Brain Metastases Of Lung Adenocarcinoma Research Articles

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung

ObjectiveTo explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma.MethodsRetrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness.ResultsIn univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group).ConclusionsRadiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.

Abstract LB014: The role of genetic alterations in metastatic development insights from a large-scale genomic database

Abstract Metastasis remains a major challenge in cancer treatment, with somatic genetic alterations being a crucial feature in progression. This study harnesses the whole genome sequencing data in the Genomics England biobank (GEL), cataloging of somatic alterations in the context of cancer metastasis. Our research aims to uncover the genetic underpinnings that facilitate the spread of cancer from its primary site to secondary locations by analyzing clinicogenomic data from 3047 cancer patients with metastatic progression. In this study, we analyzed the complex relationship between somatic alterations and cancer progression to uncover significant interactions among these alterations in a broad distribution of common tumor types among well-established oncogenic and tumor suppressor genes. In our study, we employed a Bayesian network approach, enabling us to not only pinpoint crucial genetic contributors to metastasis but also to consider other clinical variables, including patient treatment, sex, age, tumor grade and cancer types. In our analysis, LILRB2 was found to directly increase metastatic risk in hematologic cancer, with prior evidence suggesting its potential as a therapeutic biomarker in colorectal cancer and adversely related to patient prognosis in lung cancer. Similarly, MYL1, initially linked to tumor metastasis and immune infiltration in head and neck squamous cell carcinoma, was also found to increase the risk of metastasis in renal cancer, while interacting with the transcription factor E2F4, marker of poor prognosis in hepatocellular carcinoma. Our study not only confirmed the numerous key somatic gene alterations significantly linked to metastatic behavior across various cancers, but also established their significant roles.Our work highlights the potential of leveraging extensive genomic databases such as GEL to enhance our understanding of cancer metastasis. GEL’s paired germline and somatic data allows us to not only identify mutations unique to cancer cells but also to uncover germline variants that are associated with an elevated risk of cancer and the presence of more aggressive tumor characteristics. By characterizing metastatic risk features, we can explore the opportunities to stratify high-risk patients in clinical trials, with similar effort has been applied to brain metastasis in lung adenocarcinoma. Although tumor heterogeneity poses a treatment challenge due to cell diversity, previous work has demonstrated value in targeting the metastatic potential. For instance, trastuzumab target HER2-positive breast cancers prone to metastasis, markedly improving patient outcomes. Similarly, vemurafenib effectively controls metastatic Melanoma progression by targeting specific mutations like BRAFV600E. Understanding the genetic basis of metastasis could help identify high risk patients and lead to personalized combination therapies addressing various tumor subpopulations. Future research based on these findings could lead to the development of personalized combination treatment strategies that improve patient outcomes. Citation Format: Songjun Xu, Danyang Yu, Shuwei Li, Christopher Moy, Julio Molineros. The role of genetic alterations in metastatic development insights from a large-scale genomic database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB014.

Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.

Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, necessitating predictive biomarkers. MicroRNAs (MiRNAs) promote cancer cell growth, infiltration, and metastasis. However, the relationship between the miRNA expression profiles and BM occurrence in patients with LUAD remains unclear. We conducted an analysis to identify miRNAs in tissue samples that exhibited different expression levels between patients with and without BM. Using a machine learning approach, we confirmed whether the miRNA profile could be a predictive tool for BM. We performed pathway analysis of miRNA target genes using a matched mRNA dataset. We selected 25 miRNAs that consistently exhibited differential expression between the two groups of 32 samples. The 25-miRNA profile demonstrated a strong predictive potential for BM in both Group 1 and Group 2 and the entire dataset (area under the curve [AUC] = 0.918, accuracy = 0.875 in Group 1; AUC = 0.867, accuracy = 0.781 in Group 2; and AUC = 0.908, accuracy = 0.875 in the entire group). Patients predicted to have BM, based on the 25-miRNA profile, had lower survival rates. Target gene analysis of miRNAs suggested that BM could be induced through the ErbB signaling pathway, proteoglycans in cancer, and the focal adhesion pathway. Furthermore, patients predicted to have BM based on the 25-miRNA profile exhibited higher expression of the epithelial-mesenchymal transition signature, TWIST, and vimentin than those not predicted to have BM. Specifically, there was a correlation between EGFR mRNA levels and BM. This 25-miRNA profile may serve as a biomarker for predicting BM in patients with LUAD.

