Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15:81-94. [PMID: 29115304 DOI: 10.1038/nrclinonc.2017.166]

A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.

MiRNA-3163 limits ovarian cancer stem-like cells via targeting SOX-2 transcription factor

Cancer stem cells (CSCs) are pivotal in both cancer progression and the acquisition of drug resistance. MicroRNAs (miRNAs) play a crucial role in modulating CSC properties and are being explored as potential targets for therapeutic interventions. MiR-3163 is primarily known for its tumor suppressive properties in various human malignancies, with lower expression reported across different cancer types. However, its role in regulating the ovarian CSC phenotype and the underlying mechanism remain largely unknown. Here, we report a remarkable downregulation of miR-3163 in ovarian cancer stem-like cells (CSLCs). Enforced expression of miR-3163 in ovarian adherent and CSLCs, significantly disrupts the stemness phenotype. Moreover, downregulation of miR-3163 expression in ovarian cancer cells (OV2008 and OVCAR-3) inhibits the stem-like cells characterized by CD44+CD117+ expression. Sphere formation assay results reveal that overexpression of miR-3163 in ovarian cancer cells significantly inhibits spheroid formation ability, confirming the regulatory properties of miR-3163 on ovarian CSLCs. Mechanistic investigation reveals that miR-3163 depletes ovarian CSLCs via targeting SOX-2. Furthermore, we establish SOX-2 as a direct target of miR-3163 through dual-luciferase assay. Taken together, our study demonstrates that overexpression of miR-3163 could be a promising strategy for efficiently eradicating the CSC population to prevent chemoresistance and tumor relapse in ovarian cancer patients.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Targeted immunotherapy and nanomedicine for rhabdomyosarcoma: The way of the future

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood. Histology separates two main subtypes: embryonal RMS (eRMS; 60%-70%) and alveolar RMS (aRMS; 20%-30%). The aggressive aRMS carry one of two characteristic chromosomal translocations that result in the expression of a PAX3::FOXO1 or PAX7::FOXO1 fusion transcription factor; therefore, aRMS are now classified as fusion-positive (FP) RMS. Embryonal RMS have a better prognosis and are clinically indistinguishable from fusion-negative (FN) RMS. Next to histology and molecular characteristics, RMS risk groupings are now available defining low risk tumors with excellent outcomes and advanced stage disease with poor prognosis, with an overall survival of about only 20% despite intensified multimodal treatment. Therefore, development of novel effective targeted strategies to increase survival and to decrease long-term side effects is urgently needed. Recently, immunotherapies and nanomedicine have been emerging for potent and effective tumor treatments with minimal side effects, raising hopes for effective and safe cures for RMS patients. This review aims to describe the most relevant preclinical and clinical studies in immunotherapy and targeted nanomedicine performed so far in RMS and to provide an insight in future developments.

Histone deacetylases: Regulation of vascular homeostasis via endothelial cells and vascular smooth muscle cells and the role in vascular pathogenesis

Histone deacetylases (HDACs) are proteases that play a key role in chromosome structural modification and gene expression regulation, and the involvement of HDACs in cancer, the nervous system, and the metabolic and immune system has been well reviewed. Our understanding of the function of HDACs in the vascular system has recently progressed, and a significant variety of HDAC inhibitors have been shown to be effective in the treatment of vascular diseases. However, few reviews have focused on the role of HDACs in the vascular system. In this study, the role of HDACs in the regulation of the vascular system mainly involving endothelial cells and vascular smooth muscle cells was discussed based on recent updates, and the role of HDACs in different vascular pathogenesis was summarized as well. Furthermore, the therapeutic effects and prospects of HDAC inhibitors were also addressed in this review.

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.

The heterogeneity of breast cancer metastasis: a bioinformatics analysis utilizing single-cell RNA sequencing data

PURPOSE

Breast cancer is a common malignancy in women, and its metastasis is a leading cause of cancer-related deaths. Single-cell RNA sequencing (scRNA-seq) can distinguish the molecular characteristics of metastasis and identify predictor genes for patient prognosis. This article explores gene expression in primary breast cancer tumor tissue against metastatic cells in the lymph node and liver using scRNA-seq.

METHODS

Breast cancer scRNA-seq data from the Gene Expression Omnibus were used. The data were processed using R and the Seurat package. The cells were clustered and identified using Metascape. InferCNV is used to analyze the variation in copy number. Differential expression analysis was conducted for the cancer cells using Seurat and was enriched using Metascape.

RESULTS

We identified 18 distinct cell clusters, 6 of which were epithelial. CNV analysis identified significant alterations with duplication of chromosomes 1, 8, and 19. Differential gene analysis resulted in 17 upregulated and 171 downregulated genes for the primary tumor in the primary tumor vs. liver metastasis comparison and 43 upregulated and 4 downregulated genes in the primary tumor in the primary tumor vs lymph node metastasis comparison. Several enriched terms include Ribosome biogenesis, NTP synthesis, Epithelial dedifferentiation, Autophagy, and genes associated with epithelial-to-mesenchymal transitions.

CONCLUSION

No single gene or pathway can clearly explain the mechanisms behind tumor metastasis. Several mechanisms contribute to lymph node and liver metastasis, such as the loss of differentiation, epithelial-to-mesenchymal transition, and autophagy. These findings necessitate further study of metastatic tissue for effective drug development.

STARD7 could be an immunological and prognostic biomarker: from pan-cancer analysis to hepatocellular carcinoma validation

BACKGROUND

As the emergence of technologies such as sequencing and gene mapping, significant advancements have been made in understanding the landscape of tumors. However, the effective treatment of tumors continues to pose a tremendous challenge in clinical practice, which highlights the importance of predicting tumor markers and studying drug resistance mechanisms. The prognosis and differential expression of STARD7 in human pan-cancer were investigated by bioinformatic methods and experimental verification.

METHODS

The expression, diagnostic, and prognostic significance of the STARD7 gene were comprehensive analyzed using bioinformatics techniques. Furthermore, we validated our projected outcomes in liver cancer through experimental methodologies, including the use of qRT-PCR, CCK8 and transwell assays.

RESULTS

The STARD7 gene exhibits differential expression in 25 tumors, with high expression observed in 22 tumors. These distinct expression patterns within different tumor types are closely associated with poor prognosis and diagnosis. Furthermore, the STARD7 gene plays a role in regulating the tumor immune microenvironment. Methylation levels of STARD7 vary among 20 types of tumors and are correlated with survival outcomes. Furthermore, the experiment results demonstrated that STARD7 is highly expressed in hepatocellular carcinoma cells. Suppression of STARD7 significantly impedes the proliferation, migration, and invasion of HepG-2 and SMMC-7721 cells.

CONCLUSIONS

STARD7 has the potential to function as a crucial prognostic biomarker and exhibit correlation with tumor immunity in various types of human cancers. The implications of our findings extend to informing cancer immune-therapy and promoting the advancement of precision immune-oncology.

