Deciphering the role of alternative splicing in neoplastic diseases for immune-oncological therapies

Marcus Bauer1, Chiara-Maria Schöbel2, Claudia Wickenhauser1

  • 1Institute of Pathology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.

PubMed

Insights

Alternative splicing generates variants of cancer targets, impacting immunotherapy effectiveness. Analyzing splicing patterns may improve patient selection and treatment strategies for better outcomes.

Area of Science:

  • Molecular Biology
  • Immunology
  • Oncology

Background:

  • Alternative splicing (AS) is a key molecular mechanism influencing gene expression.
  • AS is tissue-specific and dysregulated in various diseases, including cancers.
  • Immuno-oncological therapies like monoclonal antibodies (mAbs) and chimeric antigen receptor (CAR) T cells show promise but have limited efficacy in many patients.

Purpose of the Study:

  • To review the role of alternative splicing in cancer and its impact on immunotherapy.
  • To explore the molecular mechanisms of AS and its clinical relevance.
  • To discuss the potential of AS analysis for improving cancer therapy response.

Main Methods:

  • Literature review of alternative splicing mechanisms and their role in cancer.
  • Analysis of how splice variants affect the efficacy of immunotherapies (mAbs, CAR T cells).
  • Examination of the prognostic potential of splicing factors in cancer patients.

Main Results:

  • Alternative splicing generates splice variants of immunotherapeutic targets, leading to immune evasion and reduced therapy efficacy.
  • Splicing patterns of targeted molecules can influence patient stratification and treatment response.
  • Certain splicing factors correlate with patient outcomes, suggesting prognostic value.

Conclusions:

  • Understanding alternative splicing is crucial for optimizing cancer immunotherapy.
  • Analyzing splicing patterns in tumors may enable better patient selection and personalized treatment strategies.
  • Further research is needed to translate AS findings into clinical applications for improved cancer patient outcomes.

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