A novel patient stratification strategy to enhance the therapeutic efficacy of dasatinib in glioblastoma

Integrative analysis of bulk RNA-seq and scRNA-seq data indicates the prognostic and immunologic values of SERPINH1 in glioma

BACKGROUND

SERPINH1 is abnormally expressed in multiple cancers and is associated with malignant progression. However, few reports detail its role in the etiopathogenesis of glioma. Hence, the aim of this article was to investigate the potential value of SERPINH1 in glioma using an integrative analysis.

METHODS

Data of RNA-seq and scRNA-seq was obtained and evaluated using online databases. The expression of SERPINH1 was confirmed by qRT-PCR and immunohistochemistry. The prognostic value of SERPINH1 was evaluated using univariate and multivariate Cox regression analyses. SERPINH1-related signaling pathways and the interaction of SERPINH1 with immunity were also investigated.

RESULTS

SERPINH1 exhibited a markedly elevated expression in glioma compared to normal brain tissues in the online databases. Similar results were confirmed by qRT-PCR and immunohistochemistry. SERPINH1 was found to be an independent prognosis factor, and high expression of SERPINH1 indicated poor survival. Moreover, a nomogram was constructed to predict prognosis more accurately and intuitively. GSEA analysis showed that SERPINH1 was involved in seven signaling pathways, including JAK-STAT pathway. Further analysis indicated SERPINH1 was significantly associated with immunity, especially in low-grade glioma. Additionally, an examination of scRNA-seq data revealed that SERPINH1 was primarily expressed in T cells of the CD4+ and CD8+ subsets.

CONCLUSIONS

SERPINH1 is a key biomarker of glioma prognosis and is immunologically relevant, which provides additional options for targeted therapy of glioma.

SERPINH1 promoted the proliferation and metastasis of colorectal cancer by activating PI3K/Akt/mTOR signaling pathway

BACKGROUND

Serpin peptidase inhibitor clade H member 1 (SERPINH1) was initially recognized as an oncogene implicated in various human malignancies. Nevertheless, the clinical relevance and functional implications of SERPINH1 in colorectal cancer (CRC) remain largely elusive.

AIM

To investigate the effects of SERPINH1 on CRC cells and its specific mechanism.

METHODS

Quantitative real-time polymerase chain reaction, western blotting analysis, The Cancer Genome Atlas data mining and immunohistochemistry were employed to examine SERPINH1 expression in CRC cell lines and tissues. A series of in-vitro assays were performed to demonstrate the function of SERPINH1 and its possible mechanisms in CRC.

RESULTS

SERPINH1 demonstrated elevated expression levels in both CRC cells and tissues, manifested at both mRNA and protein tiers. Elevated SERPINH1 levels correlated closely with advanced T stage, lymph node involvement, and distant metastasis, exhibiting a significant association with poorer overall survival among CRC patients. Subsequent investigations unveiled that SERPINH1 overexpression notably bolstered CRC cell proliferation, invasion, and migration in vitro, while conversely, SERPINH1 knockdown elicited the opposite effects. Gene set enrichment analysis underscored a correlation between SERPINH1 upregulation and genes associated with cell cycle regulation. Our findings underscored the capacity of heightened SERPINH1 levels to expedite G1/S phase cell cycle progression via phosphatidylinositol 3-kinase/AKT/mechanistic target of rapamycin pathway activation, thereby facilitating CRC cell invasion and migration.

CONCLUSION

These findings imply a crucial involvement of SERPINH1 in the advancement and escalation of CRC, potentially positioning it as a novel candidate for prognostic assessment and therapeutic intervention in CRC management.

Mechanistic insights and the clinical prospects of targeted therapies for glioblastoma: a comprehensive review

Glioblastoma (GBM) is a fatal brain tumour that is traditionally diagnosed based on histological features. Recent molecular profiling studies have reshaped the World Health Organization approach in the classification of central nervous system tumours to include more pathogenetic hallmarks. These studies have revealed that multiple oncogenic pathways are dysregulated, which contributes to the aggressiveness and resistance of GBM. Such findings have shed light on the molecular vulnerability of GBM and have shifted the disease management paradigm from chemotherapy to targeted therapies. Targeted drugs have been developed to inhibit oncogenic targets in GBM, including receptors involved in the angiogenic axis, the signal transducer and activator of transcription 3 (STAT3), the PI3K/AKT/mTOR signalling pathway, the ubiquitination-proteasome pathway, as well as IDH1/2 pathway. While certain targeted drugs showed promising results in vivo, the translatability of such preclinical achievements in GBM remains a barrier. We also discuss the recent developments and clinical assessments of targeted drugs, as well as the prospects of cell-based therapies and combinatorial therapy as novel ways to target GBM. Targeted treatments have demonstrated preclinical efficacy over chemotherapy as an alternative or adjuvant to the current standard of care for GBM, but their clinical efficacy remains hindered by challenges such as blood-brain barrier penetrance of the drugs. The development of combinatorial targeted therapies is expected to improve therapeutic efficacy and overcome drug resistance.

