Datasets Included

This page provides an overview of the datasets included in CoderData version 2.2.0. This package collects 18 diverse sets of paired molecular datasets with corresponding drug sensitivity data. All data here is reprocessed and standardized so it can be easily used as a benchmark dataset for drug response prediction machine learning models.

The dataset files are in csv format and are available at the link below:

Figshare record: https://api.figshare.com/v2/articles/28823159

Version: 2.2.0

Dataset Overview

Datasets and Modalities

Dataset	References	Sample	Drug	Drug Descriptor	Experiments	Transcriptomics	Proteomics	Mutations	Copy Number
BeatAML	[1], [2]	1022	164	X	X	X	X	X
Bladder	[3]	134	50	X	X	X		X	X
CCLE	[4]	502	24	X	X	X	X	X	X
Colorectal	[18]	61	10	X		X		X	X
CPTAC	[5]	1139				X	X	X	X
CTRPv2	[6], [7], [8]	846	459	X	X	X		X	X
FIMM	[9], [10]	52	52	X	X	X
GDSC v1	[23], [24], [25]	984	294	X		X	X	X	X
GDSC v2	[23], [24], [25]	806	171	X		X	X	X	X
gCSI	[21], [22]	569	44	X		X	X	X	X
HCMI	[11]	886				X		X	X
Liver	[19]	62	76	X		X		X	X
MPNST	[12]	50	30	X	X	X	X	X	X
NCI60	[13]	83	55157	X	X	X	X	X
Novartis	[20]	386	25	X		X		X	X
Pancreatic	[14]	70	25	X	X	X		X	X
PRISM	[15], [16]	478	1419	X	X	X
Sarcoma	[17]	36	34	X	X	X		X

The table above lists the datasets included in CoderData version 2.2.0, along with references to their original publications, counts of samples and drugs, and the types of data available for each dataset.

CoderData includes the following data:

• Sample - cell lines, patient-derived samples, or patient-derived organoids

• Drug - compounds tested for sensitivity

• Drug Descriptor - molecular descriptors for each drug (computed using RDKit)

• Experiments - dose-response experiments (various metrics such as AUC, IC50, etc.)

• Transcriptomics - gene expression (in transcripts per million, TPM)

• Proteomics - protein expression (in log2 ratio to reference)

• Mutations - gene mutations (variant calls)

• Copy Number - gene copy number variations (number of copies of each gene, 2 being diploid)

An “X” indicates the presence of a particular data type for the corresponding dataset. Each sample in the datasets corresponds to either a cancer cell line, a patient-derived xenograft, or a patient-derived organoid, depending on the specific dataset.

Dataset Summary Statistics

The following table summarizes combination counts for each dataset. This includes the number of experimental sample-drug pairs, with different molecular data types. Each column represents the number of unique combinations of samples and drugs with the specified molecular data types available. For example, the “Sample-Drug-Transcriptomics-Mutations” column indicates the number of unique sample-drug pairs that have both transcriptomics and mutation data available. These counts let you estimate how much paired data is available for tasks like building predictive models with transcriptomics and drug response.

Dataset Summary Statistics

dataset	sample_drug_pairs	sample_drug_transcript_pairs	sample_drug_transcriptomics_mutation_pairs	sample_drug_transcriptomics_copynumber_pairs	sample_drug_mutation_copynumber_pairs
beataml	31926.0	4137.0	3958.0
bladder	3300.0	840.0	640.0	640.0	3100.0
ccle	11543.0	10887.0	10792.0	10887.0	11118.0
colorectal	140.0	60.0	60.0	60.0	140.0
cptac
ctrpv2	309401.0	300507.0	295742.0	299698.0	300616.0
fimm	2663.0	2457.0	2457.0	2457.0	2611.0
gcsi	13398.0	12506.0	12338.0	12506.0	13112.0
gdscv1	247753.0	245220.0	241999.0	241240.0	242570.0
gdscv2	115440.0	114373.0	112829.0	112523.0	113133.0
hcmi
liver	4453.0	4453.0	4453.0	4453.0	4453.0
mpnst	272.0	193.0	184.0	191.0	184.0
nci60	2960756.0	2329149.0	2329132.0	2329149.0	2784474.0
novartis	1766.0	1734.0	1734.0	1723.0	1723.0
pancreatic	190.0	190.0	185.0	185.0	185.0
prism	638983.0	632078.0	630672.0	632078.0	636226.0
sarcoma	275.0	234.0	187.0

