The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index.
A Molecularly Annotated Platform of Patient-Derived Xenografts (“Xenopatients”) Identifies HER2 as an Effective Therapeutic Target in Cetuximab-Resistant Colorectal Cancer
Open accessPublished version of a preprint
Abstract
Only a fraction of patients with metastatic colorectal cancer receive clinical benefit from therapy with anti-epidermal growth factor receptor (EGFR) antibodies, which calls for the identification of novel biomarkers for better personalized medicine. We produced large xenograft cohorts from 85 patient-derived, genetically characterized metastatic colorectal cancer samples (“xenopatients”) to discover novel determinants of therapeutic response and new oncoprotein targets. Serially passaged tumors retained the morphologic and genomic features of their original counterparts. A validation trial confirmed the robustness of this approach: xenopatients responded to the anti-EGFR antibody cetuximab with rates and extents analogous to those observed in the clinic and could be prospectively stratified as responders or nonresponders on the basis of several predictive biomarkers. Genotype–response correlations indicated HER2 amplification specifically in a subset of cetuximab-resistant, KRAS/NRAS/BRAF/PIK3CA wild-type cases. Importantly, HER2 amplification was also enriched in clinically nonresponsive KRAS wild-type patients. A proof-of-concept, multiarm study in HER2-amplified xenopatients revealed that the combined inhibition of HER2 and EGFR induced overt, long-lasting tumor regression. Our results suggest promising therapeutic opportunities in cetuximab-resistant patients with metastatic colorectal cancer, whose medical treatment in the chemorefractory setting remains an unmet clinical need. Significance: Direct transfer xenografts of tumor surgical specimens conserve the interindividual diversity and the genetic heterogeneity typical of the tumors of origin, combining the flexibility of preclinical analysis with the informative value of population-based studies. Our suite of patient-derived xenografts from metastatic colorectal carcinomas reliably mimicked disease response in humans, prospectively recapitulated biomarker-based case stratification, and identified HER2 as a predictor of resistance to anti-epidermal growth factor receptor antibodies and of response to combination therapies against HER2 and epidermal growth factor receptor in this tumor setting. Cancer Discovery; 1(6); 508–23. ©2011 AACR. Read the Commentary on this article by Ciardiello and Normanno, p. 472 This article is highlighted in the In This Issue feature, p. 457
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?
In the citing paper
“HER2 overexpression is a negative predictor associated with reduced efficacy of anti-EGFR therapy [31], and >36% of cetuximab-resistant mCRC with wild-type KRAS, NRAS, BRAF, and PI3K was found to display HER2 overexpression [32].”
From Recent Advances in Therapeutic Strategies to Improve Colorectal Cancer Treatment (2024) · cites this paper as [32]
In the citing paper
“A series of studies have demonstrated that HER2 gene amplification or activating mutations can mediate resistance to anti-EGFR mAbs in CRC [27,178,179].”
From Drug Resistance in Colorectal Cancer: From Mechanism to Clinic (2022) · cites this paper as [27]
In the citing paper
“Preclinical studies have shown that the combination of HER2 and EGFR blockade can lead to CRC tumor regression [27,28,29].”
From Drug Resistance in Colorectal Cancer: From Mechanism to Clinic (2022) · cites this paper as [27]
In the citing paper
“Two independent studies have recently shown that HER2 amplification is a predictor of poor sensitivity to anti-EGFR antibodies [91, 92].”
From Primary and acquired resistance to EGFR-targeted therapies in colorectal cancer: impact on future treatment strategies (2014) · cites this paper as [91]
In the citing paper
“In retrospective studies, patients with tumors featuring HER2 amplification or heregulin overexpression and treated with cetuximab or panitumumab experienced disease progression and shorter progression-free and overall survival compared with HER2 wild-type cases [91-93].”
From Primary and acquired resistance to EGFR-targeted therapies in colorectal cancer: impact on future treatment strategies (2014) · cites this paper as [91]
In the citing paper
“only the dual targeting of HER2 and EGFR by combining a small-molecule inhibitor, such as the dual EGFR/HER2 inhibitor lapatinib, with a moAb, such as cetuximab or pertuzumab, induced overt and long-lasting tumor regressions in proof-of-concept trials in HER2-amplified xenopatients [91].”
