Influence of tumour micro-environment heterogeneity on therapeutic response

Preclinical glioma models in neuro-oncology: enhancing translational research

PURPOSE OF REVIEW

Gliomas represent approximately 25% of all primary brain and other central nervous system (CNS) tumors and 81% of malignant tumors. Unfortunately, standard treatment approaches for most CNS cancers have shown limited improvement in patient survival rates.

RECENT FINDINGS

The current drug development process has been plagued by high failure rates, leading to a shift towards human disease models in biomedical research. Unfortunately, suitable preclinical models for brain tumors have been lacking, hampering our understanding of tumor initiation processes and the discovery of effective treatments. In this review, we will explore the diverse preclinical models employed in neuro-oncology research and their contributions to translational science.

SUMMARY

By utilizing a combination of these preclinical models and fostering interdisciplinary collaborations, researchers can deepen their understanding of glioma brain tumors and develop novel therapeutic strategies to combat these devastating diseases. These models offer promising prospects for personalized and efficacious treatments for these challenging malignancies. Although it is unrealistic to fully replicate the complexity of the human body in vitro, the ultimate goal should be to achieve the closest possible resemblance to the clinical context.

Inflammation-related signature for prognostic prediction, tumor immune, genomic heterogeneity, and drug choices in prostate cancer: Integrated analysis of bulk and single-cell RNA-sequencing

BACKGROUND

Prostate cancer (PCa) ranks as the second most prevalent malignancy among males on a global scale. Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear.

METHODS

Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data.

RESULTS

We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection.

CONCLUSION

IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.

Cancer-Associated Fibroblast-Induced Remodeling of Tumor Microenvironment in Recurrent Bladder Cancer

Bladder carcinoma (BC) recurrence is a major clinical challenge, and targeting the tumor microenvironment (TME) is a promising therapy. However, the relationship between individual TME components, particularly cancer-associated fibroblasts (CAFs), and tumor recurrence is unclear. Here, TME heterogeneity in primary and recurrent BC is investigated using single-cell RNA sequence profiling of 62 460 cells. Two cancer stem cell (CSC) subtypes are identified in recurrent BC. An inflammatory CAF subtype, ICAM1+ iCAFs, specifically associated with BC recurrence is also identified. iCAFs are found to secrete FGF2, which acts on the CD44 receptor of rCSC-M, thereby maintaining tumor stemness and epithelial-mesenchymal transition. Additionally, THBS1+ monocytes, a group of myeloid-derived suppressor cells (MDSCs), are enriched in recurrent BC and interacted with CAFs. ICAM1+ iCAFs are found to secrete CCL2, which binds to CCR2 in MDSCs. Moreover, elevated STAT3, NFKB2, VEGFA, and CTGF levels in iCAFs reshape the TME in recurrent tumors. CCL2 inhibition in an in situ BC mouse model suppressed tumor growth, decreased MDSCs and Tregs, and fostered tumor immune suppression. The study results highlight the role of iCAFs in TME cell-cell crosstalk during recurrent BC. The identification of pivotal signaling factors driving BC relapse is promising for the development of novel therapies.

Multidimensional outlook on the pathophysiology of cervical cancer invasion and metastasis

Cervical cancer being one of the primary causes of high mortality rates among women is an area of concern, especially with ineffective treatment strategies. Extensive studies are carried out to understand various aspects of cervical cancer initiation, development and progression; however, invasive cervical squamous cell carcinoma has poor outcomes. Moreover, the advanced stages of cervical cancer may involve lymphatic circulation with a high risk of tumor recurrence at distant metastatic sites. Dysregulation of the cervical microbiome by human papillomavirus (HPV) together with immune response modulation and the occurrence of novel mutations that trigger genomic instability causes malignant transformation at the cervix. In this review, we focus on the major risk factors as well as the functionally altered signaling pathways promoting the transformation of cervical intraepithelial neoplasia into invasive squamous cell carcinoma. We further elucidate genetic and epigenetic variations to highlight the complexity of causal factors of cervical cancer as well as the metastatic potential due to the changes in immune response, epigenetic regulation, DNA repair capacity, and cell cycle progression. Our bioinformatics analysis on metastatic and non-metastatic cervical cancer datasets identified various significantly and differentially expressed genes as well as the downregulation of potential tumor suppressor microRNA miR-28-5p. Thus, a comprehensive understanding of the genomic landscape in invasive and metastatic cervical cancer will help in stratifying the patient groups and designing potential therapeutic strategies.

Habitat-based radiomics enhances the ability to predict lymphovascular space invasion in cervical cancer: a multi-center study

Introduction

Lymphovascular space invasion (LVSI) is associated with lymph node metastasis and poor prognosis in cervical cancer. In this study, we investigated the potential of radiomics, derived from magnetic resonance (MR) images using habitat analysis, as a non-invasive surrogate biomarker for predicting LVSI in cervical cancer.

Methods

This retrospective study included 300 patients with cervical cancer who underwent surgical treatment at two centres (centre 1 = 198 and centre 2 = 102). Using the k-means clustering method, contrast-enhanced T1-weighted imaging (CE-T1WI) images were segmented based on voxel and entropy values, creating sub-regions within the volume ofinterest. Radiomics features were extracted from these sub-regions. Pearson correlation coefficient and least absolute shrinkage and selection operator LASSO) regression methods were used to select features associated with LVSI in cervical cancer. Support vector machine (SVM) model was developed based on the radiomics features extracted from each sub-region in the training cohort.

Results

The voxels and entropy values of the CE-T1WI images were clustered into three sub-regions. In the training cohort, the AUCs of the SVM models based on radiomics features derived from the whole tumour, habitat 1, habitat 2, and habitat 3 models were 0.805 (95% confidence interval [CI]: 0.745-0.864), 0.873(95% CI: 0.824-0.922), 0.869 (95% CI: 0.821-0.917), and 0.870 (95% CI: 0.821-0.920), respectively. Compared with whole tumour model, the predictive performances of habitat 3 model was the highest in the external test cohort (0.780 [95% CI: 0.692-0.869]).

Conclusions

The radiomics model based on the tumour sub-regional habitat demonstrated superior predictive performance for an LVSI in cervical cancer than that of radiomics model derived from the whole tumour.

Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy

OBJECTIVES

This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.

MATERIALS AND METHODS

In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients).

RESULTS

TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40-3.67); PFS: (HR (95%), 2.23 (1.36-3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70-0.79) and 0.72 (0.67-0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68-0.81) and 0.72 (0.66-0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS.

CONCLUSION

By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.

Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors

BACKGROUND

Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay.

RESULTS

We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method.

CONCLUSIONS

Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

Elastin-like Recombinamer Hydrogels as Platforms for Breast Cancer Modeling

The involvement of the extracellular matrix (ECM) in tumor progression has motivated the development of biomaterials mimicking the tumor ECM to develop more predictive cancer models. Particularly, polypeptides based on elastin could be an interesting approach to mimic the ECM due to their tunable properties. Here, we demonstrated that elastin-like recombinamer (ELR) hydrogels can be suitable biomaterials to develop breast cancer models. This hydrogel was formed by two ELR polypeptides, one containing sequences biodegradable by matrix metalloproteinase and cyclooctyne and the other carrying arginylglycylaspartic acid and azide groups to allow cell adhesion, biodegradability, and suitable stiffness through "click-chemistry" cross-linking. Our findings show that breast cancer or nontumorigenic breast cells showed high viability and cell proliferation for up to 7 days. MCF7 and MCF10A formed spheroids whereas MDA-MB-231 formed cell networks, with the expression of ECM and high drug resistance in all cases, evidencing that ELR hydrogels are a promising biomaterial for breast cancer modeling.

