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  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 15_Supplement ( 2015-08-01), p. 3752-3752
    Abstract: Small cell lung cancer (SCLC) accounts for 13-15% of all new lung cancer cases and represents the sixth most commonly diagnosed cancer in the US. However, it is an understudied cancer for which no molecularly targeted approaches have shown benefit. Using high-throughput techniques such as proteomics and metabolomics we can provide a more global, unbiased characterization to the inner workings of this disease. Integrating disparate data is a challenge in data analysis and bioinformatics, but this challenge is worth confronting due to the potential for combined proteomics / metabolomics analyses to better interrogate and capture the global landscape of active pathways and networks in SCLC than either technology alone. We accomplished this by comparing SCLC cell lines with non-small cell lung cancer (NSCLC) cell lines, differentiating characteristics of these two lung cancer cell types. We first used activity-based protein profiling (ABPP) combined with LC-MS/MS to profile the ATP-binding proteome of SCLC cell lines (n = 18) and NSCLC cell lines (n = 18). ABPP uses chemical probes that are directed against the active sites of enzymes to interrogate the functional state of ATP-binding enzymes, particularly kinases, in biological samples. These experiments identified 6937 peptides (2319 proteins), of which 3891 peptides (1543 proteins) were differentially expressed. Several pathways related to metabolism, such as purine biosynthesis and glycolysis / gluconeogenesis, were identified as over-represented in this list. These results led us to perform broad spectrum UPLC-TOF-MS metabolomics on ten SCLC and ten NSCLC cell lines. Multivariate analysis demonstrated distinct metabolite profiles for SCLC and NSCLC. Over 100 metabolites with variable importance to projection greater than 1 contributed to the differentiation of the two groups. These included metabolites related to purine metabolism such as inosinic acid and adenosine monophosphate and suggest a connection between our proteomics and metabolomics results. Statistical modeling approaches such as linear modeling, nonparametric nonlinear correlation, and Bayesian network analysis were used to integrate proteomics and metabolomics data to jointly characterize the key pathways and constituent components in SCLC. Multiple regression revealed a statistically significant interaction between endoglin (ENG) and phosphatidylethanolamine (PE), both of which are involved in angiogenesis. These findings and other statistical modeling results have the potential to facilitate the identification of new subtypes in SCLC and allow for the identification of novel targeted therapies. Future work will use these results to help characterize SCLC patient-derived xenograft models and surgically resected patient tissue samples. Funded, in part, by NIH Common Fund, 1U24DK097193. Citation Format: Paul A. Stewart, Jiannong Li, Kate J. Fisher, Suraj Dhungana, Delisha Stewart, Susan Sumner, Eric Gardner, John Poirier, Charles M. Rudin, Eric A. Welsh, Steven Eschrich, Ann Chen, Eric B. Haura. Integrating proteomics and metabolomics characterizes active pathways and potential drug targets in small cell lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3752. doi:10.1158/1538-7445.AM2015-3752
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 2
    In: Nature Genetics, Springer Science and Business Media LLC, Vol. 49, No. 7 ( 2017-7), p. 1126-1132
    Type of Medium: Online Resource
    ISSN: 1061-4036 , 1546-1718
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 1494946-5
    SSG: 12
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  • 3
    In: PROTEOMICS, Wiley, Vol. 17, No. 6 ( 2017-03)
    Abstract: Discovery proteomics experiments include many options for sample preparation and MS data acquisition, which are capable of creating datasets for quantifying thousands of proteins. To define a strategy that would produce a dataset with sufficient content while optimizing required resources, we compared (1) single‐sample LC‐MS/MS with data‐dependent acquisition to single‐sample LC‐MS/MS with data‐independent acquisition and (2) peptide fractionation with label‐free (LF) quantification to peptide fractionation with relative quantification of chemically labeled peptides (sixplex tandem mass tags (TMT)). These strategies were applied to the same set of four frozen lung squamous cell carcinomas and four adjacent tissues, and the overall outcomes of each experiment were assessed. We identified 6656 unique protein groups with LF, 5535 using TMT, 3409 proteins from single‐sample analysis with data‐independent acquisition, and 2219 proteins from single‐sample analysis with data‐dependent acquisition. Pathway analysis indicated the number of proteins per pathway was proportional to the total protein identifications from each method, suggesting limited biological bias between experiments. The results suggest the use of single‐sample experiments as a rapid tissue assessment tool and digestion quality control or as a technique to maximize output from limited samples and use of TMT or LF quantification as methods for larger amounts of tumor tissue with the selection being driven mainly by instrument time limitations. Data are available via ProteomeXchange with identifiers PXD004682, PXD004683, PXD004684, and PXD005733.
