Brain metastases and lung cancer: molecular biology, natural history, prediction of response and efficacy of immunotherapy

Advancements in Gene Therapy for Non-Small Cell Lung Cancer: Current Approaches and Future Prospects

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, characterized by late diagnosis and resistance to conventional therapies. Gene therapy has emerged as a promising alternative for NSCLC therapy, especially for patients with advanced disease who have exhausted conventional treatments. This article delved into the current developments in gene therapy for NSCLC, including gene replacement and tumor suppressor gene therapy, gene silencing, CRISPR/Cas9 gene editing, and immune modulation with CAR-T cell therapy. In addition, the challenges and future prospects of gene-based therapies for NSCLC were discussed.

Distinct metastatic organotropism shapes prognosis in lung adenocarcinoma with brain metastasis

Background

Metastatic organotropism in lung cancer significantly influences prognosis, yet current treatment and clinical management guidelines are largely generalized for metastatic disease, regardless of organ site involvement. Notably, up to 30% of non-small cell lung cancer (NSCLC) patients present with brain metastases (BM) at diagnosis, underscoring the need for a more nuanced understanding of metastatic patterns. However, real-world clinical data on metastatic organotropism in well-characterized patient cohorts remain surprisingly scarce. Here, we evaluate patterns of metastasis, clinical characteristics and survival outcomes in patients with lung adenocarcinoma (LUAD), the major histological NSCLC subtype.

Methods

We performed a multi-center retrospective study including 913 stage IV LUAD patients, diagnosed and molecularly assessed in western Sweden between 2016-2021. Our primary study outcome was the distribution of specific metastatic sites and its impact on Overall Survival (OS).

Results

Out of 913 stage IV LUAD patients, 23.4% had BM. These patients exhibited markedly different metastatic patterns compared to those without BM, and median survival was significantly shorter (6 months) than those without BM (7.8 months) (p = 0.021). In addition, more than one metastatic tumor in the brain coincided with worse OS, compared to those with no, or with only one metastatic tumor in the brain. Importantly, OS was also influenced by metastasis in specific extracranial organs, like the pleura and lungs.

Conclusions

Our study highlights the distinct metastatic patterns and survival outcomes associated with BM in stage IV LUAD. These findings emphasize the need for site-specific approaches in managing metastatic disease due to BM's impact on survival.

Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas

Objective

Primary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.

Methods

The records of 86,763 primary LCs from the Surveillance, Epidemiology, and End Results (SEER) database were extracted, including 15,180 cases with brain metastasis (BM) and 71,583 without BM. Univariate and multivariate Cox regression were employed to construct a prediction model. Multiple machine learning methods were applied to validate the model. Flow cytometry and ELISA were used to explore the immune status in a real-world cohort.

Results

The research findings revealed a 17.49% prevalence of BM from LCs, with a median survival of 8 months, compared with 16 months for their counterparts (p <0.001). A nomogram was developed to predict survival at 1, 3, and 5 years on the basis of these variables, with the time-dependent area under the curve (AUC) of 0.857, 0.814, and 0.786, respectively. Moreover, several machine learning approaches have further verified the reliability of this model's performance. Flow cytometry and ELISA analysis suggested the prediction model was related the immune status.

Conclusions

BM from LCs have an inferior prognosis. Considering the substantial impact of these factors, the nomogram model is a valuable tool for guiding clinical decision-making in managing patients with this condition.

Radiomics models using machine learning algorithms to differentiate the primary focus of brain metastasis

Background

Brain metastases are common brain tumors in adults. Brain metastases from different primary tumors have special magnetic resonance imaging (MRI) features. As a new technology that can extract and quantify medical image data, and with the rapid development of artificial intelligence, the machine learning model based on radiology has been successfully applied to the diagnosis and differentiation of tumors. This study aimed to develop radiomics models from post-contrast T1-weighted images using machine learning algorithms to differentiate lung cancer from breast cancer brain metastases.

Methods

A retrospective analysis was conducted on 118 lung cancer brain metastases patients and 62 breast cancer brain metastases patients confirmed by surgery pathology or combined clinical and imaging diagnosis at The Fifth Affiliated Hospital of Sun Yat-sen University from August 2015 to September 2023. Patients were randomly divided into a training set (126 cases) and a validation set (54 cases) at a 7:3 ratio. Enhanced T1-weighted images of all patients were imported into ITK-SNAP software to manually delineate the region of interest (ROI). Radiomic features were extracted based on the ROI and feature selection was performed using the least absolute shrinkage and selection operator. Significant features were used to develop models using logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), multilayer perceptron (MLP), and light gradient boosting machine (LightGBM). The diagnostic performance of the models was assessed using the receiver operating characteristic (ROC) curve.

