Dataset for "Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia"

Article: Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia

Cancer Discovery, DOI: 10.1158/2159-8290.CD-21-0410

Data Types:

1. Clinical summary

2. Drug response data

3. Exome-sequencing data

4. RNA-sequencing data

1. Clinical summary

File_0: Common sample annotation including patient and sample IDs, stage of the disease, tissue type and availability of different data types.

File_1.1: Clinical data for 186 AML patients including clinical diagnosis, disease classification, gender, age at diagnosis, treatments, cytogenetic and molecular details. The description of the variables/column titles is given below the clinical data.

File_1.2: Description of the clinical variables in File_1.1.

2. Drug response data for 164 AML patient samples and 17 healthy samples

File_2: Drug library details for 515 chemical compounds. The compound collection includes drugs names, drug class defined by molecular targets or mode of action, concentration range used for drug testing, supplier information, solvent information and vendor information.

File_3: Drug response data including selective drug sensitivity scores (sDSS) for 515 compounds across 181 samples (164 AML patient samples and 17 healthy control samples). The DSS is modified area under the curve values and are calculated as shown in Yadav et al publication (1). The selective drug sensitivity scores (sDSS) is healthy control normalized DSS that gives estimated cancer-selective drug responses. The higher the sDSS values indicate drug sensitivities and negative sDSS values represent drug resistance.

Note: We recommend using selective DSS values instead of raw values (% inhibition, IC50, DSS given in the online manuscript supplementary data). 

Note: If the value is missing, the drug was not tested for that given sample.

File_4: Drug sensitivity and resistance testing (DSRT) assay details for 181 samples (164 AML patient samples and 17 healthy control samples). The information includes medium (MCM or CM) used for the drug testing, % cell viability after 72 h without drug testing and blast cell percentage of each sample.

Note: Column E is the ratio of luminescence values at 72 h and 0 h. The fold change in the cell viability without drug treatment was calculated as % cell viability. That is why the value could be more than 100% e.g. 70% cell viability meaning that 30% cells died during 72 h and 300% cell viability meaning that cells grew 3 times in 72 h incubation period.

3. Exome-sequencing data for 225 AML patient samples

Note: The number of samples in the manuscript is 226. The correct number used in the analyses is 225.

Mutation data. The cancer specific gene list was prepared by combining AML related genes from TCGA(2) (n=23), InToGen(3) (n=32), Papaemmanuil et al.(4) (n=111) and Census database(5) (n=616). Out of these genes, we found 340 genes as mutated across 225 AML patient samples. The mutation was called with P-values less than 0.05.

File_5: VAF (variant allele frequency) of 340 cancer-specific genes across 225 AML patient samples. The VAF was calculated using paired skin samples as a control from the same AML patient.

File_6: Binary data for 57 cancer specific genes frequently mutated (a given mutation detected in 5 or more samples) across 225 AML patient samples.

4. RNA-sequencing data for 163 AML patient samples and 4 healthy

CPM (count per million) data: The CPM values are batch corrected values used for direct comparison of gene expression.

File_7: Log2CPM values for 18,202 protein coding genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples).

File_8: Raw read count data RNA-seq library information for all 60,619 genes across 167 samples (163 AML patient samples and 4 healthy CD34+ samples). The raw read count data was used to calculate differential gene expression.

File_9: RNA-seq library information including RNA extraction method and sequencing library preparation information for 167 samples (163 AML patient samples and 4 healthy CD34+ samples).

References

1.         Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports 2014;4:5193.

2.         Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, Robertson A, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 2013;368(22):2059-74.

3.         Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A, et al. IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods 2013;10(11):1081-2.

4.         Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine 2016;374(23):2209-21.

5.         Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Research 2019;47(D1):D941-D7.