Abstract 4139: Prolyl 4-hydroxylase subunit alpha 1 acts as a novel target for lung adenocarcinoma brain metastasis

Abstract Lung cancer is the most lethal cancer worldwide, with a high incidence of distant metastasis. Many lung adenocarcinoma (LUAD) patients experience brain metastasis during the disease process with unfavorable prognosis. Our previous study has discovered that P4HA1 was exclusively upregulated in brain metastatic cells. Through the analysis of single-cell RNA sequencing of LUAD samples, we found that the expression of P4HA1 in brain metastatic tumor cells is higher than that in primary tumor cells. This finding was further confirmed by immunohistochemical staining. Significantly, a strong presence of P4HA1 in primary tumors is associated with a reduced duration of disease-free survival in individuals with LUAD. Overexpression of P4HA1 in A549 and PC9 cell lines significantly increased the incidence of brain metastasis in BALB/c nude mice, whereas knockdown of P4HA1 in high brain metastatic cells significantly decreased the incidence of brain metastasis and reduced the tumor foci in brain with improved survival. Utilization of DHB, a P4HA1 inhibitor, exerts both therapeutic and preventive effects in relieving brain metastases in nude mice. Furthermore, remarkable inhibition of tumor growth by DHB was observed in patient derived xenograft (PDX) models. RNA sequencing analysis demonstrated that the reduction of P4HA1 expression had a substantial impact on cell adhesion and extracellular matrix (ECM)-receptors interactions. Western blotting analysis verified that the decrease in P4HA1 expression led to a drop in the expression of adhesion molecules ICAM1, VCAM1, NCAM1, as well as integrin molecules ITGA2 and ITGA3. Furthermore, when endothelial cells were co-cultured with conditioned medium from tumor cells, it was observed that the expression of the tight junction molecule ZO1 was reduced after co-culturing with conditioned medium from P4HA1 knockdown A549 cells. The results from the in vitro blood-brain barrier (BBB) model and in vivo biofluorescence imaging indicate that P4HA1 promoted brain metastasis of LUAD via modulating the ability of tumor cells to cross the blood-brain barrier. In conclusion, our study reveals that overexpression of P4HA1 promotes metastatic spread to the brain, and targeting P4HA1 could be a promising therapeutic strategy for the treatment of lung adenocarcinoma brain metastasis. Citation Format: Yan He, Zhenyu Luo, Yucheng Dong, Rui Wan, Xue Zhang, Hua Bai, Jie Wang. Prolyl 4-hydroxylase subunit alpha 1 acts as a novel target for lung adenocarcinoma brain metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4139.

Single-cell RNA sequencing reveals epithelial cells driving brain metastasis in lung adenocarcinoma

Brain metastases (BM) of lung adenocarcinoma (LUAD) are the most common intracranial malignancy leading to death. However, the cellular origins and drivers of BM from LUAD have not been clarified. Cellular composition was characterized by single-cell sequencing analysis of primary lung adenocarcinoma (pLUAD), BM and lymph node metastasis (LNM) samples in GSE131907. Our study briefly analyzed the tumor microenvironment (TME), focusing on the role of epithelial cells (ECs) in BM. We have discovered a population of brain metastasis-associated epithelial cells (BMAECs) expressing SPP1, SAA1, and CDKN2A, and it has been observed that this population is mainly composed of aneuploid cells from pLUAD, playing a crucial role in brain metastasis. Our study concluded that both LNM and BM in LUAD originated from pLUAD lesions, but there is currently insufficient evidence to prove a direct association between BM lesions and LNM lesions, which provides inspiration for further investigation of the TME in BM.

Proteomics-based Model for Predicting the Risk of Brain Metastasis in Patients with Resected Lung Adenocarcinoma carrying the EGFR Mutation.

