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. 2021 Apr 15;22(1):272.
doi: 10.1186/s12864-021-07581-7.

Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

Yuanyuan Li  1 , David M Umbach  1 , Juno M Krahn  2 , Igor Shats  3 , Xiaoling Li  3 , Leping Li  4
Affiliations

Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

Yuanyuan Li et al. BMC Genomics. .

Abstract

Background: Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients' care. Tremendous progress has been made.

Results: In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data ( https://manticore.niehs.nih.gov/cancerRxTissue ). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug.

Conclusions: We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

Keywords: And CCLE; Cancer cell line; Drug sensitivity; GA/KNN; GDSC; GTEx; RNA-seq; TCGA.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Schematic diagram of the work-flow. First, GDSC cancer cell line drug sensitivity data, CCLE cancer cell gene expression data and TCGA/GTEx tissue gene expression data are combined and transformed. The CCLE gene expression data and GDSC drug sensitivity data (collectively referred to as the cell-line data) were used to build predictive models that were subsequently used to predict/impute the tissue drug sensitivity for the TCGA and GTEx samples. Broadly, for each drug, we divided the cell-line data into a training and testing set. We aimed to identify a 30-gene set whose gene expression levels are most predictive of the IC50 values of the drug for the samples in the testing set. The resulting model (a 30-gene set) was subsequently used to predict the IC50 value of the TCGA/GTEx samples. This process was repeated 100 times independently. The predicted IC50 values from the 100 runs were then averaged and taken as the predicted IC50 value of the drug for the samples. For details, see Methods
Fig. 2
Fig. 2
Scatter plot of predicted and observed ln (IC50) values for trametinib in the 571 cancer cell lines with both gene expression data and IC50 data for trametinib
Fig. 3
Fig. 3
Predicted sensitivity of tumor-types and normal tissue to trametinib. a, Violin plots of predicted ln (IC50) values of trametinib based on RNA-seq gene expression data from TCGA tumor samples from 33 tumor types. Overall COAD, READ, SKCM and UVM tumors (yellow) had the lowest predicted median IC50 values. For the description of the 33 TCGA tumor types, see supplementary data (additional file 1: Table S7A). The solid line shows the median of the medians of the predicted IC50 values for all 33 tumor types whereas the dashed line is one logarithmic unit below the solid line. b, Violin plots of the predicted ln (IC50) values of trametinib for COAD tumor (red) and normal (blue) samples from TCGA and for GTEx normal tissue samples from 15 major organs (green); here the solid line shows the median of the medians of the predicted IC50 values for all 16 normal tissues. In each violin, the red dot is located at the median; the vertical red bar extends from 25th to 75th percentiles
Fig. 4
Fig. 4
Predicted sensitivity based on mutation status. Violin plots of predicted ln (IC50) values of trametinib based on RNA-seq gene expression data from TCGA tumor samples for those with and without mutations in BRAF a, KRAS b and NRAS c. * p < 0.05; ** p < 0.005; **** p < 10− 6. Wilcoxon rank-sum test, two-sided
Fig. 5
Fig. 5
Examples of drugs that are predicted to have high tumor-to-normal sensitivity for some tumor types. Violin plots of predicted IC50 values in tumor (red) and normal (blue) tissue for trametinib a sapitinib b and luminespib c that showed the ratio of tumor-to-normal sensitivity exceeding 2.7 (1 logarithmic unit) for at least one of 14 tissue types. The ln (IC50) values of the drugs were predicted based on the RNA-seq data of the tumor and normal tissue samples from TCGA. Violin plots for normal and tumor samples from the same tissue type are shown as side-by-side pairs with their TCGA type on the X-axis. See Fig. 2 legend for additional description of the violin plots. Red star (*) indicates the difference between the median of predicted IC50 values for normal samples and the median of predicted IC50 values for tumor samples is more than one logarithmic unit
Fig. 6
Fig. 6
Selected drugs that are predicted to be tumor-type-specific. Violin plots of the predicted ln (IC50) values of Acetalax a, Alisertib b, Dasatinib c, Debrafenib d, OSI-027 e, and Sapitinib f for TCGA tumor samples from 33 tumor types. The solid line shows the median of the medians of the predicted IC50 values for all 33 tumor types; whereas the dashed line is one logarithmic unit below the solid line. See Fig. 2 legend for additional description of the violin plots
Fig. 7
Fig. 7
Basal breast tumors are predicted to be more sensitive to bleomycin than luminal A, luminal B or Her2-positive breast tumors and the sensitivity is inversely correlated with ACE expression. a, Predicted bleomycin sensitivity for the five subtypes of TCGA BRCA samples: violin plots of the predicted ln (IC50) values of bleomycin for the five subtypes of breast tumors based on gene expression data and PAM50 classification of TCGA BRCA samples. b, ACE gene expression in cancer cell lines versus sensitivity to bleomycin: ACE expression in the CCLE cancer cell lines was positively correlated with observed ln (IC50) values for bleomycin (ρs = 0.27, p-value = 6.6E-11). The red line is the least-squares regression line. c, TCGA breast cancer tumor gene expression data: violin plots of ACE expression in TCGA basal-like, Her2-positive, luminal A, luminal B, and normal-like breast tumor samples. * p < 0.05; ** p < 0.005; **** p < 10− 6. Wilcoxon rank-sum test, two-sided
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