Discovery of prognostic lncRNAs in colorectal cancer using spatial transcriptomics

Colorectal cancer (CRC) exhibits significant genetic and epigenetic diversity, evolving into sub-clonal populations with varied metastatic potentials and treatment responses. Predicting metastatic disease in CRC patients remains challenging, underscoring the need for reliable biomarkers. While most research on therapeutic targets and biomarkers has focused on proteins, non-coding RNAs such as long non-coding RNAs (lncRNAs) comprise most of the transcriptome and demonstrate superior tissue- and cancer-specific expression. We utilised spatial transcriptomics to investigate lncRNAs in CRC tumours, offering more precise cell-type-specific expression data compared to bulk RNA sequencing. Our analysis identified 301 lncRNAs linked to malignant CRC regions, which we validated with public data. Further validation using RNA-FISH revealed three lncRNAs (LINC01978, PLAC4, and LINC01303) that are detectable in stage II tumours but not in normal epithelium and are upregulated in metastatic tissues. These lncRNAs hold potential as biomarkers for early risk assessment of metastatic disease.

A basement membrane-related signature for prognosis and immunotherapy benefit in bladder cancer based on machine learning

BACKGROUND

Bladder cancer has a poor clinical outcome because of its high aggressiveness. Basement membrane plays vital functions in tumor invasion and migration. Invasion and distant metastasis of cancer are facilitated by degradation of the basement membrane and extracellular matrix.

METHODS

Ten machine learning methods were utilized to develop the basement membrane-related signature (MRS) using datasets from TCGA, GSE13507, GSE31684, GSE32984 and GSE48276. Three anti-PD1 or anti-CTLA4 datasets and several predicting scores were used to investigate the performance of MRS in predicting the immunotherapy benefits.

RESULTS

A predicting model based on the Enet algorithm (alpha = 0.1) was chosen as the optimal MRS since it had the highest average C-index being 0.72. According to TCGA data, the MRS showed good performance in predicting bladder cancer patients' clinical outcomes, with area under curves of 0.744, 0.766 and 0.817 for 1, 3, and 5-year receiver operating characteristic curve, respectively. PD1 and CTLA4 immunophenoscopes were associated with a low MRS score, as well as a lower tumor immune dysfunction and exclusion score. As MRS score increased, immune-activated cells levels decreased, tumor immune dysfunction and exclusion score decreased, immune escape score decreased, intratumor heterogeneity score decreased, PD1&CTLA4 immunophenoscore increased, and tumor mutational burden score increased in bladder cancer, suggesting better immunotherapy benefits. Bladder cancer cases with high MRS score was correlated with higher cancer related hallmark scores, including NOTCH and glycolysis signaling.

CONCLUSION

A new MRS has been developed for bladder cancer, which could be used to predict prognosis and the success of immunotherapy.

CISD3 is a prognostic biomarker and therapeutic target in pan-cancer

Recent studies indicate that CISD3 is crucial in mitochondrial function and tumorigenesis. Using various databases, we systematically analyzed its expression, prognostic value, and immune activity. Our findings show CISD3 is mainly expressed in tumor cells across cancers, with higher mRNA but lower protein levels, degraded post-translationally via the lysosomal pathway. In certain cancers, CISD3 expression is positively correlated with tumor-infiltrating immune cells. Prognostic analysis suggests dual roles as both protective and risk factors, notably an independent prognostic predictor in renal cell carcinoma (RCC). CISD3 copy number variations are linked to homologous recombination defects and tumor-specific neoantigens, negatively correlated with methylation levels. Pathway analysis reveals CISD3 involvement in oncogenic processes, such as proliferation inhibition and epithelial-mesenchymal transition. Protein interactions underline its role in mitochondrial metabolism and redox balance. Experiments confirm low CISD3 expression in cancers, with overexpression reducing proliferation, migration, invasion, and tumor growth in mice. Mechanistic studies indicate CISD3 overexpression disrupts mitochondrial function, increases ROS levels, decreases GSH/GSSG ratios and mitochondrial membrane potential, inhibiting antioxidant activity and promoting cell damage and ferroptosis, thus impeding cancer progression. This study highlights CISD3's potential as a prognostic biomarker and therapeutic target.

Systematic screening of protein-coding gene expression identified VWF as a potential key regulator in anthracycline-based chemotherapy-exacerbated metastasis of breast cancer

BACKGROUND

Breast cancer is the most commonly diagnosed cancer worldwide. Although major treatments represented by chemotherapy have shown effectiveness at the initial period, recurrence and metastasis still occur later after treatments. The alternation of the tumor microenvironment by chemotherapy is confirmed as a trigger of the elevated proliferation and migration of the remaining tumor cells.

METHODS

Using bioinformatic methods, differential gene expression analysis was used to determine DEGs between post-chemotherapy and pre-chemotherapy samples of breast cancer patients, followed by survival analysis and ELISA analysis of the potential key genes. An in vitro model of 2 breast cancer cells lines was used to demonstrate the role of VWF in the evasion and migration of breast cancer cells, using cell migration, evasion and wound healing assays, PCR and molecular docking analysis.

RESULTS

19 hub genes were further identified using GO and KEGG pathway analyses and WGCNA. The 5 secreted protein-coding genes with reported carcinogenesis effects (VWF, SVEP1, DPT, ADIPOQ, and LPL) were further analyzed in breast cancer patients and VWF was identified as a potential key regulator in the anthracycline-based chemotherapy-exacerbated metastasis. It was further confirmed that anthracycline-based chemotherapeutics doxorubicin exacerbated VWF upregulation and the evasion and migration of breast cancer cells. Based on molecular docking analysis and previous study, berberine was used as an inhibitor of VWF, and showed an effective inhibition of the doxorubicin-exacerbated VWF upregulation, migration and evasion in breast cancer.

CONCLUSIONS

Doxorubicin-exacerbated evasion and migration through VWF upregulation. Berberine as an inhibitor of VWF was able to reversed the doxorubicin-exacerbated VWF upregulation and evasion and migration in breast cancer cells.

Polybodies: Next-generation clinical antibodies

Conventional antibodies [full-length and fragments: F(ab')2, fragment antigen-binding (Fab), single-chain variable fragment (scFv), variable heavy domain of heavy chain (VHH)] are monospecific first-generation antibodies that have dominated the biopharmaceuticals field. However, advanced protein engineering technology has led to the advent of the next-generation polybody, which is a significant improvement over the conventional antibody. Polybodies comprise polyspecific and polyvalent antibodies that enable a single antibody to target multiple specific antigens simultaneously. Polybodies are superior to first-generation antibodies (more efficacious, broad-spectrum, resistance resistant, customizable, etc.) and provide a cost-effective healthcare solution. This review thoroughly addresses developments in polybodies, highlighting their superiority over conventional antibodies and offering future perspectives to encourage the generation of innovative immunotherapies.

The development of drug resistance in metastatic tumours under chemotherapy: An evolutionary perspective

We present a mathematical model of the evolutionary dynamics of a metastatic tumour under chemotherapy, comprising non-local partial differential equations for the phenotype-structured cell populations in the primary tumour and its metastasis. These equations are coupled with a physiologically-based pharmacokinetic model of drug administration and distribution, implementing a realistic delivery schedule. The model is carefully calibrated from the literature, focusing on BRAF-mutated melanoma treated with Dabrafenib as a case study. By means of long-time asymptotic and global sensitivity analyses, as well as numerical simulations, we explore the impact of cell migration from the primary to the metastatic site, physiological aspects of the tumour tissues and drug dose on the development of chemoresistance and treatment efficacy. Our findings provide a possible explanation for empirical evidence indicating that chemotherapy may foster metastatic spread and that metastases may be less impacted by the chemotherapeutic agent.