Molecular prognostic of nine parthanatos death-related genes in glioma, particularly in COL8A1 identification

Post-operative progression and chemotherapy resistance are the main causes of treatment failure in glioma patients. There is a lack of ideal prediction models for post-operative glioma patient progression and drug sensitivity. We aimed to develop a prognostic model of parthanatos mRNA biomarkers for glioma outcomes. A total of 11 parthanatos genes were obtained from ParthanatosCluster database. ConsensusClusterPlus and R "Limma" package were used to cluster The Cancer Genome Atlas (TCGA)-glioma cohort and analyze the differential mRNAs. Univariate Cox regression analysis, random survival forest model, and least absolute shrinkage and selection operator (LASSO) regression analysis were used to determine the nine ParthanatosScore prognostic genes combination. ParthanatosScore was verified by 656 patients and 979 patients in TCGA and CGCA-LGG/GBM datasets. Differences in genomic mutations, tumor microenvironments, and functional pathways were assessed. Drug response prediction was performed using pRRophetic. Kaplan-Meier survival analysis was analyzed. Finally, COL8A1 was selected to evaluate its potential biological function and drug sensitivity of temozolomide and AZD3759 in glioma cells. ParthanatosScore obtained a combination of nine glioma prognostic genes, including CD58, H19, TNFAIP6, FTLP3, TNFRSF11B, SFRP2, LOXL1, COL8A1, and FABP5P7. In the TCGA-LGG/GBM dataset, glioma prognosis was poor in high ParthanatosScore. Low-score glioma patients were sensitive to AZD3759_1915, AZD5582_1617, AZD8186_1918, Dasatinib_1079, and Temozolomide_1375, while high-score patients were less sensitive to these drugs. Compared with HA cells, COL8A1 was significantly over-expressed in LN229 and U251 cells. Silencing COL8A1 inhibited the malignant characterization of LN229 and U251 cells. Temozolomide and AZD3759 also promoted parthanatos gene expression in glioma cells. Temozolomide and AZD3759 inhibited COL8A1 expression and cell viability and promoted apoptosis in glioma cells and PGM cells. ParthanatosScore can accurately predict clinical prognosis and drug sensitivity after glioma surgery. Silencing COL8A1 inhibited the malignant characterization. Temozolomide and AZD3759 inhibited COL8A1 expression and cell viability and promoted apoptosis and parthanatos gene expression, which is a target to improve glioma.

Proteomic, Metabolomic, and Fatty Acid Profiling of Small Extracellular Vesicles from Glioblastoma Stem-Like Cells and Their Role in Tumor Heterogeneity

Glioblastoma is a deadly brain tumor for which there is no cure. The presence of glioblastoma stem-like cells (GSCs) contributes to the heterogeneous nature of the disease and makes developing effective therapies challenging. Glioblastoma cells have been shown to influence their environment by releasing biological nanostructures known as extracellular vesicles (EVs). Here, we investigated the role of GSC-derived nanosized EVs (<200 nm) in glioblastoma heterogeneity, plasticity, and aggressiveness, with a particular focus on their protein, metabolite, and fatty acid content. We showed that conditioned medium and small extracellular vesicles (sEVs) derived from cells of one glioblastoma subtype induced transcriptomic and proteomic changes in cells of another subtype. We found that GSC-derived sEVs are enriched in proteins playing a role in the transmembrane transport of amino acids, carboxylic acids, and organic acids, growth factor binding, and metabolites associated with amino acid, carboxylic acid, and sugar metabolism. This suggests a dual role of GSC-derived sEVs in supplying neighboring GSCs with valuable metabolites and proteins responsible for their transport. Moreover, GSC-derived sEVs were enriched in saturated fatty acids, while their respective cells were high in unsaturated fatty acids, supporting that the loading of biological cargos into sEVs is a highly regulated process and that GSC-derived sEVs could be sources of saturated fatty acids for the maintenance of glioblastoma cell metabolism. Interestingly, sEVs isolated from GSCs of the proneural and mesenchymal subtypes are enriched in specific sets of proteins, metabolites, and fatty acids, suggesting a molecular collaboration between transcriptionally different glioblastoma cells. In summary, this study revealed the complexity of GSC-derived sEVs and unveiled their potential contribution to tumor heterogeneity and critical cellular processes commonly deregulated in glioblastoma.