From Primary and acquired resistance to EGFR-targeted therapies in colorectal cancer: impact on future treatment strategies (2014) · cites this paper as [91]
In the citing paper
“Increased EGFR gene copy number could predict response among KRAS wild-type patients [53, 91, 111-113]”
From Primary and acquired resistance to EGFR-targeted therapies in colorectal cancer: impact on future treatment strategies (2014) · cites this paper as [91]
In the citing paper
“Strong preclinical evidence suggests that dual inhibition of ERBB2 is superior to monotherapy due to enhanced efficacy and reduced therapy resistance4648”
From Current and emerging concepts for systemic treatment of metastatic colorectal cancer (2025) · cites this paper as [46]
In the citing paper
“Bertotti et al. identified ERBB2 as a biomarker of resistance to anti-EGFR mAbs [113].”
From Resistance to anti-EGFR therapies in metastatic colorectal cancer: underlying mechanisms and reversal strategies (2021) · cites this paper as [113]
In the citing paper
“A total of 2–7% of unselected CRCs were found to have ERBB2/mutations, and this number was much higher in KRAS wild-type cases (13.6%) and in KRAS/NRAS/BRAF/PIK3CA quadruple wild-type cases (36% )[13, 113, 114].”
From Resistance to anti-EGFR therapies in metastatic colorectal cancer: underlying mechanisms and reversal strategies (2021) · cites this paper as [113]
In the citing paper
“Finally, using patient-derived colon cancer xenografts, Bertotti et al. identified HER2 gene amplification as a predictor of resistance to cetuximab among KRAS wild-type tumors (Bertotti et al., 2011).”
From ERBB Receptors: From Oncogene Discovery to Basic Science to Mechanism-Based Cancer Therapeutics (2014) · cites this paper as Bertotti et al., 2011
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
Cunningham, D., Humblet, Y., Siena, S., Khayat, D., Bleiberg, H., Santoro, A., Bets, D., Mueser, M., Harstrick, A., Verslype, C., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa033025 · Cited by 5,035
Self-citationCetuximab and Chemotherapy as Initial Treatment for Metastatic Colorectal Cancer
Van Cutsem, E., Köhne, C.-H., Hitre, E., Zaluski, J., Chang Chien, C.-R., Makhson, A., D'Haens, G., Pintér, T., Lim, R., Bodoky, G., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0805019 · Cited by 3,877
K-ras Mutations and Benefit from Cetuximab in Advanced Colorectal Cancer
Karapetis, C. S., Khambata-Ford, S., Jonker, D. J., O'Callaghan, C. J., Tu, D., Tebbutt, N. C., Simes, R. J., Chalchal, H., Shapiro, J. D., Robitaille, S., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0804385 · Cited by 3,622
ERBB receptors and cancer: the complexity of targeted inhibitors
Hynes, N. E., & Lane, H. A. (2005) · Nature reviews. Cancer · doi:10.1038/nrc1609 · Cited by 3,379
Wild-Type KRAS Is Required for Panitumumab Efficacy in Patients With Metastatic Colorectal Cancer
Amado, R. G., Wolf, M., Peeters, M., Van Cutsem, E., Siena, S., Freeman, D. J., Juan, T., Sikorski, R., Suggs, S., Radinsky, R., et al. (2008) · Journal of Clinical Oncology · doi:10.1200/jco.2007.14.7116 · Cited by 3,207
Self-citationUmar, A., Boland, C. R., Terdiman, J. P., Syngal, S., de la Chapelle, A., Rüschoff, J., Fishel, R. A., Lindor, N. M., Burgart, L. J., Hamelin, R., et al. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh034 · Cited by 3,181
The Genomic Landscapes of Human Breast and Colorectal Cancers
Wood, L. D., Parsons, D. W., Jones, S., Lin, J., Sjöblom, T., Leary, R. J., Shen, D., Boca, S. M., Barber, T., Ptak, J., et al. (2007) · Science · doi:10.1126/science.1145720 · Cited by 2,833
