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.
New prognostic factors have been recently identified in AML patient population that include frequent mutations of receptor tyrosine kinases (RTK) including KIT, PDGFR, FLT3, that are associated with higher risk of relapse. Thus, targeting RTKs could improve the therapeutic outcome in AML patients.
Relapse is a major challenge in treating patients with MDS and AML. In this study we used a genomics-informed computational biology modeling (CBM) technique to understand the mechanisms of relapse after chemotherapy treatment and to postulate new re-induction treatment options.
In AML, leukemic transformation causes clonal expansion of immature cells through de-regulated cell division cycles. CDK4/CDK6 regulates neoplastic progression, which might represent an effective strategy for treating AML. But current clinical data shows either limited efficacy or elusive results. Bromodomain and extra-terminal (BET) inhibitors interferes with transcriptional complexes and disrupting gene transcription of key oncogenes such as MYC. Also, there is need to explore usage of other receptor tyrosine kinase inhibitors.
Monosomy of chromosome 7/Del 7 (-7) or its long arm (del(7q)) is one of the most common cytogenetic abnormalities in pediatric and adult myeloid malignancies, particularly in adverse-risk acute myeloid leukemias (AMLs). Monosomy 7 with complex karyotype further worsens the prognosis. Therefore, predicting response of therapies in this segment of patients is urgently needed to improve disease management by customizing therapy to the profile genomics instead of the conventional method of trial and error or one-size-fits all treatments.
AML patients with relapsed/refractory (R/R) disease have few effective treatment options. LEN+AZA may be an active and better tolerated regimen compared with conventional chemotherapy. This combination has been tested in a phase 2 pilot study of LEN+AZA in 37 R/R older patients with a 49% overall response rate (4 complete remission (CR) / CR with incomplete recovery (CRi) and 14 with morphologic leukemia free state (MLFS).
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.
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.
Introduction: Pts with R/R AML have few effective treatment options. Len has activity in myeloid malignancies including AML, and may have additive properties with aza. Based on encouraging data using sequential aza with high dose len in untreated elderly AML pts, we designed a phase 2 pilot study exploring this therapy (tx) in pts with R/R AML.
Background: Treatment of acute myeloid leukemia (AML) remains a challenge due to short-lived responses and high rates of relapse. AML cells often possess mutations in kinases (FLT3, JAK2, etc.), resulting in uncontrolled proliferation of neoplastic cells.