SUPR
Systems Precision Medicine for Optimal Therapies in AML
Dnr:

sens2018116

Type:

SNIC SENS

Principal Investigator:

Yudi Pawitan

Affiliation:

Karolinska Institutet

Start Date:

2018-06-20

End Date:

2024-08-01

Primary Classification:

30108: Cell and Molecular Biology

Webpage:

Allocation

  • Castor /proj at UPPMAX: 20000 GiB
  • Cygnus /proj at UPPMAX: 20000 GiB
  • Castor /proj/nobackup at UPPMAX: 5000 GiB
  • Cygnus /proj/nobackup at UPPMAX: 5000 GiB
  • Bianca at UPPMAX: 10 x 1000 core-h/month

Abstract

Our goal is to develop a systems biology framework to match targeted therapies and drug combinations for specific subtypes of acute myeloid leukemia (AML), and bring these results to benefit the patients. This is a project where we will pilot strategies and opportunities in systems medicine that may in the future become of central importance to cancer diagnostics and cancer therapies in the clinical practice and create opportunities to impact on diagnostic and therapeutic development. AML is an aggressive malignancy that develops when immature blast cells arising from myeloid leukemic progenitors accumulate in the bone marrow. With major unmet clinical needs, it represents an excellent prototype disease to pilot systems medicine and precision cancer medicine. Relapse rates after standard chemotherapy in AML are as high as 30-80% and once a patient develops a treatment-refractory disease, long term survival expectation is less than 10%. Our specific aims are: 1) determine the responses of 400 AML patient samples ex vivo to 530 clinical and emerging oncology drugs and define the best drugs and drug combinations for each of the 11 genomic subtypes of AML 2) based on the extensive genotype-phenotype data available, including comprehensive proteomic profiling, build in silico systems biology models to predict effective drug combinations and the molecular and mechanistic basis of such combinations 3) validate combinatorial drug effects in other ex vivo samples from independent cohorts (n=225) of AML patients to define robust combinations for clinical testing.