Taxol is a widely used chemotherapy drug used clinically for ovarian cancer, although the response to Taxol among individuals varies due to the heterogeneity among ovarian cancer patients. In this work, we analyzed differences in the prognostic effect of gene expression and Taxol usage in the Cancer Genome Atlas (TCGA) dataset and identified specific genes associated with the Taxol effect. Using the Cox regression model, a risk model (Taxol score) was developed to assess the outcome of ovarian cancer patients who underwent chemotherapy with Taxol. According to the results, survival was significantly associated with the Taxol score. Moreover, the patients in the high and low Taxol score group had different responses to Taxol. This result was further validated in another two independent datasets. The correlation between clinicopathological indicators was also analyzed, and we determined that the Taxol score is not associated with age, pathological stage, or Taxol treatment, while there was significant correlation with tumor size and grade. Gene Set Enrichment Analysis (GSEA) showed that various signaling pathways including ECM receptor, drug metabolism and ascorbate metabolism pathways were significantly enriched in the high Taxol score group. Collectively, these results indicate that the model is robust for predicting the effectiveness of Taxol by reflecting the various cell statuses of serous ovarian carcinoma.
Hou, S., & Dai, J. (2018). Transcriptome-based signature predicts the effect of taxol in serous ovarian cancer. PLoS ONE, 13(3). https://doi.org/10.1371/journal.pone.0192812