Introduction: Epidermal growth factor receptor (EGFR) mutation is common in Chinese patients with lung adenocarcinoma (LUAD). Brain metastases (BMs) is high and associated with poor prognosis. Identification of EGFR-mutant patients at high risk of developing BMs is important to reduce or delay the incidence of BMs. Currently, there is no literature on the prediction and modeling of EGFR brain metastasis at the proteinomics level. Methods: We conducted a retrospective study of BMs in postoperative recurrent LUAD with EGFR mutation in the First Affiliated Hospital of Guangzhou Medical University. Tissue proteomic analysis was applied in the primary tumors of resected LUAD in this study using liquid chromatography-mass spectrometry (LC-MS/MS). To identify potential markers for predicting LUAD BM, comparative analyses were performed on different groups to evaluate proteins associated with high risk of BMs. Results: A combination of three potential marker proteins was found to discriminate well between distal metastasis (DM) and local recurrence (LR) of postoperative LUAD with EGFR mutation. Gene Ontology (GO) analysis of significantly altered proteins between BM and non-BM (NBM) indicated that lipid metabolism and cell cycle-related pathways were involved in BMs of LUAD. And the enriched pathways correlated with BMs were found to be quite different in the comparison groups of postoperative adjuvant therapy, tyrosine kinase inhibitor (TKI), and chemotherapy groups. Finally, we developed a random forest algorithm model with eight proteins (RRS1, CPT1A, DNM1, SRCAP, MLYCD, PCID2, IMPAD1 and FILIP1), which showed excellent predictive value (AUC: 0.9401) of BM in patients with LUAD harboring EGFR mutation. Conclusions: A predictive model based on protein markers was developed to accurately predict postoperative BM in operable LUAD harboring EGFR mutation.

Clinical Application of the Association between Genetic Alteration and Intraoperative Fluorescence Activity of 5-Aminolevulinic Acid during the Resection of Brain Metastasis of Lung Adenocarcinoma.

The primary objective of this study was to investigate the association of certain genetic alterations and intraoperative fluorescent activity of 5-aminolevulinic acid (ALA) in brain metastasis (BM) of lung adenocarcinoma. A retrospective cohort study was conducted among 72 patients who underwent surgical resection of BM of lung adenocarcinoma at our institute for five years. Cancer cell infiltration was estimated by the intraoperative fluorescent activity of 5-ALA, and genetic alterations were analyzed by next-generation sequencing (NGS). The sensitivity and specificity for detecting cancer cell infiltration using 5-ALA were 87.5% and 96.4%, respectively. Genes associated with cell cycle regulation (p = 0.003) and cell proliferation (p = 0.044) were significantly associated with positive fluorescence activity of 5-ALA in the adjacent brain tissue. Genetic alterations in cell cycle regulation and cell proliferation were also associated with shorter recurrence-free survival (p = 0.013 and p = 0.042, respectively) and overall survival (p = 0.026 and p = 0.042, respectively) in the multivariate analysis. The results suggest that genetic alterations in cell cycle regulation and cell proliferation are associated with positive fluorescence activity of 5-ALA in the adjacent infiltrative brain tissue and influence the clinical outcome of BM of lung adenocarcinoma.

Abstract C053: BACE1 cleaves EGFR to drive lung adenocarcinoma brain metastasis

Abstract Brain metastasis occurs in up to 40% of patients with non-small cell lung cancer (NSCLC). Considerable genomic heterogeneity exists between the primary lung tumour and respective brain metastasis; however, the identity of the genes capable of driving brain metastasis is incompletely understood. Here, we carried out an in vivo genome-wide CRISPR activation (CRISPRa) screen to identify molecular drivers of brain metastasis from a NSCLC patient-derived xenograft model. We discovered activation of the Alzheimer’s disease associated b-site amyloid precursor protein cleaving enzyme 1 (BACE1) led to a significant increase in brain metastasis. Furthermore, BACE1 knockout or inhibition with Verubecestat treatment blocked NSCLC brain metastasis. Mechanistically, we identified BACE1 cleaves an N-terminal fragment of EGFR to activate downstream signalling through MEK and ERK and drive this metastatic phenotype. Together our data highlights the power of in vivo CRISPR screening to unveil novel molecular drivers and potential therapeutic targets of NSCLC brain metastasis. Citation Format: Shawn C Chafe, Kui Zhai, Sheila K Singh. BACE1 cleaves EGFR to drive lung adenocarcinoma brain metastasis [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C053.

