2021) · Springer proceedings in mathematics & statistics · doi:10.1007/978-981-16-4866-3_12
Westin, S. N., Meyer, L. A., & Coleman, R. L. (2015) · Wiley eBooks · doi:10.1002/9781118468678.ch25
Lugovskoy, A. A., Curley, M. D., Lahdenranta, J., Kalra, A. V., Czibere, A. G., MacBeath, G., & Schoeberl, B. (2015) · Springer eBooks · doi:10.1007/978-1-4614-6613-0_95-1
Self-citationLugovskoy, A. A., Curley, M. D., Lahdenranta, J., Kalra, A. V., Czibere, A. G., MacBeath, G., & Schoeberl, B. (2017) · Springer eBooks · doi:10.1007/978-1-4419-0717-2_95
Self-citationChen, L., Mooso, B. A., Jathal, M. K., Madhav, A., Johnson, S. D., Spyk, E. N. V., Mikhailova, M., Ripoll, A. Z., Xue, L., Vinall, R. L., et al. (2023) · doi:10.1158/1078-0432.c.6518943
LRG1 is a novel HER3 ligand and promotes growth in colorectal cancerPreprint
Wang, R., Rathore, M. G., Wright, M., Huang, W., Martin, D., Taylor, D. J., Miyagi, M., Tang, W., Feng, H., Li, Y., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3454771/v1
Nonclinical development of monovalent and polyvalent biopharmaceuticals
Staflin, K., Korgaonkar, C. K., Narayanan, P. K., Prell, R. A., Lee, D. W., Halpern, W., & Connor, A. V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13344-2.00008-0
Signalisation et ciblage thérapeutique du récepteur tyrosine kinase AXL dans les cancers
Leconet, W. (2014) · theses.fr (ABES) · doi:10.70675/e91475a6z3b26z42ecza153zdc13a97fb5ec
Window-of-Opportunity Study Utilizing Preoperative Cetuximab in Patients with Head and Neck Cancer
Bruce, J. Y., Glazer, T. A., Berube, L., Longhurst, C. A., Yu, M., Hu, R., Wieland, A. M., Harari, P. M., Hartig, G. K., Iida, M., et al. (2026) · Cancer Research Communications · doi:10.1158/2767-9764.crc-26-0258
Momeny, M. (2015) · The University of Queensland · doi:10.14264/uql.2015.300
Metadata
- Authors
- Schoeberl, B., Faber, A. C., Li, D., Liang, M.-C., Crosby, K., Onsum, M., Burenkova, O., Pace, E., Walton, Z., Nie, L., Fulgham, A., Song, Y., Nielsen, U. B., Engelman, J. A., & Wong, K.-K.
- Affiliations
- Harvard University (US)
- Corresponding author
- Engelman, Jeffrey A.
- Wong, Kwok-Kin
- ORCID
- Venue
- Cancer Research
- Published
- 2010-03-14
- Volume
- 70
- Issue
- 6
- Pages
- 2485-2494
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- PMC
- PMC2840205
- OpenAlex
- W2120213327
- Cited by
- 264
- References
- 31
- Citations by year
- 2026: 2 · 2024: 5 · 2023: 8 · 2022: 11 · 2021: 10 · 2020: 8 · 2019: 10 · 2018: 19 · 2017: 16 · 2016: 26 · 2015: 31 · 2014: 29 · 2013: 29 · 2012: 29
- FWCI
- 14.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (R01AG2400401, K08AG024004, R01AG027757)
- National Cancer Institute (P50CA127003, CA120060, R01CA122794, R01CA140594, P50CA090578, K08CA120060, R01CA137008)
- Topics
- Health Sciences › Medicine › Oncology › HER2/EGFR in Cancer Research
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › PI3K/AKT/mTOR signaling in cancer
- Keywords
- HER3, ligand-dependent receptor activation, EGFR, HER2, MET, PI3K signaling, gefitinib resistance, cetuximab resistance, neuregulin-1, EGFR inhibitor resistance, xenograft models
- MeSH terms
- Cetuximab, Gefitinib, Animals, Antibodies, Monoclonal, Cricetulus, Cricetinae, Humans, Ligands, Neoplasms, Quinazolines, CHO Cells, Neuregulin-1, Receptor, ErbB-3, Xenograft Model Antitumor Assays, Cell Line, Tumor, Protein Kinase Inhibitors, Mice, Antibodies, Monoclonal, Humanized, ErbB Receptors
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
