Correspondingly to other cancers, also leukemias use various immune evasion mechanisms to escape the attacks of the immune system.
We have studied the anti-leukemia immune effects in chronic myeloid leukemia (CML) widely. We have previously discovered that especially the 2nd generation tyrosine kinase inhibitor (TKI) dasatinib has immunomodulatory properties (Kreutzman et al., Blood 2010; Kreutzman et al., Leukemia 2011; Mustjoki et al., Leukemia 2013; Kreutzman et al., Oncoimmunology 2014). Interestingly, the immune-related adverse events of dasatinib have been associated with better treatment responses in advanced CML. Despite the success of TKIs, it is believed that these drugs do not eradicate CML stem cells, and relapses occur often after treatment discontinuation. However, our recent studies suggest that a proportion of CML patients can discontinue the treatment and that NK cells are important in this successful treatment cessation (Ilander et al, Leukemia 2017).
Presently, we are also working with other haematological malignancies to characterize their immulonologic landscapes. Together with our collaborators, we analyze gene expression patterns in large-scale genomic data sets and characterize the immune environment in the bone marrow and blood samples with multicolor flow cytometry and multiplexed immunohistochemistry to understand the immune checkpoint molecule expression. The ultimate goal is to identify the patient subsets which may benefit from immune-based therapies such as immune checkpoint blockade.
In addition to hematological malignancies, our group has recently also begun to investigate the immune system in solid tumors (Hekim&Ilander et al, Cancer Immunol Res 2017). We are currently uncovering the in vivo immunomodulatory effects of immune checkpoint inhibitory treatment (e.g. anti-PD1) in melanoma patients concentrating especially on NK cells and charactering the immunological environment in renal cell carcinoma tumors.
Our aim is to discover novel biomarkers, by which clinicians could identify those patients who are most likely to benefit from the treatments.