Predicting response to CCRT for esophageal squamous carcinoma by a radiomics-clinical SHAP model

BACKGROUND

Radical concurrent chemoradiotherapy (CCRT) is frequently used as the first-line treatment for patients with locally advanced esophageal cancer. Unfortunately, some patients respond poorly. To predict response to radical concurrent chemoradiotherapy in pre-treatment patients with esophageal squamous carcinoma (ESCC), and compare the predicting efficacies of radiomics features of primary tumor with or without regional lymph nodes, we developed a radiomics-clinical model based on the positioning CT images. Finally, SHapley Additive exPlanation (SHAP) was used to explain the models.

METHODS

This retrospective study enrolled 105 patients with medically inoperable and/or unresectable ESCC who underwent radical concurrent chemoradiotherapy (CCRT) between October 2018 and May 2023. Patients were classified into responder and non-responder groups with RECIST standards. The 11 recently admitted patients were chosen as the validation set, previously admitted patients were randomly split into the training set (n = 70) and the testing set (n = 24). Primary tumor site (GTV), the primary tumor and the uninvolved lymph nodes at risk of microscopic disease (CTV) were identified as Regions of Interests (ROIs). 1762 radiomics features from GTV and CTV were respectively extracted and then filtered by statistical differential analysis and Least Absolute Shrinkage and Selection Operator (LASSO). The filtered radiomics features combined with 13 clinical features were further filtered with Mutual Information (MI) algorithm. Based on the filtered features, we developed five models (Clinical Model, GTV Model, GTV-Clinical Model, CTV Model, and CTV-Clinical Model) using the random forest algorithm and evaluated for their accuracy, precision, recall, F1-Score and AUC. Finally, SHAP algorithm was adopted for model interpretation to achieve transparency and utilizability.

RESULTS

The GTV-Clinical model achieves an AUC of 0.82 with a 95% confidence interval (CI) of 0.76-0.99 on testing set and an AUC of 0.97 with a 95% confidence interval (CI) of 0.84-1.0 on validation set, which are significantly higher than those of other models in predicting ESCC response to CCRT. The SHAP force map provides an integrated view of the impact of each feature on individual patients, while the SHAP summary plots indicate that radiomics features have a greater influence on model prediction than clinical factors in our model.

CONCLUSION

GTV-Clinical model based on texture features and the maximum diameter of lesion (MDL) may assist clinicians in pre-treatment predicting ESCC response to CCRT.

The role of bacteria and its derived biomaterials in cancer radiotherapy

Bacteria-mediated anti-tumor therapy has received widespread attention due to its natural tumor-targeting ability and specific immune-activation characteristics. It has made significant progress in breaking the limitations of monotherapy and effectively eradicating tumors, especially when combined with traditional therapy, such as radiotherapy. According to their different biological characteristics, bacteria and their derivatives can not only improve the sensitivity of tumor radiotherapy but also protect normal tissues. Moreover, genetically engineered bacteria and bacteria-based biomaterials have further expanded the scope of their applications in radiotherapy. In this review, we have summarized relevant researches on the application of bacteria and its derivatives in radiotherapy in recent years, expounding that the bacteria, bacterial derivatives and bacteria-based biomaterials can not only directly enhance radiotherapy but also improve the anti-tumor effect by improving the tumor microenvironment (TME) and immune effects. Furthermore, some probiotics can also protect normal tissues and organs such as intestines from radiation via anti-inflammatory, anti-oxidation and apoptosis inhibition. In conclusion, the prospect of bacteria in radiotherapy will be very extensive, but its biological safety and mechanism need to be further evaluated and studied.

Netrin-1 blockade inhibits tumor associated Myeloid-derived suppressor cells, cancer stemness and alleviates resistance to chemotherapy and immune checkpoint inhibitor

Drug resistance and cancer relapse represent significant therapeutic challenges after chemotherapy or immunotherapy, and a major limiting factor for long-term cancer survival. Netrin-1 was initially identified as a neuronal navigation cue but has more recently emerged as an interesting target for cancer therapy, which is currently clinically investigated. We show here that netrin-1 is an independent prognostic marker for clinical progression of breast and ovary cancers. Cancer stem cells (CSCs)/Tumor initiating cells (TICs) are hypothesized to be involved in clinical progression, tumor relapse and resistance. We found a significant correlation between netrin-1 expression and cancer stem cell (CSC) markers levels. We also show in different mice models of resistance to chemotherapies that netrin-1 interference using a therapeutic netrin-1 blocking antibody alleviates resistance to chemotherapy and triggers an efficient delay in tumor relapse and this effect is associated with CSCs loss. We also demonstrate that netrin-1 interference limits tumor resistance to immune checkpoint inhibitor and provide evidence linking this enhanced anti-tumor efficacy to a decreased recruitment of a subtype of myeloid-derived suppressor cells (MDSCs) called polymorphonuclear (PMN)-MDSCs. We have functionally demonstrated that these immune cells promote CSCs features and, consequently, resistance to anti-cancer treatments. Together, these data support the view of both a direct and indirect contribution of netrin-1 to cancer stemness and we propose that this may lead to therapeutic opportunities by combining conventional chemotherapies and immunotherapies with netrin-1 interfering drugs.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

Blocking LTB4 signaling-mediated TAMs recruitment by Rhizoma Coptidis sensitizes lung cancer to immunotherapy

BACKGROUND

Immune checkpoint blockade (ICB) induces durable immune responses across a spectrum of advanced cancers and revolutionizes the oncology field. However, only a subset of patients achieves long-lasting clinical benefits. Tumor-associated macrophages (TAMs) usually secrete immunosuppressive cytokines and contribute to the failure of ICB therapy. Therefore, it is crucial to mechanically manipulate the abundance and function of TAMs in the tumor microenvironment (TME), which can offer a promising molecular basis to improve the clinical response efficacy of ICB in cancer patients.

PURPOSE

This study aims to investigate TAMs in the immunosuppressive microenvironment to identify new therapeutic targets, improve the ability to predict and guide responses to clinical immunotherapy, and develop new strategies for immunotherapy of lung tumors.

METHODS

Lewis lung carcinoma (LLC) xenograft-bearing mouse models were established to analyze the antitumor activity of Rhizoma Coptidis (RC) in vivo. A systems pharmacology strategy was used to predict the correlation between RC and M2 macrophages. The effect of RC on the abundance of M2 macrophages was analyzed by flow cytometry of murine samples. Western blot was performed to analyze the expression of Leukotriene A4 hydrolase (LTA4H) and LTB4 receptor 1 (BLT1) in harvested lung cancer tissues. The impact of blocking leukotriene B4 (LTB4) signaling by RC on the recruitment of M2 macrophages was assessed in vitro and in vivo. Transwell migration assays were conducted to clarify the inhibition of macrophage migration by blocking LTB4. Lta4h-/- mice were used to investigate the sensitivity of immunotherapy to lung cancer by blocking the LTB4 signaling.

RESULTS

Here, we report that RC, an herbal medicine from the family Ranunculaceae, suppresses the recruitment and immunosuppressive function of TAMs, which in turn sensitizes lung cancer to ICB therapy. Firstly, a systems pharmacology strategy was proposed to identify combinatorial drugs for ICB therapy with a systems biology perspective of drug-target-pathway-TME phenotype. We predicted and verified that RC significantly inhibits tumor growth and the infiltration of M2-TAMs into TME of LLC tumor-bearing mice. Then, RC inhibits the recruitment of macrophages to the tumor TME via blocking LTB4 signaling, and suppresses the expression of immunosuppressive factors (IL-10, TGF-β and VEGF). As a result, RC enables CD8+ T cells to retain their proliferative and infiltrative abilities within the TME. Ultimately, these events promote cytotoxic T-cell-mediated clearance of tumor cells, which is further enhanced by the addition of anti-PD-L1 therapy. Furthermore, we employed LTA4H deficient mice (Lta4h-/- mice) to evaluate the antitumor efficiency, the results showed that the efficacy of immunotherapy was enhanced due to the synergistic effect of LTB4 signaling blockage and ICB inhibition, leading to remarkable inhibition of tumor growth in a mouse model of lung adenocarcinoma.

CONCLUSIONS

Taken together, these findings suggest that RC enhances antitumor immunity, providing a rationale for combining RC with immunotherapies as a potential anti-cancer treatment strategy.