De Roock, W., Claes, B., Bernasconi, D., De Schutter, J., Biesmans, B., Fountzilas, G., Kalogeras, K. T., Kotoula, V., Papamichael, D., Laurent-Puig, P., et al. (2010) · The Lancet Oncology · doi:10.1016/s1470-2045(10)70130-3 · Cited by 2,144
Self-citationVan Cutsem, E., Peeters, M., Siena, S., Humblet, Y., Hendlisz, A., Neyns, B., Canon, J.-L., Van Laethem, J.-L., Maurel, J., Richardson, G., et al. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2006.08.1620 · Cited by 1,979
Self-citationCetuximab for the Treatment of Colorectal Cancer
Jonker, D. J., O'Callaghan, C. J., Karapetis, C. S., Zalcberg, J. R., Tu, D., Au, H.-J., Berry, S. R., Krahn, M., Price, T., Simes, R. J., et al. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa071834 · Cited by 1,904
Cited by
Sorted by citations
Items 901–944 of 944
Vangala, D., Ladigan, S., Liffers, S. T., Noseir, S., Maghnouj, A., Götze, T.-M., Verdoodt, B., Klein‐Scory, S., Godfrey, L., Zowada, M. K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14998901
Vangala, D., Ladigan, S., Liffers, S. T., Noseir, S., Maghnouj, A., Götze, T.-M., Verdoodt, B., Klein‐Scory, S., Godfrey, L., Zowada, M. K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14998910
Vangala, D., Ladigan, S., Liffers, S. T., Noseir, S., Maghnouj, A., Götze, T.-M., Verdoodt, B., Klein‐Scory, S., Godfrey, L., Zowada, M. K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14998925
Vangala, D., Ladigan, S., Liffers, S. T., Noseir, S., Maghnouj, A., Götze, T.-M., Verdoodt, B., Klein‐Scory, S., Godfrey, L., Zowada, M. K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14998916
Vangala, D., Ladigan, S., Liffers, S. T., Noseir, S., Maghnouj, A., Götze, T.-M., Verdoodt, B., Klein‐Scory, S., Godfrey, L., Zowada, M. K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14998913
Current treatment options of HER2+ metastatic colorectal carcinoma
Liberko, M., & Soumarová, R. (2024) · Onkologie · doi:10.36290/xon.2024.039
Biological and molecular targets for targeted therapy in colorectal cancer (literature review)
Malovanna, A., Kolesnik, O., Zvirych, V. V., & Trehub, Y. S. (2024) · Practical oncology · doi:10.22141/2663-3272.7.1.2024.96
In vivo cancer modeling using mouse models
Tiwari, P. C., Chaudhary, M. J., Pal, R., & Nath, R. (2025) · Methods in cell biology · doi:10.1016/bs.mcb.2025.02.013
Miyamoto, K., Yanagi, T., Maeda, T., Kitamura, S., Nishihara, H., Iwamoto, R., Takahashi, K., & Ujiie, H. (2024) · Journal of Dermatological Science · doi:10.1016/j.jdermsci.2025.05.007
Shaikh, Z. P., & Bhise, S. B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-5787-3.ch007
Modelling Acral Melanoma in Admixed Brazilians Uncovers Genomic Drivers and Targetable PathwaysPreprint
Squiavinato, A. C. M. S., Bernardes, S. S., Aguiar, F. C., Facciolo, A. C., Herrera, M. D. C. V., Wong-Ramirez, J. R. C., Basurto‐Lozada, P., Nobre, A. B., Annamalai, G., do Nascimento, R. M. C., et al. (2025) · medRxiv · doi:10.1101/2025.08.08.25332963
Huang, W.-W., Cheng, Y., Yuan, S., Liu, Y., & Liu, F. (2025) · Clinical Colorectal Cancer · doi:10.1016/j.clcc.2025.08.004
Ceobanu, A. N., Braniste, A., Morărașu, Ș., & Dimofte, G. M. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i35.110370
Rafsanjani, M. R., Dooney, A., Suresh, R., O’Farrell, A. C., Jarzabek, M. A., Shiels, L., Byrne, A. T., Prehn, J. H. M., & Meade, A. D. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013581
Tang, W., Zheng, H., Wang, Q., Zhang, X., Zhou, S., Ge, C., & Fu, J. (2025) · Case Reports in Oncology · doi:10.1159/000549570
