Clinical efficacy and safety analysis of aumolertinib in real-world treatment of EGFR-mutated advanced non-small-cell lung cancer.

Background: This study aims to determine the efficacy and safety profile of aumolertinib in the real-word treatment setting for advanced non-small-cell lung cancer (NSCLC) patients harboring epidermal growth factor receptor (EGFR) mutations. Methods: We retrospectively analyzed the clinical data of 173 EGFR-mutated advanced NSCLC patients who received aumolertinib treatment at Henan Cancer Hospital from April 2020 to December 2022. Progression-free survival (PFS) and overall survival (OS) were evaluated using Kaplan-Meier survival curves, while a Cox regression model was used for multifactorial analysis and prognostic factor assessment. Results: Among patients administered first-line aumolertinib (n = 77), the objective remission rate (ORR) of 77.92% was observed, along with a disease control rate (DCR) of 100%. The median progression-free survival (mPFS) was 24.97 months, which did not reach the median overall survival (mOS). The patients treated with aumolertinib after progression on prior EGFR-tyrosine kinase inhibitor (TKI) therapy (n = 96) exhibited an ORR of 46.88%, a DCR of 89.58%, an mPFS of 15.17 months, and an mOS of 21.27 months. First-line treatment multivariate Cox regression analysis demonstrated a statistically significant impact of elevated creatine kinase on PFS (p = 0.016) and a similar significant influence of co-mutation on OS (p = 0.034). Furthermore, subsequent-line treatment multivariate Cox regression analysis showed a statistically significant impact of elevated creatine kinase on median PFS (p = 0.026) and a significant effect on the number of metastatic organs (p = 0.017), co-mutation (p = 0.035), and elevated creatine kinase (p = 0.014) on median OS. Conclusion: Aumolertinib has shown clinical significance and can safely be used in the real-world setting for patients with EGFR mutation-positive NSCLC.

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI.

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

cGAS regulates the DNA damage response to maintain proliferative signaling in gastric cancer cells.

The activation of some oncogenes promote cancer cell proliferation and growth, facilitate cancer progression and metastasis by induce DNA replication stress, even genome instability. Activation of the cyclic GMP-AMP synthase (cGAS) mediates classical DNA sensing, is involved in genome instability, and is linked to various tumor development or therapy. However, the function of cGAS in gastric cancer remains elusive. In this study, the TCGA database and retrospective immunohistochemical analyses revealed substantially high cGAS expression in gastric cancer tissues and cell lines. By employing cGAS high-expression gastric cancer cell lines, including AGS and MKN45, ectopic silencing of cGAS caused a significant reduction in the proliferation of the cells, tumor growth, and mass in xenograft mice. Mechanistically, database analysis predicted a possible involvement of cGAS in the DNA damage response (DDR), further data through cells revealed protein interactions of the cGAS and MRE11-RAD50-NBN (MRN) complex, which activated cell cycle checkpoints, even increased genome instability in gastric cancer cells, thereby contributing to gastric cancer progression and sensitivity to treatment with DNA damaging agents. Furthermore, the upregulation of cGAS significantly exacerbated the prognosis of gastric cancer patients while improving radiotherapeutic outcomes. Therefore, we concluded that cGAS is involved in gastric cancer progression by fueling genome instability, implying that intervening in the cGAS pathway could be a practicable therapeutic approach for gastric cancer.

Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization. Our method is built upon matrix tri-factorization enhanced by adaptive sparsity regularization for simultaneous feature dimensionality reduction and denoising. Particularly, latent grouping information of patients with distinct radiomic features is learned and utilized as supervision information to guide the feature dimensionality reduction, and noise in radiomic features is adaptively isolated in a Bayesian framework under a general assumption of Laplacian distributions of transform-domain coefficients. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and evaluation results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS.

Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning method to simultaneously stratify cancer patients into distinct risk groups based on their radiomic features and learn low-dimensional representations of the radiomic features for robust prediction of their clinical outcomes. Based on nonnegative matrix tri-factorization techniques, the proposed method applies collaborative clustering to radiomic features of cancer patients to obtain clusters of both the patients and their radiomic features so that patients with distinct imaging patterns are stratified into different risk groups and highly correlated radiomic features are grouped in the same radiomic feature clusters. Experiments on a FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method facilitates better stratification of patients with distinct survival patterns and learning of more effective low-dimensional feature representations that ultimately leads to accurate prediction of patient survival, outperforming conventional methods under comparison.