Cytoreductive surgery with or without hyperthermic intraperitoneal chemotherapy in patients with advanced ovarian cancer (OVHIPEC-1): final survival analysis of a randomised, controlled, phase 3 trial

BACKGROUND

The OVHIPEC-1 trial previously showed that the addition of hyperthermic intraperitoneal chemotherapy (HIPEC) to interval cytoreductive surgery resulted in improved progression-free and overall survival compared with cytoreductive surgery alone at 4·7 years of follow-up in patients with stage III epithelial ovarian cancer who were ineligible for primary cytoreduction. We report the final survival outcomes after 10 years of follow-up.

METHODS

In this open-label, randomised, controlled, phase 3 trial, patients with primary epithelial stage III ovarian cancer were recruited at eight HIPEC centres in the Netherlands and Belgium. Patients were eligible if they were aged 18-76 years, had not progressed during at least three cycles of neoadjuvant carboplatin plus paclitaxel, had a WHO performance status score of 0-2, normal blood counts, and adequate renal function. Patients were randomly assigned (1:1) to undergo interval cytoreductive surgery without HIPEC (surgery group) or with HIPEC (100 mg/m2 cisplatin; surgery-plus-HIPEC group). Randomisation was done centrally by minimisation with a masked web-based allocation procedure at the time of surgery when residual disease smaller than 10 mm diameter was anticipated, and was stratified by institution, previous suboptimal cytoreductive surgery, and number of abdominal regions involved. The primary endpoint was progression-free survival and a secondary endpoint was overall survival, analysed in the intention-to-treat population (ie, all randomly assigned patients). This study is registered with ClinicalTrials.gov, NCT00426257, and is closed.

FINDINGS

Between April 1, 2007, and April 30, 2016, 245 patients were enrolled and followed up for a median of 10·1 years (95% CI 8·4-12·9) in the surgery group (n=123) and 10·4 years (95% CI 9·5-13·3) in the surgery-plus-HIPEC group (n=122). Recurrence, progression, or death occurred in 114 (93%) patients in the surgery group (median progression-free survival 10·7 months [95% CI 9·6-12·0]) and 109 (89%) patients in the surgery-plus-HIPEC group (14·3 months [12·0-18·5]; hazard ratio [HR] 0·63 [95% CI 0·48-0·83], stratified log-rank p=0·0008). Death occurred in 108 (88%) patients in the surgery group (median overall survival 33·3 months [95% CI 29·0-39·1]) and 100 (82%) patients in the surgery-plus-HIPEC group (44·9 months [95% CI 38·6-55·1]; HR 0·70 [95% CI 0·53-0·92], stratified log-rank p=0·011).

INTERPRETATION

These updated survival results confirm the long-term survival benefit of HIPEC in patients with primary stage III epithelial ovarian cancer undergoing interval cytoreductive surgery.

FUNDING

Dutch Cancer Foundation (KWF Kankerbestrijding).

Bioreducible exosomes encapsulating glycolysis inhibitors potentiate mitochondria-targeted sonodynamic cancer therapy via cancer-targeted drug release and cellular energy depletion

Nanocarrier-assisted sonodynamic therapy (SDT) has shown great potential for the effective and targeted treatment of deep-seated tumors by overcoming the critical limitations of sonosensitizers. However, in vivo SDT using nanocarriers is still constrained by their intrinsic toxicity and nonspecific cargo release. In this study, we developed bioreducible exosomes for the safe and tumor-specific delivery of mitochondria-targeting sonosensitizers [triphenylphosphonium-conjugated chlorin e6 (T-Ce6)] and glycolysis inhibitors (FX11). Redox-cleavable diselenide linker-bearing lipids were embedded into exosomes to trigger drug release in response to overexpressed glutathione in the tumor microenvironment. Bioreducible exosomes facilitate the cytoplasmic release of their payload in the reducing environment of tumor cells. They significantly enhance drug release and sonodynamic effects when irradiated with ultrasound (US). The mitochondria-targeted accumulation of T-Ce6 efficiently damaged the mitochondria of the cells under US irradiation, accelerating apoptotic cell death. FX11 substantially inhibited cellular energy metabolism, potentiating the antitumor efficacy of mitochondria-targeted SDT. Bioreducible exosomes effectively suppressed tumor growth in mice without significant systemic toxicity, via a combination of mitochondria-targeted SDT and energy metabolism-targeted therapy. This study offers new insights into the use of dual stimuli-responsive exosomes encapsulating sonosensitizers for safe and targeted sonodynamic cancer therapy.

pH-gated nanoparticles selectively regulate lysosomal function of tumour-associated macrophages for cancer immunotherapy

Tumour-associated macrophages (TAMs), as one of the most abundant tumour-infiltrating immune cells, play a pivotal role in tumour antigen clearance and immune suppression. M2-like TAMs present a heightened lysosomal acidity and protease activity, limiting an effective antigen cross-presentation. How to selectively reprogram M2-like TAMs to reinvigorate anti-tumour immune responses is challenging. Here, we report a pH-gated nanoadjuvant (PGN) that selectively targets the lysosomes of M2-like TAMs in tumours rather than the corresponding organelles from macrophages in healthy tissues. Enabled by the PGN nanotechnology, M2-like TAMs are specifically switched to a M1-like phenotype with attenuated lysosomal acidity and cathepsin activity for improved antigen cross-presentation, thus eliciting adaptive immune response and sustained tumour regression in tumour-bearing female mice. Our findings provide insights into how to specifically regulate lysosomal function of TAMs for efficient cancer immunotherapy.

The construction of a prognostic model of cervical cancer based on four immune-related LncRNAs and an exploration of the correlations between the model and oxidative stress

Introduction: The immune-related lncRNAs (IRLs) are critical for the development of cervical cancer (CC), but it is still unclear how exactly ILRs contribute to CC. In this study, we aimed to examine the relationship between IRL and CC in detail. Methods: First, the RNAseq data and clinical data of CC patients were collected from The Cancer Genome Atlas (TCGA) database, along with the immune genes from the Import database. We used univariate cox and least absolute shrinkage and selection operator (lasso) to obtain IRLs for prediction after screening the variables. According to the expression levels and risk coefficients of IRLs, the riskscore were calculated. We analyzed the relationship between the model and oxidative stress. We stratified the risk model into two as the high and low-risk groups. We also evaluated the survival differences, immune cell differences, immunotherapeutic response differences, and drug sensitivity differences between the risk groups. Finally, the genes in the model were experimentally validated. Results: Based on the above analyses, we further selected four IRLs (TFAP2A.AS1, AP000911.1, AL133215.2, and LINC02078) to construct the risk model. The model was associated with oxidative-stress-related genes, especially SOD2 and OGG1. Patients in the high-risk group had a lower overall survival than those in the low-risk group. Riskscore was positively correlated with resting mast cells, neutrophils, and CD8+ T-cells. Patients in the low-risk group showed a greater sensitivity to immunosuppression therapy. In addition, we found that patients with the PIK3CA mutation were more sensitive to chemotherapeutic agents such as dasatinib, afatinib, dinaciclib and pelitinib. The function of AL133215.2 was verified, which was consistent with previous findings, and AL133215.2 exerted a pro-tumorigenic effect. We also found that AL133215.2 was closely associated with oxidative-stress-related pathways. Discussion: The results suggested that risk modeling might be useful for prognosticating patients with CC and opening up new routes for immunotherapy.

