Influence of tumour micro-environment heterogeneity on therapeutic response

Metabolic heterogeneity in tumor cells impacts immunology in lung squamous cell carcinoma

Metabolic processes are crucial in immune regulation, yet the impact of metabolic heterogeneity on immunological functions remains unclear. Integrating metabolomics into immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. To elucidate such insight in lung squamous cell carcinoma (LUSC), we analyzed 106 LUSC tumor tissues. We performed high-resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain spatial metabolic profiles, and immunohistochemistry to detect tumor-infiltrating T lymphocytes (TILs). Unsupervised k-means clustering and Simpson's diversity index were employed to assess metabolic heterogeneity, identifying five distinct metabolic tumor subpopulations. Our findings revealed that TILs are specifically associated with metabolite distributions, not randomly distributed. Integrating a validation cohort, we found that heterogeneity-correlated metabolites interact with CD8+ TIL-associated genes, affecting survival. High metabolic heterogeneity was linked to worse survival and lower TIL levels. Pathway enrichment analyses highlighted distinct metabolic pathways in each subpopulation and their potential responses to chemotherapy. This study uncovers the significant impact of metabolic heterogeneity on immune functions in LUSC, providing a foundation for tailoring therapeutic strategies.

Tumor microenvironment pH-responsive size-transformable peptide self-assembling nanocarriers for tumor-specific treatment

Peptide-based drug carriers with exceptional biodegradability offer promising avenues for tumor-targeted therapy. Nonetheless, almost all existing drug carriers harness receptor recognition to target tumors, which ultimately fall short in addressing tumor heterogeneity. Such a strategy requires intricate chemical modifications for carriers to selectively bind to specific receptors. While these modifications may induce long-term toxicity, tumor receptors are not absolutely specific but also exist in normal cells. Thus, precision therapeutic agents may inadvertently harm healthy cells as well. Tumors possess a distinctive weak acidic (pH 6.0-6.8) tumor microenvironment (TME) that contrasts with normal tissues (pH ~7.4). Hence, we developed a TME pH-triggered multilevel self-assembling peptide with simple modifications. The drug-encapsulating self-assembled peptide is size transformable from aggregates (~1.56 μm) at pH 7.4 to positively charged nanomicelles (~100 nm) at an acidic TME by protonation, which avoids being taken up by normal cells but could readily enter tumor cells, allowing TME pH-triggered tumor-specific therapy. This study establishes a breaking strategy of using peptide for TME-based tumor-specific treatment and advances the medical applications of peptide nanomaterials.

Tumors and their microenvironments: Learning from pediatric brain pathologies

Early clues to tumors and their microenvironments come from embryonic development. Here we review the literature and consider whether the embryonic brain and its pathologies can serve as a better model. Among embryonic organs, the brain is the most heterogenous and complex, with multiple lineages leading to wide spectrum of cell states and types. Its dysregulation promotes neurodevelopmental brain pathologies and pediatric tumors. Embryonic brain pathologies point to the crucial importance of spatial heterogeneity over time, akin to the tumor microenvironment. Tumors dedifferentiate through genetic mutations and epigenetic modulations; embryonic brains differentiate through epigenetic modulations. Our innovative review proposes learning developmental brain pathologies to target tumor evolution-and vice versa. We describe ways through which tumor pharmacology can learn from embryonic brains and their pathologies, and how learning tumor, and its microenvironment, can benefit targeting neurodevelopmental pathologies. Examples include pediatric low-grade versus high-grade brain tumors as in rhabdomyosarcomas and gliomas.

