Identification of CD37, cystatin A, and IL-23A gene expression in association with brain metastasis: analysis of a prospective trial

In silico evaluation of missense SNPs in cancer-associated Cystatin A protein and their potential to disrupt Cathepsin B interaction

Cystatin A (CSTA) functions as a cysteine protease inhibitor by forming tight complexes with the cathepsins. Pathogenic mutations in the CSTA gene can disrupt this interaction, potentially leading to physiological ailments. In this study, eight bioinformatics tools (SIFT, PolyPhen-2, PROVEAN, P-Mut, MutPred2, SNAP2, SNPs & GO, and PHD-SNP) were implemented to analyze non-synonymous SNPs from the dbSNP database. Five mutations (Y43C, Y43N, V48F, Y53H, and E94K) located in the conserved region were found to be highly deleterious and less stabilizing. The protein-protein interaction network found that Cathepsin B (CTSB) interacts highly with CSTA. Mutated CSTAs were created by homology modeling, and their altered binding with CTSB was examined through molecular docking and dynamics simulations. Among these, the Y53H (rs1448459675) and E94K (rs200394711) mutants were recognized as weaker inhibitors because they had 2.5 % and an 8 % lower binding affinity, respectively. Moreover, the E94K-CTSB complex, with a root mean square deviation (RMSD) above 5 Å, was found to be highly unstable during molecular dynamics. The root mean square fluctuation (RMSF) of the E94K mutant showed insufficient flexibility, indicating a reduced capacity to suppress CTSB. These findings suggest that the E94K mutation could affect the protein structure and cathepsin B interaction, potentially leading to pathological consequences as evidenced by colorectal adenocarcinoma patients in the COSMIC (Catalogue of Somatic Mutations in Cancer) database.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

Use of comprehensive genomic profiling for biomarker discovery for the management of non-small cell lung cancer brain metastases

Background

Clinical biomarkers for brain metastases remain elusive. Increased availability of genomic profiling has brought discovery of these biomarkers to the forefront of research interests.

Method

In this single institution retrospective series, 130 patients presenting with brain metastasis secondary to Non-Small Cell Lung Cancer (NSCLC) underwent comprehensive genomic profiling conducted using next generation circulating tumor deoxyribonucleic acid (DNA) (Guardant Health, Redwood City, CA). A total of 77 genetic mutation identified and correlated with nine clinical outcomes using appropriate statistical tests (general linear models, Mantel-Haenzel Chi Square test, and Cox proportional hazard regression models). For each outcome, a genetic signature composite score was created by summing the total genes wherein genes predictive of a clinically unfavorable outcome assigned a positive score, and genes with favorable clinical outcome assigned negative score.

Results

Seventy-two genes appeared in at least one gene signature including: 14 genes had only unfavorable associations, 36 genes had only favorable associations, and 22 genes had mixed effects. Statistically significant associated signatures were found for the clinical endpoints of brain metastasis velocity, time to distant brain failure, lowest radiosurgery dose, extent of extracranial metastatic disease, concurrent diagnosis of brain metastasis and NSCLC, number of brain metastases at diagnosis as well as distant brain failure. Some genes were solely associated with multiple favorable or unfavorable outcomes.

Conclusion

Genetic signatures were derived that showed strong associations with different clinical outcomes in NSCLC brain metastases patients. While these data remain to be validated, they may have prognostic and/or therapeutic impact in the future.

Statement of translation relevance

Using Liquid biopsy in NSCLC brain metastases patients, the genetic signatures identified in this series are associated with multiple clinical outcomes particularly these ones that lead to early or more numerous metastases. These findings can be reverse-translated in laboratory studies to determine if they are part of the genetic pathway leading to brain metastasis formation.

Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment

Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies.