Nanomedicines modulate the tumor immune microenvironment for cancer therapy

INTRODUCTION

In recent years, the evolution of immunotherapy as a means to trigger a robust antitumor immune response has revolutionized cancer treatment. Despite its potential, the effectiveness of cancer immunotherapy is hindered by low response rates and significant systemic side effects. Nanotechnology emerges as a promising frontier in shaping the future of cancer immunotherapy.

AREAS COVERED

This review elucidates the pivotal role of nanomedicine in reshaping the immune tumor microenvironment and explores innovative strategies pursued by diverse research groups to enhance the therapeutic efficacy of cancer immunotherapy. It discusses the hurdles encountered in cancer immunotherapy and the application of nanomedicine for small molecule immune modulators and nucleic acid therapeutics. It also highlights the advancements in DNA and mRNA vaccines facilitated by nanotechnology and outlines future trajectories in this evolving field.

EXPERT OPINION

Collectively, the integration of nanomedicine into cancer immunotherapy stands as a promising avenue to tackle the intricacies of the immune tumor microenvironment. Innovations such as immune checkpoint inhibitors and cancer vaccines have shown promise. Future developments will likely optimize nanoparticle design through artificial intelligence and create biocompatible, multifunctional nanoparticles, promising more effective, personalized, and durable cancer treatments, potentially transforming the field in the foreseeable future.

Spatial heterogeneity of infiltrating immune cells in the tumor microenvironment of non-small cell lung cancer

Tumor-infiltrating lymphocytes (TILs) are essential components of the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC). Still, it is difficult to describe due to their heterogeneity. In this study, five cell markers from NSCLC patients were analyzed. We segmented tumor cells (TCs) and TILs using Efficientnet-B3 and explored their quantitative information and spatial distribution. After that, we simulated multiplex immunohistochemistry (mIHC) by overlapping continuous single chromogenic IHCs slices. As a result, the proportion and the density of programmed cell death-ligand 1 (PD-L1)-positive TCs were the highest in the core. CD8+ T cells were the closest to the tumor (median distance: 41.71 μm), while PD-1+T cells were the most distant (median distance: 62.2μm), and our study found that most lymphocytes clustered together within the peritumoral range of 10-30 μm where cross-talk with TCs could be achieved. We also found that the classification of TME could be achieved using CD8+ T-cell density, which is correlated with the prognosis of patients. In addition, we achieved single chromogenic IHC slices overlap based on CD4-stained IHC slices. We explored the number and spatial distribution of cells in heterogeneous TME of NSCLC patients and achieved TME classification. We also found a way to show the co-expression of multiple molecules economically.

Aberrant cytoplasmic expression of UHRF1 restrains the MHC-I-mediated anti-tumor immune response

Immunotherapy successfully complements traditional cancer treatment. However, primary and acquired resistance might limit efficacy. Reduced antigen presentation by MHC-I has been identified as potential resistance factor. Here we show that the epigenetic regulator ubiquitin-like with PHD and ring finger domains 1 (UHRF1), exhibits altered expression and aberrant cytosolic localization in cancerous tissues, where it promotes MHC-I ubiquitination and degradation. Cytoplasmic translocation of UHRF1 is induced by its phosphorylation on a specific serine in response to signals provided by factors present in the tumor microenvironment (TME), such as TGF-β, enabling UHRF1 to bind MHC-I. Downregulation of MHC-I results in suppression of the antigen presentation pathway to establish an immune hostile TME. UHRF1 inactivation by genetic deletion synergizes with immune checkpoint blockade (ICB) treatment and induces an anti-tumour memory response by evoking low-affinity T cells. Our study adds to the understanding of UHRF1 in cancer immune evasion and provides a potential target to synergize with immunotherapy and overcome immunotherapeutic resistance.

Non-coding RNAs as modulators of radioresponse in triple-negative breast cancer: a systematic review

Triple-negative breast cancer (TNBC), characterized by high invasiveness, is associated with poor prognosis and elevated mortality rates. Despite the development of effective therapeutic targets for TNBC, systemic chemotherapy and radiotherapy (RdT) remain prevalent treatment modalities. One notable challenge of RdT is the acquisition of radioresistance, which poses a significant obstacle in achieving optimal treatment response. Compelling evidence implicates non-coding RNAs (ncRNAs), gene expression regulators, in the development of radioresistance. This systematic review focuses on describing the role, association, and/or involvement of ncRNAs in modulating radioresponse in TNBC. In adhrence to the PRISMA guidelines, an extensive and comprehensive search was conducted across four databases using carefully selected entry terms. Following the evaluation of the studies based on predefined inclusion and exclusion criteria, a refined selection of 37 original research articles published up to October 2023 was obtained. In total, 33 different ncRNAs, including lncRNAs, miRNAs, and circRNAs, were identified to be associated with radiation response impacting diverse molecular mechanisms, primarily the regulation of cell death and DNA damage repair. The findings highlighted in this review demonstrate the critical roles and the intricate network of ncRNAs that significantly modulates TNBC's responsiveness to radiation. The understanding of these underlying mechanisms offers potential for the early identification of non-responders and patients prone to radioresistance during RdT, ultimately improving TNBC survival outcomes.

SNRPB2 in the pan-cancer landscape: A bioinformatics exploration and validation in hepatocellular carcinoma

Aberrant splicing is a significant contributor to gene expression abnormalities in cancer. SNRPB2, a component of U2 small nuclear ribonucleoprotein particles (snRNPs), contributes to the assembly of the spliceosome, the molecular machinery responsible for splicing. To date, few studies have investigated the role of SNRPB2 in tumorigenesis. We examined data sourced from various public databases, such as The Cancer Genome Atlas(TCGA), the Clinical Proteomic Tumor Analysis Consortium(CPTAC), and Gene Expression Omnibus(GEO). Our investigation included gene expression, genomic and epigenomic scrutiny, gene set enrichment assessment(GSEA), and immune cell infiltration evaluation. Furthermore, we performed empirical validation to ascertain the impact of SNRPB2 suppression on the proliferation and migration of liver cancer cells. Analysis of gene expression revealed widespread upregulation of SNRPB2 across a spectrum of cancer types, with heightened levels of SNRPB2 expression in numerous tumors linked to unfavorable prognosis. Genomic and epigenomic assessments revealed connections between SNRPB2 expression and variations in SNRPB2 copy number, DNA methylation patterns, and RNA modifications. Through gene set enrichment analysis, the involvement of SNRPB2 in vital biological processes and pathways related to cancer was identified. Furthermore, scrutiny of immune cell infiltration suggested a potential relationship between SNRPB2 and the tumor microenvironment, which was reinforced by multiple single-cell sequencing profiles. Subsequent experimental validation revealed that silencing SNRPB2 effectively impeded the proliferation and migration of liver cancer cells. Taken together, these findings underscore the prospective utility of SNRPB2 as a prognostic biomarker and a promising candidate for immunotherapy in cancer. It is necessary to engage in additional exploration into its underlying mechanisms and clinical treatment potential.