    Type of Medium: Online Resource
    ISSN: 1615-9853 , 1615-9861
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 2037674-1
    SSG: 12
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  • 4
    In: PROTEOMICS, Wiley, Vol. 20, No. 24 ( 2020-12)
    Abstract: Analysis of tyrosine kinase signaling is critical for the development of targeted cancer therapy. Currently, immunoprecipitation of phosphotyrosine (pY) peptides prior to liquid chromatography‐tandem mass spectrometry (LC‐MS/MS) is used to profile tyrosine kinase substrates. A typical protocol requests 10 mg of total protein from ≈10 8 cells or 50–100 mg of tissue. Large sample requirements can be cost prohibitive or not feasible for certain experiments. Sample multiplexing using chemical labeling reduces the protein amount required for each sample, and newer approaches use a material‐rich reference channel as a calibrator to trigger detection and quantification for smaller samples. Here, it is demonstrated that the tandem mass tag (TMT) calibrator approach reduces the sample input for pY profiling tenfold (to ≈1 mg total protein per sample from 10 7 cells grown in one plate), while maintaining the depth of pY proteome sampling and the biological content of the experiment. Data are available through PRIDE (PXD019764 for label‐free and PXD018952 for TMT). This strategy opens more opportunities for pY profiling of large sample cohorts and samples with limited protein quantity such as immune cells, xenograft models, and human tumors.
    Type of Medium: Online Resource
    ISSN: 1615-9853 , 1615-9861
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2037674-1
    SSG: 12
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  • 5
    In: Oncotarget, Impact Journals, LLC, Vol. 10, No. 8 ( 2019-01-25), p. 810-824
    Type of Medium: Online Resource
    ISSN: 1949-2553
    URL: Issue
    Language: English
    Publisher: Impact Journals, LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2560162-3
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  • 6
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2015
    In:  Cancer Research Vol. 75, No. 15_Supplement ( 2015-08-01), p. 1817-1817
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 15_Supplement ( 2015-08-01), p. 1817-1817
    Abstract: Lung cancer (NSCLC) is responsible for the most cancer-related deaths, and treatment options are limited regardless of histological type. Lung adenocarcinomas frequently exhibit genetic alterations, including EGFR and KRAS mutations, resulting in oncogene addiction; however, ∼50% of these tumors have no known oncogenic drivers. When genetic aberrations and their resulting phenotypes are well understood, targeted kinase inhibitors can be developed. Therefore, identification of dominant signaling pathways in tumors with no known oncogenic drivers is a key step in development of novel treatment options or repurposing of existing drugs.  In this study, we used activity-based protein profiling (ABPP) using ATP analog probes (ActivX, Thermo) as well as quantification of tyrosine phosphorylation (pY) levels to identify subsets of patients with unknown oncogenic drivers that would benefit from targeted therapy. In parallel experiments, liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) is used to quantify ABPP-labeled peptides and pY peptides to evaluate kinase signaling in tumors. Additionally, changes in ABPP and pY-LC-MRM signals after inhibitor treatment can be quantified. Building on previous analysis of lung squamous cell carcinomas (SCC), LC-MRM for enrichment and quantification of kinase ATP uptake and tyrosine phosphorylation was carried out on frozen tumors from 50 adenocarcinoma patients; 15 had matched control tissue. The panel of ABPP enriched proteins consists of & gt;250 kinases (∼15 tyrosine kinases) and pY-LC-MRM quantifies & gt;375 tyrosine phosphorylation sites. ABPP-LC-MRM analysis of lung adenocarcinoma cell lines with WT EGFR and WT KRAS included H2073, H1755, H1395, H1993, and H2342 cells.  Because cancer cells have many drivers and significant plasticity in signaling, the ABPP-LC-MRM strategy enables readout of multiple kinases as targets for inhibition, which could support the design of rational combinations using differential kinase activity between tumor and normal lung tissues, as well as classification of patients based on the kinase activity profiles. The kinase activity levels between adenocarcinoma and SCC are also compared to find common potential therapies. In cell lines matched to the tumors based on kinase profiles, viability assays were used to examine synergy of kinase inhibitors suggested by ABPP-LC-MRM.  