IMMU-49. BETA-ADRENERGIC BLOCKADE LICENSES THE USE OF IMMUNOTHERAPY IN PRIMARY AND METASTATIC BRAIN TUMORS

Abstract Immunotherapy is less effective against intracranial metastases compared to extracranial metastases and ineffective against primary brain tumors such as glioblastoma. Brain tumors present a unique challenge to in the context of immunotherapy, as these patients are immunosuppressed not just locally at the tumor, but also outside the CNS. Importantly, systemic immunosuppression observed in brain tumor patients limits the capacity of the immune system to respond to immunotherapy. Through this work we present a novel axis of immunosuppression in patients with intracranial tumors and demonstrate that overactive adrenergic signaling is a major barrier to immunotherapeutic success.Our data indicate that combining beta-adrenergic blockade with immunotherapy provides a survival benefit in the setting of brain tumors, where immunotherapy alone has proven ineffective. We demonstrate that this survival benefit is driven by a remodeling of the tumor microenvironment with an increase in cDC1s and CD8+ T cells, as well as an increase in CD40-CD40L signaling, suggesting the immune system is poised to respond. Further, we demonstrate that beta-adrenergic blockade can overcome systemic immunosuppression to restore the capacity of T cells to respond to an immune stimulus. Further, using the SEER-Medicaid database, we retrospectively examined outcomes in patients with melanoma and lung adenocarcinoma brain metastasis who received checkpoint blockade therapy alone vs those concurrently receiving beta-blocker therapy. We found that patients receiving combination therapy showed increased overall survival compared to those receiving checkpoint blockade alone. We then validated these findings in preclinical models of glioma, demonstrating that combining immunotherapy and propranolol, a widely-prescribed FDA-approved beta-blocker, extended survival. Moving forward, we suggest that combination therapy with beta-blocker and immunotherapy, particularly checkpoint blockade, represents a promising translational treatment platform for patients with primary or metastatic intracranial malignancies.

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis

BackgroundThe precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks.MethodsTo comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights.ResultsThe experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization.ConclusionWith the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.

The Impact of Co-Alterations on Outcomes after Local Therapy for Patients with KRAS-Mutant Lung Adenocarcinoma Brain Metastases

Brain metastases are common in NSCLC with up to 25% of patients having brain metastases (BMs) at the time of diagnosis and 30% developing BMs during their disease course. KRAS is an oncogenic driver in approximately 25% of lung adenocarcinomas. Genomic alterations co-occurring with KRAS are associated with distinct biological landscapes which may influence prognosis. Herein, we sought to identify correlations between genomic profiles, intracranial progression free survival (iPFS), and overall survival (OS). We retrospectively reviewed 156 patients with KRAS-mutant lung adenocarcinoma BM who underwent SRS for their BMs at MSKCC from 2010-2022. Each patient had at least one tumor sample profiled with MSK-IMPACT, a custom FDA-cleared next-generation sequencing. Mutations, copy number alterations, and fusions were filtered for driver alterations using OncoKB. Survival outcomes were calculated from date of MRI indicating metastatic brain disease. Of the 156 patients, 80 patients presented with BMs at diagnosis whereas 76 developed BMs during their disease course, with a median 2 lines of therapy prior to BM diagnosis. The most common KRAS mutation was G12C (n = 64; 41%), G12V (n = 26, 17%), G12D (n = 17; 11%), and G12A (n = 11; 7%). The most frequently co-altered genes were TP53 (n = 71, 46%), STK11 (n = 51, 33%), CDKN2A (n = 27, 17%), KEAP1 (n = 17, 11%), and SMARCA4 (n = 10, 6%). The presence of a KEAP1 co-occurring alteration was associated with inferior iPFS (HR 1.95, 95% CI 1.05 - 3.59, p = 0.035) and the presence of SMARCA4 was also associated with inferior iPFS (HR 2.28, 95% CI 1.05 - 4.95, p = 0.038). The presence of an STK11 mutation was associated with worse OS (HR 1.57, 95% 1.01 - 2.43, p = 0.045). In a multi-variate clinico-genomic model, KEAP1 and STK11 co-occurring alterations remained significantly associated with iPFS. Patients with KEAP1-altered tumors had an increased incidence of intracranial regional progression. The 24-month cumulative incidence of regional progression amongst KEAP1-altered tumors was 57% (95% CI, 29%-77%) compared with 37% (95% CI, 29%-46%) among KEAP1-wildtype tumors (P = 0.041). Patients with CDKN2A-altered tumors had an increased incidence of leptomeningeal disease (LMD) as a form of intracranial progression. The 24-month cumulative incidence of LMD amongst CDKN2A-altered tumors was 11% (95% CI, 2.7%-27%) compared with 4.1% (95% CI, 1.5%-8.8%) among CDKN2A-wildtype tumors (P = 0.023). In our cohort of molecularly profiled KRAS-mutant lung adenocarcinoma BM patients treated with SRS, we found that co-occurring KEAP1 and STK11 were significantly associated with worse iPFS. We also observed that CDKN2A co-altered tumors had an increased incidence of LMD. These findings have implications for future efforts to personalize brain metastasis management based on comprehensive genomic profiling.

