• Read more about iCare 1: A prospective clinical trial to predict treatment response based on genomics-informed computational biology in patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS).

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.

• Read more about A Study to Predict Response to Azacitidine or Lenalidomide/Azacitidine Combination Therapy in MDS and AML Patients Using a Computational Biological Modeling (CBM) Approach

Background: Several treatment options are available for patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML); however, the majority of patients will fail treatment or relapse. Predicting response is imperative in determining the right treatment regimen for each patient but currently no validated method exists.

• Read more about Computational Biology Approach to Predict Hypomethylating Agent (HMA) Response Using Genomic and Clinical Characteristics in Myelodsyplastic Syndromes (MDS)

Background: HMAs (e.g., azacitidine (AZA), decitabine (DAC)) are approved agents for the treatment of patients with MDS. Despite their widespread use, only 50% of patients respond to HMA without reliable biomarkers of response despite knowing the patient’s genomic and clinical (non-genomic) characteristics prior to treatment.

• Read more about Digital Drug Screening and Computational Modeling Identifies Treatment Opportunities Despite Lack of Directly Actionable Mutations in Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)

Background: Although virtually all patients (pts) with AML or MDS harbor at least one somatic mutation, a minority of pts possess genetic mutations considered directly targetable by a drug. Moreover, in pts with multiple gene mutations, single-gene/single-drug matching often produces conflicting drug recommendations.

• Read more about Icare 1: A Prospective Clinical Trial to Predict Treatment Response Based on Mutanome-Informed Computational Biology in Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS)

• Read more about A computational biology method to predict HMA or lenalidomide treatment response in non-Del(5q) MDS

60% of myelodysplastic syndromes (MDS) patients fail to achieve clinical improvement with hypomethylating agents (HMAs).

• Read more about iCare 1: A prospective clinical trial to predict treatment response based on mutanome-informed computational biology in patients with AML and MDS

Hypomethylating agents (HMAs) (azacitidine (aza), decitabine (dec)) and lenalidomide (len) are approved agents and used to treat patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML).

• Read more about Computational Analysis of Genomic Abnormalities from a Phase 3 Trial of Rigosertib in Higher-Risk MDS: Simulation of a Predictive Signature for Clinical Response

Background: MDS is a marrow stem cell disorder with limited treatments. Due to outcome heterogeneity of MDS, it is imperative to identify prognostic tools for patients in clinical trials. Rigosertib (RIG) is a RAS-mimetic that inhibits cellular signaling pathways by binding to the RAS-binding Domain found in RAS effector proteins.

• Read more about A Prospective Clinical Trial to Predict Treatment Response Based on Mutanome-Informed Computational Biology in Patient with AML and MDS

Background: Hypomethylating agents (HMAs) (e.g., azacitidine (aza), decitabine (dec)) and lenalidomide (len) are approved agents and used in the treatment of patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML).

• Read more about A Genomic Rule Predicting HMA Treatment Response in MDS Identified By Protein Network Mapping and Validated By Clinical Trial Simulation

Background: Hypomethyating agents (HMAs) for the treatment of the myelodysplastic syndromes (MDS) fail to achieve clinical improvement in nearly 60% of patients.