9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Comparative analysis of drug response and gene profiling of HER2-targeted tyrosine kinase inhibitors

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Human epidermal growth factor 2 (HER2/ERBB2) is frequently amplified/mutated in cancer. The tyrosine kinase inhibitors (TKIs) lapatinib, neratinib, and tucatinib are FDA-approved for the treatment of HER2-positive breast cancer. Direct comparisons of the preclinical efficacy of the TKIs have been limited to small-scale studies. Novel biomarkers are required to define beneficial patient populations.

          Methods

          In this study, the anti-proliferative effects of the three TKIs were directly compared using a 115 cancer cell line panel. Novel TKI response/resistance markers were identified through cross-analysis of drug response profiles with mutation, gene copy number and expression data.

          Results

          All three TKIs were effective against HER2-amplified breast cancer models; neratinib showing the most potent activity, followed by tucatinib then lapatinib. Neratinib displayed the greatest activity in HER2-mutant and EGFR-mutant cells. High expression of HER2, VTCN1, CDK12, and RAC1 correlated with response to all three TKIs. DNA damage repair genes were associated with TKI resistance. BRCA2 mutations were correlated with neratinib and tucatinib response, and high expression of ATM, BRCA2, and BRCA1 were associated with neratinib resistance.

          Conclusions

          Neratinib was the most effective HER2-targeted TKI against HER2-amplified, -mutant, and EGFR-mutant cell lines. This analysis revealed novel resistance mechanisms that may be exploited using combinatorial strategies.

          Related collections

          Most cited references47

          • Record: found
          • Abstract: found
          • Article: not found

          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

            The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Next-generation characterization of the Cancer Cell Line Encyclopedia

              Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous backbone upon which to study genetic variants, candidate targets, small molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from various lineages and ethnicities. Integrating these data with functional characterizations such as drug-sensitivity data, short hairpin RNA knockdown and CRISPR–Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource to accelerate cancer research using model cancer cell lines.
                Bookmark

                Author and article information

                Contributors
                neil.conlon@dcu.ie
                Journal
                Br J Cancer
                Br J Cancer
                British Journal of Cancer
                Nature Publishing Group UK (London )
                0007-0920
                1532-1827
                21 January 2021
                21 January 2021
                30 March 2021
                : 124
                : 7
                : 1249-1259
                Affiliations
                [1 ]GRID grid.15596.3e, ISNI 0000000102380260, National Institute of Cellular Biotechnology, , Dublin City University, ; Glasnevin, Dublin, Ireland
                [2 ]Netherlands Translational Research Center B.V., Kloosterstraat 9, 5349 AB Oss, The Netherlands
                [3 ]GRID grid.476660.5, ISNI 0000 0004 0585 0952, Puma Biotechnology, Inc., ; 10880 Wilshire Boulevard, Suite 2150, Los Angeles, CA 90024 USA
                [4 ]GRID grid.412751.4, ISNI 0000 0001 0315 8143, Department of Medical Oncology, , St Vincent’s University Hospital, ; Dublin, Ireland
                Author information
                http://orcid.org/0000-0001-5921-7114
                Article
                1257
                10.1038/s41416-020-01257-x
                8007737
                33473169
                d6f1d05d-7a4b-4f66-9135-3c255e4831ce
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 August 2020
                : 9 December 2020
                : 17 December 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002081, Irish Research Council (An Chomhairle um Thaighde in Éirinn);
                Award ID: EPSPD/2020/24
                Award ID: EPSD/2020/24
                Award Recipient :
                Funded by: Cancer Clinical Research Trust (Charity no. CHY12210) Puma Biotechnology Inc
                Funded by: Cancer Clinical Research Trust (Charity no. CHY12210) Puma Biotechnology Inc.
                Categories
                Article
                Custom metadata
                © Cancer Research UK 2021

                Oncology & Radiotherapy
                tumour biomarkers,targeted therapies
                Oncology & Radiotherapy
                tumour biomarkers, targeted therapies

                Comments

                Comment on this article