Table 1 Pharmaceuticals for which PharmacoDB analysis reveals a predictive drug response association with the transcription from the RFS profile loci.

From: Genome-wide DNA methylation patterns reveal clinically relevant predictive and prognostic subtypes in human osteosarcoma

Drug

Median IC50 (μM)

Median regression coefficient

Mechanism of action

GMX-1778

0.021

0.190

NAMPTi

tanespimycin + gemcitabine

1.189

0.175

HSP90i + nucleoside analog

CUDC-101

1.634

0.196

HDACi + EDGFR/HER2i

alistertib + navitoclax

1.692

0.194

AURKAi/BCLi

mirdametinib

2.863

0.248

MEKi

vorinostat + navitoclax

3.116

0.162

HDACi/BCLi

ceranib-2

4.434

0.188

ceramidase i

BRD-K34222889

4.464

0.195

GSTP1i

navitoclax + piperlongumine

4.976

0.178

BCLi/GSTP1i

necrosulfonamide

6.260

0.194

MLKLi

Genetech Cpd 10

7.190

0.190

AURKA/Bi

UNC0638 + navitoclax

9.552

0.230

G9ai/GLPi + BCLi

navitoclax + sorafenib

10.182

0.186

BCLi + c-RASi

alisertib

10.196

0.184

AURKAi

BX-912

11.335

0.182

PDK1i

CL-1040

12.805

0.188

MEKi

carboplatin + etoposide

19.099

0.207

DNA crosslinking + TOPIIi

tretinoin + navitoclax

27.382

0.188

RAR/TERTi + BCLi

  1. Median IC50 values are obtained across all OSA cell lines in the GDSC dataset though PharmacoDB. The median regression coefficient is calculated from the significant gene–drug interactions used to identify the drugs. Bolded drugs act through epigenetic mechanisms. Underlined drugs have more significant response correlations (p < 0.05) with the RFS methylation profile than at least 90% of 1000 randomly generated CpG site sets in OSA cell lines.
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