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. 2022 Sep 11;23(18):10539.
doi: 10.3390/ijms231810539.

Comprehensive Transcriptomic and Proteomic Analyses Identify a Candidate Gene Set in Cross-Resistance for Endocrine Therapy in Breast Cancer

Chung-Liang Li  1   2   3 , Sin-Hua Moi  4 , Huei-Shan Lin  1   2 , Ming-Feng Hou  1   2   5 , Fang-Ming Chen  1   2 , Shen-Liang Shih  1   2 , Jung-Yu Kan  1   2 , Chieh-Ni Kao  1   2   6 , Yi-Chia Wu  1   2   7 , Li-Chun Kao  1   2 , Ying-Hsuan Chen  1   2 , Yi-Chen Lee  8 , Chih-Po Chiang  1   2   9
Affiliations

Comprehensive Transcriptomic and Proteomic Analyses Identify a Candidate Gene Set in Cross-Resistance for Endocrine Therapy in Breast Cancer

Chung-Liang Li et al. Int J Mol Sci. .

Abstract

Endocrine therapy (ET) of selective estrogen receptor modulators (SERMs), selective estrogen receptor downregulators (SERDs), and aromatase inhibitors (AIs) has been used as the gold standard treatment for hormone-receptor-positive (HR+) breast cancer. Despite its clinical benefits, approximately 30% of patients develop ET resistance, which remains a major clinical challenge in patients with HR+ breast cancer. The mechanisms of ET resistance mainly focus on mutations in the ER and related pathways; however, other targets still exist from ligand-independent ER reactivation. Moreover, mutations in the ER that confer resistance to SERMs or AIs seldom appear in SERDs. To date, little research has been conducted to identify a critical target that appears in both SERMs/SERDs and AIs. In this study, we conducted comprehensive transcriptomic and proteomic analyses from two cohorts of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) to identify the critical targets for both SERMs/SERDs and AIs of ET resistance. From a treatment response cohort with treatment response for the initial ET regimen and an endocrine therapy cohort with survival outcomes, we identified candidate gene sets that appeared in both SERMs/SERDs and AIs of ET resistance. The candidate gene sets successfully differentiated progress/resistant groups (PD) from complete response groups (CR) and were significantly correlated with survival outcomes in both cohorts. In summary, this study provides valuable clinical implications for the critical roles played by candidate gene sets in the diagnosis, mechanism, and therapeutic strategy for both SERMs/SERDs and AIs of ET resistance for the future.

Keywords: The Cancer Genome Atlas (TCGA); aromatase inhibitors (AIs); breast cancer; cross-resistance; endocrine therapy resistance; selective estrogen receptor degraders (SERDs); selective estrogen receptor modulators (SERMs).

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the study design; collection, analyses, or interpretation of data; writing of the manuscript, or decision to publish the results.

Figures

Figure 1
Figure 1
The workflow of the study.
Figure 2
Figure 2
The candidate gene set of RNA−seq and RPPA in both SERM/SERD and AI of ET resistance. (A,B) The Venn diagram analysis shows that there are 1470 targets of RNA−seq and seven targets of RPPA in both SERM/SERD and AI of ET resistance. (C–F) Based on a series of analyses and selection, the expression of candidate gene sets from RNA−seq of ten targets and RPPA of five targets is displayed as box plots in the treatment response cohort (TR cohort) and heatmap in the treatment response (TR cohort) and endocrine therapy cohort (ET cohort).
Figure 3
Figure 3
The diagnostic value of the candidate gene set between CR and PD groups. (A,B) The risk score from RNA-seq, RPPA, and RNA-seq + RPPA analyses showed excellent and outstanding discrimination ability in identifying PD and CR groups. A p value less than * p < 0.05, ** p < 0.01, and *** p < 0.001 was considered statistically significant. Red line represents the ROC curve for a line of identity. (C) The scatter plot provides a quick view of the co-expression of both RNA-seq and RPPA cumulative risk scores of study cohorts with different clinical outcomes. Blue circles represent the group of CR and red circles represents the group of PD.
Figure 4
Figure 4
The survival outcomes with cumulative risk score in the treatment response and endocrine therapy cohort. (A) The original outcomes of PFS/OS between CR and PD groups. (B–D) With the mean and median value of the cumulative risk score, the survival outcomes show a significant difference between low- and high-risk groups in the treatment response and endocrine therapy cohort.
Figure 5
Figure 5
Correlation matrix and GSEA analysis of candidate gene set with 15 targets. (A,B) Most of the candidate gene sets were significantly correlated with each other in both cohorts. (C) The main function of the candidate gene set was associated with cell death, metabolic process, kinase activity, neuregulin, DNA double−strand break repair, and cancer−related signaling pathways. A p value less than * p < 0.05, ** p < 0.01, and *** p < 0.001 was considered statistically significant.
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References

    1. Siegel R.L., Miller K.D., Fuchs H.E., Jemal A. Cancer statistics, 2022. CA Cancer J. Clin. 2022;72:7–33. doi: 10.3322/caac.21708. - DOI - PubMed
    1. Perou C.M., Sorlie T., Eisen M.B., van de Rijn M., Jeffrey S.S., Rees C.A., Pollack J.R., Ross D.T., Johnsen H., Akslen L.A., et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. doi: 10.1038/35021093. - DOI - PubMed
    1. Pusztai L., Mazouni C., Anderson K., Wu Y., Symmans W.F. Molecular classification of breast cancer: Limitations and potential. Oncologist. 2006;11:868–877. doi: 10.1634/theoncologist.11-8-868. - DOI - PubMed
    1. Hammond M.E., Hayes D.F., Dowsett M., Allred D.C., Hagerty K.L., Badve S., Fitzgibbons P.L., Francis G., Goldstein N.S., Hayes M., et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J. Clin. Oncol. 2010;28:2784–2795. doi: 10.1200/JCO.2009.25.6529. - DOI - PMC - PubMed
    1. Nilsson S., Makela S., Treuter E., Tujague M., Thomsen J., Andersson G., Enmark E., Pettersson K., Warner M., Gustafsson J.A. Mechanisms of estrogen action. Physiol. Rev. 2001;81:1535–1565. doi: 10.1152/physrev.2001.81.4.1535. - DOI - PubMed