NRF2 connects Src tyrosine kinase to ferroptosis resistance in glioblastoma

Glioblastoma is a severe brain tumor characterized by an extremely poor survival rate of patients. Glioblastoma cancer cells escape to standard therapeutic protocols consisting of a combination of ionizing radiation and temozolomide alkylating drugs that trigger DNA damage by rewiring of signaling pathways. In recent years, the up-regulation of factors that counteract ferroptosis has been highlighted as a major driver of cancer resistance to ionizing radiation, although the molecular connection between the activation of oncogenic signaling and the modulation of ferroptosis has not been clarified yet. Here, we provide the first evidence for a molecular connection between the constitutive activation of tyrosine kinases and resistance to ferroptosis. Src tyrosine kinase, a central hub on which deregulated receptor tyrosine kinase signaling converge in cancer, leads to the stabilization and activation of NRF2 pathway, thus promoting resistance to ionizing radiation-induced ferroptosis. These data suggest that the up-regulation of the Src-NRF2 axis may represent a vulnerability for combined strategies that, by targeting ferroptosis resistance, enhance radiation sensitivity in glioblastoma.

Diffusion tensor imaging-based machine learning for IDH wild-type glioblastoma stratification to reveal the biological underpinning of radiomic features

INTRODUCTION

This study addresses the lack of systematic investigation into the prognostic value of hand-crafted radiomic features derived from diffusion tensor imaging (DTI) in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM), as well as the limited understanding of the biological interpretation of individual DTI radiomic features and metrics.

AIMS

To develop and validate a DTI-based radiomic model for predicting prognosis in patients with IDH wild-type GBM and reveal the biological underpinning of individual DTI radiomic features and metrics.

RESULTS

The DTI-based radiomic signature was an independent prognostic factor (p < 0.001). Incorporating the radiomic signature into a clinical model resulted in a radiomic-clinical nomogram that predicted survival better than either the radiomic model or clinical model alone, with a better calibration and classification accuracy. Four categories of pathways (synapse, proliferation, DNA damage response, and complex cellular functions) were significantly correlated with the DTI-based radiomic features and DTI metrics.

CONCLUSION

The prognostic radiomic features derived from DTI are driven by distinct pathways involved in synapse, proliferation, DNA damage response, and complex cellular functions of GBM.

Machine learning-based identification of lower grade glioma stemness subtypes discriminates patient prognosis and drug response

Glioma stem cells (GSCs) remodel their tumor microenvironment to sustain a supportive niche. Identification and stratification of stemness related characteristics in patients with glioma might aid in the diagnosis and treatment of the disease. In this study, we calculated the mRNA stemness index in bulk and single-cell RNA-sequencing datasets using machine learning methods and investigated the correlation between stemness and clinicopathological characteristics. A glioma stemness-associated score (GSScore) was constructed using multivariate Cox regression analysis. We also generated a GSC cell line derived from a patient diagnosed with glioma and used glioma cell lines to validate the performance of the GSScore in predicting chemotherapeutic responses. Differentially expressed genes (DEGs) between GSCs with high and low GSScores were used to cluster lower-grade glioma (LGG) samples into three stemness subtypes. Differences in clinicopathological characteristics, including survival, copy number variations, mutations, tumor microenvironment, and immune and chemotherapeutic responses, among the three LGG stemness-associated subtypes were identified. Using machine learning methods, we further identified genes as subtype predictors and validated their performance using the CGGA datasets. In the current study, we identified a GSScore that correlated with LGG chemotherapeutic response. Through the score, we also identified a novel classification of the LGG subtype and associated subtype predictors, which might facilitate the development of precision therapy.

Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells

Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.

