Functional Genomic Landscape of Acute Myeloid Leukemia

By J. Tyner, C. Tognon, D. Bottomly, Beth Wilmot, S. Kurtz, Samantha Savage, Nicola Long, Anna Reister Schultz, E. Traer, Melissa L. Abel and 78 more. Nature, 2018

1,171 citations recorded by the source corpus.

The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset—accessible through the Beat AML data viewer (Vizome)—that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML. Analyses of samples from patients with acute myeloid leukaemia reveal that drug response is associated with mutational status and gene expression; the generated dataset provides a basis for future clinical and functional studies of this disease.

Subfields: Cancer Research, Hematology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/42fd93462b58a3ef4bf06eaf79a5f88461640781

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In Cancer Research

1. Su-In Lee
2. Scott M. Lundberg

In Molecular Biology

1. Su-In Lee
2. Scott M. Lundberg
3. Joseph D. Janizek
4. Gabriel Erion-Barner
5. Pascal Sturmfels

Citations in the corpus

• Improving performance of deep learning models with axiomatic attribution priors and expected gradients (2020, methodology): “We downloaded publicly available gene expression and drug response data for patients with acute myeloid leukaemia (AML, a type of blood cancer) and tried to predict patients’ drug response from their gene expression 29 .”
• Learning Explainable Models Using Attribution Priors (2019, methodology): “We downloaded publicly available gene expression and drug response data for patients with acute myeloid leukemia (AML, a type of blood cancer) and tried to predict patients’ drug response from their gene expression [38].”