MAPK inhibitors(Hendrix College)

Cancer cells present neoantigens dominantly through MHC class I (MHCI) to drive tumor rejection through cytotoxic CD8+ T cells. There is growing recognition that a subset of tumors express MHC class II (MHCII), causing recognition of antigens by TCRs of CD4+ T cells that contribute to the antitumor response. We found that mouse BrafV600E-driven anaplastic thyroid cancers (ATCs) responded markedly to the RAF plus MEK inhibitors dabrafenib and trametinib (dab/tram) and that this was associated with upregulation of MhcII in cancer cells and increased CD4+ T cell infiltration. A subset of recurrent tumors lost MhcII expression due to silencing of Ciita, the master transcriptional regulator of MhcII, despite preserved IFN-γ signal transduction, which could be rescued by EZH2 inhibition. Orthotopically implanted Ciita-/- and H2-Ab1-/- ATC cells into immune-competent mice became unresponsive to the MAPK inhibitors. Moreover, depletion of CD4+, but not CD8+, T cells also abrogated the response to dab/tram. These findings implicate MHCII-driven CD4+ T cell activation as a key determinant of the response of Braf-mutant ATCs to MAPK inhibition.

Acute myeloid leukemia (AML) is a heterogeneous malignancy with diverse genetic mutations and oncogenic pathways influencing treatment response. Despite therapeutic advances, relapse and resistance remain persistent issues. This study integrates genomic and transcriptomic profiling to identify biomarkers of high-risk AML, informing personalized medicine strategies. Nonnegative matrix factorization was applied to RNA sequencing data from the BeatAML cohort (N = 462) for patient subtyping, with survival analysis using the Kaplan-Meier method. Immune profiling via xCell and Gene Set Variation Analysis assessed the tumor microenvironment, with findings validated in the Cancer Genome Atlas AML cohort (N = 173). Using a random forest machine learning model, we developed a 20-gene signature identifying a high-risk subgroup comprising approximately 20% of patients with AML. The high-risk AML subtype was enriched for recurrent FLT3, NPM1, and DNMT3A mutations, activation of PI3K/AKT/mTOR, and complement pathways. Immune profiling revealed an immunosuppressive microenvironment with increased M2 macrophages and mesenchymal stem cells. The 20-gene signature predicted high-risk AML with high accuracy (area under the curve = 0.995, F1 = 0.89). AML cell lines representing high- and low-risk phenotypes identified using the 20-gene signature were tested for drug sensitivity, including the standard-of-care cytarabine, and two targeted therapies, the PI3K inhibitor LY294002 and the MAPK inhibitor selumetinib, selected based on enriched pathways in high-risk AML. High-risk AML cell lines exhibited reduced cytarabine sensitivity but greater responsiveness to PI3K and MAPK/ERK inhibitors, consistent with pathway enrichment results. These findings support molecular stratification and predictive signatures as tools to guide therapy in high-risk AML. Further clinical validation is warranted.