Identification and Biological Characterization of the Pyrazolo[3,4-d]pyrimidine Derivative SI388 Active as Src Inhibitor

Src is a non-receptor tyrosine kinase (TK) whose involvement in cancer, including glioblastoma (GBM), has been extensively demonstrated. In this context, we started from our in-house library of pyrazolo[3,4-d]pyrimidines that are active as Src and/or Bcr-Abl TK inhibitors and performed a lead optimization study to discover a new generation derivative that is suitable for Src kinase targeting. We synthesized a library of 19 compounds, 2a-s. Among these, compound 2a (SI388) was identified as the most potent Src inhibitor. Based on the cell-free results, we investigated the effect of SI388 in 2D and 3D GBM cellular models. Interestingly, SI388 significantly inhibits Src kinase, and therefore affects cell viability, tumorigenicity and enhances cancer cell sensitivity to ionizing radiation.

Proteomic analysis reveals microvesicles containing NAMPT as mediators of radioresistance in glioma

Tumor-initiating cells contained within the aggressive brain tumor glioma (glioma stem cells, GSCs) promote radioresistance and disease recurrence. However, mechanisms of resistance are not well understood. Herein, we show that the proteome-level regulation occurring upon radiation treatment of several patient-derived GSC lines predicts their resistance status, whereas glioma transcriptional subtypes do not. We identify a mechanism of radioresistance mediated by the transfer of the metabolic enzyme NAMPT to radiosensitive cells through microvesicles (NAMPT-high MVs) shed by resistant GSCs. NAMPT-high MVs rescue the proliferation of radiosensitive GSCs and fibroblasts upon irradiation, and upon treatment with a radiomimetic drug or low serum, and increase intracellular NAD(H) levels. Finally, we show that the presence of NAMPT within the MVs and its enzymatic activity in recipient cells are necessary to mediate these effects. Collectively, we demonstrate that the proteome of GSCs provides unique information as it predicts the ability of glioma to resist radiation treatment. Furthermore, we establish NAMPT transfer via MVs as a mechanism for rescuing the proliferation of radiosensitive cells upon irradiation.

Overexpression of heat-shock protein 47 impacts survival of patients with oral squamous cell carcinoma

BACKGROUND

The expression of heat-shock protein 47 (HSP47) has been linked to collagen synthesis control and implicated in fibrotic disorders, but more recent studies have demonstrated its role in solid tumors. In this study, we explored the prognostic impact of HSP47 in oral squamous cell carcinomas (OSCC) and determined the in vitro effects of its loss-of-function on viability, proliferation, migration, invasion, and resistance to cisplatin of OSCC cells.

METHODS

The HSP47 expression in tumor samples was assessed by immunohistochemistry in two independent cohorts totaling 339 patients with OSCC, and protein levels were associated with clinicopathological features and survival outcomes. The OSCC cell lines HSC3 and SCC9 were transduced with lentivirus expressing short hairpin RNA to stably silence HSP47 and used in assays to measure cellular viability, proliferation, migration, and invasion.

RESULTS

HSP47 was overexpressed in OSCC samples, and its overexpression was significantly and independently associated with poor disease-specific survival and shortened disease-free survival in both OSCC cohorts. The knockdown of HSP47 showed no effects on cell viability or cisplatin sensitivity, but impaired significantly proliferation, migration, and invasion of OSCC cells, with stronger effects on SCC9 cells.

CONCLUSION

Our results show a significant prognostic impact of HSP47 overexpression in OSCC and reveal that HSP47 inhibition impairs the proliferation, migration, and invasion of OSCC cells. HSP47 may represent a potential therapeutic target for OSCC.

Inference of glioblastoma migration and proliferation rates using single time-point images

Cancer cell migration is a driving mechanism of invasion in solid malignant tumors. Anti-migratory treatments provide an alternative approach for managing disease progression. However, we currently lack scalable screening methods for identifying novel anti-migratory drugs. To this end, we develop a method that can estimate cell motility from single end-point images in vitro by estimating differences in the spatial distribution of cells and inferring proliferation and diffusion parameters using agent-based modeling and approximate Bayesian computation. To test the power of our method, we use it to investigate drug responses in a collection of 41 patient-derived glioblastoma cell cultures, identifying migration-associated pathways and drugs with potent anti-migratory effects. We validate our method and result in both in silico and in vitro using time-lapse imaging. Our proposed method applies to standard drug screen experiments, with no change needed, and emerges as a scalable approach to screen for anti-migratory drugs.