Limitations of current therapeutic strategies for the gastrointestinal disorders
Sharma, S., & Tumdam, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36372-6.00001-x · Cited by 1
Mazzeschi, M., Sgarzi, M., Romaniello, D., Gelfo, V., Cavallo, C., Ambrosi, F., Morselli, A., Miano, C., Laprovitera, N., Girone, C., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19451499
Cascianelli, S., Barbera, C., Ulla, A. A., Grassi, E., Lupo, B., Pasini, D., Bertotti, A., Trusolino, L., Médico, E., Isella, C., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22827057
Self-citationLu, X., Li, Y., Li, Y., Zhang, X., Shi, J., Feng, H., Yu, Z., & Gao, Y. (2024) · Figshare · doi:10.6084/m9.figshare.26641327
Vangala, D., Ladigan, S., Liffers, S. T., Noseir, S., Maghnouj, A., Götze, T.-M., Verdoodt, B., Klein‐Scory, S., Godfrey, L., Zowada, M. K., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14998907
Lu, X., Li, Y., Li, Y., Zhang, X., Shi, J., Feng, H., Yu, Z., & Gao, Y. (2024) · Figshare · doi:10.6084/m9.figshare.24580446
Lu, X., Li, Y., Li, Y., Zhang, X., Shi, J., Feng, H., Yu, Z., & Gao, Y. (2024) · Figshare · doi:10.6084/m9.figshare.26641330
Cascianelli, S., Barbera, C., Ulla, A. A., Grassi, E., Lupo, B., Pasini, D., Bertotti, A., Trusolino, L., Médico, E., Isella, C., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22827024
Self-citationKim, S., Shin, D., Min, A., Kim, M., Na, D., Lee, H.-B., Ryu, H. S., Yang, Y., 우고운, Lee, K.-H., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13535429
Lu, X., Li, Y., Li, Y., Zhang, X., Shi, J., Feng, H., Yu, Z., & Gao, Y. (2023) · Figshare · doi:10.6084/m9.figshare.24580296
Target Hyperactive ERK Signaling for Cancer Therapy
Yap, J., & Hu, J. (2025) · Springer eBooks · doi:10.1007/978-3-032-00771-1_257
Actionable subgroups (II): HER2+, NTRK/RET/ALK and MSI-H/TMB high
Vivas, C. S., Pena, M. B., Gimeno, D. D., & Rodríguez, J. C. R. (2026) · Revisiones en cáncer · doi:10.20960/revcancer.00135
Threadgill, D. W., Carter, K., McGill, M. P., Leach, W., Porter, W., Si, M., & Thomas, M. (2026) · Research Square · doi:10.21203/rs.3.rs-8802187/v1
Nguyen, L. C. (2019) · theses.fr (ABES) · doi:10.70675/df2ce30ez1a6bz4ba3z9798z284f80a7fa8d
Kaneko, S., Sawada, K., Hatanaka, K., Dazai, M., Nakatsumi, H., Ando, T., Kotaka, M., Tsuji, Y., Nakamura, M., Muto, O., et al. (2026) · International Journal of Clinical Oncology · doi:10.1007/s10147-026-03193-x
HER2 in Metastatic Colorectal Cancer: Diagnostic and Therapeutic Opportunities and Challenges
Zhao-Tao, P. A. N., Feng-Yu, G. A. I., Chen, C., Tong, L. I., & Yan-Ping, Q. I. N. G. (2026) · PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS · doi:10.3724/j.pibb.2025.0480
Özdemir, Ö., Günenç, D., Taşkaynatan, H., Peker, P., Kahraman, E. G., Biter, S., Paydaş, S., Önder, T., Ateş, Ö., Er, M. M., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15103979
New insights in tumor-on-a-chip models for studying cancer drug resistance
Wang, Y., Shi, L., Li, J., Zhang, X., Ren, J., Jia, Q., Lv, L., Yang, L., Zhang, Z., Yang, J., et al. (2026) · Pathology - Research and Practice · doi:10.1016/j.prp.2026.156562
Personalisierte Arzneimitteltherapie des kolorektalen Karzinoms
Schulte, N. (2026) · Die Innere Medizin · doi:10.1007/s00108-026-02133-1
Lee, J., Sim, J., Lee, Y., Lee, G.-W., Jung, H. S., Kim, K.-P., Kim, T.-Y., Ryu, H., Ryu, M., Ahn, M. S., et al. (2026) · International Journal of Clinical Oncology · doi:10.1007/s10147-026-03080-5
Moiola, C. P., López-Gil, C., Rebull, M., Sotoca, A. M., de la Calle, I., García, Á., Denizli, M., Salazar-Huayna, L., van der Vleuten, M. A. J., Bebia, V., et al. (2026) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2026.119830