Improved Antitumor Efficacy of Combined Vaccine Based on the Induced HUVECs and DC-CT26 Against Colorectal Carcinoma.

Angiogenesis is essential for the development, growth, and metastasis of solid tumors. Vaccination with viable human umbilical vein endothelial cells (HUVECs) has been used for antitumor angiogenesis. However, the limited immune response induced by HUVECs hinders their clinical application. In the present study, we found that HUVECs induced by a tumor microenvironment using the supernatant of murine CT26 colorectal cancer cells exerted a better antiangiogenic effect than HUVECs themselves. The inhibitory effect on tumor growth in the induced HUVEC group was significantly better than that of the HUVEC group, and the induced HUVEC group showed a strong inhibition in CD31-positive microvessel density in the tumor tissues. Moreover, the level of anti-induced HUVEC membrane protein antibody in mouse serum was profoundly higher in the induced HUVEC group than in the HUVEC group. Based on this, the antitumor effect of a vaccine with a combination of induced HUVECs and dendritic cell-loading CT26 antigen (DC-CT26) was evaluated. Notably, the microvessel density of tumor specimens was significantly lower in the combined vaccine group than in the control groups. Furthermore, the spleen index, the killing effect of cytotoxic T lymphocytes (CTLs), and the concentration of interferon-γ in the serum were enhanced in the combined vaccine group. Based on these results, the combined vaccine targeting both tumor angiogenesis and tumor cells may be an attractive and effective cancer immunotherapy strategy.

Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis.

Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study (e.g. difference in patients) for learning discriminative low dimensional representations. To achieve feature dimensionality reduction with improved discriminative power and robustness to noisy radiomic features, we develop an adaptive sparsity regularization based collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively. Our method is built on adaptive sparsity regularized matrix tri-factorization for simultaneous feature denoising and dimension reduction so that the noise is adaptively isolated from the features, and grouping information of patients with distinctive features provides latent supervision information to guide feature dimension reduction. The sparsity regularization is grounded on distribution modeling of transform-domain coefficients in a Bayesian framework. Experiments on synthetic data have demonstrated the effectiveness of the proposed approach in data clustering, and empirical results on an FDG-PET/CT dataset of rectal cancer patients have demonstrated that the proposed method outperforms alternative methods in terms of both patient stratification and prediction of patient clinical outcomes.

Human umbilical vein endothelial cell vaccine suppresses the angiogenesis of esophageal squamous cell carcinoma in a humanized mouse model.

The antitumor effect of the human umbilical vein endothelial cell (HUVEC) vaccine has been well documented; however, its anti­angiogenic effects on human esophageal squamous cell carcinoma (ESCC) have yet to be studied. In the present study, a 'humanized' mouse model was established by transplanting NOD/SCID mice with human peripheral blood mononuclear cells (PBMC). After 4 weeks, the level of cluster of differentiation (CD)­45+ human T­lymphocytes in mouse peripheral blood was >0.1%, which indicated that mouse reconstruction and the human immune system transformation had been successful. The humanized mice were used to evaluate the anti­angiogenic effect of the HUVEC vaccine on human ESCC. After immunization with the HUVEC vaccine for 5 consecutive weeks, the humanized mice were subcutaneously transplanted with EC9706 cells. The results indicated that the HUVEC vaccine reduced the size of human esophageal carcinoma xenografts by suppressing angiogenesis. In addition, the HUVEC­immunized mice exhibited reduced expression of angiogenesis­associated antigens (vascular endothelial growth factor receptor 2 and VE­Cadherin) in the tumor specimens, and increased levels of angiogenesis­associated antibodies in the serum. Notably, the HUVEC vaccine also increased the infiltration of human T­lymphocytes into the spleen of humanized mice. In conclusion, the present study demonstrated the anti­angiogenic effect of the HUVEC vaccine on ESCC in a humanized mouse model, and set an experimental foundation for the application of the HUVEC vaccine in ESCC patients.

Metformin inhibits esophageal squamous cell carcinoma-induced angiogenesis by suppressing JAK/STAT3 signaling pathway.