Results

The LightGBM radiomics model exhibited the best diagnostic performance, with an area under the curve (AUC) of 0.875 [95% confidence interval (CI): 0.819-0.931] in the training set and 0.866 (95% CI: 0.740-0.993) in the validation set.

Conclusions

The enhanced MRI radiomics model, especially the LightGBM model, can accurately predict the primary lesion types of brain metastases from lung cancer and breast cancer origins.

Roles of SPOCK1 in the Formation Mechanisms and Treatment of Non-Small-Cell Lung Cancer and Brain Metastases from Lung Cancer

Lung cancer is a malignant tumor with high morbidity and mortality in China and worldwide. Once it metastasizes to the brain, its prognosis is very poor. Brain metastases are found in about 20% of newly diagnosed non-small-cell lung cancer (NSCLC) patients. About 30% of NSCLC patients develop brain metastases during treatment. NSCLC that is positive for EGFR, ALK, and ROS1 variations is especially likely to metastasize to the brain. SPOCK1 is a proteoglycan with systemic physiological functions. It regulates the self-renewal of brain metastasis-initiating cells, regulates invasion and metastasis from the lung to the brain, plays an important role in tumor progression and treatment resistance, and has higher expression in metastatic tumor tissues than other tissues. Current treatments for NSCLC brain metastases include surgery, whole-brain radiotherapy, stereotactic radiotherapy, targeted therapy, and chemotherapy. SPOCK1 is involved in many signaling pathways, by which it influences a variety of NSCLC treatment methods. In this paper, the progress of research on the treatment of NSCLC brain metastases is reviewed to guide decisions on treatment options in clinical practice.

Metastatic brain tumors: from development to cutting-edge treatment

Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting-edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody-drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood-brain barrier to achieve high intracranial concentrations, including small-molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.

Brain metastases in newly diagnosed lung cancer: epidemiology and conditional survival

Background

The brain serves as the primary site for metastasis in patients with both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The presence of lung cancer with brain metastasis (LCBM) is a debilitating condition associated with considerable morbidity and mortality. The objective of this study was to assess the incidence and survival rates of LCBM in the United States population.

Methods

We analyzed a total of 9,212 patients diagnosed with LCBM between 2010 and 2015, extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Our analysis assessed the incidence, relative survival, and conditional survival (CS) of LCBM. We utilized the Kaplan-Meier method to estimate overall survival and determine CS at year y+x after x years of survival, following the formula CS(y|x) = CS(y+x)/CS(x). Prognostic factor selection was performed using the least absolute shrinkage and selection operator (LASSO) regression approach, and multivariate Cox regression was employed to demonstrate the impact of these predictors on outcomes and construct a CS-based nomogram.

Results

The overall age-adjusted incidence rate of LCBM was 5.82 cases per 100,000, with a slight decline observed during our study period. Patient relative survival showed a continuous decline with increasing age. CS analysis revealed that the 5-year CS rate for patients initially diagnosed with LCBM adjusted from 3% to 13%, 28%, 52%, and 73% over successive years of survival (1-4 years). Identified predictors included age at diagnosis, sex, race, tumor size, tumor grade, surgery, radiotherapy, and chemotherapy. These predictors, along with the CS formula, were employed to develop a CS-based nomogram for real-time prognosis prediction. Calibration curve, area under the time-dependent receiver operating characteristic (ROC) curve, concordance index (c-index), and decision curve analysis (DCA) demonstrated the model's strong predictive capabilities.

Conclusions

This study deepened our understanding of LCBM patients, summarizing their epidemiological characteristics and CS patterns. We successfully developed a novel CS-based nomogram model for dynamic survival estimation, offering real-time and personalized prognostic information that is clinically valuable.

In-depth exploration of the focus issues of TKI combined with radiotherapy for EGFR-mutant lung adenocarcinoma patients with brain metastasis: a systematic analysis based on literature metrology, meta-analysis, and real-world observational data

BACKGROUND

There is a growing interest in utilizing a combination of brain radiotherapy (RT) and tyrosine kinase inhibitors (TKIs) for patients diagnosed with brain metastases (BM) in epidermal growth factor receptor (EGFR) mutation-positive lung adenocarcinoma (LAC). The current status of this treatment strategy remains a subject of debate.

