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response, overcoming what appears to be tumor resistance. To increase their impact, such models may need
[63]
to integrate into Bayesian adaptive trials .

One theme borrowed from the physics community is to develop simplified abstract models. Such models
can generate powerful predictions, derived from the concept of universality [239] . The observation of
unexpected dynamical patterns in the immune system such as oscillations over several days [240] suggests that
phenomenological models have an important role to play.

Detailed mechanistic models employ knowledge at the molecular or cell level to explain and predict
phenomena [241] . A recent attempt to model the cancer-immune system interaction using 12 immune cell
types and 13 cytokines plus cancer cells finds steady state “basins” corresponding to escape, elimination,
and equilibrium phases in immunoediting, while also finding oscillatory states [242] . The interested reader
is referred to volumes focused on cancer [243] and the immune system [244] . A textbook on computational
immunology has recently been released [245] .

From an artificial intelligence perspective, therapy can be viewed as planning in the presence of uncertainty.
The idea that the immune system can be “steered” has been demonstrated by proof-of-concept in silico
work [246] . Cancer cells can be treated as adversaries in a game theory context [247] . In the clinical trials arena,
reinforcement learning approaches promise a model-free approach to sequential treatment selection [248,249] .

CONCLUSION
After a long history of doubt and failure, checkpoint blockade therapy has opened the door for cancer
immunotherapy [250] . With this key modality now accepted, the full weight of technological progress can be
brought to bear. New tools provide windows through which the process of disease and treatment can be
viewed. Their integration will allow increasingly sophisticated descriptions of immune system and tumor
state. Neoantigen cancer vaccines in particular are beneficiaries of this new environment and are poised to
lead the way to more precise and effective therapies.

While neoantigen vaccines can now be created with workflows that are increasingly standardized and almost
routine, many challenges lie ahead to elicit their true potential. Foremost is gaining a better understanding
of primary and secondary resistance. This can be viewed in the light of theories in which cancer cells and the
immune system train each other, for better or worse, over decades.

Combination therapies are now pursued as the next step forwards. The examination of all possible protocols
may however become infeasible or at least inefficient. Principled methods will need to be developed to
systematically identify promising approaches and learn from both successes and failures. This complexity is
also an opportunity to formalize therapy as a strategy and not simply an application of magic bullets. Over
the longer term, this promises growth in novel interventions that integrate technology, data, models, and
algorithms as part of an interdisciplinary biomedical science.

DECLARATIONS
Acknowledgments
The authors wish to express their gratitude to Rong (Shirly) Li, who created Figures 1 and 2. They also wish
to thank Qiubin Lin, Bin Yu, Gang Fang, Antoine Danchin, Chenli Liu, and Chung-I Wu for enlightening
conversations.

Authors’ contributions
Conception: Huang JD
Design: Tokuyasu TA, Huang JD