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Huemer F, Leisch M, Geisberger R, Melchardt T, Rinnerthaler G, Zaborsky N, Greil R. Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence. Int J Mol Sci 2020; 21:E2856. [PMID: 32325898 PMCID: PMC7215892 DOI: 10.3390/ijms21082856] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 12/23/2022] Open
Abstract
The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.
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Affiliation(s)
- Florian Huemer
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Paracelsus Medical University, 5020 Salzburg, Austria; (F.H.); (M.L.); (T.M.); (G.R.)
| | - Michael Leisch
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Paracelsus Medical University, 5020 Salzburg, Austria; (F.H.); (M.L.); (T.M.); (G.R.)
| | - Roland Geisberger
- Salzburg Cancer Research Institute-Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR), 5020 Salzburg, Austria; (R.G.); (N.Z.)
| | - Thomas Melchardt
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Paracelsus Medical University, 5020 Salzburg, Austria; (F.H.); (M.L.); (T.M.); (G.R.)
| | - Gabriel Rinnerthaler
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Paracelsus Medical University, 5020 Salzburg, Austria; (F.H.); (M.L.); (T.M.); (G.R.)
- Cancer Cluster Salzburg, 5020 Salzburg, Austria
| | - Nadja Zaborsky
- Salzburg Cancer Research Institute-Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR), 5020 Salzburg, Austria; (R.G.); (N.Z.)
- Cancer Cluster Salzburg, 5020 Salzburg, Austria
| | - Richard Greil
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Paracelsus Medical University, 5020 Salzburg, Austria; (F.H.); (M.L.); (T.M.); (G.R.)
- Salzburg Cancer Research Institute-Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR), 5020 Salzburg, Austria; (R.G.); (N.Z.)
- Cancer Cluster Salzburg, 5020 Salzburg, Austria
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