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      FGFR blockade inhibits targeted therapy-tolerant persister in basal FGFR1- and FGF2-high cancers with driver oncogenes

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          Abstract

          Cancer cell resistance arises when tyrosine kinase inhibitor (TKI)-targeted therapies induce a drug-tolerant persister (DTP) state with growth via genetic aberrations, making DTP cells potential therapeutic targets. We screened an anti-cancer compound library and identified fibroblast growth factor receptor 1 (FGFR1) promoting alectinib-induced anaplastic lymphoma kinase (ALK) fusion-positive DTP cell’s survival. FGFR1 signaling promoted DTP cell survival generated from basal FGFR1- and fibroblast growth factor 2 (FGF2)-high protein expressing cells, following alectinib treatment, which is blocked by FGFR inhibition. The hazard ratio for progression-free survival of ALK-TKIs increased in patients with ALK fusion-positive non-small cell lung cancer with FGFR1- and FGF2-high mRNA expression at baseline. The combination of FGFR and targeted TKIs enhanced cell growth inhibition and apoptosis induction in basal FGFR1- and FGF2-high protein expressing cells with ALK-rearranged and epidermal growth factor receptor (EGFR)-mutated NSCLC, human epidermal growth factor receptor 2 (HER2)-amplified breast cancer, or v-raf murine sarcoma viral oncogene homolog B1 (BRAF)-mutated melanoma by preventing compensatory extracellular signal-regulated kinase (ERK) reactivation. These results suggest that a targeted TKI-induced DTP state results from an oncogenic switch from activated oncogenic driver signaling to the FGFR1 pathway in basal FGFR1- and FGF2-high expressing cancers and initial dual blockade of FGFR and driver oncogenes based on FGFR1 and FGF2 expression levels at baseline is a potent treatment strategy to prevent acquired drug resistance to targeted TKIs through DTP cells regardless of types of driver oncogenes.

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          Most cited references47

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          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 .
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            Osimertinib in Untreated EGFR-Mutated Advanced Non–Small-Cell Lung Cancer

            Osimertinib is an oral, third-generation, irreversible epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) that selectively inhibits both EGFR-TKI-sensitizing and EGFR T790M resistance mutations. We compared osimertinib with standard EGFR-TKIs in patients with previously untreated, EGFR mutation-positive advanced non-small-cell lung cancer (NSCLC).
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              Sensitivity Analysis in Observational Research: Introducing the E-Value.

              Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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                Author and article information

                Contributors
                yoshiura.shigeki97@chugai-pharm.co.jp
                Journal
                NPJ Precis Oncol
                NPJ Precis Oncol
                NPJ Precision Oncology
                Nature Publishing Group UK (London )
                2397-768X
                25 October 2023
                25 October 2023
                2023
                : 7
                : 107
                Affiliations
                [1 ]GRID grid.515733.6, ISNI 0000 0004 1756 470X, Product Research Department, , Chugai Pharmaceutical Co., Ltd., ; 216 Totsuka-cho, Totsuka-ku, Kanagawa 244-8602 Japan
                [2 ]GRID grid.515733.6, ISNI 0000 0004 1756 470X, Biometrics Department, , Chugai Pharmaceutical Co., Ltd., ; 2-1-1 Nihonbashi-muromachi, Chuo-ku, Tokyo 103-8324 Japan
                Author information
                http://orcid.org/0009-0008-9626-2027
                http://orcid.org/0000-0002-8747-1395
                http://orcid.org/0000-0002-2401-148X
                Article
                462
                10.1038/s41698-023-00462-0
                10600219
                37880373
                70523cf7-b4ca-458a-9be3-53c887996df9
                © Nature Publishing Group UK 2023

                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
                : 8 December 2022
                : 6 October 2023
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                © Nature Publishing Group UK 2023

                lung cancer,target identification
                lung cancer, target identification

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