RESUMEN
Background: Identification of cancer metastasis-relevant molecular networks is desired to provide the basis for understanding and developing intervention strategies. Here we address the role of GIPC1 in the process of MACC1-driven metastasis. MACC1 is a prognostic indicator for patient metastasis formation and metastasis-free survival. MACC1 controls gene transcription, promotes motility, invasion and proliferation of colon cancer cells in vitro, and causes tumor growth and metastasis in mice. Methods: By using yeast-two-hybrid assay, mass spectrometry, co-immunoprecipitation and peptide array we analyzed GIPC1 protein binding partners, by using the MACC1 gene promoter and chromatin immunoprecipitation and electrophoretic mobility shift assay we probed for GIPC1 as transcription factor. We employed GIPC1/MACC1-manipulated cell lines for in vitro and in vivo analyses, and we probed the GIPC1/MACC1 impact using human primary colorectal cancer (CRC) tissue. Results: We identified MACC1 and its paralogue SH3BP4 as protein binding partners of the protein GIPC1, and we also demonstrated the binding of GIPC1 as transcription factor to the MACC1 promoter (TSS to -60 bp). GIPC1 knockdown reduced endogenous, but not CMV promoter-driven MACC1 expression, and diminished MACC1-induced cell migration and invasion. GIPC1 suppression reduced tumor growth and metastasis in mice intrasplenically transplanted with MACC1-overexpressing CRC cells. In human primary CRC specimens, GIPC1 correlates with MACC1 expression and is of prognostic value for metastasis formation and metastasis-free survival. Combination of MACC1 and GIPC1 expression improved patient survival prognosis, whereas SH3BP4 expression did not show any prognostic value. Conclusions: We identified an important, dual function of GIPC1 - as protein interaction partner and as transcription factor of MACC1 - for tumor progression and cancer metastasis.
RESUMEN
Patient-derived xenograft (PDX) tumor models are essential for identifying new biomarkers, signaling pathways and novel targets, to better define key factors of therapy response and resistance mechanisms. Therefore, this study aimed at establishing pancreas carcinoma (PC) PDX models with thorough molecular characterization, and the identification of signatures defining responsiveness toward drug treatment. In total, 45 PC-PDXs were generated from 120 patient tumor specimens and the identity of PDX and corresponding patient tumors was validated. The majority of engrafted PDX models represent ductal adenocarcinomas (PDAC). The PDX growth characteristics were assessed, with great variations in doubling times (4 to 32 days). The mutational analyses revealed an individual mutational profile of the PDXs, predominantly showing alterations in the genes encoding KRAS, TP53, FAT1, KMT2D, MUC4, RNF213, ATR, MUC16, GNAS, RANBP2 and CDKN2A. Sensitivity of PDX toward standard of care (SoC) drugs gemcitabine, 5-fluorouracil, oxaliplatin and abraxane, and combinations thereof, revealed PDX models with sensitivity and resistance toward these treatments. We performed correlation analyses of drug sensitivity of these PDX models and their molecular profile to identify signatures for response and resistance. This study strongly supports the importance and value of PDX models for improvement in therapies of PC.