Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validated. In-silico predictive models based on analysis of a plethora of data have been proposed by Lombardo et al. as an innovative tool for more efficacious immunotherapy against NBM. In particular, knowledge gained on intracellular signaling pathways linked to the development of NBM was used to predict how the different phenotypes could be modulated to respond to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. Prediction or forecasting are important targets of artificial intelligence and machine learning. Hopefully, similar systems could provide a reliable opportunity for a more targeted approach in the near future.

Giuseppe Giglia, G.G. (2020). Through Predictive Personalized Medicine. BRAIN SCIENCES, 10(9), 1-4 [10.3390/brainsci10090594].

Through Predictive Personalized Medicine

Giuseppe Giglia;Giuditta Gambino;Pierangelo Sardo
2020-01-01

Abstract

Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validated. In-silico predictive models based on analysis of a plethora of data have been proposed by Lombardo et al. as an innovative tool for more efficacious immunotherapy against NBM. In particular, knowledge gained on intracellular signaling pathways linked to the development of NBM was used to predict how the different phenotypes could be modulated to respond to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. Prediction or forecasting are important targets of artificial intelligence and machine learning. Hopefully, similar systems could provide a reliable opportunity for a more targeted approach in the near future.
2020
Settore BIO/09 - Fisiologia
Giuseppe Giglia, G.G. (2020). Through Predictive Personalized Medicine. BRAIN SCIENCES, 10(9), 1-4 [10.3390/brainsci10090594].
File in questo prodotto:
File Dimensione Formato  
Through Predictive Personalized medicine_Giglia 2020.pdf

accesso aperto

Tipologia: Versione Editoriale
Dimensione 190.81 kB
Formato Adobe PDF
190.81 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/431631
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 1
social impact