Evaluating [18F]FDG and [18F]FLT Radiotracers as Biomarkers of Response for Combined Therapy Outcome in Triple-Negative and Estrogen-Receptor-Positive Breast Cancer Models

Breast cancer (BC) is the most frequent cancer and the second leading cause of death in women. A typical feature of BC cells is the metabolic shift toward increased glycolysis, which has become an interesting therapeutic target for metabolic drugs such as metformin (MET). Recently, the administration of the antihypertensive syrosingopine (SYRO) in combination with MET has shown a synergistic effect toward a variety of cancers. However, a fundamental need remains, which is the development of in vivo biomarkers that are able to detect early clinical response. In this study, we exploited a triple-negative murine BC cell line (4T1) and a metastatic ER+ murine BC cell line (TS/A) in order to investigate, in vivo, the early response to treatment, based on MET and/or SYRO administration, evaluating [18F]FDG and [18F]FLT as potential biomarkers via PET/CT. The study provides evidence that SYRO plus MET has a synergistic effect on tumor growth inhibition in both 4T1 and TS/A experimental models and has showed the highest efficacy on the TNBC xenograft mice (4T1) via the expression reduction in the lactate transporter MCT4 and in the epithelial-mesenchymal transition biomarker Snail, promoting its potential application in therapy settings. In addition, the selective reduction in the [18F]FLT tumor uptake (at 7 dd), observed in the SYRO plus MET treated mice in comparison with the vehicle group, suggests that this radiotracer could be potentially used as a biomarker for the early detection of therapy response, in both evaluated xenografts models.

CD44+ lung cancer stem cell-derived pericyte-like cells cause brain metastases through GPR124-enhanced trans-endothelial migration

Brain metastasis of lung cancer causes high mortality, but the exact mechanisms underlying the metastasis remain unclear. Here we report that vascular pericytes derived from CD44+ lung cancer stem cells (CSCs) in lung adenocarcinoma (ADC) potently cause brain metastases through the G-protein-coupled receptor 124 (GPR124)-enhanced trans-endothelial migration (TEM). CD44+ CSCs in perivascular niches generate the majority of vascular pericytes in lung ADC. CSC-derived pericyte-like cells (Cd-pericytes) exhibit remarkable TEM capacity to effectively intravasate into the vessel lumina, survive in the circulation, extravasate into the brain parenchyma, and then de-differentiate into tumorigenic CSCs to form metastases. Cd-pericytes uniquely express GPR124 that activates Wnt7-β-catenin signaling to enhance TEM capacity of Cd-pericytes for intravasation and extravasation, two critical steps during tumor metastasis. Furthermore, selective disruption of Cd-pericytes, GPR124, or the Wnt7-β-catenin signaling markedly reduces brain and liver metastases of lung ADC. Our findings uncover an unappreciated cellular and molecular paradigm driving tumor metastasis.

Stromal cells in the tumor microenvironment: accomplices of tumor progression?

The tumor microenvironment (TME) is made up of cells and extracellular matrix (non-cellular component), and cellular components include cancer cells and non-malignant cells such as immune cells and stromal cells. These three types of cells establish complex signals in the body and further influence tumor genesis, development, metastasis and participate in resistance to anti-tumor therapy. It has attracted scholars to study immune cells in TME due to the significant efficacy of immune checkpoint inhibitors (ICI) and chimeric antigen receptor T (CAR-T) in solid tumors and hematologic tumors. After more than 10 years of efforts, the role of immune cells in TME and the strategy of treating tumors based on immune cells have developed rapidly. Moreover, ICI have been recommended by guidelines as first- or second-line treatment strategies in a variety of tumors. At the same time, stromal cells is another major class of cellular components in TME, which also play a very important role in tumor metabolism, growth, metastasis, immune evasion and treatment resistance. Stromal cells can be recruited from neighboring non-cancerous host stromal cells and can also be formed by transdifferentiation from stromal cells to stromal cells or from tumor cells to stromal cells. Moreover, they participate in tumor genesis, development and drug resistance by secreting various factors and exosomes, participating in tumor angiogenesis and tumor metabolism, regulating the immune response in TME and extracellular matrix. However, with the deepening understanding of stromal cells, people found that stromal cells not only have the effect of promoting tumor but also can inhibit tumor in some cases. In this review, we will introduce the origin of stromal cells in TME as well as the role and specific mechanism of stromal cells in tumorigenesis and tumor development and strategies for treatment of tumors based on stromal cells. We will focus on tumor-associated fibroblasts (CAFs), mesenchymal stem cells (MSCs), tumor-associated adipocytes (CAAs), tumor endothelial cells (TECs) and pericytes (PCs) in stromal cells.

Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects

Neoadjuvant and adjuvant therapies have made significant progress in cancer treatment. However, tumor adjuvant therapy still faces challenges due to the intrinsic heterogeneity of cancer, genomic instability, and the formation of an immunosuppressive tumor microenvironment. Functional materials possess unique biological properties such as long circulation times, tumor-specific targeting, and immunomodulation. The combination of functional materials with natural substances and nanotechnology has led to the development of smart biomaterials with multiple functions, high biocompatibilities, and negligible immunogenicities, which can be used for precise cancer treatment. Recently, subcellular structure-targeting functional materials have received particular attention in various biomedical applications including the diagnosis, sensing, and imaging of tumors and drug delivery. Subcellular organelle-targeting materials can precisely accumulate therapeutic agents in organelles, considerably reduce the threshold dosages of therapeutic agents, and minimize drug-related side effects. This review provides a systematic and comprehensive overview of the research progress in subcellular organelle-targeted cancer therapy based on functional nanomaterials. Moreover, it explains the challenges and prospects of subcellular organelle-targeting functional materials in precision oncology. The review will serve as an excellent cutting-edge guide for researchers in the field of subcellular organelle-targeted cancer therapy.

(-)-Guaiol inhibit epithelial-mesenchymal transition in lung cancer via suppressing M2 macrophages mediated STAT3 signaling pathway

In the context of cancer expansion, epithelial-mesenchymal transition (EMT) plays an essential role in driving invasion and metastasis potential of cancer cells. Tumor-associated macrophages (TAMs)-derived factors involved in the initiation and progression of EMT. We assess the role of M2 macrophage in suppressing lung tumors of a natural compound (-)-Guaiol by using macrophage depleted model. Bone marrow-derived monocytes (BMDMs) were extracted and induced to M2-like phenotype in vitro. The co-culture of M2 macrophage and lung cancer cells was established to observe that inhibition of lung tumor growth by (-)-Guaiol requires presence of macrophages. This suppressed effect of (-)-Guaiol was alleviated when mice macrophage was depleted. The expression of M2-like macrophages was strongly reduced by (-)-Guaiol treated mice, but not the changes of M1-like macrophages. In vitro studies, we demonstrated that (-)-Guaiol suppressed M2 polarization of BMDMs, as well as migration, invasion, and EMT of lung cancer cells in co-culture. M2 macrophage-derived interleukin 10 (IL-10) was investigated as a critical signaling molecule between M2 macrophage and lung cancer cells. We have also verified that the mechanism of (-)-Guaiol inhibiting the EMT process of lung cancer is related to the activation of IL-10-mediated signal transducer and activator of transcription 3 (STAT3). These results suggested that the suppressive effect role of (-)-Guaiol in M2 macrophage promoting EMT of lung cancer, which was associated with inhibition of IL-10 mediated STAT3 signaling pathway.

Noncellular components in the liver cancer stem cell niche: Biology and potential clinical implications

Cancer stem cells (CSCs) are now recognized as one of the major root causes of therapy failure and tumor recurrence in hepatocellular carcinoma (HCC). Early studies in the field focused primarily on the intrinsic regulators of CSC maintenance, but in recent years, mounting evidence has demonstrated the presence and role of extrinsic regulators in the tumor microenvironment (TME) in the control of liver CSCs. In addition to direct interaction with cellular components, noncellular components, including the extracellular matrix, hypoxia, nutrient deprivation, and secreted molecules within the tumor stroma and hepatitis viruses, also play a critical role in shaping the CSC niche. In this review, we highlight how various noncellular components in the TME play a role in regulating CSCs and how CSCs secrete components to interact with the TME to generate their own niche, working hand in hand to drive tumor physiology in HCC. In addition, we describe the potential clinical applications of these findings and propose perspectives on future research of noncellular components in the liver CSC niche.