Caspase-8 as a novel mediator linking Src kinase signaling to enhanced glioblastoma malignancy

Caspase-8 is a cysteine protease that plays an essential role in apoptosis. Consistently with its canonical proapoptotic function, cancer cells may genetically or epigenetically downregulate its expression. Unexpectedly, Caspase-8 is often retained in cancer, suggesting the presence of alternative mechanisms that may be exploited by cancer cells to their own benefit. In this regard, we reported that Src tyrosine kinase, which is aberrantly activated in many tumors, promotes Caspase-8 phosphorylation on Tyrosine 380 (Y380) preventing its full activation. Here, we investigated the significance of Caspase-8 expression and of its phosphorylation on Y380 in glioblastoma, a brain tumor where both Caspase-8 expression and Src activity are often aberrantly upregulated. Transcriptomic analyses identified inflammatory response as a major target of Caspase-8, and in particular, NFκB signaling as one of the most affected pathways. More importantly, we could show that Src-dependent phosphorylation of Caspase-8 on Y380 drives the assembly of a multiprotein complex that triggers NFκB activation, thereby inducing the expression of inflammatory and pro-angiogenic factors. Remarkably, phosphorylation on Y380 sustains neoangiogenesis and resistance to radiotherapy. In summary, our work identifies a novel interplay between Src kinase and Caspase-8 that allows cancer cells to hijack Caspase-8 to sustain tumor growth.

Integrative genomic analysis facilitates precision strategies for glioblastoma treatment

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

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A multiomics-based classification of GBM was established

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Single-cell transcriptomic profiling of GBM subclasses was revealed using Scissor

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A robust prognostic risk model was developed for GBM by machine learning method

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Prediction of potential agents based on molecular and prognostic risk stratification

Collagen Remodeling along Cancer Progression Providing a Novel Opportunity for Cancer Diagnosis and Treatment

The extracellular matrix (ECM) is a significant factor in cancer progression. Collagens, as the main component of the ECM, are greatly remodeled alongside cancer development. More and more studies have confirmed that collagens changed from a barrier to providing assistance in cancer development. In this course, collagens cause remodeling alongside cancer progression, which in turn, promotes cancer development. The interaction between collagens and tumor cells is complex with biochemical and mechanical signals intervention through activating diverse signal pathways. As the mechanism gradually clears, it becomes a new target to find opportunities to diagnose and treat cancer. In this review, we investigated the process of collagen remodeling in cancer progression and discussed the interaction between collagens and cancer cells. Several typical effects associated with collagens were highlighted in the review, such as fibrillation in precancerous lesions, enhancing ECM stiffness, promoting angiogenesis, and guiding invasion. Then, the values of cancer diagnosis and prognosis were focused on. It is worth noting that several generated fragments in serum were reported to be able to be biomarkers for cancer diagnosis and prognosis, which is beneficial for clinic detection. At a glance, a variety of reported biomarkers were summarized. Many collagen-associated targets and drugs have been reported for cancer treatment in recent years. The new targets and related drugs were discussed in the review. The mass data were collected and classified by mechanism. Overall, the interaction of collagens and tumor cells is complicated, in which the mechanisms are not completely clear. A lot of collagen-associated biomarkers are excavated for cancer diagnosis. However, new therapeutic targets and related drugs are almost in clinical trials, with merely a few in clinical applications. So, more efforts are needed in collagens-associated studies and drug development for cancer research and treatment.

Propofol enhances stem-like properties of glioma via GABAAR-dependent Src modulation of ZDHHC5-EZH2 palmitoylation mechanism

Background

Propofol is a commonly used anesthetic. However, its effects on glioma growth and recurrence remain largely unknown.

Methods

The effect of propofol on glioma growth was demonstrated by a series of in vitro and in vivo experiments (spheroidal formation assay, western blotting, and xenograft model). The acyl-biotin exchange method and liquid chromatography-mass spectrometry assays identified palmitoylation proteins mediated by the domain containing the Asp-His-His-Cys family. Western blotting, co-immunoprecipitation, quantitative real-time polymerase chain reaction, co-immunoprecipitation, chromatin immunoprecipitation, and luciferase reporter assays were used to explore the mechanisms of the γ-aminobutyric acid receptor (GABA_AR)/Src/ZDHHC5/EZH2 signaling axis in the effects of propofol on glioma stem cells (GSCs).

Results

We found that treatment with a standard dose of propofol promoted glioma growth in nude mice compared with control or low-dose propofol. Propofol-treated GSCs also led to larger tumor growth in nude mice than did vector-treated tumors. Mechanistically, propofol enhances the stem-like properties of gliomas through GABA_AR to increase Src expression, thereby enhancing the palmitoylation of ZDHHC5-mediated EZH2 and Oct4 expression.