SYST-05 BETA-ADRENERGIC BLOCKADE LICENSES THE USE OF IMMUNOTHERAPY IN PRIMARY AND METASTATIC BRAIN TUMORS

Abstract Brain tumors present a unique challenge to in the context of immunotherapy, as these patients are immunosuppressed not just at the tumor but also outside the CNS. The systemic immunosuppression observed in brain tumor patients limits the capacity of the immune system to respond to immunotherapy. Brain tumor patients, particularly those with metastatic disease, represent a patient population that is on the rise and in need of targeted treatment approaches. Through this work we present a novel axis of immunosuppression in brain tumor patients and demonstrate that overactive adrenergic signaling is a major barrier to immunotherapeutic success. Our data indicate that combining beta-adrenergic blockade with immunotherapy provides a survival benefit in the setting of brain tumors, where immunotherapy alone has proven ineffective. Using the SEER-Medicaid database, we retrospectively examined outcomes in patients with melanoma and lung adenocarcinoma brain metastasis who received checkpoint blockade therapy alone vs those concurrently on beta-blocker therapy. We found that patients receiving combination therapy showed increased overall survival compared to those receiving checkpoint blockade alone. We then validated these findings in preclinical models of both glioma and melanoma brain metastases, demonstrating that combining immunotherapy and propranolol, a widely-prescribed FDA-approved beta-blocker, extended survival. Moving forward, we suggest that combination therapy with propranolol and immunotherapy, particularly checkpoint blockade, represents a promising translational treatment platform for patients with primary or metastatic intracranial malignancies.

Combination beta-adrenergic blockade and immunotherapy for the treatment of primary and metastatic brain cancer.

e14017 Background: Brain tumors present a unique challenge to immunotherapeutic success, as immune responses in patients with brain tumors are suppressed both within the tumor microenvironment and peripherally. The systemic immunosuppression observed in brain tumor patients limits the capacity of the immune system to respond to immunotherapy. Brain tumor patients, particularly those with primary brain tumors like glioblastoma, represent a population in dire need of more thoughtful, targeted treatment approaches. Through this work we present an immunosuppressive axis specifically activated in the context of intracranial malignancies that inhibits immunotherapeutic success in these patients. We demonstrate that overactive adrenergic signaling is a major barrier to immunotherapeutic success. More specifically, beta-adrenergic signaling is known to suppress immune responses in various contexts. Our data indicate that combining beta-adrenergic blockade with immunotherapy provides a survival benefit in the setting of brain tumors, where immunotherapy alone has proven ineffective. Methods: Using the SEER Medicaid database, we retrospectively examined outcomes in patients with melanoma and lung adenocarcinoma brain metastasis who received checkpoint blockade therapy alone vs those concurrently on beta-blocker therapy. Results: We found that patients receiving combination therapy showed increased overall survival compared to those receiving checkpoint blockade alone. We then validated these findings in preclinical models of both glioma and melanoma brain metastases, demonstrating that combining immunotherapy and propranolol, a highly-prescribed FDA-approved beta-blocker, extended survival. Conclusions: Moving forward, we suggest that combination therapy with propranolol and immunotherapy, particularly checkpoint blockade, represents a promising translational treatment platform for patients with primary or metastatic intracranial malignancies.