LC-MRM quantification of kinase ATP uptake enables the evaluation of activity of ∼50% of known protein kinases in a single experiment; typical tumor readouts provide data for 100-150 kinases.  ABPP- and pY-LC-MRM analysis in cell lines and subsequent comparison of tumor and normal tissue specimens indicates the ability to detect signaling pathways that may be tumor drivers in NSCLC.  This platform can assist with classifying patients for molecularly-driven selection of targeted therapy combinations. Citation Format: Melissa Martinez, Bin Fang, Jiannong Li, Y. Ann Chen, Stephen Brantley, Wei Guan, Fumi Kinose, Eric Welsh, Steven A. Eschrich, Eric B. Haura, John M. Koomen. Quantitative proteomics identifies unique signaling phenotypes in NSCLC. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1817. doi:10.1158/1538-7445.AM2015-1817
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 7
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2014
    In:  Molecular Cancer Research Vol. 12, No. 12_Supplement ( 2014-12-01), p. A31-A31
    In: Molecular Cancer Research, American Association for Cancer Research (AACR), Vol. 12, No. 12_Supplement ( 2014-12-01), p. A31-A31
    Abstract: KRAS mutations account for development of approximately 30% of lung adenocarcinomas. While RAF-MEK-ERK signaling cascade is a key regulator of RAS-mediated proliferation and survival, the clinical efficacy of MEK inhibitors is still underwhelming in RAS-driven cancers. We treated KRAS mutant non-small cell lung cancer (NSCLC) cell lines with two MEK inhibitors (AZD6244 and MEK162) currently in clinical development. In all cell lines tested (N=11), we failed to observe significant effects on cell viability or induction of apoptosis. Because increasing evidence indicates that cancer cells undergo adaptive proteomic rewiring in response to kinase inhibitors, we performed activity-based protein profiling (ABPP) after treating 11 KRAS mutant NSCLC cell lines with MEK inhibitors in order to identify global enzymatic changes potentially involved in these adaptive processes. Our ABPP approach employs desthiobiotin-ATP probe, which enables selective labeling of active sites in ATP-binding proteins including kinases, chaperones and metabolic enzymes. Labeled proteins were subsequently purified and analyzed by mass spectrometry-based proteomics technique. Our ABPP results revealed that MEK inhibitors increased levels of multiple receptor tyrosine kinases (RTK) including EGFR and MET. MEK inhibitors induced MET phosphorylation and EGF-mediated AKT phosphorylation was markedly enhanced in the presence of MEK inhibitors, indicating increased RTK activity in adaptive response to MEK inhibition. We also found that MEK inhibition led to increased multiple heat shock proteins including HSP90 and HSP70, suggesting the possibility of chaperone-mediated adaptive resistance. Finally, MEK inhibitors globally increased enzymes involved in glucose utilization pathways implicating metabolic alterations in adaptive response to MEK inhibition. Collectively, our systematic activity-based proteomic profiling approach provides novel insights into adaptive resistance mechanisms to MEK inhibitors, suggesting rational combination treatment strategies for KRAS mutant NSCLC. Updated works will be presented. Citation Format: Jae-Young Kim, Bin Fang, Eric A. Welsh, Fumi Kinose, Jiannong Li, Steven A. Eschrich, John M. Koomen, Eric B. Haura. H. Activity-based protein profiling reveals adaptive response to pharmacological MEK inhibition in KRAS mutant non-small cell lung cancer. [abstract]. In: Proceedings of the AACR Special Conference on RAS Oncogenes: From Biology to Therapy; Feb 24-27, 2014; Lake Buena Vista, FL. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(12 Suppl):Abstract nr A31. doi: 10.1158/1557-3125.RASONC14-A31
    Type of Medium: Online Resource
    ISSN: 1541-7786 , 1557-3125
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2014
    detail.hit.zdb_id: 2097884-4
    SSG: 12
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  • 8
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 4543-4543
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 4543-4543
