FBXL2 counteracts Grp94 to destabilize EGFR and inhibit EGFR-driven NSCLC growth

Abnormal activation of epidermal growth factor receptor (EGFR) drives non-small cell lung cancer (NSCLC) development. EGFR mutations-mediated resistance to tyrosine-kinase inhibitors (TKIs) is a major hurdle for NSCLC treatment. Here, we show that F-box protein FBXL2 targets EGFR and EGFR TKI-resistant mutants for proteasome-mediated degradation, resulting in suppression of EGFR-driven NSCLC growth. Reduced FBXL2 expression is associated with poor clinical outcomes of NSCLC patients. Furthermore, we show that glucose-regulated protein 94 (Grp94) protects EGFR from degradation via blockage of FBXL2 binding to EGFR. Moreover, we have identified nebivolol, a clinically used small molecule inhibitor, that can upregulate FBXL2 expression to inhibit EGFR-driven NSCLC growth. Nebivolol in combination with osimertinib or Grp94-inhibitor-1 exhibits strong inhibitory effects on osimertinib-resistant NSCLC. Together, this study demonstrates that the FBXL2-Grp94-EGFR axis plays a critical role in NSCLC development and suggests that targeting FBXL2-Grp94 to destabilize EGFR may represent a putative therapeutic strategy for TKI-resistant NSCLC.

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection NDP.view2 viewing software to IHC image data collection; Image lab 3.0 to western blot data collection; Quantum GX II Micro-CT Imaging System to Micro-CT data collection; Pearl Trilogy Imagers (LI-COR Biosciences) to assess EGFR expression on lung tumors data collection.

Data analysis
Image-Pro Plus 6.0 (MD, USA) to quantify IHC staining by integrated optical density (IOD); GraphPad 8.0 and Excel to do the graph figures and statistics; MIM software (7.0.7, MIM Software Inc., Beijing, China) to analysis the Micro-CT images; Octet Pro software analysis (10.0.1.6) to calculated the binding affinity (KD) value; Vina Score (1.1.2) and Cyscore (2.0) to score protein-ligand binding affinity. LAS_X software (23.0) to analysis fluorescent images. Gromacs software 5.0.7 was used to a short time (1 ns) molecular simulation on the structure For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

April 2020
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability The structures of FBXO3-ApaG domain (PDB ID: 5HDW), EGFR kinase domain (PDB 6V66), or FBXL2 (PDB ID: 6O60) were obtained from Protein Data Bank (https:// www.rcsb.org/). The FBXL2 expression in TCGA lung cancer datasets was analyzed on the following website: https://portal.gdc.cancer.gov. The KM plotter lung cancer dataset was obtained from http://kmplot.com/analysis. All data generated or analyzed during this study are included in this article and its Supplementary Information files. The uncropped gel or blot figures and original data underlying Figs. 1-7 and Supplementary Figs. 1-11 are provided as a Source Data file. Source data are provided with this paper. All data supporting the findings of this study are available from the corresponding authors upon reasonable request.

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Life sciences study design
All studies must disclose on these points even when the disclosure is negative.

Sample size
For in vitro experiments, at least three biologically independent experiments were performed for all experiments unless otherwise stated. No statistical method was used to predetermine sample size. Such sample sizes are typical for the in vitro experiments and sufficient for a statistical analysis. For in vivo experiments, a sample size of n = 4-6 mice per group were used, which is sufficient to generate statistically significant results. No statistical method was used to predetermine sample size. The sample size for animal studies were designed according to previous report (PMID: 24250214) and were chosen based on prior experience with the same experimental design.
Data exclusions No data were excluded from the analyses Replication All attempts for replication were successful by different co-authors of this study. For in vitro experiments, at least three biologically independent experiments were performed for all experiments unless otherwise stated. For in vivo experiments, n=4-6 mice/group mice were used.
Randomization Yes, mice were randomly divided into different experimental groups and housed under standard conditions.

Blinding
The lab technician who measured the mice were blinded to the treatment groups, and experimenters who performed IHC analyses were blinded to group allocation. We performed other experiments, such as Western blotting and MTS assays, in a non-blinded manner, since the experimental design was complicated and the researchers were limited.

Authentication
All cell lines were obtained from a trusted source and were kept at low passages in order to maintain their identity. We have not authenticated these cell lines by ourselves.

Mycoplasma contamination
Cell lines used in this study were routinely tested to be negative for mycoplasma contamination.
Commonly misidentified lines (See ICLAC register) No commonly misidentified cell lines were used.

Animals and other organisms
Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research