Although it has been known that the tumor microenvironment affects angiogenesis, the precise mechanism remains unclear. In this study, we simulated the microenvironment of human esophageal squamous cell carcinoma (ESCC) by tumor conditioned medium (TCM) to assess the influence on normal endothelial cells (NECs). We found that the TCM-induced NECs showed enhanced angiogenic properties, such as migration, invasion and tube formation. Moreover, the TCM-induced NECs expressed tumor endothelial cells (TECs) markers at higher levels, which indicated that TCM probably promoted tumor angiogenesis by coercing NECs to change toward TECs. The microarray gene expression analysis indicated that TCM induced great changes in the genome of NECs and altered many regulatory networks, especially c-MYC and JAK/STAT3 signaling pathway. More importantly, we investigated the anti-angiogenic effect of metformin, and found that metformin abrogated the ESCC microenvironment-induced transition of NECs toward TECs by inhibiting JAK/STAT3/c-MYC signaling pathway. Furthermore, we verified the anti-angiogenic activity of metformin in vivo by a human ESCC patient-derived xenograft (PDX) mouse model for the first time. Taken together, our research provides a novel mechanism for the anti-angiogenic effect of metformin, and sets an experimental basis for the development of new anti-angiogenic drugs by blocking the transition of NECs toward TECs, which possibly open new avenues for targeted treatment of cancer.

Dendritic cells loading autologous tumor lysate promote tumor angiogenesis.

Dendritic cells (DC) have been exploited for vaccination against cancer for years. DC loading autologous tumor lysate (ATL-DC) have been assessed in ongoing clinical trials, but frequently do not meet expectation. In this study, we found that mice immunized with ATL-DC induced less protective anti-tumor effect than immunized with DC alone. The percentage of CD8+ T cells and the lysis efficiency of CTLs to auto tumor cells in ATL-DC vaccination group was less than that of DC group. Moreover, vaccination of mice with ATL-DC also promoted tumor angiogenesis by analyzing the CD31 positive microvessel density and hemoglobin content of tumor specimens. Human umbilical vein endothelial cells (HUVEC) have been proved effective in the anti-angiogenesis immunity against cancer. However, in the following research we found that the anti-tumor effect was attenuated while immunized mice with HUVEC combined with ATL-DC (HUVEC + ATL-DC). Furthermore, immunized mice with HUVEC + ATL-DC profoundly increased the tumor angiogenesis by analyzing the microvessel density and hemoglobin content of tumor specimens. These data suggest that vaccination using ATL-DC antagonized HUVEC induced anti-angiogenesis effect. Our research for the first time indicated that ATL-DC have the potential to promote the process of tumor angiogenesis in vivo. As vaccines based on DC loading autologous tumor lysate have been used in clinical, this find warned that the safety of this kind of vaccine should be taken into consideration seriously.

Genome-wide analysis of the effect of esophageal squamous cell carcinoma on human umbilical vein endothelial cells.

A large volume of data indicates that controlling tumor-associated angiogenesis is a promising therapy against cancer. However, angiogenesis is a complex process, little is known about the differential gene expression in the process of normal endothelial cell differentiation toward tumor vascular endothelial cells induced by tumor microenvironment. The aim of this study is to investigate the effect of tumor microenvironment simulated by the supernatant of esophageal squamous cancer cells (KYSE70) on normal endothelial cells (HUVECs) at the whole genome level. The gene expression profile was studied through gene ontology and signal pathway analysis. Compared with the normal HUVECs, a total of 3769 differentially expressed genes in induced HUVECs were detected, including 1609 upregulated genes and 2160 downregulated genes. Moreover, the microarray data analysis showed that 11 significant biological processes and 10 significant signaling pathways changed most, which are associated with angiogenesis and cell differentiation. According to the different expression levels in the microarrays and their functions, four differentially expressed genes involved in tumor angiogenesis and cell differentiation (IL6, VEGFA, S1PR1, TYMP) were selected and analyzed by qRT-PCR. The qRT-PCR results were consistent with the microarray data. Furthermore, we simulated the tumor microenvironment by human esophageal carcinoma tissue homogenate to investigate its effect on HUVECs, the qRT-PCR results indicated that the above genes were highly expressed in HUVECs after induction by esophageal carcinoma tissue homogenate. In conclusion, tumor microenvironment impact on normal endothelial cells differentiated toward tumor vascular endothelial cells, and the selected genes, which are associated with tumor angiogenesis, would be anti-angiogenesis targets against esophageal carcinoma.