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The difficulty in translating the preclinical success of combined TGFβ and immune checkpoint inhibition to clinical trial
Immune checkpoint inhibitors (ICIs) have transformed the treatment paradigm for solid tumors. However, even in cancers generally considered ICI-sensitive, responses can vary significantly. Thus, there is an ever-increasing interest in identifying novel means of improving therapeutic responses, both...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9706619/ https://www.ncbi.nlm.nih.gov/pubmed/36455409 http://dx.doi.org/10.1016/j.ebiom.2022.104380 |
Sumario: | Immune checkpoint inhibitors (ICIs) have transformed the treatment paradigm for solid tumors. However, even in cancers generally considered ICI-sensitive, responses can vary significantly. Thus, there is an ever-increasing interest in identifying novel means of improving therapeutic responses, both for cancers in which ICIs are indicated and those for which they have yet to show significant anti-tumor activity. To this end, Transforming Growth Factor β (TGFβ) signaling is emerging as an important barrier to the efficacy of ICIs. Accordingly, several preclinical studies now support the use of combined TGFβ and immune checkpoint blockade, with near-uniform positive results across a wide range of tumor types. However, as these approaches have started to emerge in clinical trials, the addition of TGFβ inhibitors has often failed to show a meaningful benefit beyond the current generation of ICIs alone. Here, we summarize landmark clinical studies exploring combined TGFβ and immune checkpoint blockade. These studies not only reinforce the difficulty in translating results from rodents to clinical trials in immune-oncology but also underscore the need to re-evaluate the design of trials exploring this approach, incorporating both mechanism-driven combination strategies and novel, predictive biomarkers to identify the patients most likely to derive clinical benefit. |
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