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Sökning: WFRF:(Cheng XT)

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  • Hosaka, K, et al. (författare)
  • Therapeutic paradigm of dual targeting VEGF and PDGF for effectively treating FGF-2 off-target tumors
  • 2020
  • Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 3704-
  • Tidskriftsartikel (refereegranskat)abstract
    • FGF-2 displays multifarious functions in regulation of angiogenesis and vascular remodeling. However, effective drugs for treating FGF-2+ tumors are unavailable. Here we show that FGF-2 modulates tumor vessels by recruiting NG2+ pricytes onto tumor microvessels through a PDGFRβ-dependent mechanism. FGF-2+ tumors are intrinsically resistant to clinically available drugs targeting VEGF and PDGF. Surprisingly, dual targeting the VEGF and PDGF signaling produces a superior antitumor effect in FGF-2+ breast cancer and fibrosarcoma models. Mechanistically, inhibition of PDGFRβ ablates FGF-2-recruited perivascular coverage, exposing anti-VEGF agents to inhibit vascular sprouting. These findings show that the off-target FGF-2 is a resistant biomarker for anti-VEGF and anti-PDGF monotherapy, but a highly beneficial marker for combination therapy. Our data shed light on mechanistic interactions between various angiogenic and remodeling factors in tumor neovascularization. Optimization of antiangiogenic drugs with different principles could produce therapeutic benefits for treating their resistant off-target cancers.
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  • Menden, MP, et al. (författare)
  • Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
  • 2019
  • Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
  • Tidskriftsartikel (refereegranskat)abstract
    • The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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