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Druggable genome and precision medicine in cancer: current challenges

Camille Amandine Dupont1, Kristina Riegel1, Malvika Pompaiah1

  • 1Cell Biology Unit, University Medical Center of the Johannes Gutenberg University, Mainz, Germany.

The FEBS Journal
|February 24, 2021
PubMed

Insights

Targeted cancer therapies show promise but face resistance. Future strategies involve exploring the druggable genome, combining treatments, and utilizing patient-derived models with comprehensive data for personalized medicine.

Area of Science:

  • Oncology
  • Genomics
  • Immunotherapy

Background:

  • Significant progress in developing targeted cancer therapeutics based on multiomics, particularly genomics.
  • Tailor-made solutions targeting specific oncogenes have shown success but are limited by acquired resistance.
  • Next-generation therapeutics integrate multidisciplinary approaches and immune checkpoint inhibitors to overcome treatment resistance.

Purpose of the Study:

  • To summarize current advances and challenges in cancer therapeutics.
  • To highlight the understudied druggable genome and its role in the immune system.
  • To emphasize the importance of biobanking and longitudinal data for personalized cancer treatment.

Main Methods:

  • Review of advancements in targeted therapies and multiomics.
  • Discussion of synergistic therapeutic regimes and immune checkpoint inhibitors.
  • Exploration of patient-derived organoid models for preclinical validation.

Main Results:

  • Targeted therapies offer personalized treatment but face rapid resistance.
  • A significant portion of the druggable genome remains unexplored, particularly concerning its immunological functions.
  • Patient-derived organoid models accelerate the preclinical testing of novel drugs.

Conclusions:

  • Further research into the druggable genome and its immune interactions is crucial.
  • Biobanking and longitudinal data collection are essential for developing future tailor-made cancer therapies.
  • Integrating multiomics, advanced models, and comprehensive data will drive personalized oncology.
Keywords:
biobankingdrug resistancedruggable genomeoncogenesorganoidspersonalized therapeuticsprecision medicine

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