Person:
Cuesta Martínez, Ángel

Profile Picture
First Name
Ángel
Last Name
Cuesta Martínez
URI
https://hdl.handle.net/20.500.14352/77488
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Farmacia
Department
Bioquímica y Biología Molecular
Area
Bioquímica y Biología Molecular
Identifiers
UCM identifierORCIDScopus Author IDWeb of Science ResearcherIDDialnet ID

Filters

2018 - 20192
Reset filters

Settings

Author: Acker, Till × Subject: 577.1 ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    PHD3 Controls Lung Cancer Metastasis and Resistance to EGFR Inhibitors through TGFα
    (Cancer Research, 2018) Dopeso, Higinio; Jiao, Hui-Ke; Cuesta Martínez, Ángel; Henze, Anne-Theres; Jurida, Liane; Kracht, Michael; Acker-Palmer, Amparo; Boyan K. Garvalov; Acker, Till
    Lung cancer is the leading cause of cancer-related death worldwide, in large part due to its high propensity to metastasize and to develop therapy resistance. Adaptive responses to hypoxia and epithelial–mesenchymal transition (EMT) are linked to tumor metastasis and drug resistance, but little is known about how oxygen sensing and EMT intersect to control these hallmarks of cancer. Here, we show that the oxygen sensor PHD3 links hypoxic signaling and EMT regulation in the lung tumor microenvironment. PHD3 was repressed by signals that induce EMT and acted as a negative regulator of EMT, metastasis, and therapeutic resistance. PHD3 depletion in tumors, which can be caused by the EMT inducer TGFβ or by promoter methylation, enhanced EMT and spontaneous metastasis via HIF-dependent upregulation of the EGFR ligand TGFα. In turn, TGFα stimulated EGFR, which potentiated SMAD signaling, reinforcing EMT and metastasis. In clinical specimens of lung cancer, reduced PHD3 expression was linked to poor prognosis and to therapeutic resistance against EGFR inhibitors such as erlotinib. Reexpression of PHD3 in lung cancer cells suppressed EMT and metastasis and restored sensitivity to erlotinib. Taken together, our results establish a key function for PHD3 in metastasis and drug resistance and suggest opportunities to improve patient treatment by interfering with the feedforward signaling mechanisms activated by PHD3 silencing. Significance: This study links the oxygen sensor PHD3 to metastasis and drug resistance in cancer, with implications for therapeutic improvement by targeting this system.
  • Thumbnail Image
    Item
    Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body
    (Cell, 2019) Pan, Chenchen; Schoppe, Oliver; Parra-Damas, Arnaldo; Cai, Ruiyao; Todorov, Mihail Ivilinov; Gondi, Gabor; Neubeck, Bettina von; Böğürcü-Seidel, Nuray; Seidel, Sascha; Sleiman, Katia; Veltkamp, Christian; Förstera, Benjamin; Mai, Hongcheng; Rong, Zhouyi; Trompak, Omelyan; Ghasemigharagoz, Alireza; Reimer, Madita Alice; Javier Coronel; Jeremias, Irmela; Saur, Dieter; Acker-Palmer, Amparo; Acker, Till; Garvalov, Boyan K.; Menze, Bjoern; Zeidler, Reinhard; Ertürk, Ali; Cuesta Martínez, Ángel
    Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of cancer cells more than 100-fold by applying the vDISCO method to image metastasis in transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantification in 5 different metastatic cancer models including breast, lung, and pancreatic cancer with distinct organotropisms allowed us to systematically analyze features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in entire mice. DeepMACT can thus considerably improve the discovery of effective antibody-based therapeutics at the pre-clinical stage.