Drug Curve Metrics Collected

The following table summarizes the number of drugs associated with each dose-response metric across the datasets.

Drug Curve Metrics Summary

dataset

num_drugs

aac

abc

auc

dss

fit_auc

fit_ec50

fit_ec50se

fit_einf

fit_hs

fit_ic50

fit_r2

lmm

mRESCIST

published_auc

TGI

beataml

164

X

X

X

X

X

X

X

X

X

X

bladder

50

X

X

X

X

X

X

X

X

X

X

ccle

24

X

X

X

X

X

X

X

X

X

X

colorectal

10

X

X

X

X

X

X

X

X

X

X

ctrpv2

459

X

X

X

X

X

X

X

X

X

X

fimm

52

X

X

X

X

X

X

X

X

X

X

gcsi

44

X

X

X

X

X

X

X

X

X

X

gdscv1

294

X

X

X

X

X

X

X

X

X

X

gdscv2

171

X

X

X

X

X

X

X

X

X

X

liver

76

X

X

X

X

X

X

X

X

X

X

mpnst

30

X

X

X

X

X

X

X

X

X

X

X

X

X

X

nci60

55157

X

X

X

X

X

X

X

X

X

X

novartis

25

X

X

X

X

pancreatic

25

X

X

X

X

X

X

X

X

X

X

prism

1419

X

X

X

X

X

X

X

X

X

X

sarcoma

34

X

Types of dose-response metrics collected include:

• AAC - Area above the response curve; the complement value of AUC.

• ABC - Area between curves, the difference between the AUC of the control and the treated cells.

• AUC - Area under the fitted hill slope curve across all doses present. Lower AUC signifies lower levels of growth.

• DSS - A multiparametric dose response value that takes into account control and treated cells.

• fit_auc - Area under the fitted hill slope curve across the common interval of −log10[M], where the molar concentration ranges from 10⁻⁴ to 10⁻¹⁰.

• fit_ec50 - The fitted curve prediction of the −log10M concentration at which 50% of the maximal effect is observed.

• fit_ec50se - Standard error of the Fit_EC50 estimate.

• fit_einf - The fraction of cells that are unaffected even at an infinite dose concentration. Calculated as the lower asymptote of the hill slope function.

• fit_hs - The estimated hill slope binding cooperativity, calculated as the slope of the sigmoidal hill curve.

• fit_ic50 - The fitted curve prediction of the −log10M concentration required to reduce tumor growth by 50%.

• fit_r2 - Coefficient of determination between observed growth and the fitted hill slope curve, indicating goodness of fit.

• lmm - The resulting “time and treatment interaction” in a linear mixed model with fixed effects as time and treatment and patient as a random effect. Indicates how much the treatment changes the slope of log(volume) over time compared to the control.

• mRESCIST - Disease status classified into PD (progressive disease), SD (stable disease), PR (partial response), and CR (complete response), based on percent volume change and cumulative average response.

• published_auc - Published Area Under the Curve

• TG - Tumor growth inhibition between the control and treatment time-volume curves.

