Integrative Analysis of Drug Response and Clinical Outcome in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a new dataset, which has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort validation and identify new features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally impacting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers new avenues for mechanistic exploration and drug development, and reveals new tools for predicting outcome in AML.

Citation: Bottomly, D., Long, N., Schultz, A. R., Kurtz, S. E., Tognon, C. E., Johnson, K., … & Tyner, J. W. (2022). Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell, 40(8), 850-864. Article Link

Workflow

Code to reproduce manuscript figures/tables is available here

Metadata

• Sample Identifier Mapping

Harmonized Data (Across all four waves)

To download .txt files Right Click + Save Link As...

• Clinical Summary
• Normalized Expression
• WES/targeted Sequencing Mutation Calls
• Inhibitor AUC values
• Raw Inhibitor values
• Inhibitor Families
• WGCNA module Information
• Malignant Cell Types

Non-Harmonized Data

• Gene-level count matrix

Frequently Asked Questions

1. Q: These identifiers do not match!!?
   • A: The BeatAML project has three types of identifiers and the Sample ID Mapping File can help determine correspondance between them:
     1. labIds ([0-9]{2}-[0-9]{5}) and patientIds ([0-9]+) are internal identifiers assigned by the specimen tracking database.
     2. seq_ids (also [0-9]{2}-[0-9]{5}) are assigned as distinct barcodes per sample to the RNA and DNA sequencing FASTQ files prior to processing.
     3. dbgap_sample_id (BA[0-9]+[DR]) and dbgap_subject_id ([0-9]+) are those provided to dbGaP upon submission and are also leveraged by the NCI Genomic Data Commons (GDC).
2. Q: How do I access the raw sequencing data?
   • A: FASTQ file access is available from the NCI Genomic Data Commons
3. Q: Mutation calls in the Clinical Summary are different than those in WES/Targeted Sequencing Calls?
   • A: With the exception of FLT3 (ITD), NPM1, RUNX1, ASXL1 and TP53 mutations, which are consensus calls, mutations described in the Clinical Summary are derived solely from clinical genotyping results.
4. Q: How can I find information on healthy control samples with RNASeq data?
   • A: Annotation for these samples can be found in the Sample ID Mapping File after filtering on the rna_control column. The three types of controls are:
     1. Healthy pooled CD34+
     2. Healthy Individual BM MNC
     3. Healthy Individual CD34+
5. Q: What version of the human genome was used for alignments?
   • A: To be consistent with the original BeatAML publication the GRCh37 build was used for all alignments.
6. Q: Licensing
   • A: The processed data provided here is licensed under: CC-BY-4.0