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.
Pediatric AML (pAML) treatment outcomes can vary due to genomic heterogeneity. Thus, selecting the right drugs for a given patient is challenging. There is a need for a priori means of predicting treatment responses based on tumor “omics”. Computational biology modeling (CBM) is a precision medicine approach by which biological pathways of tumorigenesis are mapped using mathematical principles to yield a virtual, interactive tumor model. This model can be customized based on a patient’s omics and analyzed virtually for response to therapies.
Multiple myeloma (MM) is a malignancy of plasma cells accounting for around 10% of all hematologic cancers. MM is an incurable heterogeneous malignancy which impacts the response rate due to complex nature of the disease. Although with standard of care treatment, including proteasome inhibitors (PI), a significant response and remission is achieved, the majority of patients still develops resistance. There is no precise method for determining the pathways which govern the acquired resistance to PI.
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.
Multiple myeloma (MM) is characterized by the invasion of malignant plasma cells into the bone marrow. While first line treatment options result in significant clinical benefit to patients, spatiotemporal clonal evolution results in disease relapse and mortality. Advances in genomics have armed clinicians with unprecedented insight into the molecular architecture of MM cells, however, the clinical benefit derived by genomics-guided intervention has been limited.
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).
Multiple myeloma (MM) is an incurable and heterogeneous haematological malignancy in which immune suppression and complex biology affect the disease and its response to treatment. Several new treatments have been approved for MM in recent years providing numerous options for patients with relapsed/refractory disease. However, there is no validated method for selecting the best treatment combination for each patient, making patient management difficult. The ability to predict treatment response based on disease characteristics could improve clinically outcomes.
Azacitidine (AZA) is currently a drug of choice for most of high-risk MDS patients. However, only 40-50% of MDS patients achieve clinical improvement with AZA. There is a need for a predictive clinical decision support tool that can identify MDS patients with higher or lower likelihood of AZA response. Ideally, patients with no chance of response would be spared of life-threatening toxicities and expense; while patients with high chance for response would receive maximized treatment.