Bio-Responsive Macromolecular Drug and Small-Molecular Drug Conjugates: Nanoparticulate Prodrugs for Tumor Microenvironment Heterogeneity Management and Therapeutic Response Enhancement

How to break through the poor response of current drug therapy, which often resulted from tumor microenvironment heterogeneity (TMH), remains an enormous challenge in the treatment of critical diseases. In this work, a practical solution on bio-responsive dual-drug conjugates for overcoming TMH and improving antitumor treatment, which integrates the advantages of macromolecular drugs and small-molecular drugs, is proposed. Nanoparticulate prodrugs based on small-molecular drug and macromolecular drug conjugates are designed as a robust weapon for programmable multidrug delivery at tumor-specific sites: the tumor microenvironment acid condition triggers delivery of macromolecular aptamer drugs (AX102) to manage TMH (including tumor stroma matrix, interstitial fluid pressure, vasculature network, blood perfusion, and oxygen distribution), and intracellular lysosomal acid condition activates rapid release of small-molecular drugs (doxorubicin and dactolisib) to enhance curative effects. As compared with doxorubicin chemotherapy, the tumor growth inhibition rate is enhanced by 47.94% after multiple tumor heterogeneity management. This work verifies that the nanoparticulate prodrugs facilitate TMH management and therapeutic response enhancements, as well as elucidates synergetic mechanisms for drug resistance reversal and metastasis inhibition. It is hoped that the nanoparticulate prodrugs will be an excellent demonstration of the co-delivery of small-molecular drugs and macromolecular drugs.

COPS6 promotes tumor progression and reduces CD8+ T cell infiltration by repressing IL-6 production to facilitate tumor immune evasion in breast cancer

Due to poor T cell infiltration, tumors evade immune surveillance. Increased CD8+ T cell infiltration in breast cancer suggests a satisfactory response to immunotherapy. COPS6 has been identified as an oncogene, but its role in regulating antitumor immune responses has not been defined. In this study, we investigated the impact of COPS6 on tumor immune evasion in vivo. Tumor transplantation models were established in C57BL/6 J mice and BALB/c nude mice. Flow cytometry was conducted to identify the role of COPS6 on tumor-infiltrating CD8+ T cells. By analyzing the TCGA and GTEx cohort, we found that COPS6 expression was significantly up-regulated in a variety of cancers. In human osteosarcoma cell line U2OS and non-small cell lung cancer cell line H1299, we showed that p53 negatively regulated COPS6 promoter activity. In human breast cancer MCF-7 cells, COPS6 overexpression stimulated p-AKT expression as well as the proliferation and malignant transformation of tumor cells, whereas knockdown of COPS6 caused opposite effects. Knockdown of COPS6 also significantly suppressed the growth of mouse mammary cancer EMT6 xenografts in BALB/c nude mice. Bioinformatics analysis suggested that COPS6 was a mediator of IL-6 production in the tumor microenvironment and a negative regulator of CD8+ T cell tumor infiltration in breast cancer. In C57BL6 mice bearing EMT6 xenografts, COPS6 knockdown in the EMT6 cells increased the number of tumor-infiltrating CD8+ T cells, while knockdown of IL-6 in COPS6KD EMT6 cells diminished tumor infiltrating CD8+ T cells. We conclude that COPS6 promotes breast cancer progression by reducing CD8+ T cell infiltration and function via the regulation of IL-6 secretion. This study clarifies the role of p53/COPS6/IL-6/CD8+ tumor infiltrating lymphocytes signaling in breast cancer progression and immune evasion, opening a new path for development of COPS6-targeting therapies to enhance tumor immunogenicity and treat immunologically "cold" breast cancer.

JMJD6 in tumor-associated macrophage regulates macrophage polarization and cancer progression via STAT3/IL-10 axis

The tumor-associated macrophage (TAM) is the most abundant group of immune cells in the tumor microenvironment (TME), which plays a critical role in the regulation of tumor progression and treatment resistance. Based on different polarization status, TAMs may also induce antitumor immune responses or immunosuppression. The present study identified JMJD6 (Jumonji domain-containing 6) as a novel modulator of TAM activation, the upregulation of which was associated with the immunosuppressive activities of TAMs. JMJD6 deficiency attenuated the growth of both Lewis lung carcinoma (LLC) tumors and B16F10 melanomas by reversing M2-like activation of macrophages, and sensitized tumors to immune checkpoint blockades (ICBs). Moreover, the JMJD6-induced inhibition of M2 polarization was potentially mediated by the STAT3/IL-10 signaling. These findings highlight the regulatory activities of JMJD6 in TAM polarization, and the therapeutic potential of JMJD6/STAT3/IL-10 axis blockades to enhance the efficacy of ICBs in cancer treatment.

Single-Nucleus Transcriptome Profiling of Locally Advanced Cervical Squamous Cell Cancer Identifies Neural-Like Progenitor Program Associated with the Efficacy of Radiotherapy

Radiotherapy is the first-line treatment for locally advanced cervical squamous cell cancer (CSCC). However, ≈50% of patients fail to respond to therapy and, in some cases, tumors progress after radical radiotherapy. Here, single-nucleus RNA-seq is performed to construct high-resolution molecular landscapes of various cell types in CSCC before and during radiotherapy, to better understand radiotherapy related molecular responses within tumor microenvironment. The results show that expression levels of a neural-like progenitor (NRP) program in tumor cells are significantly higher after radiotherapy and these are enriched in the tumors of nonresponding patients. The enrichment of the NRP program in malignant cells from the tumors of nonresponders in an independent cohort analyzed by bulk RNA-seq is validated. In addition, an analysis of The Cancer Genome Atlas dataset shows that NRP expression is associated with poor prognosis in CSCC patients. In vitro experiments on the CSCC cell line demonstrate that downregulation of neuregulin 1 (NRG1), a key gene from NRP program, is associated with decreased cell growth and increased sensitivity to radiation. Immunohistochemistry staining in cohort 3 validated key genes, NRG1 and immediate early response 3 from immunomodulatory program, as radiosensitivity regulators. The findings reveal that the expression of NRP in CSCC can be used to predict the efficacy of radiotherapy.

Biology-guided deep learning predicts prognosis and cancer immunotherapy response

Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

Targeting Melanoma-Associated Fibroblasts (MAFs) with Activated γδ (Vδ2) T Cells: An In Vitro Cytotoxicity Model

The tumor microenvironment (TME) has gained considerable scientific attention by playing a role in immunosuppression and tumorigenesis. Besides tumor cells, TME is composed of various other cell types, including cancer-associated fibroblasts (CAFs or MAFs when referring to melanoma-derived CAFs) and tumor-infiltrating lymphocytes (TILs), a subpopulation of which is labeled as γδ T cells. Since the current anti-cancer therapies using γδ T cells in various cancers have exhibited mixed treatment responses, to better understand the γδ T cell biology in melanoma, our research group aimed to investigate whether activated γδ T cells are capable of killing MAFs. To answer this question, we set up an in vitro platform using freshly isolated Vδ2-type γδ T cells and cultured MAFs that were biobanked from our melanoma patients. This study proved that the addition of zoledronic acid (1-2.5 µM) to the γδ T cells was necessary to drive MAFs into apoptosis. The MAF cytotoxicity of γδ T cells was further enhanced by using the stimulatory clone 20.1 of anti-BTN3A1 antibody but was reduced when anti-TCR γδ or anti-BTN2A1 antibodies were used. Since the administration of zoledronic acid is safe and tolerable in humans, our results provide further data for future clinical studies on the treatment of melanoma.

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers

Background

Neoadjuvant chemotherapy (NAC) plays a significant role in breast cancer (BC) management; however, its efficacy varies among patients. Current evaluation methods may lead to delayed treatment alterations, and traditional imaging modalities often yield inaccurate results. Radiomics, an emerging field in medical imaging, offers potential for improved tumor characterization and personalized medicine. Nevertheless, its application in early and accurately predicting NAC response remains underinvestigated.