Repurposing Nelfinavir: AIM2 inflammasome-associated anti-tumor effects in Glioblastoma
Meena, D., Sha, L., Chhipa, S., Solanki, P., & Jha, S. (2026) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2026.168428 · Cited by 1
Sagawa, T., Nagashima, H., & Fujikawa, K. (2026) · Journal of Gastrointestinal Cancer · doi:10.1007/s12029-026-01564-1
Liu, J., Jiang, D., Zhang, X., Chen, H., Zhao, S., Wen, D., & Ji, Y. (2026) · The New England Journal of Statistics in Data Science · doi:10.51387/26-nejsds109
Sadanandam, A., O’Farrell, A. C., Miller, I. S., Hari, P. S., Lärkeryd, A., Venken, T., Arijs, I., Zanella, E. R., Cottino, F., Pinnelli, M., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.09.014
Self-citationSeemann, H. (2013) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-4083
Metadata
- Authors
- Bertotti, A., Migliardi, G., Galimi, F., Sassi, F., Torti, D., Isella, C., Corà, D., Di Nicolantonio, F., Buscarino, M., Petti, C., Ribero, D., Russolillo, N., Muratore, A., Massucco, P., Pisacane, A., Molinaro, L., Valtorta, E., Sartore-Bianchi, A., Risio, M., Capussotti, L., Gambacorta, M., Siena, S., Medico, E., Sapino, A., Marsoni, S., Comoglio, P. M., Bardelli, A., & Trusolino, L.
- Affiliations
- IFOM (IT)
- Authors' Affiliations: 1Laboratory of Molecular Pharmacology, 2Laboratory of Oncogenomics, 3Laboratory of Systems Biology, 4Laboratory of Molecular Genetics, 5Division of Surgical Oncology, 6Unit of Pathology, 7Clinical Coordination Unit, and 8Molecular Clinical Oncology, Institute for Cancer Research and Treatment, Torino; Departments of 9Oncological Sciences and 10Biomedical Sciences and Human Oncology, University of Torino Medical School, Torino; 11Department of Surgery, Mauriziano Umberto I Hospital, Torino; 12FIRC Institute of Molecular Oncology, Milano; 13Division of Pathology, 14Falck Division of Medical Oncology, Ospedale Niguarda Ca' Granda, Milano; and 15Southern Europe New Drug Organization Foundation, Milano, Italy
- ORCID
- Venue
- Cancer Discovery
- Published
- 2011-11-01
- Volume
- 1
- Issue
- 6
- Pages
- 508-523
- ISSN
- 2159-8274 (ISSN-L)
- 2159-8290
- OpenAlex
- W1987443121
- Cited by
- 939
- References
- 56
- Citations by year
- 2026: 24 · 2025: 42 · 2024: 46 · 2023: 79 · 2022: 72 · 2021: 74 · 2020: 71 · 2019: 86 · 2018: 86 · 2017: 90 · 2016: 71 · 2015: 62 · 2014: 60 · 2013: 48 · 2012: 23
- FWCI
- 21.44
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- Cancer Discovery (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
- Funders
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Associazione Italiana per la Ricerca sul Cancro
- Regione Piemonte
- Genentech
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Treatments and Studies
- Health Sciences › Medicine › Oncology › HER2/EGFR in Cancer Research
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Keywords
- patient-derived xenografts, metastatic colorectal cancer, cetuximab resistance, HER2 amplification, anti-EGFR therapy, predictive biomarkers, combination therapy, KRAS wild-type, BRAF wild-type, treatment resistance, tumor heterogeneity
- MeSH terms
- Cetuximab, Aged, Aged, 80 and over, Animals, Antibodies, Monoclonal, Female, Humans, Male, Mice, Inbred C57BL, Mice, Transgenic, Middle Aged, Prospective Studies, Biomarkers, Tumor, Colorectal Neoplasms, ras Proteins, Receptor, ErbB-2, Erb-b2 Receptor Tyrosine Kinases, Drug Resistance, Neoplasm, Xenograft Model Antitumor Assays, Mice, Molecular Targeted Therapy, 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