Synthesis, SAR, and application of JQ1 analogs as PROTACs for cancer therapy

JQ1 is a wonder therapeutic molecule that selectively inhibits the BRD4 signaling pathway and is thus widely used in the anticancer drug discovery program. Due to its unique selective BRD4 binding property, its applications are further extended in the design and synthesis of bi-functional PROTAC molecules. This BRD4 targeting PROTAC molecule selectively degrades the protein by proteolysis. There are several modifications of JQ1 known to date and extensively explored for their applications in PROTAC technology by several research groups in academia as well as industry for targeting oncogenic genes. In this review, we have covered the discovery and synthesis of the JQ1 molecule. The SAR of the JQ1 analogs will help researchers develop potent JQ1 compounds with improved inhibitory properties against malignant cells. Furthermore, we explored the potential application of JQ1 analogs in PROTAC technology. The brief history of the bromodomain family of proteins, as well as the obstacles connected with PROTAC technology, can help comprehend the context of the current research, which has the potential to improve the drug development process. Overall, this review comprehensively appraises JQ1 molecules and their prior implementation in PROTAC technology and cancer therapy.

Improving glioma drug delivery: A multifaceted approach for glioma drug development

Glioma is one of the most common central nervous system (CNS) cancers that can be found within the brain and the spinal cord. One of the pressing issues plaguing the development of therapeutics for glioma originates from the selective and semipermeable CNS membranes: the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB). It is difficult to bypass these membranes and target the desired cancerous tissue because the purpose of the BBB and BSCB is to filter toxins and foreign material from invading CNS spaces. There are currently four varieties of Food and Drug Administration (FDA)-approved drug treatment for glioma; yet these therapies have limitations including, but not limited to, relatively low transmission through the BBB/BSCB, despite pharmacokinetic characteristics that allow them to cross the barriers. Steps must be taken to improve the development of novel and repurposed glioma treatments through the consideration of pharmacological profiles and innovative drug delivery techniques. This review addresses current FDA-approved glioma treatments' gaps, shortcomings, and challenges. We then outline how incorporating computational BBB/BSCB models and innovative drug delivery mechanisms will help motivate clinical advancements in glioma drug delivery. Ultimately, considering these attributes will improve the process of novel and repurposed drug development in glioma and the efficacy of glioma treatment.

Non-coding RNAs as potential targets in metformin therapy for cancer

Metformin, a widely used oral hypoglycemic drug, has emerged as a potential therapeutic agent for cancer treatment. While initially known for its role in managing diabetes, accumulating evidence suggests that metformin exhibits anticancer properties through various mechanisms. Several cellular or animal experiments have attempted to elucidate the role of non-coding RNA molecules, including microRNAs and long non-coding RNAs, in mediating the anticancer effects of metformin. The present review summarized the current understanding of the mechanisms by which non-coding RNAs modulate the response to metformin in cancer cells. The regulatory roles of non-coding RNAs, particularly miRNAs, in key cellular processes such as cell proliferation, cell death, angiogenesis, metabolism and epigenetics, and how metformin affects these processes are discussed. This review also highlights the role of lncRNAs in cancer types such as lung adenocarcinoma, breast cancer, and renal cancer, and points out the need for further exploration of the mechanisms by which metformin regulates lncRNAs. In addition, the present review explores the potential advantages of metformin-based therapies over direct delivery of ncRNAs, and this review highlights the mechanisms of non-coding RNA regulation when metformin is combined with other therapies. Overall, the present review provides insights into the molecular mechanisms underlying the anticancer effects of metformin mediated by non-coding RNAs, offering novel opportunities for the development of personalized treatment strategies in cancer patients.

A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids

Genotypic and phenotypic diversity, which generates heterogeneity during disease evolution, is common in cancer. The identification of features specific to each patient and tumor is central to the development of precision medicine and preclinical studies for cancer treatment. However, the complexity of the disease due to inter- and intratumor heterogeneity increases the difficulty of effective analysis. Here, we introduce a sequential deep learning model, preprocessing to organize the complexity due to heterogeneity, which contrasts with general approaches that apply a single model directly. We characterized morphological heterogeneity using microscopy images of patient-derived organoids (PDOs) and identified gene subsets relevant to distinguishing differences among original tumors. PDOs, which reflect the features of their origins, can be reproduced in large quantities and varieties, contributing to increasing the variation by enhancing their common characteristics, in contrast to those from different origins. This resulted in increased efficiency in the extraction of organoid morphological features sharing the same origin. Linking these tumor-specific morphological features to PDO gene expression data enables the extraction of genes strongly correlated with intertumor differences. The relevance of the selected genes was assessed, and the results suggest potential applications in preclinical studies and personalized clinical care.

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

The therapeutic potential of bee venom-derived Apamin and Melittin conjugates in cancer treatment: A systematic review

The therapeutic potential of bee venom-derived peptides, particularly apamin and melittin, in cancer treatment has garnered significant attention as a promising avenue for advancing oncology. This systematic review examines preclinical studies highlighting the emerging role of these peptides in enhancing cancer therapies. Melittin and apamin, when conjugated with other therapeutic agents or formulated into novel delivery systems, have demonstrated improved efficacy in targeting tumor cells. Key findings indicate that melittin-based conjugates, such as polyethylene glycol (PEG)ylated versions, show potential in enhancing therapeutic outcomes and minimizing toxicity across various cancer models. Similarly, apamin-conjugated formulations have improved the efficacy of established anti-cancer drugs, contributing to enhanced targeting and reduced systemic toxicity. These developments underscore a growing interest in leveraging bee venom-derived peptides as adjuncts in cancer therapy. The integration of these peptides into treatment regimens offers a promising strategy to address current limitations in cancer treatment, such as drug resistance and off-target effects. However, comprehensive validation through clinical trials is essential to confirm their safety and effectiveness in human patients. This review highlights the global emergence of bee venom-derived peptides in cancer treatment, advocating for continued research and development to fully realize their therapeutic potential.

Leveraging the Photofunctions of Transition Metal Complexes for the Design of Innovative Phototherapeutics

Despite the advent of various medical interventions for cancer treatment, the disease continues to pose a formidable global health challenge, necessitating the development of new therapeutic approaches for more effective treatment outcomes. Photodynamic therapy (PDT), which utilizes light to activate a photosensitizer to produce cytotoxic reactive oxygen species (ROS) for eradicating cancer cells, has emerged as a promising approach for cancer treatment due to its high spatiotemporal precision and minimal invasiveness. However, the widespread clinical use of PDT faces several challenges, including the inefficient production of ROS in the hypoxic tumor microenvironment, the limited penetration depth of light in biological tissues, and the inadequate accumulation of photosensitizers at the tumor site. Over the past decade, there has been increasing interest in the utilization of photofunctional transition metal complexes as photosensitizers for PDT applications due to their intriguing photophysical and photochemical properties. This review provides an overview of the current design strategies used in the development of transition metal complexes as innovative phototherapeutics, aiming to address the limitations associated with PDT and achieve more effective treatment outcomes. The current challenges and future perspectives on the clinical translation of transition metal complexes are also discussed.