    Abstract: AXL is an attractive drug target because of its role in EMT-mediated resistance to EGFR tyrosine kinase inhibitor (TKI) in lung cancer (LC). Lack of genetic alterations and the role of stroma-mediated AXL activation in cancer cells, underscore the need to better characterize AXL TKIs, understand their effects on signaling and phenotype of cells, and develop assays to visualize active AXL signaling complexes. For this, 25 LC cells were analyzed for total (t) and phosphorylated (p) AXL expression. AXL TKIs, RXDX106, R428 and Cabozantinib, were profiled using western blotting (WB), viability assay and activity-based protein profiling (ABPP). Phosphoproteins (pSTY) altered by RXDX106 were identified using mass spectrometry. Effects of RXDX106 on signaling, viability and migration of LC cells were also evaluated. Cell line models of EMT-mediated acquired drug resistance, treated with a combination of AXL and EGFR TKIs, were analyzed for changes in signaling, cell viability and EMT. Immunoprecipitation (IP) identified adaptors of AXL signaling, and Proximity Ligation Assays (PLA) were developed to detect these active complexes in situ. H1299 cells, expressing highest levels of p and t AXL among the LC lines screened, was used in this study. RXDX106 and Cabozantinib potently inhibited pAXL in H1299 cells, but did not affect cell viability at these doses. R428 reduced cell viability at doses that did not efficiently inhibit pAXL, suggesting AXL independent phenotypic effects. Our ABPP data shows that apart from AXL, these TKIs target other overlapping and distinct subsets of proteins. R428 has the highest number of off targets and its unique ability to inhibit the FoxO pathway may explain the AXL independent phenotypic effects of R428. The pSTY data shows that RXDX106 deregulates phosphorylation of proteins involved in PI3K signaling, receptor endocytosis and cell migration pathways in H1299 cells. WB and phenotypic assays support these results by showing that RXDX106 inhibits pAXL, downstream pAKT but not pERK, and migration/invasion in these cells. In EGFR TKI resistant cells, EGFR and AXL TKI combination fails to alter downstream signaling, cell viability or EMT. Consistent with the WB and pSTY analyses, IP identifies PI3KR1 as an AXL interactor. PLAs to detect active AXL:PI3KR1 and AXL:pY100 signaling complexes show high basal PLA foci in H1299 and Calu1 cells that are abrogated by AXL TKI. HCC827 cells, which lack ligand independent pAXL, do not show significant labeling by either PLA. Overall, we demonstrate that different AXL TKIs have distinct target profiles and that inhibition of AXL suppresses downstream PI3K/AKT signaling and migration/ invasion of LC cells. We also show that AXL TKI fails to suppress downstream signaling, cell viability or EMT in EGFR TKI resistant cell lines. We have also established a PLA to annotate AXL adaptor foci that could be developed as a tool to measure drug-targetable active AXL complexes in patient tissues. Citation Format: Anurima Majumder, Guolin Zhang, Emma Adhikari, Bin Fang, Eric A. Welsh, John M. Koomen, Eric B. Haura. Proteomic characterization of AXL kinase inhibitors and signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4543.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 9
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 3410-3410
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 3410-3410
    Abstract: Covalent inhibitors of KRASG12C mutants allow specific targeting of KRAS driven signaling yet early studies suggest bypass signaling as a mechanism of resistance. Understanding adaptive signaling mechanisms enabling cells to survive KRASG12C inhibition could identify combination therapies and associated predictive biomarkers. We employed KRASG12C mutant lung cancer cell lines and evaluated their sensitivity in 2D cell culture to mimic the resistant state. We combined small molecule targeted agents (N=292 compounds) screens alongside KRASG12C inhibition as well as global mass spectrometry based phosphoproteomics (pY/S/T) to examine adaptive signaling changes. KRASG12C mutant lung cancer cell lines have differential sensitivity to KRASG12C inhibition with ARS-1620. Signaling analysis indicates inhibition of p-ERK and to some degree pAKT after 6 hours with evidence of rebound by 24 hr. To expand our understanding of cell type specific response and pathway reactivation upon KRASG12C inhibition, TMT-based quantitative phosphoproteomics was performed after phosphotyrosine (pY) enrichment in 3 cell lines (H358, Calu1 and H1792) after ARS-1620 treatment (6h and 24h). We identified 2275 