Network biology of lapatinib resistance in different types of HER2-positive breast cancers informs the prioritization of the combination anticancer treatments as anti-resistance interventions

Abstract

Background: With widespread therapeutic advancement, targeted anticancer therapeutics are taking over traditional treatment protocols. Nevertheless, the resistance to targeted therapeutics has halted the enthusiastic treatment response. An example of targeted therapy is Lapatinib. Lapatinib is a tyrosine kinase inhibitor used in HER2-positive breast cancer. It is widely used in HER2-positive metastatic breast cancer in combination with other drugs. Methods: This study's main objective was to provide a plausible mechanistic insight into lapatinib’s resistance in two HER2 positive breast cancer cell lines, SKBR3 and BT-474. We performed gene set analysis to identify the differentially expressed genes (DEG) in response to treatment with lapatinib from the gene expression profiles obtained from GSE38376 and GSE16179. The DEG was then analyzed by Ingenuity Pathway Analysis (IPA). Results: The IPA analysis showed that the increased expression of Hypoxia-inducible factor-1 alpha (HIF-1α) and Wnt/β-catenin and their related networks were associated with resistance and poor prognosis in SKBR3 and BT-474 cell lines, respectively. Although both cell lines are categorized as HER2 positive cell lines and in some reports were used interchangeably, in our hands, the two cell lines exhibited different biological pathways underlying resistance to lapatinib. In addition, among the other top canonical pathways, TNF was identified as the top upstream regulator in SKBR3 cell lines, whereas the Microphthalmia-associated transcription factor (MITF) was predicted as a top regulator in BT-474 cell lines. Conclusions: This study highlights the relevance of HIF-1α and Wnt/β-catenin compensatory networks in resistance toward lapatinib. Our findings outline the activation of angiogenesis and invasion processes in resistant cells with differential underlining gene networks in two different HER2 positive cell lines. The two cell lines reflect two different types of breast cancer, and hence the treatment strategy to avoid resistance should be planned differently.