CCL20-CCR6 signaling in tumor microenvironment: Functional roles, mechanisms, and immunotherapy targeting

Chemokine CC motif ligand 20 (CCL20) is a molecule with immunomodulatory properties that is involved in the regulation of diseases such as chronic inflammation, autoimmune diseases, and cancer. It operates by binding to its specific receptor, CC chemokine receptor type 6 (CCR6), and activating a complex intracellular signaling network. Building on its established role in inflammatory diseases, recent research has expanded our understanding of CCL20 to encompass its critical contributions to the tumor microenvironment (TME), highlighting its significance in cancer progression. Numerous studies have emphasized its prominent role in regulating immune responses. Consequently, Monoclonal antibodies against CCL20 and inhibitors of CCR6 have been successfully developed to block downstream signaling, making the CCL20-CCR6 axis a promising and critical target in the TME. This offers potential immunotherapeutic strategies for cancers. In this review, we summarize the biological consequences of CCL20-CCR6 mediated signaling, its role and mechanisms in the TME, and its potential applications. We suggest that the CCL20-CCR6 axis may be a novel biomarker for tumor diagnosis and prognosis, as well as a therapeutic target in various cancers.

A vascularized tumors-on-a-chip model for studying tumor-angiogenesis interplay, heterogeneity and drug responses

Current tumor models struggle to replicate the complexity of the tumor microenvironment, particularly endothelial sprouting and vascular-tumor interactions. To address these limitations, we developed a vascularized tumors-on-a-chip model by fusing tumor spheroids with HUVEC spheroids to simulate angiogenesis. The model incorporates hypoxia-driven cytokine secretion and dynamic endothelial penetration, enabling accurate recapitulation of angiogenic processes. Spheroids were optimized for size and viability, and four cancer types were studied, with GBM and A549 exhibiting the highest angiogenic potential, as confirmed by Z-stack imaging and qRT-PCR. Encapsulation in GelMA and integration into PDMS-based microfluidic chips provided a dynamic flow environment, mimicking in vivo drug delivery while enabling high-throughput drug screening. This chip-based system allows simultaneous testing of multiple drugs or tumors under physiologically relevant conditions, enhancing its translational potential. The platform was validated using doxorubicin and bevacizumab, revealing reduced VEGF secretion and dynamic cytokine responses, replicating vascular barriers. Further validation in murine models demonstrated its capacity to promote angiogenesis and mimic tumor-vessel interactions. This advanced tumors-on-a-chip model addresses critical shortcomings of conventional 2D and 3D systems and offers a transformative tool for preclinical drug evaluation and the development of precision oncology strategies, bridging the gap between in vitro testing and in vivo relevance.

Cancer-associated fibroblasts promote growth and dissemination of esophageal squamous cell carcinoma cells by secreting WNT family member 5A

Esophageal squamous cell carcinoma (ESCC) is a common and aggressive subtype of esophageal cancer. This research investigates the functions of cancer-associated fibroblasts (CAFs) in the malignant phenotype of ESCC and probes the underpinning mechanism. Key CAF-associated proteins in ESCC were identified using bioinformatics analyses. ESCC cell lines were co-cultured with CAFs, followed by the addition of neutralizing antibodies against WNT family member 5A (WNT5A) (Anti-WNT5A; AW) and frizzled class receptor 5 (FZD5) (Anti-FZD5; AF), or a human recombinant protein of WNT5A (rWNT5A; rW). The effects of CAF stimulation and the neutralizing or recombinant proteins on the growth and dissemination of ESCC cells were investigated. In addition, ESCC cells were transplanted into nude mice for in vivo assessment of tumor growth and metastasis. WNT5A was identified as a CAF-associated protein linked to poor prognosis in ESCC. Co-culturing with CAFs augmented proliferation, mobility, and apoptosis resistance of ESCC cells. These effects were negated by the AW or AF but restored by rW. WNT5A interacted with FZD5 to activate the WNT signaling in ESCC cells. The rW treatment also enhanced tumorigenesis and metastasis of xenograft tumors in nude mice, with these effects diminished by AW or AF treatment. This study suggests that CAFs promote growth and dissemination of ESCC cell primarily through the secretion of WNT5A.

Predicting the early therapeutic response to hepatic artery infusion chemotherapy in patients with unresectable HCC using a contrast-enhanced computed tomography-based habitat radiomics model: a multi-center retrospective study

OBJECTIVE

Predicting the therapeutic response before initiation of hepatic artery infusion chemotherapy (HAIC) with fluorouracil, leucovorin, and oxaliplatin (FOLFOX) remains challenging for patients with unresectable hepatocellular carcinoma (HCC). Herein, we investigated the potential of a contrast-enhanced CT-based habitat radiomics model as a novel approach for predicting the early therapeutic response to HAIC-FOLFOX in patients with unresectable HCC.

