Retinoids as differentiating agents in oncology: a network of interactions with intracellular pathways as the basis

Enrico Garattini1, Maurizio Gianni, Mineko Terao

  • 1Laboratory of Molecular Biology, Centro Catullo e Daniela Borgomainerio, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy. egarattini@marionegri.it

Insights

Retinoids show promise in cancer treatment but face challenges like resistance and toxicity. Combining retinoids with other drugs could improve their effectiveness and safety in oncology.

Area of Science:

  • Oncology
  • Pharmacology
  • Molecular Biology

Background:

  • Retinoids, including retinoic acid, are potent anti-neoplastic agents with therapeutic and chemopreventive potential.
  • Despite successes like treating acute promyelocytic leukemia, widespread retinoid use in cancer is limited by resistance and toxicity.
  • Understanding retinoid mechanisms is key to overcoming these limitations.

Purpose of the Study:

  • To review and critically discuss current knowledge on retinoid mechanisms in cancer.
  • To provide a framework for designing effective retinoid combination therapies.
  • To enhance the therapeutic index of retinoids in oncological applications.

Main Methods:

  • Literature review of biochemical and molecular mechanisms of retinoid activity.
  • Analysis of cellular actions: differentiation, growth inhibition, and apoptosis.
  • Exploration of nuclear receptor-dependent and -independent pathways.

Main Results:

  • Retinoids exert anti-cancer effects through cell differentiation, growth inhibition, and apoptosis.
  • Retinoid action involves nuclear retinoic acid receptor (RAR)-dependent and -independent pathways.
  • Intra- and extra-cellular pathways modulate retinoid activity, offering targets for combination therapy.

Conclusions:

  • Rational drug combinations can enhance retinoid efficacy and reduce toxicity.
  • Targeting modulating pathways presents opportunities for novel therapeutic strategies.
  • Further research into retinoid combinations can improve cancer treatment outcomes.

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