Over the past decade, large-scale cancer genome studies have revealed genetic and epigenetic alterations that drive the development and progression of cancers. This knowledge has improved our understanding of cancer biology, advanced the development of targeted therapies, and enabled diagnostic tests that identify patients who may benefit from these therapies. Several targeted drugs have been approved by the FDA and become standard of care of cancer treatment. However, most therapies targeting those oncogenic alterations alone yielded modest clinical benefit or caused acquired resistance in cancer patients. Better understanding how those genetic and epigenetic alterations affect the cancer cells and their niche will uncover the novel therapeutic targets and rational combinations.
Checkpoint immunotherapy has yielded meaningful responses in subsets of cancer patients. However, many questions remain to be answered, in regard to identification of predictive biomarkers, understanding of resistance mechanisms, and development of effective combinations. Recently, the FDA approved treatment with PD-1 inhibitor for all MMR-deficient, MSI-high, or tumor mutational burden–high solid tumors, opening a new horizon for biomarker-driven immunotherapy. There is a growing recognition of the importance of identifying the molecular subtypes that better respond to a certain immunotherapy. Hence, elucidating the impact of cancer genomics on responsiveness to immunotherapy facilitate the development of novel immunotherapies and combinatorial strategies for precision oncology.
This Research Topic welcomes Original Research and Review articles covering, but not limited to, the following sub-topics:
1) Investigate the impact of the cancer genome on responsiveness to immunotherapies
2) Develop biomarker-driven immunotherapy or combination therapies by targeting the interactions between cancer cells and immune cells.
Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation are considered out of scope of this section.
Over the past decade, large-scale cancer genome studies have revealed genetic and epigenetic alterations that drive the development and progression of cancers. This knowledge has improved our understanding of cancer biology, advanced the development of targeted therapies, and enabled diagnostic tests that identify patients who may benefit from these therapies. Several targeted drugs have been approved by the FDA and become standard of care of cancer treatment. However, most therapies targeting those oncogenic alterations alone yielded modest clinical benefit or caused acquired resistance in cancer patients. Better understanding how those genetic and epigenetic alterations affect the cancer cells and their niche will uncover the novel therapeutic targets and rational combinations.
Checkpoint immunotherapy has yielded meaningful responses in subsets of cancer patients. However, many questions remain to be answered, in regard to identification of predictive biomarkers, understanding of resistance mechanisms, and development of effective combinations. Recently, the FDA approved treatment with PD-1 inhibitor for all MMR-deficient, MSI-high, or tumor mutational burden–high solid tumors, opening a new horizon for biomarker-driven immunotherapy. There is a growing recognition of the importance of identifying the molecular subtypes that better respond to a certain immunotherapy. Hence, elucidating the impact of cancer genomics on responsiveness to immunotherapy facilitate the development of novel immunotherapies and combinatorial strategies for precision oncology.
This Research Topic welcomes Original Research and Review articles covering, but not limited to, the following sub-topics:
1) Investigate the impact of the cancer genome on responsiveness to immunotherapies
2) Develop biomarker-driven immunotherapy or combination therapies by targeting the interactions between cancer cells and immune cells.
Manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by robust and relevant validation are considered out of scope of this section.