METHODS

A total of 148 patients with unresectable HCC who received HAIC-FOLFOX combined with targeted therapy or immunotherapy at three tertiary care medical centers were enrolled retrospectively. Tumor habitat features were extracted from subregion radiomics based on CECT at different phases using k-means clustering. Logistic regression was used to construct the model. This CECT-based habitat radiomics model was verified by bootstrapping and compared with a model based on clinical variables. Model performance was evaluated using the area under the curve (AUC) and a calibration curve.

RESULTS

Three intratumoral habitats with high, moderate, and low enhancement were identified to construct a habitat radiomics model for therapeutic response prediction. Patients with a greater proportion of high-enhancement intratumoral habitat showed better therapeutic responses. The AUC of the habitat radiomics model was 0.857 (95% CI: 0.798-0.916), and the bootstrap-corrected concordance index was 0.842 (95% CI: 0.785-0.907), resulting in a better predictive value than the clinical variable-based model, which had an AUC of 0.757 (95% CI: 0.679-0.834).

CONCLUSION

The CECT-based habitat radiomics model is an effective, visualized, and noninvasive tool for predicting the early therapeutic response of patients with unresectable HCC to HAIC-FOLFOX treatment and could guide clinical management and decision-making.

Perfusion-based ex vivo culture of frozen ovarian cancer tissues with preserved tumor microenvironment

Ovarian cancer (OC) poses significant treatment challenges due to late-stage diagnosis and a complex tumor microenvironment contributing to therapy resistance. We optimized a U-CUP perfusion-based bioreactor method to culture patient-derived primary and metastatic OC specimens, demonstrating that perfusion better preserves cancer cell viability and proliferation, both when fresh and slow-frozen tissues were used. Perfused cultures maintained key microenvironment components, including cancer-associated fibroblasts, endothelial and immune cells. Genetic analysis confirmed the retention in culture of tumor-specific driver mutations. We hence challenged ad hoc generated cisplatin-sensitive and resistant OC cells with cisplatin during growth in U-CUP, validating our system for the testing of drug response. Finally, treatment of slow-frozen OC tissues with carboplatin/paclitaxel revealed different degrees of response to treatment, as indicated by variations in tumor necrosis and number of residual PAX8+ cells, providing the bases for the prompt evaluation of OC standard chemotherapy efficacy in our ex vivo system.

"Actual" peptide properties required for nanoparticle development in precision cancer therapeutic delivery

Functionalizing nanoparticles with peptides (3-30 amino acids) reduces premature clearance and increases colloidal stability and targeting capacity of cancer therapeutics. Glutamate/lysine-rich zwitterionic and hydrophilic/neutral peptides minimize reticuloendothelial digestion of nanomedicine through reducing particle hydrophobicity and depressing plasma anti-PEG immunoglobulin that disrupts the PEG-based particle stealth. Anionic peptides negate protein corona formation and subsequent particle aggregation in vivo enabling efficient nanoparticles biodistribution and drug targeting by facilitating their endothelial/extracellular matrix pore diffusion. Cationic and hydrophobic peptides display a strong affinity for anionic cancer cell membrane and mediate membrane porosification or receptor binding leading to particle uptake and endocytosis. The peptide ionic and hydrophobicity/hydrophilicity attributes collectively facilitate endosomal escape, and nuclear and mitochondria targeting of nanoparticles. Peptides are required to present with different physicochemical attributes from administration site, through blood and extracellular matrix, to cancer site of action. Charge/hydrophilicity-hydrophobicity switching and projection of receptor-specific domain of peptides are attainable through pH-pKa interplay and labile bond hydrolysis of "unwanted" domain to give rise to new functional domains in response to pH, thermal and enzymatic stimuli. Co-introducing all functional attributes on a single peptide is challenging. Use of peptide blends risks leaching during nanoparticles production. Peptides-nanoparticles conjugation risks peptide conformational alterations and loss of acidic/basic termini affecting their roles in nanoparticle stabilization, targeting, membrane permeabilization and subcellular delivery.