Objective

This study aims to develop an automated breast volume scanner (ABVS)-based radiomics model to facilitate early detection of suboptimal NAC response, ultimately promoting personalized therapeutic approaches for BC patients.

Methods

This retrospective study involved 248 BC patients receiving NAC. Standard guidelines were followed, and patients were classified as responders or non-responders based on treatment outcomes. ABVS images were obtained before and during NAC, and radiomics features were extracted using the PyRadiomics toolkit. Inter-observer consistency and hierarchical feature selection were assessed. Three machine learning classifiers, logistic regression, support vector machine, and random forest, were trained and validated using a five-fold cross-validation with three repetitions. Model performance was comprehensively evaluated based on discrimination, calibration, and clinical utility.

Results

Of the 248 BC patients, 157 (63.3%) were responders, and 91 (36.7%) were non-responders. Radiomics feature selection revealed 7 pre-NAC and 6 post-NAC ABVS features, with higher weights for post-NAC features (min >0.05) than pre-NAC (max <0.03). The three post-NAC classifiers demonstrated AUCs of approximately 0.9, indicating excellent discrimination. DCA curves revealed a substantial net benefit when the threshold probability exceeded 40%. Conversely, the three pre-NAC classifiers had AUCs between 0.7 and 0.8, suggesting moderate discrimination and limited clinical utility based on their DCA curves.

Conclusion

The ABVS-based radiomics model effectively predicted suboptimal NAC responses in BC patients, with early post-NAC classifiers outperforming pre-NAC classifiers in discrimination and clinical utility. It could enhance personalized treatment and improve patient outcomes in BC management.

Inhibition of hyaluronic acid degradation pathway suppresses glioma progression by inducing apoptosis and cell cycle arrest

BACKGROUND

Abnormal hyaluronic acid (HA) metabolism is a major factor in tumor progression, and the metabolic regulation of HA mainly includes HA biosynthesis and catabolism. In glioma, abnormal HA biosynthesis is intimately involved in glioma malignant biological properties and the formation of immunosuppressive microenvironment; however, the role of abnormal HA catabolism in glioma remains unclear.

METHODS

HA catabolism is dependent on hyaluronidase. In TCGA and GEPIA databases, we found that among the 6 human hyaluronidases (HYAL1, HYAL2, HYAL3, HYAL4, HYALP1, SPAM1), only HYAL2 expression was highest in glioma. Next, TCGA and CGGA database were further used to explore the correlation of HYAL2 expression with glioma prognosis. Then, the mRNA expression and protein level of HYAL2 was determined by qRT-PCR, Western blot and Immunohistochemical staining in glioma cells and glioma tissues, respectively. The MTT, EdU and Colony formation assay were used to measure the effect of HYAL2 knockdown on glioma. The GSEA enrichment analysis was performed to explore the potential pathway regulated by HYAL2 in glioma, in addition, the HYAL2-regulated signaling pathways were detected by flow cytometry and Western blot. Finally, small molecule compounds targeting HYAL2 in glioma were screened by Cmap analysis.

RESULTS

In the present study, we confirmed that Hyaluronidase 2 (HYAL2) is abnormally overexpressed in glioma. Moreover, we found that HYAL2 overexpression is associated with multiple glioma clinical traits and acts as a key indicator for glioma prognosis. Targeting HYAL2 could inhibit glioma progression by inducing glioma cell apoptosis and cell cycle arrest.

CONCLUSION

Collectively, these observations suggest that HYAL2 overexpression could promote glioma progression. Thus, treatments that disrupt HA catabolism by altering HYAL2 expression may serve as effective strategies for glioma treatment.

Hyperthermia-triggered biomimetic bubble nanomachines

Nanoparticle-based drug delivery systems have gained much attention in the treatment of various malignant tumors during the past decades. However, limited tumor penetration of nanodrugs remains a significant hurdle for effective tumor therapy due to the existing biological barriers of tumoral microenvironment. Inspired by bubble machines, here we report the successful fabrication of biomimetic nanodevices capable of in-situ secreting cell-membrane-derived nanovesicles with smaller sizes under near infrared (NIR) laser irradiation for synergistic photothermal/photodynamic therapy. Porous Au nanocages (AuNC) are loaded with phase transitable perfluorohexane (PFO) and hemoglobin (Hb), followed by oxygen pre-saturation and indocyanine green (ICG) anchored 4T1 tumor cell membrane camouflage. Upon slight laser treatment, the loaded PFO undergoes phase transition due to surface plasmon resonance effect produced by AuNC framework, thus inducing the budding of outer cell membrane coating into small-scale nanovesicles based on the pore size of AuNC. Therefore, the hyperthermia-triggered generation of nanovesicles with smaller size, sufficient oxygen supply and anchored ICG results in enhanced tumor penetration for further self-sufficient oxygen-augmented photodynamic therapy and photothermal therapy. The as-developed biomimetic bubble nanomachines with temperature responsiveness show great promise as a potential nanoplatform for cancer treatment.

Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer

Lysosome dysfunction has been shown to play an important role in cancer progression. However, few research studies have reported the role of lysosomes in head and neck squamous cell carcinoma (HNSCC) progression. Lysosome-related genes (LRGs) were collected from the Molecular Signatures Database. Differentially expressed lysosome-related genes (DELRGs) were identified from the TCGA-HNSCC dataset. The least absolute shrinkage and selection operator and multivariate Cox regression analysis were used to identify the prognostic genes. The prognostic values and expression of hub DELRGs were further validated by GEO datasets. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data and the single-sample gene set enrichment analysis were applied to evaluate the correlation between cathepsin G (CTSG) and immune infiltrates. Twenty-two DELRGs were identified. Among them, CTSG was an independent prognostic biomarker for HNSCC patients. Gene set enrichment analysis indicated that the potential mechanism of CTSG in regulating HNSCC was associated with the immune- and inflammation-related pathways. CTSG expression was highly correlated with immune cell infiltration. Finally, two potential compounds (CH and MAN) targeting CTSG protein were identified, and their reliability was validated through molecular docking analysis. CTSG was associated with immune infiltration and had prognostic value in HNSCC patients, which may be a potential biomarker for predicting the outcome of immunotherapy.

Current immune therapeutic strategies in advanced or metastatic non-small cell lung cancer

ABSTRACT

Immune escape mechanisms in non-small cell lung cancer (NSCLC) can disrupt every step of the anti-cancer immune response. In recent years, an increased understanding of the specific mechanisms fueling immune escape has allowed for the development of numerous immunotherapeutic treatments that have been introduced into the clinical practice. The advent of immunotherapy has dramatically changed the current treatment landscape of advanced or metastatic NSCLC because of its durable efficacy and manageable toxicity. In this review, we will first present a brief overview of recent evidence on immune escape mechanisms in NSCLC. We will then discuss the current promising immunotherapeutic strategies in advanced or metastatic NSCLC tumors.

Emerging role of exosome-derived non-coding RNAs in tumor-associated angiogenesis of tumor microenvironment

The tumor microenvironment (TME) is an intricate ecosystem that is actively involved in various stages of cancer occurrence and development. Some characteristics of tumor biological behavior, such as proliferation, migration, invasion, inhibition of apoptosis, immune escape, angiogenesis, and metabolic reprogramming, are affected by TME. Studies have shown that non-coding RNAs, especially long-chain non-coding RNAs and microRNAs in cancer-derived exosomes, facilitate intercellular communication as a mechanism for regulating angiogenesis. They stimulate tumor growth, as well as angiogenesis, metastasis, and reprogramming of the TME. Exploring the relationship between exogenous non-coding RNAs and tumor-associated endothelial cells, as well as their role in angiogenesis, clinicians will gain new insights into treatment as a result.

Pan-cancer analysis reveals that G6PD is a prognostic biomarker and therapeutic target for a variety of cancers

BACKGROUND

Despite accumulating evidence revealing that Glucose-6-phosphate dehydrogenase (G6PD) is highly expressed in many tumor tissues and plays a remarkable role in cancer tumorigenesis and progression, there is still a lack of G6PD pan-cancer analysis. This study was designed to analyze the expression status and prognostic significance of G6PD in pan-cancer.

