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Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

Florian Huemer¹, Michael Leisch¹, Roland Geisberger²

¹Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Paracelsus Medical University, 5020 Salzburg, Austria.

International Journal of Molecular Sciences

|April 25, 2020

Summary

Immune-checkpoint inhibitors (ICI) show promise in cancer, but many patients need combination therapies. This review explores combining ICI with other treatments and using AI to predict patient responses for better outcomes.

Area of Science:

Oncology
Immunotherapy
Biotechnology

Background:

Immune-checkpoint inhibitors (ICI) offer long-term survival for some advanced cancer patients.
Most patients do not respond to single-agent ICI, necessitating novel combination strategies.
Chimeric antigen receptor (CAR-)T cell therapy shows success in certain blood cancers but limited efficacy in solid tumors.

Purpose of the Study:

To review combination partners for immune-checkpoint inhibitors (ICI).
To explore the role of immune-checkpoints in CAR-T cell therapy resistance.
To discuss the application of artificial intelligence (AI) in predicting ICI therapy responses.

Main Methods:

Literature review of combination therapies with ICI.
Analysis of immune-checkpoint roles in CAR-T cell therapy.
Exploration of biomarkers and AI for predicting ICI response.

Main Results:

Combination strategies involving chemotherapy, kinase inhibitors, and vaccinations are under investigation to enhance ICI efficacy.
Immune-checkpoints represent a therapeutic target to overcome resistance in CAR-T cell therapy for both hematologic and solid tumors.
Biomarkers like PD-L1 and TMB aid patient selection, but their absence doesn't preclude response. Antigen presentation pathway alterations and radiomics show potential for predicting immunogenicity and ICI response.

Conclusions:

Combining ICI with other therapeutic modalities is crucial for improving patient outcomes.
Targeting immune-checkpoints can enhance CAR-T cell therapy effectiveness.
AI and novel biomarkers hold promise for personalizing ICI treatment strategies.

Keywords:

CAR-T cell HLA PD-1 PD-L1 T cell exhaustion kinase inhibitor radiomics resistance mechanism tumor neoantigen vaccination

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