LncRNA DARS-AS1 in human cancers: A comprehensive review of its potency as a biomarker and therapeutic target

Long non-coding RNAs have emerged as important players in cancer biology. Increasing evidence has uncovered their potency in improving cancer management as they can be used as a credible prognostic and diagnostic biomarker. Recently, DARS-AS1 has gained significant attention for its involvement in facilitating tumor progression. So far, numerous research has been reported its upregulation in different malignancies of human body systems and revealed its association with cancer hallmarks as well as clinicopathological characteristics. Importantly, targeting DARS-AS1 holds promise in cancer therapy. In the current study, we provide an in-depth analysis of its expression status and explore the underlying mechanisms through which DARS-AS1 contributes to tumor initiation, growth, invasion, and metastasis. Additionally, we examine the correlation between DARS-AS1 expression and clinicopathological features of cancer patients, shedding light on its potential as a cancer biomarker. Furthermore, we discuss the therapeutic potential of targeting DARS-AS1 in cancer treatment, highlighting emerging strategies, such as RNA interference and small molecule inhibitors. Boosting the understanding of its functional role can open new avenues for precision medicine, thus resulting in better outcomes for cancer patients.

Synergistic anticancer immunity in metastatic triple-negative breast cancer through an in situ amplifying Peptide-Drug Conjugate

Despite significant progress in combining cancer immunotherapy with chemotherapy to treat triple negative breast cancer (TNBC), challenges persist due to target depletion and tumor heterogeneity, especially in metastasis. Chemotherapy lacks precise targeting abilities, and targeted therapy is inadequate in addressing the diverse heterogeneity of tumors. To address these challenges, we introduce RGDEVD-DOX as a tumor-specific immunogenic agent, namely TPD1, which targets integrin αvβ3 and gets continuously activated by apoptosis. TPD1 facilitates the caspase-3-mediated in situ amplification that results in tumor-specific accumulation of doxorubicin. This local concentration of doxorubicin induces immunogenic cell death and promotes the recruitment of immune cells to the tumor site. Notably, the tumor-targeting capabilities of TPD1 help bypass the systemic immunotoxicity of doxorubicin. Consequently, this alters the tumor microenvironment, converting it into a 'hot' tumor that is more susceptible to immune checkpoint inhibition. We demonstrated the anti-metastatic and anti-cancer efficacy of this treatment using various xenograft and metastatic models. This study underscores the high potential of caspase-3 cleavable peptide-drug conjugates to be used in conjunction with anti-cancer immunotherapies.

Impact of Duration of Adjuvant Therapy on Patients with Initially Unresectable Hepatocellular Carcinoma After Conversion Surgery: A Propensity Score Matching Study

Background

This study aimed to assess the effect of adjuvant therapy with different durations in patients with initially unresectable hepatocellular carcinoma (uHCC) after conversion surgery.

Methods

This study included 85 patients with initially uHCC who received conversion surgery between May 2019 and November 2022. They were divided into the long duration group (n = 57) and short duration group (n = 28) based on postoperative medication duration. Recurrence-free survival (RFS) and overall survival (OS) were analyzed and compared between the cohorts.

Results

No significant difference in RFS or OS was found between the two groups [RFS: hazard ratio (HR) = 0.486; 95% confidence interval (CI), 0.229-1.034, P = 0.061; OS: HR = 0.377; 95% CI, 0.119-1.196, P = 0.098]. Patients without major pathologic response (MPR) in the long duration group had better RFS and OS results compared to those in the short duration group (RFS: HR = 0.242; 95% CI, 0.092-0.634, P = 0.004; OS: HR = 0.264; 95% CI, 0.079-0.882, P = 0.031). No significant difference was detected in RFS or OS between the two groups in patients with MPR (RFS: HR = 1.250; 95% CI, 0.373-4.183, P = 0.718; OS: HR = 7.389; 95% CI, 0.147-372.4, P = 0.317). After propensity score matching, 25 pairs of patients were selected and the results remained consistent.

Conclusion

At least 6 months of adjuvant therapy may be beneficial for patients without MPR after conversion surgery. However, in patients with MPR, the effect of adjuvant therapy remains unclear. Further studies are needed to confirm the optimal duration of adjuvant therapy.

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment

Background

Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene signature (HPRGS) and explore its clinical application value.

Methods

TCGA-LIHC cohort was used for training, and the LIRI-JP cohort and HCC cDNA microarray were used for validation. Machine learning algorithms constructed a prognostic gene label for HCC. Kaplan-Meier (K-M), ROC curve, multiple analyses, algorithms, and online databases were used to analyze differences between high- and low-risk populations. A nomogram was constructed to facilitate clinical application.

Results

We identified 119 differential genes based on transcriptome sequencing data from five independent HCC cohorts, and 53 of these genes were associated with overall survival (OS). Using 101 machine learning algorithms, the 10 most prognostic genes were selected. We constructed an HCC HPRGS with four genes (SOCS2, LCAT, ECT2, and TMEM106C). Good predictive performance of the HPRGS was confirmed by ROC, C-index, and K-M curves. Mutation analysis showed significant differences between the low- and high-risk patients. The low-risk group had a higher response to transcatheter arterial chemoembolization (TACE) and immunotherapy. Treatment response of high- and low-risk groups to small-molecule drugs was predicted. Linifanib was a potential drug for high-risk populations. Multivariate analysis confirmed that HPRGS were independent prognostic factors in TCGA-LIHC. A nomogram provided a clinical practice reference.

Conclusion

We constructed an HPRGS for HCC, which can accurately predict OS and guide the treatment decisions for patients with HCC.

A logic-activated nanoswitch for killing cancer cells according to assessment of drug-resistance

A logic-activated nanoswitch that could diagnose the differences between drug-resistant and non-drug-resistant cancer cells and control the release of drugs was developed for enhanced chemo-gene therapy using a standalone system. Compared to traditional treatments, the nanoswitch displayed improved anti-tumor efficiency in vitro.

Tumor-induced endothelial RhoA activation mediates tumor cell transendothelial migration and metastasis

The endothelial barrier plays an active role in transendothelial tumor cell migration during metastasis, however, the endothelial regulatory elements of this step remain obscure. Here we show that endothelial RhoA activation is a determining factor during this process. Breast tumor cell-induced endothelial RhoA activation is the combined outcome of paracrine IL-8-dependent and cell-to-cell contact β 1 integrin-mediated mechanisms, with elements of this pathway correlating with clinical data. Endothelial-specific RhoA blockade or in vivo deficiency inhibited the transendothelial migration and metastatic potential of human breast tumor and three murine syngeneic tumor cell lines, similar to the pharmacological blockade of the downstream RhoA pathway. These findings highlight endothelial RhoA as a potent, universal target in the tumor microenvironment for anti-metastatic treatment of solid tumors.

Negative-predictive value of SUVmax for Ascertaining the efficacy of osimertinib in EGFR mutation-positive non-small cell lung cancer

BACKGROUND

Fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) is routinely used to stage non-small cell lung cancer (NSCLC). However, whether 18F-FDG accumulation in primary tumors affects the efficacy of osimertinib in patients with epidermal growth factor receptor (EGFR) mutation-positive NSCLC remains unclear.