METHODS

G6PD expression data were obtained from multiple data resources including the Genotype-Tissue Expression, the Cancer Genome Atlas, and the Tumor Immunity Estimation Resource. These data were used to assess the G6PD expression, prognostic value, and clinical characteristics. The ESTIMATE algorithms were used to analyze the association between G6PD expression and immune-infiltrating cells and the tumor microenvironment. The functional enrichment analysis was also performed across pan-cancer. In addition, the GDSC1 database containing 403 drugs was utilized to explore the relationship between drug sensitivity and G6PD expression levels. Furthermore, we also performed clinical validation and in vitro experiments to further validate the role of G6PD in hepatocellular carcinoma (HCC) cells and its correlation with prognosis. The R software was used for statistical analysis and data visualization.

RESULTS

G6PD expression was upregulated in most cancers compared to their normal counterparts. The study also revealed that G6PD expression was a prognostic indicator and high levels of G6PD expression were correlated with worse clinical prognosis including overall survival, disease-specific survival, and progression-free interval in multiple cancers. Furthermore, the G6PD level was also related to cancer immunity infiltration in most of the cancers, especially in KIRC, LGG, and LIHC. In addition to this, G6PD expression was positively related to pathological stages of KIRP, BRCA, KIRC, and LIHC. Functional analysis and protein-protein interactions network results revealed that G6PD was involved in metabolism-related activities, immune responses, proliferation, and apoptosis. Drug sensitivity analysis showed that IC50 values of most identified anti-cancer drugs were positively correlated with the G6PD expression. Notably, in vitro functional validation showed that G6PD knockdown attenuated the phenotypes of proliferation in HCC.

CONCLUSION

G6PD may serve as a potential prognostic biomarker for cancers and may be a potential therapeutic target gene for tumor therapy.

Transcriptomic characterization revealed that METTL7A inhibits melanoma progression via the p53 signaling pathway and immunomodulatory pathway

METTL7A is a protein-coding gene expected to be associated with methylation, and its expression disorder is associated with a range of diseases. However, few research have been carried out to explore the relationship between METTL7A and tumor malignant phenotype as well as the involvement potential mechanism. We conducted our research via a combination of silico analysis and molecular biology techniques to investigate the biological function of METTL7A in the progression of cancer. Gene expression and clinical information were extracted from the TCGA database to explore expression variation and prognostic value of METTL7A. In vitro, CCK8, transwell, wound healing and colony formation assays were conducted to explore the biological functions of METT7A in cancer cell. GSEA was performed to explore the signaling pathway involved in METTL7A and validated via western blotting. In conclusion, METTL7A was downregulated in most cancer tissues and its low expression was associated with shorter overall survival. In melanoma, METTL7A downregulation was associated with poorer clinical staging, lower levels of TIL infiltration, higher IC50 levels of chemotherapeutic agents, and poorer immunotherapy outcomes. QPCR results confirm that METTL7A is down-regulated in melanoma cells. Cell function assays showed that METTL7A knockdown promoted proliferation, invasion, migration and clone formation of melanoma cells. Mechanistic studies showed that METTL7A inhibits tumorigenicity through the p53 signaling pathway. Meanwhile, METTL7A is also a potential immune regulatory factor.

Tumor organoid model of colorectal cancer (Review)

The establishment of self-organizing 'mini-gut' organoid models has brought about a significant breakthrough in biomedical research. Patient-derived tumor organoids have emerged as valuable tools for preclinical studies, offering the retention of genetic and phenotypic characteristics of the original tumor. These organoids have applications in various research areas, including in vitro modelling, drug discovery and personalized medicine. The present review provided an overview of intestinal organoids, focusing on their unique characteristics and current understanding. The progress made in colorectal cancer (CRC) organoid models was then delved into, discussing their role in drug development and personalized medicine. For instance, it has been indicated that patient-derived tumor organoids are able to predict response to irinotecan-based neoadjuvant chemoradiotherapy. Furthermore, the limitations and challenges associated with current CRC organoid models were addressed, along with proposed strategies for enhancing their utility in future basic and translational research.

Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data

In oncology, intratumoural heterogeneity is closely linked with the efficacy of therapy, and can be partially characterized via tumour biopsies. Here we show that intratumoural heterogeneity can be characterized spatially via phenotype-specific, multi-view learning classifiers trained with data from dynamic positron emission tomography (PET) and multiparametric magnetic resonance imaging (MRI). Classifiers trained with PET-MRI data from mice with subcutaneous colon cancer quantified phenotypic changes resulting from an apoptosis-inducing targeted therapeutic and provided biologically relevant probability maps of tumour-tissue subtypes. When applied to retrospective PET-MRI data of patients with liver metastases from colorectal cancer, the trained classifiers characterized intratumoural tissue subregions in agreement with tumour histology. The spatial characterization of intratumoural heterogeneity in mice and patients via multimodal, multiparametric imaging aided by machine-learning may facilitate applications in precision oncology.

The mirrored cationic peptide as miRNA vehicle for efficient lung cancer therapy

Gene therapy has emerged as a potential approach for lung cancer therapy. However, the application of gene therapy is still limited by their properties, such as low specificity to the cancer cells, negatively charged groups, short systemic circulation time, and rapid degradation by nucleases. The progression of lung adenocarcinoma (LUAD) can be promoted through the methylation process of miR-148a-3p promoter, as confirmed by our previous research. In the current study, we are the first to design a mirrored Arg-Gly-Asp (RGD)-modified cationic peptide (RD24) as a microRNA (miRNA) vehicle, which enabled to pack the miRNA (miR-148a-3p) efficiently and generate RD24/miR-148a-3p nanoparticles (RPRIN) by self-assembling. RPRIN exhibited a high transfection efficiency in lung cancer cells via the conjugation between RGD and integrins on the surface of lung cancer cells. Furthermore, RD24 showed matrix metallopeptidase 2 (MMP2) responsiveness, which improved lung cancer cell inhibition induced by the miRNA intracellularly. In addition, RPRIN exhibits several advantages, such as prolonged circulation duration, reduced toxicity, and immune escape. Experiments conducted both in vitro and in vivo revealed that RPRIN effectively suppressed the growth and progression of lung cancer. Thus, the mirrored RGD-modified cationic peptide showed great potential in transducing miRNA for lung cancer therapy.

Self-assembled nanoformulations of paclitaxel for enhanced cancer theranostics

Chemotherapy has occupied the critical position in cancer therapy, especially towards the post-operative, advanced, recurrent, and metastatic tumors. Paclitaxel (PTX)-based formulations have been widely used in clinical practice, while the therapeutic effect is far from satisfied due to off-target toxicity and drug resistance. The caseless multi-components make the preparation technology complicated and aggravate the concerns with the excipients-associated toxicity. The self-assembled PTX nanoparticles possess a high drug content and could incorporate various functional molecules for enhancing the therapeutic index. In this work, we summarize the self-assembly strategy for diverse nanodrugs of PTX. Then, the advancement of nanodrugs for tumor therapy, especially emphasis on mono-chemotherapy, combinational therapy, and theranostics, have been outlined. Finally, the challenges and potential improvements have been briefly spotlighted.

An agarose-alginate microfluidic device for the study of spheroid invasion, ATRA inhibits CAFs-mediated matrix remodeling

Growing evidence demonstrates that cancer-associated fibroblasts (CAF) are responsible for tumor genesis, growth, metastasis, and treatment response. Therefore, targeting these cells may contribute to tumor control. It has been proposed that targeting key molecules and pathways of proliferative functions can be more effective than killing CAFs. In this regard, multicellular aggregates, like spheroids, can be used as human tumor models. Spheroids closely resemble human tumors and mimic many of their features. Microfluidic systems are ideal for cultivation and study of spheroids. These systems can be designed with different biological and synthetic matrices in order to have a more realistic simulation of the tumor microenvironment (TME). In this study, we investigated the effect of all-trans retinoic acid (ATRA) on 3D spheroid invasion of MDA-MB cells exposed to hydrogel matrix derived from CAFs. The number of invasive cells significantly decreased in CAF-ECM hydrogel treated with ATRA (p < 0.05), which indicates that ATRA could be effective for CAFs normalization. This experiment was done using an agarose-alginate microfluidic chip. As compared with common methods, such hydrogel casting is an easier method for chip fabrication and can even reduce costs.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10616-023-00578-y.

