Myelodysplastic syndrome (MDS) patients who are refractory to hypomethylating agents (HMAs) have a poor prognosis with median survival <6 months and few treatment options.
Patients with previously treated metastatic TETs appear to benefit from everolimus. Molecular testing of these tumors using targeted NGS panel revealed an association with several key genes, which may help to guide patient selection in the future.
Active GTPase-Rac1 is associated with cellular processes involved in carcinogenesis and expression of Bcl-2 endows cells with the ability to evade apoptosis. Here we provide evidence that active Rac1 and Bcl-2 work in a positive feedforward loop to promote sustained phosphorylation of Bcl-2 at serine-70 (S70pBcl-2), which stabilizes its anti-apoptotic activity.
Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues.
Patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) are generally older and have more comorbidities. Therefore, identifying personalized treatment options for each patient early and accurately is essential. To address this, we developed a computational biology modeling (CBM) and digital drug simulation platform that relies on somatic gene mutations and gene CNVs found in malignant cells of individual patients.
Tumor metabolism is the hallmark of cancer cells. Cancer cells utilize different nutrient sources to drive the metabolic pathways to sustain tumor growth. Glucose (Glu) and Glutamine (Gln) are the primary nutrient sources on which cancer cells thrive. Developing precision diet based on patient’s molecular characteristics can help treat the cancer with dietary modulations along with traditional approaches.
Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is an aggressive hematological malignancy for which optimal therapeutic approaches are poorly characterized. Using computational biology modeling (CBM) in conjunction with genomic data from cell lines and individual patients, we generated disease-specific protein network maps that were used to identify unique characteristics associated with the mutational profiles of ETP-ALL compared to non-ETP-ALL (T-ALL) cases and simulated cellular responses to a digital library of FDA-approved and investigational agents.
Despite advances in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), overall survival rates remain low. The ability to predict treatment response based on individual cancer genomics using computational modeling will aid in the development of novel therapeutics and personalize care.