human phosphosites in total, corresponding to 1153 proteins in 987 protein groups, out of which 433 phosphosites were differentially expressed (1.5-fold change and p-value & lt; 0.05) between any treatment time point and control. Preliminary analysis revealed some common and cell line specific response to ARS-1620. MAPK1, MAPK3, MET, CTNND1, and VTI1B exhibit the strongest down-regulation, across all time points in all cell lines. We observed 84 phosphosites corresponding to 77 proteins that were more abundant in ARS-1620 treated cells indicating a potential for adaptive resistance signaling. Results from the small molecule screens indicate compounds targeting ERBB/HER family of kinases and PI3K pathway showed better synergistic effects with ARS-1620. Signaling analysis in H358, H23 and H2122 cells revealed more abundant tyrosine phosphorylated and total HER2 and HER3 upon ARS-1620 treatment. Further suppression of pERK and pAKT was observed when afatinib was added to ARS-1620 resulting in more combined effects on cell viability. When compared to more specific EGFR inhibition (erlotinib), afatinib enhanced inhibition of pERK and pAKT by ARS-1620 and had more effects on cell viability. However, in H1792 cells, phosphoproteomics identified increased tyrosine phosphorylation of FGFR1 following ARS-1620 and this correlated with combination effects on cell viability of ARS-1620 with the FGFR inhibitor AZD-4547. Lastly, our results suggest that cells sensitive to combination with either HER TKI or FGFR TKI with ARS-1620 are also demonstrating combination effects with SHP2 inhibition (SHP099). Our combined chemical biology and phosphoproteomics results suggest widespread adaptive signaling changes, some of which are contributing to adaptive resistance. Results suggest a more important role of pan-HER inhibition compared with EGFR inhibition. These data may guide biomarker development strategies, especially those that reflect adaptive signaling responses, to predict clinically effective combination strategies in diverse group of KRASG12C mutant lung cancer. Citation Format: Hitendra Singh Solanki, Bin Fang, Eric A. Welsh, Aik C. Tan, Fumi Kinose, Uwe Rix, Eric B. Haura. Adaptive resistance mechanisms to KRASG12C specific inhibitors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3410.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 10
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2021
    In:  Clinical Cancer Research Vol. 27, No. 9 ( 2021-05-01), p. 2533-2548
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 27, No. 9 ( 2021-05-01), p. 2533-2548
    Abstract: Covalent inhibitors of KRASG12C specifically target tumors driven by this form of mutant KRAS, yet early studies show that bypass signaling drives adaptive resistance. Although several combination strategies have been shown to improve efficacy of KRASG12C inhibitors (KRASi), underlying mechanisms and predictive strategies for patient enrichment are less clear. Experimental Design: We performed mass spectrometry–based phosphoproteomics analysis in KRASG12C cell lines after short-term treatment with ARS-1620. To understand signaling diversity and cell type–specific markers, we compared proteome and phosphoproteomes of KRASG12C cells. Gene expression patterns of KRASG12C cell lines and lung tumor tissues were examined. Results: Our analysis suggests cell type–specific perturbation to ERBB2/3 signaling compensates for repressed ERK and AKT signaling following ARS-1620 treatment in epithelial cell type, and this subtype was also more responsive to coinhibition of SHP2 and SOS1. Conversely, both high basal and feedback activation of FGFR or AXL signaling were identified in mesenchymal cells. Inhibition of FGFR signaling suppressed feedback activation of ERK and mTOR, while AXL inhibition suppressed PI3K pathway. In both cell lines and human lung cancer tissues with KRASG12C, we observed high basal ERBB2/3 associated with epithelial gene signatures, while higher basal FGFR1 and AXL were observed in cells/tumors with mesenchymal gene signatures. Conclusions: Our phosphoproteomic study identified cell type–adaptive responses to KRASi. Markers and targets associated with ERBB2/3 signaling in epithelial subtype and with FGFR1/AXL signaling in mesenchymal subtype should be considered in patient enrichment schemes with KRASi.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 1225457-5
    detail.hit.zdb_id: 2036787-9
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