Biomarkers and subtypes of breast cancerBreast cancer is not a single disease. Clinically, breast cancer has been known to have distinct prognosis and response to chemotherapies based on the immunohistochemical (IHC) subtypes [e.g., estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2)] (1,2). hormone receptor (HR)-positive (ER and/or PgR-positive)/HER2-negative breast cancers have a good response to hormone therapy and favourable prognosis. HER2-positive and triple-negative (TN: ER, PgR and HER2-negative) breast cancers have poor baseline prognosis, and good response to HER2 targeted therapy and chemotherapy, respectively. In 2000, molecular subtypes (luminal A, luminal B, HER2 enriched, and basal) in breast cancer were reported (3). mRNA expression patterns in a Review Article on Neoadjuvant/adjuvant Treatment for Early Breast Cancer
Trastuzumab-emtansine (T-DM1) is a therapeutic agent molecularly targeting human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC), and it is especially effective for MBC with resistance to trastuzumab. Although several reports have described T-DM1 resistance, few have examined the mechanism underlying T-DM1 resistance after the development of acquired resistance to trastuzumab. We previously reported that YES1, a member of the Src family, plays an important role in acquired resistance to trastuzumab in HER2-amplified breast cancer cells. We newly established a trastuzumab/T-DM1-dual-resistant cell line and analyzed the resistance mechanisms in this cell line. At first, the T-DM1 effectively inhibited the YES1-amplified trastuzumab-resistant cell line, but resistance to T-DM1 gradually developed. YES1 amplification was further enhanced after acquired resistance to T-DM1 became apparent, and the knockdown of the YES1 or the administration of the Src inhibitor dasatinib restored sensitivity to T-DM1. Our results indicate that YES1 is also strongly associated with T-DM1 resistance after the development of acquired resistance to trastuzumab, and the continuous inhibition of YES1 is important for overcoming resistance to T-DM1.
Cancer‐associated fibroblasts (CAFs) are a major component of the tumor microenvironment that mediate resistance of cancer cells to anticancer drugs. Tranilast is an antiallergic drug that suppresses the release of cytokines from various inflammatory cells. In this study, we investigated the inhibitory effect of tranilast on the interactions between non–small cell lung cancer (NSCLC) cells and the CAFs in the tumor microenvironment. Three EGFR‐mutant NSCLC cell lines, two KRAS‐mutant cell lines, and three CAFs derived from NSCLC patients were used. To mimic the tumor microenvironment, the NSCLC cells were cocultured with the CAFs in vitro, and the molecular profiles and sensitivity to molecular targeted therapy were assessed. Crosstalk between NSCLC cells and CAFs induced multiple biological effects on the NSCLC cells both in vivo and in vitro, including activation of the STAT3 signaling pathway, promotion of xenograft tumor growth, induction of epithelial‐mesenchymal transition (EMT), and acquisition of resistance to molecular‐targeted therapy, including EGFR‐mutant NSCLC cells to osimertinib and of KRAS‐mutant NSCLC cells to selumetinib. Treatment with tranilast led to inhibition of IL‐6 secretion from the CAFs, which, in turn, resulted in inhibition of CAF‐induced phospho‐STAT3 upregulation. Tranilast also inhibited CAF‐induced EMT in the NSCLC cells. Finally, combined administration of tranilast with molecular‐targeted therapy reversed the CAF‐mediated resistance of the NSCLC cells to the molecular‐targeted drugs, both in vitro and in vivo. Our results showed that combined administration of tranilast with molecular‐targeted therapy is a possible new treatment strategy to overcome drug resistance caused by cancer‐CAF interaction.
BackgroundPrevious studies of immune-related gene signatures (IGSs) in breast cancer have attempted to predict the response to chemotherapy or prognosis and were performed using different patient cohorts. The purpose of this study was to evaluate the predictive functions of various IGSs using the same patient cohort that included data for response to chemotherapy as well as the prognosis after surgery. MethodsWe applied ve previously described IGS models in a public dataset of 508 breast cancer patients treated with neoadjuvant chemotherapy. The prognostic and predictive values of each model were evaluated, and their correlations were compared. ResultsWe observed a high proportion of expression concordance among the IGS models (r: 0.56-1). Higher gene expression scores of IGSs were detected in aggressive breast cancer subtypes (basal and HER2-enriched) (P < 0.001). Four of the ve IGSs could predict chemotherapy responses and two could predict 5-year relapse-free survival in cases with hormone receptor-positive (HR+) tumors. However, the models showed no signi cant differences in their predictive abilities for hormone receptor-negative (HR-) tumors. ConclusionsIGSs are, to some extent, useful for predicting prognosis and chemotherapy response; moreover, they show substantial agreement for speci c breast cancer subtypes. However, it is necessary to identify more compelling biomarkers for both prognosis and response to chemotherapy in HR-and HER2+ cases.
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