Translating RB1 predictive value in clinical cancer therapy: Are we there yet?

Paola Indovina1, Francesca Pentimalli2, Daniele Conti3

  • 1Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA.

Insights

The retinoblastoma (RB1) gene, a key tumor suppressor, influences cancer cell response to therapies. Understanding RB1

Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • The retinoblastoma (RB1) gene, identified as the first tumor suppressor, plays a critical role in cell cycle regulation.
  • RB1 loss-of-function and pathway alterations are prevalent in human cancers, often correlating with prognosis.
  • RB1 status significantly impacts cellular responses to various stimuli and therapeutic interventions.

Purpose of the Study:

  • To provide an overview of RB1's role in response to diverse anti-cancer therapies.
  • To review the predictive value of RB1 status in clinical practice.
  • To discuss emerging non-canonical RB1 functions influencing treatment outcomes.

Main Methods:

  • Literature review of studies on RB1 and therapeutic responses.
  • Analysis of RB1's role in conventional cytotoxic/cytostatic treatments.
  • Review of RB1's predictive value in response to targeted therapies (e.g., CDK4/6 inhibitors, kinase inhibitors) and immunotherapy.

Main Results:

  • RB1 status is a crucial determinant of cell response to both traditional and modern anti-cancer strategies.
  • The predictive value of RB1 is underutilized due to functional complexity, context-dependency, and technical challenges.
  • RB1 influences outcomes across various cancer types, including hormone-dependent cancers.

Conclusions:

  • RB1's complex role necessitates further investigation for optimal clinical application.
  • Understanding RB1's canonical and non-canonical functions is vital for predicting and improving anti-cancer treatment efficacy.
  • Integrating RB1 status into clinical decision-making holds potential for personalized cancer therapy.

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