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, 12 (1), 17

Comprehensive Pharmacogenomic Characterization of Gastric Cancer

Affiliations

Comprehensive Pharmacogenomic Characterization of Gastric Cancer

Jason K Sa et al. Genome Med.

Abstract

Background: Gastric cancer is among the most lethal human malignancies. Previous studies have identified molecular aberrations that constitute dynamic biological networks and genomic complexities of gastric tumors. However, the clinical translation of molecular-guided targeted therapy is hampered by challenges. Notably, solid tumors often harbor multiple genetic alterations, complicating the development of effective treatments.

Methods: To address such challenges, we established a comprehensive dataset of molecularly annotated patient derivatives coupled with pharmacological profiles for 60 targeted agents to explore dynamic pharmacogenomic interactions in gastric cancers.

Results: We identified lineage-specific drug sensitivities based on histopathological and molecular subclassification, including substantial sensitivities toward VEGFR and EGFR inhibition therapies in diffuse- and signet ring-type gastric tumors, respectively. We identified potential therapeutic opportunities for WNT pathway inhibitors in ALK-mutant tumors, a significant association between PIK3CA-E542K mutation and AZD5363 response, and transcriptome expression of RNF11 as a potential predictor of response to gefitinib.

Conclusions: Collectively, our results demonstrate the feasibility of drug screening combined with tumor molecular characterization to facilitate personalized therapeutic regimens for gastric tumors.

Keywords: Gastric cancer; Gefitinib; PIK3CA-E542K; Pharmacogenomics; RNF11.

Conflict of interest statement

Do-Hyun Nam is the CEO of AimedBio Inc. and owns shares of AimedBio Inc. which owns IPs for Avatascan. The remaining authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Mutational landscape of gastric cancer. a Mutational landscape of gastric cancers based on molecular subclassification; EBV-positive, LCNA, and HCNA tumors. All mutations with variant allele frequency of > 5% and depth of > 20 reads are shown. b Ternary diagram depicting mutation frequencies in EBV-positive, LCNA, and HCNA tumors. The size of each node represents the number of tumors with the respective mutation, and the color spectrum indicates its relative frequency. c Three-dimensional bubble plot showing the frequency of non-synonymous cancer-driver genomic mutations exclusively in tissue (black, left axis), in PDCs (blue, right axis), or in both (gray, upper axis). The position of each dot or mutation is located on the quadrant based on its shared or private rate between primary tumor tissues and matched PDCs, and the distance reflects the number of cases that harbor respective mutation
Fig. 2
Fig. 2
Gastric cancer subgroup-specific drug sensitivity. a Heatmap representation of drug sensitivities in gastric cancer based on molecular, histological, and pathological subclassification. Only significant associations are marked based on sensitivity (red) or resistance (blue). Drugs were clustered based on their known target classes. b Violin plots demonstrating pathway enrichment scores of each corresponding pathway. The activity scores were measured using ssGSEA. Horizontal lines within the violin plots represent 0.25, 0.50, and 0.75 quantiles. P values in a, b were derived from two-sided Wilcoxon rank-sum tests
Fig. 3
Fig. 3
Pharmacogenomic interactions in gastric cancer. a Volcano plot representation of pharmacogenomic interactions in gastric cancer with fold-change drug comparison (x-axis) and its significance (y-axis). Each node represents a single genomic alteration-drug interaction, and the size is proportional to the number of tumors with the respective genomic variation. b Violin plots of drug AUC values for tumors with thegenomic alteration compared to those without from selected gene-drug interactions. Horizontal lines within the violin plots represent 0.25, 0.50, and 0.75 quantiles. c Box plots of AZD5363 AUC values among tumors with different PIK3CA variations. Box plots span from the first to third quartiles, and the whiskers represent the 1.5 interquartile range. d Cell proliferation assay of gastric cancer cell-lines. e Effects of AZD5363 on the PI3K/AKT/mTOR signaling pathway in gastric cancer cell-lines with different mutations of PIK3CA or the wild-type gene. f Scatter plot of AZD5363 AUCs in our cohort (left panel). The AUC of the PDC that was isolated from the indicated patient (right panel) is highlighted in a red circle. Dotted green and orange horizontal lines represent relative resistance and sensitivity, respectively. T1-weighted contrast-enhanced magnetic resonance images of tumor samples from the gastric cancer patient who received AZD5363 treatment. The red arrow indicates measurable or progressed tumor; the orange arrow represents partial response. P values in a, b were derived from two-sided Wilcoxon rank-sum tests, the P value in c from one-way ANOVA
Fig. 4
Fig. 4
Transcriptome correlates of gefitinib sensitivity. a Elastic-net regression results of transcriptome features that predict pharmacological response to gefitinib. The bottom scatter plot represents drug response for gefitinib-treated tumors. The upper heatmap shows the top extracted features in the model. The left bar graph shows the averaged weight of each predictive feature. The number of appearances in 100 bootstraps is indicated in parentheses. b Scatter plot revealing a linear correlation between gefitinib AUC and RNF11 transcriptome expression. Correlation coefficients and P values were obtained by Pearson correlation analysis. c Immunoblot analysis of RNF11, p-EGFR, EGFR in gastric cancer cell-lines. β-Actin was used as a loading control (left panel). Cell proliferation assay in EGFR-activated gastric cancer cell-lines (right panel). Cancer cells were exposed to gefitinib for 72 h, and then, cell viability was measured. d Gastric cancer cell-lines with high (SNU5; left panel) and low (SNU638; right panel) RNF11 expression were transiently transfected with 10 nM of siRNF11 and treated with gefitinib for 72 h the next day. The results are represented as the mean ± SD of triplicate wells and are representative of three independent experiments. e Immunoblot analysis of EGFR signaling-related molecules, including p-EGFR, EGFR, p-AKT, and AKT in gastric cancer cell-lines that were transiently transfected with 10 nM of siRNF11 and treated with gefitinib for 4 h the next day. P values in c, d were derived from two-sided Student’s t tests

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