References

[1]

Bottomly D, Long N, Schultz AR, et al. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell. 2022;40(8):850-864.e9. doi:10.1016/j.ccell.2022.07.002

[2]

Pino JC, Posso C, Joshi SK, et al. Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia. Cell Rep Med. 2024;5(1):101359. doi:10.1016/j.xcrm.2023.101359

[3]

Lee SH, Hu W, Matulay JT, et al. Tumor Evolution and Drug Response in Patient-Derived Organoid Models of Bladder Cancer. Cell. 2018;173(2):515-528.e17. doi:10.1016/j.cell.2018.03.017

[4]

Barretina J, Caponigro G, Stransky N, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603-607. doi:10.1038/nature11003

[5]

Lindgren CM, Adams DW, Kimball B, et al. Simplified and Unified Access to Cancer Proteogenomic Data. J Proteome Res. 2021;20(4):1902-1910. doi:10.1021/acs.jproteome.0c00919

[6]

Rees MG, Seashore-Ludlow B, Cheah JH, et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat Chem Biol. 2016;12(2):109-116. doi:10.1038/nchembio.1986

[7]

Seashore-Ludlow B, Rees MG, Cheah JH, et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov. 2015;5(11):1210-1223. doi:10.1158/2159-8290.CD-15-0235

[8]

Basu A, Bodycombe NE, Cheah JH, et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell. 2013;154(5):1151-1161. doi:10.1016/j.cell.2013.08.003

[9]

Mpindi JP, Yadav B, Östling P, et al. Consistency in drug response profiling. Nature. 2016;540(7631):E5-E6. doi:10.1038/nature20171

[10]

Pemovska T, Kontro M, Yadav B, et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 2013;3(12):1416-1429. doi:10.1159/2159-8290.CD-13-0350

[11]

Human Cancer Models Initiative (HCMI). dbGaP accession phs001486. https://cancer.gov/ccg/research/functional-genomics/hcmi

[12]

Dehner C, Moon CI, Zhang X, et al. Chromosome 8 gain is associated with high-grade transformation in MPNST. JCI Insight. 2021;6(6):e146351. doi:10.1172/jci.insight.146351

[13]

Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer. 2006;6(10):813-823. doi:10.1038/nrc1951

[14]

Tiriac H, Belleau P, Engle DD, et al. Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. Cancer Discov. 2018;8(9):1112-1129. doi:10.1158/2159-8290.CD-18-0349

[15]

Corsello SM, Nagari RT, Spangler RD, et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer. 2020;1(2):235-248. doi:10.1038/s43018-019-0018-6

[16]

Yu C, Mannan AM, Yvone GM, et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat Biotechnol. 2016;34(4):419-423. doi:10.1038/nbt.3460

[17]

Al Shihabi A, Tebon PJ, Nguyen HTL, et al. The landscape of drug sensitivity and resistance in sarcoma. Cell Stem Cell. 2024;31(10):1524-1542.e4. doi:10.1016/j.stem.2024.08.010

[18]

van de Wetering M, Francies HE, Francis JM, et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell. 2015;161(4):933-945. doi:10.1016/j.cell.2015.03.053

[19]

Ji S, Feng L, Fu Z, et al. Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology. Sci Transl Med. 2023;15(706):eadg3358. doi:10.1126/scitranslmed.adg3358

[20]

Gao H, Korn JM, Ferretti S, et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med. 2015;21(11):1318–1325. doi:10.1038/nm.3954

[21]

Haverty PM, Lin E, Tan J, et al. Reproducible pharmacogenomic profiling of cancer cell line panels. Nature. 2016;533(7603):333–337. doi:10.1038/nature17987

[22]

Klijn C, Durinck S, Stawiski EW, et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat Biotechnol. 2015;33(3):306–312. doi:10.1038/nbt.3080

[23] (1,2)

Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483(7391):570–575. doi:10.1038/nature11005

[24] (1,2)

Iorio F, Knijnenburg TA, Vis DJ, et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016;166(3):740–754. doi:10.1016/j.cell.2016.06.017

[25] (1,2)

Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41(Database issue):D955–D961. doi:10.1093/nar/gks1111