Ultrasound-Triggered Cascade Amplification of Nanotherapy

Ultrasound (US)-triggered cascade amplification of nanotherapies has attracted considerable attention as an effective strategy for cancer treatment. With the remarkable advances in materials chemistry and nanotechnology, a large number of well-designed nanosystems have emerged that incorporate presupposed cascade amplification processes and can be activated to trigger therapies such as chemotherapy, immunotherapy, and ferroptosis, under exogenous US stimulation or specific substances generated by US actuation, to maximize antitumor efficacy and minimize detrimental effects. Therefore, summarizing the corresponding nanotherapies and applications based on US-triggered cascade amplification is essential. This review comprehensively summarizes and highlights the recent advances in the design of intelligent modalities, consisting of unique components, distinctive properties, and specific cascade processes. These ingenious strategies confer unparalleled potential to nanotherapies based on ultrasound-triggered cascade amplification and provide superior controllability, thus overcoming the unmet requirements of precision medicine and personalized treatment. Finally, the challenges and prospects of this emerging strategy are discussed and it is expected to encourage more innovative ideas and promote their further development.

Targeting NEDD8-activating enzyme for cancer therapy: developments, clinical trials, challenges and future research directions

NEDDylation, a post-translational modification through three-step enzymatic cascades, plays crucial roles in the regulation of diverse biological processes. NEDD8-activating enzyme (NAE) as the only activation enzyme in the NEDDylation modification has become an attractive target to develop anticancer drugs. To date, numerous inhibitors or agonists targeting NAE have been developed. Among them, covalent NAE inhibitors such as MLN4924 and TAS4464 currently entered into clinical trials for cancer therapy, particularly for hematological tumors. This review explains the relationships between NEDDylation and cancers, structural characteristics of NAE and multistep mechanisms of NEDD8 activation by NAE. In addition, the potential approaches to discover NAE inhibitors and detailed pharmacological mechanisms of NAE inhibitors in the clinical stage are explored in depth. Importantly, we reasonably investigate the challenges of NAE inhibitors for cancer therapy and possible development directions of NAE-targeting drugs in the future.

Dysregulated Signalling Pathways Driving Anticancer Drug Resistance

One of the leading causes of death worldwide, in both men and women, is cancer. Despite the significant development in therapeutic strategies, the inevitable emergence of drug resistance limits the success and impedes the curative outcome. Intrinsic and acquired resistance are common mechanisms responsible for cancer relapse. Several factors crucially regulate tumourigenesis and resistance, including physical barriers, tumour microenvironment (TME), heterogeneity, genetic and epigenetic alterations, the immune system, tumour burden, growth kinetics and undruggable targets. Moreover, transforming growth factor-beta (TGF-β), Notch, epidermal growth factor receptor (EGFR), integrin-extracellular matrix (ECM), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), phosphoinositol-3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR), wingless-related integration site (Wnt/β-catenin), Janus kinase/signal transducers and activators of transcription (JAK/STAT) and RAS/RAF/mitogen-activated protein kinase (MAPK) signalling pathways are some of the key players that have a pivotal role in drug resistance mechanisms. To guide future cancer treatments and improve results, a deeper comprehension of drug resistance pathways is necessary. This review covers both intrinsic and acquired resistance and gives a comprehensive overview of recent research on mechanisms that enable cancer cells to bypass barriers put up by treatments, and, like "satellite navigation", find alternative routes by which to carry on their "journey" to cancer progression.

KRAS mutation-driven angiopoietin 2 bestows anti-VEGF resistance in epithelial carcinomas

Defining reliable surrogate markers and overcoming drug resistance are the most challenging issues for improving therapeutic outcomes of antiangiogenic drugs (AADs) in cancer patients. At the time of this writing, no biomarkers are clinically available to predict AAD therapeutic benefits and drug resistance. Here, we uncovered a unique mechanism of AAD resistance in epithelial carcinomas with KRAS mutations that targeted angiopoietin 2 (ANG2) to circumvent antivascular endothelial growth factor (anti-VEGF) responses. Mechanistically, KRAS mutations up-regulated the FOXC2 transcription factor that directly elevated ANG2 expression at the transcriptional level. ANG2 bestowed anti-VEGF resistance as an alternative pathway to augment VEGF-independent tumor angiogenesis. Most colorectal and pancreatic cancers with KRAS mutations were intrinsically resistant to monotherapies of anti-VEGF or anti-ANG2 drugs. However, combination therapy with anti-VEGF and anti-ANG2 drugs produced synergistic and potent anticancer effects in KRAS-mutated cancers. Together, these data demonstrate that KRAS mutations in tumors serve as a predictive marker for anti-VEGF resistance and are susceptible to combination therapy with anti-VEGF and anti-ANG2 drugs.

Leptomeningeal Metastases in Melanoma Patients: An Update on and Future Perspectives for Diagnosis and Treatment

Leptomeningeal disease (LMD) is a devastating complication of cancer with a particularly poor prognosis. Among solid tumours, malignant melanoma (MM) has one of the highest rates of metastasis to the leptomeninges, with approximately 10-15% of patients with advanced disease developing LMD. Tumour cells that metastasise to the brain have unique properties that allow them to cross the blood-brain barrier, evade the immune system, and survive in the brain microenvironment. Metastatic colonisation is achieved through dynamic communication between metastatic cells and the tumour microenvironment, resulting in a tumour-permissive milieu. Despite advances in treatment options, the incidence of LMD appears to be increasing and current treatment modalities have a limited impact on survival. This review provides an overview of the biology of LMD, diagnosis and current treatment approaches for MM patients with LMD, and an overview of ongoing clinical trials. Despite the still limited efficacy of current therapies, there is hope that emerging treatments will improve the outcomes for patients with LMD.

Species-Deconvolved Proteomics for In Situ Investigation of Tumor-Stroma Interactions after Treatment of Pancreatic Cancer Patient-Derived Xenografts with Combined Gemcitabine and Paclitaxel

Tumor-stroma interactions are critical in pancreatic ductal adenocarcinoma (PDAC) progression and therapeutics. Patient-derived xenograft (PDX) models recapitulate tumor-stroma interactions, but the conventional antibody-based immunoassay is inadequate to discriminate tumor and stromal proteins. Here, we describe a species-deconvolved proteomics approach embedded in IonStar that can unambiguously quantify the tumor (human-derived) and stromal (mouse-derived) proteins in PDX samples, enabling unbiased investigation of tumor and stromal proteomes with excellent quantitative reproducibility. With this strategy, we studied tumor-stroma interactions in PDAC PDXs that responded differently to Gemcitabine combined with nab-Paclitaxel (GEM+PTX) treatment. By analyzing 48 PDX animals 24 h/192 h after treatment with/without GEM+PTX, we quantified 7262 species-specific proteins under stringent cutoff criteria, with high reproducibility. For the PDX sensitive to GEM+PTX, the drug-dysregulated proteins in tumor cells were involved in suppressed oxidative phosphorylation and the TCA cycle, and in the stroma, inhibition of glycolytic activity was predominant, suggesting a relieved reverse Warburg effect by the treatment. In GEM+PTX-resistant PDXs, protein changes suggested extracellular matrix deposition and activation of tumor cell proliferation. Key findings were validated by immunohistochemistry (IHC). Overall, this approach provides a species-deconvolved proteomic platform that could advance cancer therapeutic studies by enabling unbiased exploration of tumor-stroma interactions in the large number of PDX samples required for such investigations.