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Predictive Immune Modeling of Solid Tumors
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Cancer predictive studies.

Ivano Amelio1, Riccardo Bertolo2,3, Pierluigi Bove2,3

  • 1Torvergata Oncoscience Research Centre of Excellence, TOR, Department of Experimental Medicine, University of Rome Tor Vergata, via Montpellier 1, 00133, Rome, Italy. ivano.amelio@uniroma2.it.

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|October 15, 2020
PubMed
Summary
This summary is machine-generated.

Identifying predictive genetic alterations in neuroblastoma (NB) can stratify patients for targeted therapies. This approach aims to improve treatment outcomes for aggressive stages 3-4 NB, enhancing therapeutic success and minimizing harm.

Keywords:
MicrobiotaNeuroblastomaOmicsPrecision oncology

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Area of Science:

  • Oncology
  • Genetics
  • Molecular Biology

Background:

  • Neuroblastoma (NB) is a common childhood extracranial tumor with distinct stages.
  • Stages 3-4 NB represent aggressive, chemotherapy-resistant disease phases.
  • Current treatments show limited improvement for advanced NB stages.

Purpose of the Study:

  • To explore how cancer prediction studies, using NB as a model, can guide research.
  • To identify specific genetic landscapes for improved therapeutic strategies.
  • To enhance treatment success and reduce adverse effects in cancer patients.

Main Methods:

  • Utilizing neuroblastoma as a model system for genetic analysis.
  • Investigating predictive genetic alterations.
  • Stratifying patients into distinct cohorts based on genetic profiles.

Main Results:

  • Predictive genetic alterations can aid in defining cancer treatment outcomes.
  • Patient stratification allows for selective therapeutic protocols.
  • Focusing on genetic landscapes can steer preclinical and clinical research.

Conclusions:

  • Cancer prediction studies are crucial for advancing neuroblastoma treatment.
  • Identifying specific genetic alterations can lead to more effective and personalized therapies.
  • This approach holds potential for maximizing therapeutic success and minimizing harm in cancer patients.