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Gene variations in GAGA samples Single

Immunoblot and Annexin V assays of GAGA6 and GAGA6-R PDX tumors after drug treatment with AZD4547, sunitinib or 1-AKP.

Preclinical models of diffuse-type gastric cancer (DGC) that reliably predict clinical activity of novel compounds are lacking. To overcome the problem of poor tumor cellularity in DGC, we used cells from malignant ascites to establish DGC patient-derived xenograft (PDX) models that recapitulate the primary cancer. Cells in PDX model GAGA6 with FGFR2 amplification were sensitive to AZD4547, a potent FGFR inhibitor that is being clinically evaluated for FGFR-aberrant cancer types. Intermittent in vivo treatment of GAGA6 tumors with AZD4547 gave rise to PDX tumors with acquired resistance to AZD4547, GAGA6-R. Surprisingly, there were no mutations in the FGFR2 gene in GAGA6-R, negating gatekeeper mutations as a mechanism of drug resistance. Phosphorylation of FGFR2 and downstream signaling molecules AKT/PKB and MAPK/ERK remained inhibited by AZD4547. Further analysis of signaling pathways identified AKT-independent phosphorylation and inhibition of GSK3β as a mechanism of drug resistance in GAGA6-R cells. Treatment of GAGA6-R cells with protein kinase C (PKC) inhibitor H7 in combination with AZD4547 led to dephosphorylation and activation of GSK3β with concomitant downregulation of MCL-1 and BCL-XL. Combined treatment with AZD4547 and H7 in vitro synergistically enhanced cell death in GAGA6-R but not GAGA6 cells. Furthermore, midostaurin, a multikinase inhibitor with PKC-inhibiting activity, in part reversed resistance of GAGA6-R tumor to AZD4547 in vivo. Our results suggest that upon challenge with FGFR inhibitors, FGFR2-amplified tumors that are highly dependent on FGFR2 signaling for survival rapidly develop resistance by switching to a PKC-mediated inhibition of GSK3β to gain a survival advantage. Mol Cancer Ther; 17(1); 232–42. ©2017 AACR.

PDF file - 130KB, Supplementary Figure S3 shows fractionation of CD44v+ cells for limiting dilution analysis in vitro and in vivo.

PDF file - 20KB, Supplementary Table S1 shows patient clinical information.

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PDF file - 24KB, Supplementary Table S3 shows effects of CD44 variant over-expression and knockdown on tumor initiation potential.

PDF file - 25KB, Supplementary Table S2 shows estimation of CSC frequency in unsorted, EpCAM+CD133+ and EpCAM+CD133- fractions from GC16 xenografts.

PDF file - 118KB, Supplementary Figure S1 shows gating strategies for flow cytometry, quantitation for EpCAM expression level and EpCAM+ cells initiated tumors in mice.

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PDF file - 20KB, Supplementary Table S4 shows p53 mutation status and proportion of CD44+ cells in gastric tumor xenografts.

PDF file - 126KB, Supplementary Figure S2 shows characterization of CD44v8-10 antibody by flow cytometry and gating strategy and isotype control for Figure 3.

PDF file - 93KB, Supplementary Figure S4 shows gating strategy for sorting of CD44 depleted TMK1 cells for tumor initiation assay and xenograft tumors formed.

Multiple three-dimensional (3D) tumour organoid models assisted by multi-omics and Artificial Intelligence (AI) have contributed greatly to preclinical drug development and precision medicine. The intrinsic ability to maintain genetic and phenotypic heterogeneity of tumours allows for the reconciliation of shortcomings in traditional cancer models. While their utility in preclinical studies have been well established, little progress has been made in translational research and clinical trials. In this review, we identify the major bottlenecks preventing patient-derived tumour organoids (PDTOs) from being used in clinical setting. Unsuitable methods of tissue acquisition, disparities in establishment rates and a lengthy timeline are the limiting factors for use of PDTOs in clinical application. Potential strategies to overcome this include liquid biopsies via circulating tumour cells (CTCs), an automated organoid platform and optical metabolic imaging (OMI). These proposed solutions accelerate and optimize the workflow of a clinical organoid drug screening. As such, PDTOs have the potential for potential applications in clinical oncology to improve patient outcomes. If remarkable progress is made, cancer patients can finally benefit from this revolutionary technology.