METHODS

We retrospectively investigated 74 patients with advanced or postoperative recurrent EGFR mutation-positive NSCLC who underwent 18F-FDG PET/CT and were treated with osimertinib as first-line therapy between September 2018 and March 2023 at Kumamoto University Hospital. The maximum standardized uptake value (SUVmax) of each primary tumor was measured, and the patients were divided into two groups according to the median SUVmax. The effects of SUVmax on progression-free survival (PFS) and overall survival (OS) were assessed using a multivariate Cox proportional hazards model.

RESULTS

The median SUVmax was 8.2 (interquartile range: 5.5-11.4). The median PFS in the high SUVmax group (≥8.2) was significantly shorter than that in the low SUVmax group (<8.2). The respective median PFSs were 11.2 months (95% confidence interval [CI]: 3.1-19.3 months) vs. 22.9 months (95% CI: 12.4-33.4 months) (P = 0.015), although the OS values did not differ significantly. Multivariate analysis showed that a high SUVmax was an independent negative predictive factor for PFS in patients treated with osimertinib (hazard ratio, 2.25; 95% CI: 1.15-4.39, P = 0.017).

CONCLUSIONS

High primary-lesion SUVmax in patients with EGFR mutation-positive NSCLC correlated with shorter PFS with first-line osimertinib therapy, suggesting that SUVmax is a useful predictive marker for the antitumor efficacy of osimertinib.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Harnessing curcumin in a multifunctional biodegradable metal-organic framework (bio-MOF) for targeted colorectal cancer theranostics

Despite significant advancements in managing colorectal cancer (CRC), the issues of efficient diagnosis and targeted therapy remain demanding. To address these challenges and improve treatment outcomes while reducing the cost and side effects, there is a need for more effective theranostic systems that combine diagnostic techniques with therapeutic modalities. This study introduces a pioneering approach for the synthesis of a porous bio-MOF (biodegradable metal-organic framework) using iron as the metal component and curcumin as the pharmaceutical ingredient. Subsequently, the developed drug delivery system was equipped with the anticancer drug doxorubicin (DOX), coated with biocompatible polyethylene glycol (PEG), and targeted with a CRC-specific aptamer (EpCAM). The physicochemical characterization confirmed the successful synthesis of the bio-MOF, demonstrating high encapsulation efficiency and pH-dependent release of DOX. In vitro studies for anticancer activity, cellular uptake, and mechanism of cell death demonstrated that in the case of positive EpCAM HT-29 cells, Apt-PEG-MOF@DOX had enhanced internalization that resulted in massive apoptosis. In vivo studies of the nanoparticles were then conducted in immunocompromised C57BL/6 mice bearing HT-29 tumors. These studies showed that the targeted platform could induce efficient tumor regression with reduced systemic toxicity. The targeted bio-MOF also exhibited MRI imaging properties useful for monitoring tumors. Significantly, the biocompatibility of the introduced bio-MOF was enhanced by pursuing the green synthesis method, which does not engage toxic solvents and strong acids. Overall, this multimodal system acts diversely as a tumor imaging agent and a therapeutic delivery platform suitable for CRC theranostics.

Single-cell RNA sequencing to map tumor heterogeneity in gastric carcinogenesis paving roads to individualized therapy

Gastric cancer (GC) is a highly heterogeneous disease with a complex tumor microenvironment (TME) that encompasses multiple cell types including cancer cells, immune cells, stromal cells, and so on. Cancer-associated cells could remodel the TME and influence the progression of GC and therapeutic response. Single-cell RNA sequencing (scRNA-seq), as an emerging technology, has provided unprecedented insights into the complicated biological composition and characteristics of TME at the molecular, cellular, and immunological resolutions, offering a new idea for GC studies. In this review, we discuss the novel findings from scRNA-seq datasets revealing the origin and evolution of GC, and scRNA-seq is a powerful tool for investigating transcriptional dynamics and intratumor heterogeneity (ITH) in GC. Meanwhile, we demonstrate that the vital immune cells within TME, including T cells, B cells, macrophages, and stromal cells, play an important role in the disease progression. Additionally, we also overview that how scRNA-seq facilitates our understanding about the effects on individualized therapy of GC patients. Spatial transcriptomes (ST) have been designed to determine spatial distribution and capture local intercellular communication networks, enabling a further understanding of the relationship between the spatial background of a particular cell and its functions. In summary, scRNA-seq and other single-cell technologies provide a valuable perspective for molecular and pathological disease characteristics and hold promise for advancing basic research and clinical practice in GC.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

Introduction

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

Methods

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

Results

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Discussion

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Biological Barriers for Drug Delivery and Development of Innovative Therapeutic Approaches in HIV, Pancreatic Cancer, and Hemophilia A/B

Biological barriers remain a major obstacle for the development of innovative therapeutics. Depending on a disease's pathophysiology, the involved tissues, cell populations, and cellular components, drugs often have to overcome several biological barriers to reach their target cells and become effective in a specific cellular compartment. Human biological barriers are incredibly diverse and include multiple layers of protection and obstruction. Importantly, biological barriers are not only found at the organ/tissue level, but also include cellular structures such as the outer plasma membrane, the endolysosomal machinery, and the nuclear envelope. Nowadays, clinicians have access to a broad arsenal of therapeutics ranging from chemically synthesized small molecules, biologicals including recombinant proteins (such as monoclonal antibodies and hormones), nucleic-acid-based therapeutics, and antibody-drug conjugates (ADCs), to modern viral-vector-mediated gene therapy. In the past decade, the therapeutic landscape has been changing rapidly, giving rise to a multitude of innovative therapy approaches. In 2018, the FDA approval of patisiran paved the way for small interfering RNAs (siRNAs) to become a novel class of nucleic-acid-based therapeutics, which-upon effective drug delivery to their target cells-allow to elegantly regulate the post-transcriptional gene expression. The recent approvals of valoctocogene roxaparvovec and etranacogene dezaparvovec for the treatment of hemophilia A and B, respectively, mark the breakthrough of viral-vector-based gene therapy as a new tool to cure disease. A multitude of highly innovative medicines and drug delivery methods including mRNA-based cancer vaccines and exosome-targeted therapy is on the verge of entering the market and changing the treatment landscape for a broad range of conditions. In this review, we provide insights into three different disease entities, which are clinically, scientifically, and socioeconomically impactful and have given rise to many technological advancements: acquired immunodeficiency syndrome (AIDS) as a predominant infectious disease, pancreatic carcinoma as one of the most lethal solid cancers, and hemophilia A/B as a hereditary genetic disorder. Our primary objective is to highlight the overarching principles of biological barriers that can be identified across different disease areas. Our second goal is to showcase which therapeutic approaches designed to cross disease-specific biological barriers have been promising in effectively treating disease. In this context, we will exemplify how the right selection of the drug category and delivery vehicle, mode of administration, and therapeutic target(s) can help overcome various biological barriers to prevent, treat, and cure disease.

