PRMT1 Regulates EGFR and Wnt Signaling Pathways and Is a Promising Target for Combinatorial Treatment of Breast Cancer

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Abstract

Identifying new therapeutic strategies for triple-negative breast cancer (TNBC) patients is a priority as these patients are highly prone to relapse after chemotherapy. Here, we found that protein arginine methyltransferase 1 (PRMT1) is highly expressed in all breast cancer subtypes. PRMT1 depletion decreases cell survival by inducing DNA damage and apoptosis in various breast cancer cell lines. Transcriptomic analysis and chromatin immunoprecipitation revealed that PRMT1 regulates the epidermal growth factor receptor (EGFR) and the Wnt signaling pathways, reported to be activated in TNBC. PRMT1 enzymatic activity is also required to stimulate the canonical Wnt pathway. Type I PRMT inhibitors decrease breast cancer cell proliferation and show anti-tumor activity in a TNBC xenograft model. These inhibitors display synergistic interactions with some chemotherapies used to treat TNBC patients as well as erlotinib, an EGFR inhibitor. Therefore, targeting PRMT1 in combination with these chemotherapies may improve existing treatments for TNBC patients.

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A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients.

Balázs Győrffy, András Lánczky, Aron C. Eklund … (2010)

Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.

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Breast cancer

Sibylle Loibl, Philip Poortmans, Monica Morrow … (2021)

Breast cancer is still the most common cancer worldwide. But the way breast cancer is viewed has changed drastically since its molecular hallmarks were extensively characterised, now including immunohistochemical markers (eg, ER, PR, HER2 [ERBB2], and proliferation marker protein Ki-67 [MKI67]), genomic markers (eg, BRCA1, BRCA2, and PIK3CA), and immunomarkers (eg, tumour-infiltrating lymphocytes and PD-L1). New biomarker combinations are the basis for increasingly complex diagnostic algorithms. Neoadjuvant combination therapy, often including targeted agents, is a standard of care (especially in HER2-positive and triple-negative breast cancer), and the basis for de-escalation of surgery in the breast and axilla and for risk-adapted post-neoadjuvant strategies. Radiotherapy remains an important cornerstone of breast cancer therapy, but de-escalation schemes have become the standard of care. ER-positive tumours are treated with 5-10 years of endocrine therapy and chemotherapy, based on an individual risk assessment. For metastatic breast cancer, standard therapy options include targeted approaches such as CDK4 and CDK6 inhibitors, PI3K inhibitors, PARP inhibitors, and anti-PD-L1 immunotherapy, depending on tumour type and molecular profile. This range of treatment options reflects the complexity of breast cancer therapy today.