METHODS

We initiated our study by conducting a comprehensive literature search using the SCI-expanded database of Web of Science Core Collection (WoSCC). We utilized the VOSPviewer software to analyze various aspects of the research, including the year of publication, authorship, keywords, and country.Subsequently, we performed an extensive and systematic literature search on popular online databases. Our primary outcome measures were overall survival (OS) and intracranial progression-free survival (iPFS), both quantified by hazard ratios (HRs). Additionally, for data verification, we included data from patients in non-small cell lung cancer with brain metastasis who underwent therapeutic intervention at the Cancer Prevention and Treatment Center of Sun Yat-sen University and the Radiotherapy Department of Hanzhong Central Hospital between August 2012 and November 2021.

RESULTS

The bibliometric analysis revealed an increasing trend in research focused on the combination of RT and TKIs for the management of lung cancer brain metastases over the previous decade. Then, nine studies consistent with the research direction were included for meta-analysis. The meta-analysis showed that the OS (HR = 0.81, 95% confidence interval: 0.69-0.94; P = 0.007) and iPFS (HR = 0.71, 95% confidence interval: 0.61-0.82; P < 0.001) of the combination therapy were significantly prolonged. Finally, 168 EGFR-mutated BM advanced LAC patients in the real world were verified, and the median iPFS of the combination therapy (n = 88 and EGFR-TKIs alone (n = 80) were 16.0 and 9.0 months, respectively, (P < 0.001). The median OS was 29.0 and 27.0 months, respectively, with no dramatic difference (P = 0.188).

CONCLUSIONS

Research on EGFR-mutant LAC brain metastasis has turned towards exploring optimal treatment strategies for this condition. Our meta-analysis and real-world data analysis consistently demonstrate that combination therapy offers a substantial improvement in patient survival compared to EGFR-TKI monotherapy. Notably, among patients undergoing salvage radiotherapy (RT), our subgroup analysis reveals that those initially treated with third-generation TKIs experience more significant benefits than those treated with first- or second-generation TKIs.

Non-Small-Cell Lung Cancers (NSCLCs) Harboring RET Gene Fusion, from Their Discovery to the Advent of New Selective Potent RET Inhibitors: "Shadows and Fogs"

RET fusions are relatively rare in Non-Small-Cell Lung Cancers (NSCLCs), being around 1-2% of all NSCLCs. They share the same clinical features as the other fusion-driven NSCLC patients, as follows: younger age, adenocarcinoma histology, low exposure to tobacco, and high risk of spreading to the brain. Chemotherapy and immunotherapy have a low impact on the prognosis of these patients. Multitargeted RET inhibitors have shown modest activity jeopardized by high toxicity. New potent and selective RET inhibitors (RET-Is) (pralsetinib and selpercatinib) have achieved a higher efficacy minimizing the known toxicities of the multitargeted agents. This review will describe the sensitivity of immune-checkpoint inhibitors (ICIs) in RET fusion + NSCLC patients, as well their experiences with the 'old' multi-targeted RET inhibitors. This review will focus on the advent of new potent and selective RET-Is. We will describe their efficacy as well as the main mechanisms of resistance to them. We will further proceed to deal with the new drugs and strategies proposed to overcome the resistance to RET-Is. In the last section, we will also focus on the safety profile of RET-Is, dealing with the main toxicities as well as the rare but severe adverse events.

FOXP3: A Player of Immunogenetic Architecture in Lung Cancer

The transcription factor forkhead box protein 3 (FOXP3) is considered to be a prominent component of the immune system expressed in regulatory T cells (Tregs). Tregs are immunosuppressive cells that regulate immune homeostasis and self-tolerance. FOXP3 was originally thought to be a Tregs-specific molecule, but recent studies have pinpointed that FOXP3 is expressed in a diversity of benign tumors and carcinomas. The vast majority of the data have shown that FOXP3 is correlated with an unfavorable prognosis, although there are some reports indicating the opposite function of this molecule. Here, we review recent progress in understanding the FOXP3 role in the immunogenetic architecture of lung cancer, which is the leading cause of cancer-related death. We discuss the prognostic significance of tumor FOXP3 expression, tumor-infiltrating FOXP3-lymphocytes, tumor FOXP3 in tumor microenvironments and the potential of FOXP3-targeted therapy.