The histologic classification of thymic epithelial tumors (TETs) is based on the description of both epithelial cell morphology and relative abundance of lymphocytes. Here, we used a computational biological model (CBM) approach on The Cancer Genome Atlas (TCGA) dataset to identify molecular subtypes of TETs and associated predicted therapeutic options.
Emerging data suggest that KRAS mutated non-small cell lung cancer (NSCLC) is a heterogeneous disease based on the presence of co-mutations. These co-mutations may impact PD-L1 expression, a predictive biomarker for PD-1/PD-L1 immunotherapy, and may result in differential responses to immunotherapy.
Hypomethylating agents (HMA) and lenalidomide (LEN) are approved and used in the treatment of patients (pts) with MDS, though these drugs fail in most pts. No method exists to predict drug response beyond associating single actionable mutations with a single drug's response.
Cytotoxic agents (7+3, HiDAC) and hypomethylating agents (HMAs) fail in the majority of MDS and AML pts. The aim of this study is to determine the predictive values of a genomics-informed computational biology method (CBM) in pts who are treated with standard of care (SOC) therapy.
Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies.