The prospect of combination therapies with the third-generation EGFR-TKIs to overcome the resistance in NSCLC.

Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) bring significant benefits to non-small cell lung cancer patients with EGFR mutations, which represent a breakthrough in lung cancer therapy. However, patients will ultimately develop the acquired resistance to the first- or second-generation EGFR-TKIs after a period of treatment, and EGFR T790M mutation is the most common resistant mechanism. The third-generation EGFR-TKIs target T790M mutation and show potent anti-tumor efficacy, especially in central neural system response. Unfortunately, patients inevitably get resistant to the third-generation EGFR-TKIs due to various mechanisms, which can be mainly divided into EGFR-dependent and -independent ones. EGFR-dependent mechanism refers to manifold EGFR mutations while EGFR-independent mechanisms include bypass signal activation, histologic transformation and so on. To precisely address this issue and improve clinical outcomes, various other therapies (e.g. chemotherapy, radiotherapy, etc.) in combination with the third-generation EGFR-TKIs are designed. However, the current results of combination therapies are insufficient and ambiguous, which remain further exploration. Herein, we provide an updated landscape of the third-generation EGFR-TKIs and elaborate on the complex resistant mechanisms. Notably, we summarize the combination therapies with third-generation EGFR-TKIs and discuss their limitations and future perspective, aiming at providing insights to clinicians from bench to bedside.

A genomic instability-related lncRNA model for predicting prognosis and immune checkpoint inhibitor efficacy in breast cancer.

Breast cancer has overtaken lung cancer as the most frequently diagnosed cancer type and is the leading cause of death for women worldwide. It has been demonstrated in published studies that long non-coding RNAs (lncRNAs) involved in genomic stability are closely associated with the progression of breast cancer, and remarkably, genomic stability has been shown to predict the response to immune checkpoint inhibitors (ICIs) in cancer therapy, especially colorectal cancer. Therefore, it is of interest to explore somatic mutator-derived lncRNAs in predicting the prognosis and ICI efficacy in breast cancer patients. In this study, the lncRNA expression data and somatic mutation data of breast cancer patients from The Cancer Genome Atlas (TCGA) were downloaded and analyzed thoroughly. Univariate and multivariate Cox proportional hazards analyses were used to generate the genomic instability-related lncRNAs in a training set, which was subsequently used to analyze a testing set and combination of the two sets. The qRT-PCR was conducted in both normal mammary and breast cancer cell lines. Furthermore, the Kaplan-Meier and receiver operating characteristic (ROC) curves were applied to validate the predictive effect in the three sets. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the association between genomic instability-related lncRNAs and immune checkpoints. As a result, a six-genomic instability-related lncRNA signature (U62317.4, MAPT-AS1, AC115837.2, EGOT, SEMA3B-AS1, and HOTAIR) was identified as the independent prognostic risk model for breast cancer patients. Compared with the normal mammary cells, the qRT-PCR showed that HOTAIR was upregulated while MAPT-AS1, EGOT, and SEMA3B-AS1 were downregulated in breast cancer cells. The areas under the ROC curves at 3 and 5 years were 0.711 and 0.723, respectively. Moreover, the patients classified in the high-risk group by the prognostic model had abundant negative immune checkpoint molecules. In summary, this study suggested that the prognostic model comprising six genomic instability-related lncRNAs may provide survival prediction. It is necessary to identify patients who are suitable for ICIs to avoid severe immune-related adverse effects, especially autoimmune diseases. This model may predict the ICI efficacy, facilitating the identification of patients who may benefit from ICIs.