Prognostic and immunological significance of an M1 macrophage-related gene signature in osteosarcoma

As the most abundant infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) are pivotal in tumor development and treatment. The present investigation endeavors to explore the potential of M1 macrophage-related genes (MRGs) as biomarkers for assessing risk in individuals with osteosarcoma. RNA-sequence data and clinical data were derived from TCGA and GEO databases. The CIBERSORT method was utilized to discern subtypes of tumor-infiltrating immune cells. Identification of MRGs was achieved through Pearson correlation analysis. A prognostic risk model for MRGs was developed using Cox and LASSO regression analyses. A tripartite gene signature comprising CD37, GABRD, and ARHGAP25 was an independent prognostic indicator and was employed to develop a risk score model. The internal and external validation cohort confirmed the results. The area under the ROC curve (AUC) was determined for survival periods of 1 year, three years, and five years, yielding values of 0.746, 0.839, and 0.850, respectively. The C-index of the risk score was found to be superior to clinicopathological factors. GO/KEGG enrichment showed that the differences between high- and low-risk groups were predominantly associated with immune response pathways. Immune-related analysis related to proportions of immune cells, immune function, and expression levels of immune checkpoint genes all showed differences between the high- and low-risk groups. The qRT-PCR and Western blotting results indicate that CD37 expression was markedly higher in MG63 and U2OS cell lines when compared to normal osteoblast hFOB1.19. In U2OS cell line, GABRD expression levels were significantly upregulated. ARHGAP25 expression levels were elevated in both 143B and U2OS cell lines. In summary, utilizing a macrophage genes signature demonstrates efficacy in predicting both the prognosis and therapy response of OS. Additionally, immune analysis confirms a correlation between the risk score and the tumor microenvironment. Our findings, therefore, provide a cogent account for the disparate prognoses observed among patients and furnish a justification for further inquiry into biomarkers and anti-tumor treatment strategies.

Cytokine Landscape in Central Nervous System Metastases

The central nervous system is the location of metastases in more than 40% of patients with lung cancer, breast cancer and melanoma. These metastases are associated with one of the poorest prognoses in advanced cancer patients, mainly due to the lack of effective treatments. In this review, we explore the involvement of cytokines, including interleukins and chemokines, during the development of brain and leptomeningeal metastases from the epithelial-to-mesenchymal cell transition and blood–brain barrier extravasation to the interaction between cancer cells and cells from the brain microenvironment, including astrocytes and microglia. Furthermore, the role of the gut–brain axis on cytokine release during this process will also be addressed.

Identifying Lung Cancer Cell Markers with Machine Learning Methods and Single-Cell RNA-Seq Data

Non-small cell lung cancer is a major lethal subtype of epithelial lung cancer, with high morbidity and mortality. The single-cell sequencing technique plays a key role in exploring the pathogenesis of non-small cell lung cancer. We proposed a computational method for distinguishing cell subtypes from the different pathological regions of non-small cell lung cancer on the basis of transcriptomic profiles, including a group of qualitative classification criteria (biomarkers) and various rules. The random forest classifier reached a Matthew’s correlation coefficient (MCC) of 0.922 by using 720 features, and the decision tree reached an MCC of 0.786 by using 1880 features. The obtained biomarkers and rules were analyzed in the end of this study.

Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy

Background

The incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death.

Methods

We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization.

Results

We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference.

Conclusion

A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.

The Network of Cytokines in Brain Metastases

Brain metastases are the most common of all intracranial tumors and a major cause of death in patients with cancer. Cytokines, including chemokines, interferons, interleukins, lymphokines, and tumor necrosis factors are key regulators in the formation of brain metastases. They regulate the infiltration of different cellular subsets into the tumor microenvironment and affect the therapeutic outcomes in patients. Elucidating the cancer cell-cytokine interactions in the setting of brain metastases is crucial for the development of more accurate diagnostics and efficacious therapies. In this review, we focus on cytokines that are found in the tumor microenvironment of brain metastases and elaborate on their trends of expression, regulation, and roles in cellular recruitment and tumorigenesis. We also explore how cytokines can alter the anti-tumor response in the context of brain metastases and discuss ways through which cytokine networks can be manipulated for diagnosis and treatment.

CD37 in B Cell Derived Tumors-More than Just a Docking Point for Monoclonal Antibodies

CD37 is a tetraspanin expressed prominently on the surface of B cells. It is an attractive molecular target exploited in the immunotherapy of B cell-derived lymphomas and leukemia. Currently, several monoclonal antibodies targeting CD37 as well as chimeric antigen receptor-based immunotherapies are being developed and investigated in clinical trials. Given the unique role of CD37 in the biology of B cells, it seems that CD37 constitutes more than a docking point for monoclonal antibodies, and targeting this molecule may provide additional benefit to relapsed or refractory patients. In this review, we aimed to provide an extensive overview of the function of CD37 in B cell malignancies, providing a comprehensive view of recent therapeutic advances targeting CD37 and delineating future perspectives.