三阴性乳腺癌
肿瘤微环境
癌症研究
乳腺癌
免疫疗法
生物标志物
医学
癌症
转移
雌激素受体
靶向治疗
生物
肿瘤科
内科学
生物化学
作者
Harshita Tiwari,Swati Singh,Sonal Sharma,Priyamvada Gupta,Ashish Verma,Amrit Chattopadhaya,Brijesh Kumar,Sakshi Agarwal,Rajiv Kumar,Sanjeev Gupta,Vibhav Gautam
摘要
Abstract Triple negative breast cancer (TNBC) displays a notable challenge in clinical oncology due to its invasive nature which is attributed to the absence of progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor (HER‐2). The heterogenous tumor microenvironment (TME) of TNBC is composed of diverse constituents that intricately interact to evade immune response and facilitate cancer progression and metastasis. Based on molecular gene expression, TNBC is classified into four molecular subtypes: basal‐like (BL1 and BL2), luminal androgen receptor (LAR), immunomodulatory (IM), and mesenchymal. TNBC is an aggressive histological variant with adverse prognosis and poor therapeutic response. The lack of response in most of the TNBC patients could be attributed to the heterogeneity of the disease, highlighting the need for more effective treatments and reliable prognostic biomarkers. Targeting certain signaling pathways and their components has emerged as a promising therapeutic strategy for improving patient outcomes. In this review, we have summarized the interactions among various components of the dynamic TME in TNBC and discussed the classification of its molecular subtypes. Moreover, the purpose of this review is to compile and provide an overview of the most recent data about recently discovered novel TNBC biomarkers and targeted therapeutics that have proven successful in treating metastatic TNBC. The emergence of novel therapeutic strategies such as chemoimmunotherapy, chimeric antigen receptor (CAR)‐T cells‐based immunotherapy, phytometabolites‐mediated natural therapy, photodynamic and photothermal approaches have made a significant positive impact and have paved the way for more effective interventions.
科研通智能强力驱动
Strongly Powered by AbleSci AI