Thymoma habitat segmentation and risk prediction model using CT imaging and K-means clustering

BACKGROUND

Thymomas, though rare, present a wide range of clinical behaviors, from indolent to aggressive forms, making accurate risk stratification crucial for treatment planning. Traditional methods such as histopathology and radiological assessments often lack the ability to capture tumor heterogeneity, which can impact prognosis. Radiomics, combined with machine learning, provides a method to extract and analyze quantitative imaging features, offering the potential to improve tumor classification and risk prediction. By segmenting tumors into distinct habitat zones, it becomes possible to assess intratumoral heterogeneity more effectively. This study employs radiomics and machine learning techniques to enhance thymoma risk prediction, aiming to improve diagnostic consistency and reduce variability in radiologists' assessments.

OBJECTIVE

This study aims to identify different habitat zones within thymomas through CT imaging feature analysis and to establish a predictive model to differentiate between high and low-risk thymomas. Additionally, the study explores how this model can assist radiologists.

METHODS

We obtained CT imaging data from 133 patients with thymoma who were treated at the Affiliated Hospital of Guangdong Medical University from 2015 to 2023. Images from the plain scan phase, venous phase, arterial phase, and their differential images (subtracted images) were used. Tumor regions were segmented into three habitat zones using K-Means clustering. Imaging features from each habitat zone were extracted using the PyRadiomics (van Griethuysen, 2017) library. The 28 most distinguishing features were selected through Mann-Whitney U tests (Mann, 1947) and Spearman's correlation analysis (Spearman, 1904). Five predictive models were built using the same machine learning algorithm (Support Vector Machine [SVM]): Habitat1, Habitat2, Habitat3 (trained on features from individual tumor habitat regions), Habitat All (trained on combined features from all regions), and Intra (trained on intratumoral features), and their performances were evaluated for comparison. The models' diagnostic outcomes were compared with the diagnoses of four radiologists (two junior and two experienced physicians).

RESULTS

The AUC (area under curve) for habitat zone 1 was 0.818, for habitat zone 2 was 0.732, and for habitat zone 3 was 0.763. The comprehensive model, which combined data from all habitat zones, achieved an AUC of 0.960, outperforming the model based on traditional radiomic features (AUC of 0.720). The model significantly improved the diagnostic accuracy of all four radiologists. The AUCs for junior radiologists 1 and 2 increased from 0.747 and 0.775 to 0.932 and 0.972, respectively, while for experienced radiologists 1 and 2, the AUCs increased from 0.932 and 0.859 to 0.977 and 0.972, respectively.

CONCLUSION

This study successfully identified distinct habitat zones within thymomas through CT imaging feature analysis and developed an efficient predictive model that significantly improved diagnostic accuracy. This model offers a novel tool for risk assessment of thymomas and can aid in guiding clinical decision-making.

Harnessing Photo-Energy Conversion in Nanomaterials for Precision Theranostics

The rapidly advancing field of theranostics aims to integrate therapeutic and diagnostic functionalities into a single platform for precision medicine, enabling the simultaneous treatment and monitoring of diseases. Photo-energy conversion-based nanomaterials have emerged as a versatile platform that utilizes the unique properties of light to activate theranostics with high spatial and temporal precision. This review provides a comprehensive overview of recent developments in photo-energy conversion using nanomaterials, highlighting their applications in disease theranostics. The discussion begins by exploring the fundamental principles of photo-energy conversion in nanomaterials, including the types of materials used and various light-triggered mechanisms, such as photoluminescence, photothermal, photoelectric, photoacoustic, photo-triggered SERS, and photodynamic processes. Following this, the review delves into the broad spectrum of applications of photo-energy conversion in nanomaterials, emphasizing their role in the diagnosis and treatment of major diseases, including cancer, neurodegenerative disorders, retinal degeneration, and osteoarthritis. Finally, the challenges and opportunities of photo-energy conversion-based technologies for precision theranostics are discussed, aiming to advance personalized medicine.