Tumour immune characterisation of primary triple-negative breast cancer using automated image quantification of immunohistochemistry-stained immune cells

The tumour immune microenvironment (TIME) in breast cancer is acknowledged with an increasing role in treatment response and prognosis. With a growing number of immune markers analysed, digital image analysis may facilitate broader TIME understanding, even in single-plex IHC data. To facilitate analyses of the latter an open-source image analysis pipeline, Tissue microarray MArker Quantification (TMArQ), was developed and applied to single-plex stainings for p53, CD3, CD4, CD8, CD20, CD68, FOXP3, and PD-L1 (SP142 antibody) in a 218-patient triple negative breast cancer (TNBC) cohort with complementary pathology scorings, clinicopathological, whole genome sequencing, and RNA-sequencing data. TMArQ's cell counts for analysed immune markers were on par with results from alternative methods and consistent with both estimates from human pathology review, different quantifications and classifications derived from RNA-sequencing as well as known prognostic patterns of immune response in TNBC. The digital cell counts demonstrated how immune markers are coexpressed in the TIME when considering TNBC molecular subtypes and DNA repair deficiency, and how combination of immune status with DNA repair deficiency status can improve the prognostic stratification in chemotherapy treated patients. These results underscore the value and potential of integrating TIME and specific tumour intrinsic alterations/phenotypes for the molecular understanding of TNBC.

Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer

BACKGROUND

Lung cancer is one of the most common cancers worldwide, with the incidence increasing each year. It is crucial to improve the prognosis of patients who have lung cancer. Non-Small Cell Lung Cancer (NSCLC) accounts for the majority of lung cancer. Though its prognostic significance in NSCLC has not been often documented, Endoplasmic Reticulum (ER) stress has been identified to be implicated in tumour malignant behaviours and resistance to treatment.

OBJECTIVE

This work aimed to develop a gene profile linked to ER stress that could be applied to predictive and risk assessment for non-small cell lung cancer.

METHODS

Data from 1014 NSCLC patients were sourced from The Cancer Genome Atlas (TCGA) database, integrating clinical and Ribonucleic Acid (RNA) information. Diverse analytical techniques were utilized to identify ERS-associated genes associated with patients' prognoses. These techniques included Kaplan-Meier analysis, univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression analysis (LASSO) regression, and Pearson correlation analysis. Using a risk score model obtained from multivariate Cox analysis, a nomogram was created and validated to classify patients into high- and low-risk groups. The study employed the CIBERSORT algorithm and Single-Sample Gene Set Eenrichment Analysis (ssGSEA) to investigate the tumour immune microenvironment. We used the Genomics of Drug Sensitivity in Cancer (GDSC) database and R tools to identify medicines that could be responsive.

RESULTS

Four genes - FABP5, C5AR1, CTSL, and LTA4H - were chosen to create the risk model. Overall Survival (OS) was considerably lower (P< 0.05) in the high-risk group. When it came to predictive accuracy, the risk model outperformed clinical considerations. Several medication types that are sensitive to high-risk groups were chosen.

CONCLUSION

Our study has produced a gene signature associated with ER stress that may be employed to forecast the prognosis and therapeutic response of non-small cell lung cancer patients.

Advancements and Challenges in Personalized Therapy for BRAF-Mutant Melanoma: A Comprehensive Review

Over the past several decades, advancements in the treatment of BRAF-mutant melanoma have led to the development of BRAF inhibitors, BRAF/MEK inhibitor combinations, anti-PD-1 therapy, and anti-CTLA4 therapy. Although these therapies have shown substantial efficacy in clinical trials, their sustained effectiveness is often challenged by the tumor microenvironment, which is a highly heterogeneous and complex milieu of immunosuppressive cells that affect tumor progression. The era of personalized medicine holds substantial promise for the tailoring of treatments to individual genetic profiles. However, tumor heterogeneity and immune evasion mechanisms contribute to the resistance to immunotherapy. Despite these challenges, tumor-infiltrating lymphocyte (TIL) therapy, as exemplified by lifileucel, has demonstrated notable efficacy against BRAF V600-mutant melanoma. Additionally, early response biomarkers, such as COX-2 and MMP2, along with FDG-PET imaging, offer the potential to improve personalized immunotherapy by predicting patient responses and determining the optimal treatment duration. Future efforts should focus on reducing the T-cell harvesting periods and costs associated with TIL therapy to enhance efficiency and accessibility.

A pH-Responsive Protein Assembly through Clustering of a Charge-Tunable Single Amino Acid Repeat

Specific targeting of tumor cells is a key to achieving high therapeutic efficacy while minimizing off-target side effects. As a general approach to targeting diverse tumor cells, considerable attention has been paid to the tumor microenvironment, particularly its slightly acidic pH (6.5-6.8). However, existing pH-sensitive nanomaterials, based on organic polymers and proteins, often lack sufficient pH sensitivity and specificity. Here, we demonstrate a strategy to construct a pH-responsive protein assembly through clustering of a single amino acid repeat as a charge-tunable moiety. As a proof of concept, a histidine peptide with varying lengths was displayed on the surface of a ferritin assembly composed of 24 subunits by genetic fusion to a subunit. The resulting self-assembled ferritin particles, termed "pHerricle (pH-responsive ferritin particle)", were shown to exhibit a specific binding to tumor cells in response to pH changes through cooperative effects of histidine peptides. Increasing the histidine peptide length from 0 to 12 residues increased the pHerricle's cell-binding capacity by 21-fold and allowed modulation of the targetable pH range. General applicability as a tumor cell-targeting platform was shown by specific delivery of a cytotoxic cargo by the pHerricle into tumor cells of various origins in a pH-dependent manner.

Enhancing Anti-Cancer Immune Response by Acidosis-Sensitive Nanobody Display

One of the main challenges with many cancer immunotherapies is that biomarkers are needed for targeting. These biomarkers are often associated with tumors but are not specific to a particular tumor and can lead to damage in healthy tissues, resistance to treatment, or the need for customization for different types of cancer due to variations in targets. A promising alternative approach is to target the acidic microenvironment found in most solid tumor types. This can be achieved using the pH (Low) Insertion Peptide (pHLIP), which inserts selectively into cell membranes under acidic conditions, sparing healthy tissues. pHLIP has shown potential for imaging, drug delivery, and surface display. For instance, we previously used pHLIP to display epitopes on the surfaces of cancer cells, enabling antibody-mediated immune cell recruitment and selective killing of cancer cells. In this study, we further explored this concept by directly fusing an anti-CD16 nanobody, which activates natural killer (NK) cells, to pHLIP, eliminating the need for antibody recruitment. Our results demonstrated the insertion of pH-sensitive agents into cancer cells, activation of the CD16 receptor on effector cells, and successful targeting and destruction of cancer cells by high-affinity CD16+ NK cells in two cancer cell lines.

Highly selective detection of breast cancer cells mediated by multi-aptamer and dye-loaded mesoporous silica nanoparticles

Multi-aptamer recognition of breast cancer cells (MCF-7) is utilized to achieve high specificity. The method comprises two parts, aptamer-functionalized mesoporous silica nanoparticles (MSNs) loaded with dissimilar dyes (thymolphthalein or curcumin) as signal transducers and aptamer-modified magnetic beads (MBs) as capture agents, which worked together to detect MCF-7 cells sensitively and accurately. The results indicated that the aptasensor has a linear detection range of 100 to 4000 cells and a detection threshold of 10 cells/mL. The method had been successfully employed to detect breast cancer cells in real blood samples to distinguish between breast cancer patients and healthy individuals. In conclusion, the development of the multi-aptamer-based colorimetric sensor offered a novel method for the highly selective detection of MCF-7 cells, contributing to the accurate identification of breast cancer.

MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction

BACKGROUND AND OBJECTIVE

Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating information from various modalities, such as genetic and clinical data, and developing multi-modal deep learning models can enhance prediction accuracy. However, most existing multi-modal deep learning methods either overlook patient similarities that benefit prognosis prediction or fail to effectively capture diverse information due to measuring patient similarities from a single perspective. To address these issues, a novel framework called multi-modal multi-view graph convolutional networks (MMGCN) is proposed for cancer prognosis prediction.

METHODS

Initially, we utilize the similarity network fusion (SNF) algorithm to merge patient similarity networks (PSNs), individually constructed using gene expression, copy number alteration, and clinical data, into a fused PSN for integrating multi-modal information. To capture diverse perspectives of patient similarities, we treat the fused PSN as a multi-view graph by considering each single-edge-type subgraph as a view graph, and propose multi-view graph convolutional networks (GCNs) with a view-level attention mechanism. Moreover, an edge homophily prediction module is designed to alleviate the adverse effects of heterophilic edges on the representation power of GCNs. Finally, comprehensive representations of patient nodes are obtained to predict cancer prognosis.

RESULTS

Experimental results demonstrate that MMGCN outperforms state-of-the-art baselines on four public datasets, including METABRIC, TCGA-BRCA, TCGA-LGG, and TCGA-LUSC, with the area under the receiver operating characteristic curve achieving 0.827 ± 0.005, 0.805 ± 0.014, 0.925 ± 0.007, and 0.746 ± 0.013, respectively.

CONCLUSIONS

Our study reveals the effectiveness of the proposed MMGCN, which deeply explores patient similarities related to different modalities from a broad perspective, in enhancing the performance of multi-modal cancer prognosis prediction. The source code is publicly available at https://github.com/ping-y/MMGCN.

Metabolic profiling of glioblastoma stem cells reveals pyruvate carboxylase as a critical survival factor and potential therapeutic target

BACKGROUND

Glioblastoma (GBM) is a highly aggressive tumor with unmet therapeutic needs, which can be explained by extensive intra-tumoral heterogeneity and plasticity. In this study, we aimed to investigate the specific metabolic features of Glioblastoma stem cells (GSC), a rare tumor subpopulation involved in tumor growth and therapy resistance.

METHODS

We conducted comprehensive analyses of primary patient-derived GBM cultures and GSC-enriched cultures of human GBM cell lines using state-of-the-art molecular, metabolic, and phenotypic studies.

RESULTS

We showed that GSC-enriched cultures display distinct glycolytic profiles compared with differentiated tumor cells. Further analysis revealed that GSC relies on pyruvate carboxylase (PC) activity for survival and self-renewal capacity. Interestingly, inhibition of PC led to GSC death, particularly when the glutamine pool was low, and increased differentiation. Finally, while GSC displayed resistance to the chemotherapy drug etoposide, genetic or pharmacological inhibition of PC restored etoposide sensitivity in GSC, both in vitro and in orthotopic murine models.

CONCLUSIONS

Our findings demonstrate the critical role of PC in GSC metabolism, survival, and escape to etoposide. They also highlight PC as a therapeutic target to overcome therapy resistance in GBM.

High-throughput fluorescence lifetime imaging flow cytometry

Flow cytometry is a vital tool in biomedical research and laboratory medicine. However, its accuracy is often compromised by undesired fluctuations in fluorescence intensity. While fluorescence lifetime imaging microscopy (FLIM) bypasses this challenge as fluorescence lifetime remains unaffected by such fluctuations, the full integration of FLIM into flow cytometry has yet to be demonstrated due to speed limitations. Here we overcome the speed limitations in FLIM, thereby enabling high-throughput FLIM flow cytometry at a high rate of over 10,000 cells per second. This is made possible by using dual intensity-modulated continuous-wave beam arrays with complementary modulation frequency pairs for fluorophore excitation and acquiring fluorescence lifetime images of rapidly flowing cells. Moreover, our FLIM system distinguishes subpopulations in male rat glioma and captures dynamic changes in the cell nucleus induced by an anti-cancer drug. FLIM flow cytometry significantly enhances cellular analysis capabilities, providing detailed insights into cellular functions, interactions, and environments.

Nab-paclitaxel plus S-1 followed by gemcitabine-oxaliplatin as first-line alternating sequential treatment of pancreatic ductal adenocarcinoma

BACKGROUND

Alternating sequential administration of drugs may be a promising approach to overcome chemotherapy resistance in advanced pancreatic ductal adenocarcinoma (PDAC).

METHODS

This study was an open-label, single-arm, and prospective trial included patients with untreated advanced PDAC. They received 2 cycles of NS regimen (nab-paclitaxel:125 mg/m2, intravenously injected on days 1 and 8, plus S-1:40-60 mg, orally twice per day for 1-14 days) followed by 2 cycles of GemOx regimen (gemcitabine, intravenously injected on days 1 and 8, and oxaliplatin: 130 mg/m2, intravenously injected on day 1). The primary efficacy endpoint was a progression-free survival rate at 6 months (PFSR-6m). The secondary efficacy endpoints included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and adverse events (AEs). Specific mRNA transcripts were used to explore survival associated genes.

RESULTS

Forty-two patients received a minimum of one treatment cycle, and of these, 30 patients completed one alternating treatment consisting of 4 cycles. The PFSR-6m was 71% (95% CI = 58%-87%). The median PFS and OS were 6.53 months (95% CI = 6.03-8.43) and 11.4 months (95% CI = 9.8-14.4), respectively. Common grades 3-4 hematological AEs included neutropenia 30.9%, leukopenia 26.2%, anemia 2.4%, and thrombocytopenia in 11.9%. Patients with OS > 10 months showed high expression of HLA-DQA2 while melanoma-associated antigen genes (MAGE) were notably upregulated in patients with OS < 10 months.

CONCLUSION

The alternating sequential administration of the NS and GemOx regimens may be a novel approach for first-line chemotherapy in patients with advanced PDAC requiring further study (ClinicalTrials.gov Identifier: ChiCTR1900024867).

Mutational profile of the KIT gene and its heterogeneity in primary and metastatic melanomas

BACKGROUND

This study investigates the mutational profile of the KIT gene in primary and metastatic melanomas, highlighting the significance of genetic heterogeneity.

METHODS

This research is a retrospective cohort that includes formalin-fixed and paraffin-embedded melanoma samples obtained from Hospital São Paulo, Brazil, between the years of 1996 and 2010. The research encompasses primary melanomas of the superficial spreading (SSM) and acral lentiginous (AL) subtypes and their metastases, using next-generation sequencing to explore genetic heterogeneity.

RESULTS

Despite losing 57 samples due to quality issues, 27 samples from 20 patients were analyzed, revealing a nearly equal distribution between AL and SSM subtypes. Both histological subtypes revealed KIT gene variants, including previously undescribed variants and polymorphisms, emphasizing the role of such mutations in melanoma pathogenesis and the potential for targeted therapies. Tumor heterogeneity was also observed in both histological subtypes.

CONCLUSIONS

The study underscores the complexity of melanoma, driven by diverse mutational landscapes within and across tumors and advocates for personalized treatment approaches based on detailed molecular profiling. Despite limitations like sample size, this research lays the groundwork for further investigation into melanoma's genetic intricacies and therapeutic vulnerabilities.