Identification of RAC1 in promoting brain metastasis of lung adenocarcinoma using single-cell transcriptome sequencing

This study aims to give a new perspective to the biomarkers in the lung adenocarcinoma (LUAD) brain metastasis, pathways involved and potential therapeutics. We performed a comprehensive single-cell level transcriptomic analysis on one LUAD patient with circulating tumor cells (CTCs), primary tumor tissue and metastatic tumor tissue using scRNA-seq approach to identify metastasis related biomarkers. Further scRNA-seq were performed on 7 patients to validate the cancer metastatic hallmark. with single cells collected from either metastatic or primary LUAD tissues. Pathological and functional studies were also performed to evidence the critical role of RAC1 in the LUAD metastasis. Hallmark gene was verified based on immunohistochemistry staining, cytological experiment, survival information from The Cancer Genome Atlas (TCGA), and staining results from Human Protein Atlas (HPA) databases. PCA analysis revealed that CTCs were in the intermediate place between the metastatic group and primary group. In the unsupervised clustering analysis CTCs were closer to one of the metastatic tumor cells, implying heterogeneity of the metastatic tumor and origin of the CTCs were from metastatic site. Transitional phase related gene analysis identified RAC1 was enriched in metastatic tumor tissue (MTT) preferred gene set functioning as regulated cell death and apoptosis as well as promoted macromolecule organization. Compared with normal tissue, expression levels of RAC1 increased significantly in LUAD tissue based on HPA database. High expression of RAC1 predicts worse prognosis and higher-risk. EMT analysis identified the propensity of mesenchymal state in primary cells while epithelial signals were higher in the metastatic site. Functional clustering and pathway analyses suggested genes in RAC1 highly expressed cells played critical roles in adhesion, ECM and VEGF signaling pathways. Inhibition of RAC1 attenuates the proliferation, invasiveness and migration ability of lung cancer cells. Besides, through MRI T2WI results, we proved that RAC1 can promote brain metastasis in the RAC1-overexpressed H1975 cell burden nude mouse model. RAC1 and its mechanisms might promote drug design against LUAD brain metastasis.

Tumor lineage-specific immune response in brain metastatic disease: opportunities for targeted immunotherapy regimen?

Metastases in the brain are the most severe and devastating complication of cancer. The incidence of brain metastasis is increasing. Therefore, the need of finding specific druggable targets for brain metastasis is demanding. The aim of this study was to compare the brain (immune) response to brain metastases of the most common tumor lineages, viz., lung adenocarcinoma and breast cancer. Targeted gene expression profiles of 11 brain metastasis of lung adenocarcinoma (BM-LUAD) were compared to 11 brain metastasis of breast cancer (BCBM) using NanoString nCounter PanCancer IO 360™ Panel. The most promising results were validated spatially using the novel GeoMx™ Digital Spatial Profiler (DSP) Technology. Additionally, Immune cell profiles and expression of drug targets were validated by multiplex immunohistochemistry. We found a more active immune response in BM-LUAD as compared to BCBM. In the BM-LUAD, 138 genes were upregulated as compared to BCBM (adj. p ≤ 0.05). Conversely, in BCBM 28 genes were upregulated (adj. p ≤ 0.05). Additionally, genes related to CD45 + cells, T cells, and cytotoxic T cells showed to be expressed higher in BM-LUAD compared to BCBM (adj. p = 0.01, adj. p = 0.023, adj. p = 0.023, respectively). The spatial quantification of the immune cells using the GeoMx DSP technique revealed the significantly higher quantification of CD14 and CD163 in tumor regions of BM-LUAD as compared to BCBM. Importantly, the immune checkpoint VISTA and IDO1 were identified as highly expressed in the BM-LUAD. Multiplex immunohistochemistry confirmed the finding and showed that VISTA is expressed mainly in BM-LUAD tumor cells, CD3 + cells, and to fewer levels in some microglial cells in BM-LUAD. This is the first report on differences in the brain immune response between metastatic tumors of different lineages. We found a far more extensive infiltration of immune cells in BM-LUAD as compared to BCBM. In addition, we found higher expression of VISTA and IDO1 in BM-LUAD. Taken together, targeted immune therapy should be considered to treat patients with BM-LUAD.