Tumour and microenvironment crosstalk in NSCLC progression and response to therapy

The treatment landscape of non-small-cell lung cancer (NSCLC) is evolving rapidly, driven by advances in the development of targeted agents and immunotherapies. Despite this progress, some patients have suboptimal responses to treatment, highlighting the need for new therapeutic strategies. In the past decade, the important role of the tumour microenvironment (TME) in NSCLC progression, metastatic dissemination and response to treatment has become increasingly evident. Understanding the complexity of the TME and its interactions with NSCLC can propel efforts to improve current treatment modalities, overcome resistance and develop new treatments, which will ultimately improve the outcomes of patients. In this Review, we provide a comprehensive view of the NSCLC TME, examining its components and highlighting distinct archetypes characterized by spatial niches within and surrounding tumour nests, which form complex neighbourhoods. Next, we explore the interactions within these components, focusing on how inflammation and immunosuppression shape the dynamics of the NSCLC TME. We also address the emerging influences of patient-related factors, such as ageing, sex and health disparities, on the NSCLC-TME crosstalk. Finally, we discuss how various therapeutic strategies interact with and are influenced by the TME in NSCLC. Overall, we emphasize the interconnectedness of these elements and how they influence therapeutic outcomes and tumour progression.

Immune-metabolic crosstalk in HNSCC: mechanisms and therapeutic opportunities

Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy, characterized by metabolic reprogramming. This reprogramming creates an acidic and hypoxic environment within tumor cells to adapt to metabolic changes. Experimental data indicate that in HNSCC, the metabolic reprogramming of tumor cells regulates immune cells via metabolites or signaling pathways, thereby promoting cancer progression or immune evasion. This article reviews the metabolic reprogramming in HNSCC, including glucose, fatty acids, amino acids, and nucleotide metabolism. These metabolic pathways play crucial roles in the proliferation, differentiation, and effector functions of immune cells, and influence immunosuppressive checkpoints. Additionally, this review explores the potential relationships between metabolic reprogramming, tumor immunity, and related treatments. Thus, targeting metabolic reprogramming and interactions between immune cells may help overcome therapeutic resistance in HNSCC patients.

Artificial Intelligence-Guided Cancer Engineering for Tumor Normalization Executed by Tumor Lysosomal-Triggered Supramolecular Chiral Peptide

Cancer engineering for tumor normalization offers a promising therapeutic strategy to reverse malignant cells and their supportive tumor microenvironment into a more benign state. Herein, an artificial intelligence (AI) approach was developed using mRNA data from patients with lung adenocarcinoma to facilitate the identification of aberrant signaling pathways, specifically focusing on PD-L1, Wnt, and macropinocytosis. Targeting these characteristics, we have developed a supramolecular construct called cancer corrector (CCtor) with the aim of harnessing the enhanced macropinocytosis observed in cancer cells. Undergoing cleavage and subsequent drug release triggered by the lysosomal protease in cancer cells, CCtor rectifies the aberrant hyperactivity of both Wnt and PD-L1 signaling pathways. This dual-action therapeutic strategy not only restores normalcy to cancer cells but also exerts an exceptionally robust therapeutic effect. This work exemplifies a future direction for cancer therapies by combining AI with molecular engineering to significantly improve patient outcomes through tumor behavior normalization.

HR eye & MMR eye: one-day assessment of DNA repair-defective tumors eligible for targeted therapy

Homologous recombination (HR) and mismatch repair (MMR) act as guardians of the human genome, and defects in HR or MMR are causative in at least a quarter of all malignant tumors. Although these DNA repair-deficient tumors are eligible for effective targeted therapies, fully reliable diagnostic strategies based on functional assay have yet to be established, potentially limiting safe and proper application of the molecular targeted drugs. Here we show that transient transfection of artificial DNA substrates enables ultrarapid detection of HR and MMR. This finding led us to develop a diagnostic strategy that can determine the cellular HR/MMR status within one day without the need for control cells or tissues. Notably, the accuracy of this method allowed the discovery of a pathogenic RAD51D mutation, which was missed by existing companion diagnostic tests. Our methods, termed HR eye and MMR eye, are applicable to frozen tumor tissues and roughly predict the response to therapy. Overall, the findings presented here could pave the way for accurately assessing malignant tumors with functional defects in HR or MMR, a step forward in accelerating precision medicine.

