Ten challenges and opportunities in computational immuno-oncology

Area of Science:

• Oncology
• Immunology
• Biomedical Data Science

Background:

• Immuno-oncology therapies have revolutionized cancer treatment, becoming standard care for many histologies.
• However, a significant number of patients do not achieve lasting clinical benefits from current immunotherapies.
• This necessitates continuous innovation and advancement in the field of immuno-oncology.

Purpose of the Study:

• To review critical challenges and opportunities in computational immuno-oncology.
• To emphasize the role of computational strategies in accelerating the development of effective and safe cancer immunotherapies.
• To highlight the importance of interdisciplinary collaboration in addressing the evolving landscape of immuno-oncology.

Main Methods:

• This review synthesizes current knowledge and identifies key areas for development in computational immuno-oncology.
• It focuses on the intersection of data science, oncology, immunology, and clinical research.
• The review outlines 10 critical challenges and opportunities within the field.

Main Results:

• Computational immuno-oncology is emerging as a crucial discipline for advancing cancer treatment.
• Addressing 10 critical challenges can significantly impact the development pipeline.
• Robust computational strategies are essential for translating laboratory findings to clinical application.

Conclusions:

• Computational immuno-oncology offers a powerful approach to overcome limitations in current cancer immunotherapies.
• Interdisciplinary collaboration and advanced computational methods are vital for future progress.
• Accelerating the development of effective and safe immuno-oncology treatments requires a concerted effort.