Glycodelin is a potential biomarker for malignant tumors

Objective of the study to analyze and summarize the available data on the role of glycodelin in carcinogenesis and its expression in various cancers.Material and Methods. A literature search was conducted in Medline, PubMed Central, NCBI databases in the time interval from January 1983 to October 2019 using the key words glycodelin and cancer. Of the 104 publications found, 21 were used to write the review.Results. This paper presents the overview of the findings in current research focusing on the properties of glycodelin, the major lipocalin protein of the human reproductive system. Some lipocalins are known to play a key role in cancer development as well as influence signaling pathways in the regulation of cell motility, differentiation and neovascularization. Most likely they can be used as cancer markers. Glycodelin A is determined in serum and, due to its special immunoregulatory properties, can serve as a useful prognostic marker and a promising target for future anti-cancer therapies. The presence of glycodelin A in breast cancer tissue is known to be mostly linked to a better prognosis than is attributed to glycodelin-negative tissue, as glycodelin is a protein typical of differentiated tissue. On the other hand, glycodelin might play a role in neovascularisation, thereby promoting tumor growth. Glycodelin is a biomarker of aggressive malignant pleural mesothelioma and a prognostic biomarker of metastatic non-small cell lung cancer at late stages. Glycodelin hyperexpression is associated with brain metastasis in lung adenocarcinoma, and its determination can be used as an additional prognostic factor.Conclusion. The review refects basic scientifc data and results of clinical trials, as well as identifes future prospects that allow the development of new methods for cancer detection and treatment. It should be noted that glycodelin plays an important role in tumor development, progression, angiogenesis, and the formation of distant metastases, and therefore can serve as a useful diagnostic and prognostic marker. Further studies of the functional properties of glycodelin are needed to develop promising strategies in cancer therapy.

Identification of FKBP10 prognostic value in lung adenocarcinoma patients with surgical resection of brain metastases: A retrospective single-institution cohort study

ObjectiveTo explore the expression levels and clinical value of FKBP10 in lung adenocarcinoma brain metastases. DesignA retrospective single-institution cohort study. PatientsThe perioperative records of 71 patients with lung adenocarcinoma brain metastases who underwent surgical resection at the authors’ institution between November 2012 and June 2019 were retrospectively analyzed. MethodsThe authors evaluated FKBP10 expression levels using immunohistochemistry in tissue arrays of these patients. Kaplan-Meier survival curves were constructed, and a Cox proportional hazards regression model was used to identify independent prognostic biomarkers. A public database was used to detect FKBP10 expression and its clinical value in primary lung adenocarcinoma. ResultsThe authors found that the FKBP10 protein was selectively expressed in lung adenocarcinoma brain metastases. Survival analysis showed that FKBP10 expression (p = 0.02, HR = 2.472, 95% CI [1.156, 5.289]), target therapy (p < 0.01, HR = 0.186, 95% CI [0.073, 0.477]), and radiotherapy (p = 0.006, HR = 0.330, 95% CI [0.149, 0.731]) were independent prognostic factors for survival in lung adenocarcinoma patients with brain metastases. The authors also detected FKBP10 expression in primary lung adenocarcinoma using a public database, found that FKBP10 is also selectively expressed in primary lung adenocarcinoma, and affects the overall survival and disease-free survival of patients. LimitationsThe number of enrolled patients was relatively small and patients’ treatment options varied. ConclusionsA combination of surgical resection, adjuvant radiotherapy, and precise target therapy may benefit the survival of selected patients with lung adenocarcinoma brain metastases. FKBP10 is a novel biomarker for lung adenocarcinoma brain metastases, which is closely associated with survival time and may serve as a potential therapeutic target.

Conservation of pyramidal tract in radiosurgery for brain metastases of lung adenocarcinoma: Three-dimensional analysis of biologically effective dose

