BioMedical Data Manifest

The TARGET Acute Myeloid Leukemia projects employed comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of high-risk or hard-to-treat childhood cancers. Acute myeloid leukemia (AML) is a cancer that originates in the bone marrow from immature white blood cells known as myeloblasts. About 25% of all children with leukemia have AML. Although survival rates have increased since the 1970s, approximately half of all childhood AML cases relapse despite intensive treatment. Additional therapies following relapse are often unsuccessful and can be especially difficult and damaging for children. These patients would clearly benefit from targeted therapeutic approaches.

Through comprehensive genome-wide characterization, TARGET researchers are identifying the genetic and epigenetic alterations of relapsed disease. The ultimate goal is to translate their discoveries into novel treatments that will improve outcomes for children with AML. To learn more about pediatric AML and current treatment strategies, visit the NCI pediatric AML website.

TARGET AML molecular characterization analyses include gene expression array, copy number array, DNA methylation, Whole Genome Sequencing, Whole Exome Sequencing, RNA-seq, miRNA-seq and Targeted Capture Sequencing.

• Data Links:
  • Raw:
    • dbGaP
    • Genomic Data Commons
  • Processed:
    • Genomic Data Commons
• Documentation (If different):
  • For GDC specific workflows see this link, otherwise accompanying dataset manuscripts

• Data Manifest aggregated from GDC

• Current Version: Data Release 42.0
• DOI: N/A
• Release Date: January 30, 2025
• Last Updated: N/A

• Hematopoietic and reticuloendothelial systems
• Myeloid Leukemias

• Contact Dataset Owners/Publishers for ways to contribute

• N/A

Dataset Contacts:
• Soheil Meshinchi, MD, PhD. Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
• Robert Arceci, MD, PhD. Children's Hospital, Phoenix, AZ, USA.

• N/A

• Disease-Specific (Pediatric Cancer Research): Use of the data must be related to Pediatric Cancer Research.
• Use of protected TARGET datasets should be for research projects that can only be conducted using pediatric data (i.e., the research objectives cannot be accomplished using data from adults) and that have likely relevance to developing more effective treatments, diagnostic tests, or prognostic markers for childhood cancers. Applications proposing methods, software, or other tool development would not be considered acceptable uses of the data.

• N/A

• N/A

When using this dataset please cite:

• Use of data from dbGaP: Please cite/reference the use of dbGaP data by including the dbGaP accession phs000465
• Acknowledgement Statement: The results published here are in whole or part based upon data generated by the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative, phs000218, managed by the NCI. The data used for this analysis are available through the GDC [https://gdc.cancer.gov/about-data/publications#/?groups=&years=&programs=TARGET&order=desc]. Information about TARGET can be found at https://www.cancer.gov/ccg/research/genome-sequencing/target/about.

• Each experimental unit refers to a patient sample
• Each patient (Case) can have multiple experimental units corresponding to for example different timepoints or tissue sources etc.

Above: Overall statistics of the open dataset.

Open Access
Size of Dataset	25.027 GB
Number of Cases	2,492
Number of Files	10,479
File Types	TSV,MAF,TXT,IDAT,XLSX

Above: Overall statistics of the controlled dataset.

Controlled Access
Size of Dataset	105.216 TB
Number of Cases	2,465
Number of Files	41,424
File Types	BEDPE,BAM,TSV,VCF,MAF,CEL,TAR

• Modalities:
  • Simple Nucleotide Variation
  • Sequencing Reads
  • Combined Nucleotide Variation
  • Biospecimen
  • Clinical
  • Copy Number Variation
  • Transcriptome Profiling
  • DNA Methylation
  • Somatic Structural Variation
  • Structural Variation
• Missingness:
  • Clinical data due to lack of access to records or curation, missing typically represented by blank, NA, Unknown entries etc
  • Not all genomics data is captured for each patient sample
  • Additionally, all genomic assays may have regions below LOD/background
• Sampling:
  • N/A
• Data anomalies/errors:
  • N/A

SUMMARY OF INSTANCES AND DATATYPES

Inclusion: AML patient samples were obtained from the Children's Oncology Group (most from study AAML0531) and chosen for inclusion in the TARGET project based on the following criteria: bone marrow and peripheral blood blast counts of > 50% and availability of DNA and RNA for comprehensive molecular characterization of the genome, transcriptome and epigenome. The majority of cases in the project had 3 or fewer cytogenetic abnormalities and the patients achieved a remission following the standard two rounds of induction therapy, which allowed the post-induction "normal" tissue to be used as tumor comparator. However, there does exist subsets of cases within the AML dataset that express a greater number of cytogenetic abnormalities and/or failed induction.

Confidentiality: Usage of Open Access data has been deemed safe for general usage with minimal risk to patients. Restricted Access data could be used to potentially re-identify a given patient and so needs special requirements to access.

Considerations for external resources utilized: N/A

• REB/IRB approval for data collection?: Generally, for these studies all patients have provided informed consent but see dbGaP for more details

• Sample were collected from patients in the following countries:
  • N/A
• Known ELS issues introduced by preprocessing: N/A
• Known problematic proxies: N/A
• Was a data protection impact analysis performed?: N/A

• IP / Terms of Use:
  • Raw data available from the Genomic Data Commons with access granted through dbGaP and adherence to their policies
• Third party intellectual property considerations:
  • N/A
• Export controls or other regulations impacting dataset access/download or storage:
  • N/A

Description of the data collection methodology: N/A

Specific data collection devices:

• N/A

Dataset Curation: N/A

Dataset Validation:

• N/A

Data collection timeframe: N/A

Preprocessing steps/workflow:

• Pre-processing: Generation and processing of raw data is described in detail in existing publications
• Post-processing and derived features: Generation of processed data by the Genomic Data Commons is described here