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Ten challenges and opportunities in computational immuno-oncology
Riyue Bao1,2, Alan Hutson3, Anant Madabhushi4,5,6
1UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Alan.Hutson@roswellpark.org baor@upmc.edu anantm@emory.edu meerzamd@mail.nih.gov song.liu@roswellpark.org eliezerm_vanallen@dfci.harvard.edu xingyi@chop.edu.
None:
Immuno-oncology has transformed the treatment of cancer, with several immunotherapies becoming the standard treatment across histologies. Despite these advancements, the majority of patients do not experience durable clinical benefits, highlighting the imperative for ongoing advancement in immuno-oncology. Computational immuno-oncology emerges as a forefront discipline that draws on biomedical data science and intersects with oncology, immunology, and clinical research, with the overarching goal to accelerate the development of effective and safe immuno-oncology treatments from the laboratory to the clinic. In this review, we outline 10 critical challenges and opportunities in computational immuno-oncology, emphasizing the importance of robust computational strategies and interdisciplinary collaborations amid the constantly evolving interplay between clinical needs and technological innovation.
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