Conclusion

These results demonstrate that propofol may promote glioma growth through the GABA_AR-Src-ZDHHC5-EZH2 mechanism and are helpful in guiding the clinical use of propofol to obtain a better patient prognosis after the surgical resection of tumors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-022-03087-5.

Anti-Survival Effect of SI306 and Its Derivatives on Human Glioblastoma Cells

Glioblastoma (GBM) is the most common adult brain tumor and, although many efforts have been made to find valid therapies, the onset of resistance is the main cause of recurrence. Therefore, it is crucial to identify and target the molecular mediators responsible for GBM malignancy. In this context, the use of Src inhibitors such as SI306 (C1) and its prodrug (C2) showed promising results, suggesting that SI306 could be the lead compound useful to derivate new anti-GBM drugs. Therefore, a new prodrug of SI306 (C3) was synthesized and tested on CAS-1 and U87 human GBM cells by comparing its effect to that of C1 and C2. All compounds were more effective on CAS-1 than U87 cells, while C2 was the most active on both cell lines. Moreover, the anti-survival effect was associated with a reduction in the expression of epidermal growth factor receptor (EGFR)WT and EGFR-vIII in U87 and CAS-1 cells, respectively. Collectively, our findings demonstrate that all tested compounds are able to counteract GBM survival, further supporting the role of SI306 as progenitor of promising new drugs to treat malignant GBM.

In Vitro Validation of the Therapeutic Potential of Dendrimer-Based Nanoformulations against Tumor Stem Cells

Tumor cells with stem cell properties are considered to play major roles in promoting the development and malignant behavior of aggressive cancers. Therapeutic strategies that efficiently eradicate such tumor stem cells are of highest clinical need. Herein, we performed the validation of the polycationic phosphorus dendrimer-based approach for small interfering RNAs delivery in in vitro stem-like cells as models. As a therapeutic target, we chose Lyn, a member of the Src family kinases as an example of a prominent enzyme class widely discussed as a potent anti-cancer intervention point. Our selection is guided by our discovery that Lyn mRNA expression level in glioma, a class of brain tumors, possesses significant negative clinical predictive value, promoting its potential as a therapeutic target for future molecular-targeted treatments. We then showed that anti-Lyn siRNA, delivered into Lyn-expressing glioma cell model reduces the cell viability, a fact that was not observed in a cell model that lacks Lyn-expression. Furthermore, we have found that the dendrimer itself influences various parameters of the cells such as the expression of surface markers PD-L1, TIM-3 and CD47, targets for immune recognition and other biological processes suggested to be regulating glioblastoma cell invasion. Our findings prove the potential of dendrimer-based platforms for therapeutic applications, which might help to eradicate the population of cancer cells with augmented chemotherapy resistance. Moreover, the results further promote our functional stem cell technology as suitable component in early stage drug development.

Detection of Label-Free Drugs within Brain Tissue Using Orbitrap Secondary Ion Mass Spectrometry as a Complement to Neuro-Oncological Drug Delivery

Historically, pre-clinical neuro-oncological drug delivery studies have exhaustively relied upon overall animal survival as an exclusive measure of efficacy. However, with no adopted methodology to both image and quantitate brain parenchyma penetration of label-free drugs, an absence of efficacy typically hampers clinical translational potential, rather than encourage re-formulation of drug compounds using nanocarriers to achieve greater tissue penetration. OrbiSIMS, a next-generation analytical instrument for label-free imaging, combines the high resolving power of an OrbiTrapTM mass spectrometer with the relatively high spatial resolution of secondary ion mass spectrometry. Here, we develop an ex vivo pipeline using OrbiSIMS to accurately detect brain penetration of drug compounds. Secondary ion spectra were acquired for a panel of drugs (etoposide, olaparib, gemcitabine, vorinostat and dasatinib) under preclinical consideration for the treatment of isocitrate dehydrogenase-1 wild-type glioblastoma. Each drug demonstrated diagnostic secondary ions (all present molecular ions [M-H]− which could be discriminated from brain analytes when spiked at >20 µg/mg tissue. Olaparib/dasatinib and olaparib/etoposide dual combinations are shown as exemplars for the capability of OrbiSIMS to discriminate distinct drug ions simultaneously. Furthermore, we demonstrate the imaging capability of OrbiSIMS to simultaneously illustrate label-free drug location and brain chemistry. Our work encourages the neuro-oncology community to consider mass spectrometry imaging modalities to complement in vivo efficacy studies, as an analytical tool to assess brain distribution of systemically administered drugs, or localised brain penetration of drugs released from micro- or nano-scale biomaterials.