Search Results
You are looking at 1 - 3 of 3 items for
- Author: A De Luca x
- Refine by access: All content x
Search for other papers by N Normanno in
Google Scholar
PubMed
Search for other papers by A De Luca in
Google Scholar
PubMed
Search for other papers by D Aldinucci in
Google Scholar
PubMed
Search for other papers by M R Maiello in
Google Scholar
PubMed
Search for other papers by M Mancino in
Google Scholar
PubMed
Search for other papers by A D’Antonio in
Google Scholar
PubMed
Search for other papers by R De Filippi in
Google Scholar
PubMed
Search for other papers by A Pinto in
Google Scholar
PubMed
Significant relief of bone pain in patients with bone metastases was observed in a clinical trial of the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib in breast cancer. Osteoclast activation and differentiation are regulated by bone marrow stromal cells (BMSC), a heterogeneous cell compartment that comprehends undifferentiated mesenchymal stem cells (MSC) and their specialized progeny. In this regard, we found that human primary BMSCs express immunoreactive EGFR. Expression of EGFR mRNA and protein was also demonstrated in two human, continuous MSC-like cell lines, HDS-1 and HDS-2 cells. Treatment of HDS cells with EGF produced a significant increase in the levels of activated EGFR which was not observed in the presence of gefitinib. A significant reduction in the basal levels of activation of the EGFR and of Akt was observed in HDS cells following treatment with gefitinib. Treatment of HDS cells with gefitinib produced a significant reduction in the levels of secreted macrophage colony-stimulating factor (M-CSF) and cell-associated receptor activator of NF-κB ligand (RANKL) in both cell lines, as assessed by using specific ELISA and Western blotting techniques. Finally, the ability to sustain the differentiation of pre-osteoclasts of conditioned medium from gefitinib-treated HDS cells was reduced by approximately 45% as compared with untreated HDS cells. These data have demonstrated for the first time that the EGFR regulates the ability of BMSCs to induce osteoclast differentiation and strongly support clinical trials of gefitinib in breast cancer patients with bone disease.
Search for other papers by N Normanno in
Google Scholar
PubMed
Search for other papers by C Bianco in
Google Scholar
PubMed
Search for other papers by A De Luca in
Google Scholar
PubMed
Search for other papers by M R Maiello in
Google Scholar
PubMed
Search for other papers by D S Salomon in
Google Scholar
PubMed
The ErbB receptors and their cognate ligands that belong to the epidermal growth factor (EGF) family of peptides are involved in the pathogenesis of different types of carcinomas. In fact, the ErbB receptors and the EGF-like growth factors are frequently expressed in human tumors. These proteins form a complex system that regulates the proliferation and the survival of cancer cells. Therefore, ErbB receptors and their ligands might represent suitable targets for novel therapeutic approaches in human carcinomas. In this regard, different target-based agents that are directed against the ErbB receptors have been developed in the past two decades. One of these compounds, the humanized anti-ErbB-2 monoclonal antibody trastuzumab has been approved for the treatment of patients with metastatic breast cancer. The anti-EGF receptor (EGFR) antibody C225, as well as EGFR tyrosine kinase inhibitors ZD1839 and OSI-774 are currently in phase III clinical development. Several other ErbB tyrosine kinase inhibitors are in phase I/II studies. These compounds have generally been shown to have an acceptable toxicity profile and promising anti-tumor activity in heavily pretreated patients. The mechanisms of action of these compounds, as well as the potential therapeutic strategies to improve their efficacy are discussed in this review with particular regard to the combinations of anti-ErbB agents with cytotoxic drugs, or combinations of different ErbB-targeting agents.
Department of Urology IEO European Institute of Oncology, IRCCS, Via Ripamonti, Milan, Italy
Università degli Studi di Milano, Milan, Italy
Search for other papers by Letizia Maria Ippolita Jannello in
Google Scholar
PubMed
Department of Neurosciences, Science of Reproduction and Odontostomatology, University of Naples Federico II, Naples, Italy
Search for other papers by Simone Morra in
Google Scholar
PubMed
Department of Urology, Medical University of Graz, Graz, Austria
Search for other papers by Lukas Scheipner in
Google Scholar
PubMed
Università degli Studi di Milano, Milan, Italy
Department of Urology, IRCCS Policlinico San Donato, Milan, Italy
Search for other papers by Andrea Baudo in
Google Scholar
PubMed
Department of Urology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
Search for other papers by Carolin Siech in
Google Scholar
PubMed
Division of Experimental Oncology/Unit of Urology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
Search for other papers by Mario de Angelis in
Google Scholar
PubMed
Search for other papers by Nawar Touma in
Google Scholar
PubMed
Search for other papers by Zhe Tian in
Google Scholar
PubMed
Search for other papers by Jordan A Goyal in
Google Scholar
PubMed
Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
Search for other papers by Stefano Luzzago in
Google Scholar
PubMed
Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
Search for other papers by Francesco A Mistretta in
Google Scholar
PubMed
Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
Search for other papers by Mattia Luca Piccinelli in
Google Scholar
PubMed
Search for other papers by Fred Saad in
Google Scholar
PubMed
Search for other papers by Felix K H Chun in
Google Scholar
PubMed
Search for other papers by Alberto Briganti in
Google Scholar
PubMed
Search for other papers by Sascha Ahyai in
Google Scholar
PubMed
Department of Urology, IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy
Search for other papers by Luca Carmignani in
Google Scholar
PubMed
Search for other papers by Nicola Longo in
Google Scholar
PubMed
Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
Search for other papers by Ottavio de Cobelli in
Google Scholar
PubMed
Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
Search for other papers by Gennaro Musi in
Google Scholar
PubMed
Search for other papers by Pierre I Karakiewicz in
Google Scholar
PubMed
We developed a novel contemporary population-based model for predicting cancer-specific survival (CSS) in adrenocortical carcinoma (ACC) patients and compared it with the established 8th edition of the American Joint Committee on Cancer staging system (AJCC). Within the Surveillance, Epidemiology, and End Results database (2004–2020), we identified 1056 ACC patients. Univariable Cox regression model addressed CSS. Harrell’s concordance index (C-index) quantified accuracy after 2000 bootstrap resamples for internal validation. The multivariable Cox regression model included the most informative, statistically significant predictors. Calibration and decision curve analyses (DCAs) tested the multivariable model as well as AJCC in head-to-head comparisons. Age at diagnosis (>60 vs ≤60 years), surgery, T, N, and M stages were included in the multivariable model. Multivariable model C-index for 3-year CSS prediction was 0.795 vs 0.757 for AJCC. Multivariable model outperformed AJCC in DCAs for the majority of possible CSS-predicted values. Both models exhibited similar calibration properties. Finally, the range of the multivariable model CSS predicted probabilities raged 0.02–75.3% versus only four single AJCC values, specifically 73.2% for stage I, 69.7% for stage II, 46.6% for stage III, and 15.5% for stage IV. The greatest benefit of the multivariable model-generated CSS probabilities applied to AJCC stage I and II patients. The multivariable model was more accurate than AJCC staging when CSS predictions represented the endpoint. Additionally, the multivariable model outperformed AJCC in DCAs. Finally, the AJCC appeared to lag behind the multivariable model when discrimination addressed AJCC stage I and II patients.