BackgroundGamma knife surgery (GKS) for brain metastases (BMs) adjacent to the pyramidal tract (PT) is still a challenge to conduct. PT visualization and biologically effective dose (BED) calculation on a voxel-by-voxel basis may provide data to establish clinically safe values. We aimed to assess the relationship of parameters extracted from the BED-volume histogram with outcomes of PT after GKS-treating target (adjacent BM of lung adenocarcinoma). MethodsWe formed BED-volume histograms for 672 BMs in a retrospective cohort, using 3-dimensional (3D) coordinate values of PT, target, and each iso-centre to calculate the 3D BED distribution in a 200 × 200 × 200 matrix. PT conservation failure (PTCF) was judged clinically and radiologically and classified as lesion progression and radionecrosis. Cox proportional hazards models were used to analyse 3D BED parameters. Internal validation of models was performed by bootstrapping. ResultsThere were 116 (17.3 %) subjects with PTCF in the cohort, of which 74 (11.0 %) and 42 (6.3 %) were caused by lesion progression and radionecrosis, respectively. Multivariate analysis showed that DLesion_min BED and DLesion_90% BED significantly predicted lesion progression (P <.001). DPT_Max BED and VPT_ BED40 significantly predicted radionecrosis (P <.001). The model predicting PTCF showed fair discrimination and calibration of DLesion_min BED + DLesion_90% BED and DPT_Max BED + VPT_ BED40. ConclusionsThe conservation of PT in GKS for BMs of lung adenocarcinoma depends on the combination of PT-tolerated BED and target effective control BED. Therefore, a BED-volume histogram with a 3D BED algorithm is proposed to assess plan quality.

Immersion bioprinting of hyaluronan and collagen bioink-supported 3D patient-derived brain tumor organoids

Organoids, and in particular patient-derived organoids, have emerged as crucial tools for cancer research. Our organoid platform, which has supported patient-derived tumor organoids (PTOs) from a variety of tumor types, has been based on the use of hyaluronic acid (HA) and collagen, or gelatin, hydrogel bioinks. One hurdle to high throughput PTO biofabrication is that as high-throughput multi-well plates, bioprinted volumes have increased risk of contacting the sides of wells. When this happens, surface tension causes bioinks to fall flat, resulting in 2D cultures. To address this problem, we developed an organoid immersion bioprinting method—inspired by the FRESH printing method—in which organoids are bioprinted into support baths in well plates. The bath—in this case an HA solution—shields organoids from the well walls, preventing deformation. Here we describe an improvement to our approach, based on rheological assessment of previous gelatin baths versus newer HA support baths, combined with morphological assessment of immersion bioprinted organoids. HA print baths enabled more consistent organoid volumes and geometries. We optimized the printing parameters of this approach using a cell line. Finally, we deployed our optimized immersion bioprinting approach into a drug screening application, using PTOs derived from glioma biospecimens, and a lung adenocarcinoma brain metastasis. In these studies, we showed a general dose dependent response to an experimental p53 activator compound and temozolomide (TMZ), the drug most commonly given to brain tumor patients. Responses to the p53 activator compound were effective across all PTO sets, while TMZ responses were observed, but less pronounced, potentially explained by genetic and epigenetic states of the originating tumors. The studies presented herein showcase a bioprinting methodology that we hope can be used in increased throughput settings to help automate biofabrication of PTOs for drug development-based screening studies and precision medicine applications.

Micro-ribonucleic acids (miRNAs) and a proteomic profile in lung adenocarcinoma cases with brain metastasis.

Brain metastasis (BM) is the main cause of death of individuals with lung adenocarcinoma (LAC). Biomarkers with high sensitivity and specificity for the early detection and treatment of BM of LAC urgently need to be identified. In this study, we analyzed the pathogenesis of LAC-induced BM by detecting micro-ribonucleic acid (miRNA) and proteome expression differences between primary LAC lesion and BM tissue specimens to identify biomarkers of LAC-associated BM and develop potential therapeutic targets. The miRNA and protein profiles of non-metastatic primary LAC and BM cases were examined to further explore the mechanism of BM. The roles and interactions of differential miRNAs and proteins were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The interactions of differential miRNAs and proteins were analyzed by R software and depicted using Cytoscape. Compared to the LAC tissue specimens, 16 and 4 miRNAs showed increased and reduced levels, respectively, in the BM tissue specimens, and 53 proteins were upregulated, and 35 proteins were downregulated. The enrichment pathway analysis showed the nuclear factor kappa B (NF-κB) signaling and the primary immunodeficiency pathways played important roles in the pathogenetic mechanisms of BM in LAC. This study extended understandings of the regulatory network of miRNAs and proteins and provided novel insights into the pathogenic mechanisms of BM in LAC at the miRNA and protein levels.