A Systems Bioinformatics Analysis Indicates that Disruption of the lncRNA SFTA1P Network is Consistent with Impairing Surfactant Homeostasis and Respiratory Function Observed in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the leading global health challenges wherein novel therapeutic targets are much needed. In this systems bioinformatics study, we report that disruption of the long noncoding RNA (lncRNA) SFTA1P-centered network, respiratory gaseous exchange and surfactant-associated Biological Network (rgsBNet), is consistent with impairing surfactant homeostasis and respiratory function, and thus warrants attention for future drug discovery and development. We analyzed data from The Cancer Genome Atlas LUAD cohort to identify differentially expressed mRNAs, lncRNAs, and microRNAs (miRNAs), followed by correlational analysis to examine the coexpression network of lncRNA SFTA1P and its potential role in LUAD pathogenesis. We observed the downregulation of lncRNA SFTA1P and its coexpressed network in LUAD. Intriguingly, this network appears to be associated with disrupting surfactant homeostasis and perturbing respiratory function, suggesting a potential role in LUAD progression. Additionally, we identified key transcription factors that correlate with the expression of genes crucial for respiratory gaseous exchange and surfactant homeostasis. The attendant regulatory mechanisms suggested that SFTA1P may act as a "sponge" for certain miRNAs, sequestering them away from their mRNA targets. In conclusion, this work uncovers novel insights into the molecular mechanisms governing surfactant homeostasis in LUAD and offers a possible avenue for therapeutic interventions aimed at ameliorating lung function and improving disease management. The downregulation of lncRNA SFTA1P and its coexpressed network highlights their potential as regulators of lung function and opens doors for further investigation into their role in LUAD progression and as potential therapeutic targets.

Exosome-Mediated Chemoresistance in Cancers: Mechanisms, Therapeutic Implications, and Future Directions

Chemotherapy resistance represents a formidable obstacle in oncological therapeutics, substantially compromising the efficacy of adjuvant chemotherapy regimens and contributing to unfavorable clinical prognoses. Emerging evidence has elucidated the pivotal involvement of exosomes in the dissemination of chemoresistance phenotypes among tumor cells and within the tumor microenvironment. This review delineates two distinct intra-tumoral resistance mechanisms orchestrated by exosomes: (1) the exosome-mediated sequestration of chemotherapeutic agents coupled with enhanced drug efflux in neoplastic cells, and (2) the horizontal transfer of chemoresistance to drug-sensitive cells through the delivery of bioactive molecular cargo, thereby facilitating the propagation of resistance phenotypes across the tumor population. Furthermore, the review covers current in vivo experimental data focusing on targeted interventions against specific genetic elements and exosomal secretion pathways, demonstrating their potential in mitigating chemotherapy resistance. Additionally, the therapeutic potential of inhibiting exosome-mediated transporter transfer strategy is particularly examined as a promising strategy to overcome tumor resistance mechanisms.

Emerging Multifunctional Biomaterials for Addressing Drug Resistance in Cancer

Drug resistance remains a major barrier to effective cancer treatment, contributing to poor patient outcomes. Multifunctional biomaterials integrating electrical and catalytic properties offer a transformative strategy to target diverse resistance mechanisms. This review explores their ability to modulate cellular processes, remodel the tumor microenvironment (TME), and enhance drug delivery. Electrically active biomaterials enhance drug uptake and apoptotic sensitivity by altering membrane potentials, ion channels, and intracellular signaling, synergizing with chemotherapy. Catalytic biomaterials generate reactive oxygen species (ROS), activate prodrugs, reprogram hypoxic and acidic TME, and degrade the extracellular matrix (ECM) to improve drug penetration. Hybrid nanomaterials (e.g., conductive hydrogels, electrocatalytic nanoparticles), synergize electrical and catalytic properties for localized, stimuli-responsive therapy and targeted drug release, minimizing systemic toxicity. Despite challenges in biocompatibility and scalability, future integration with immunotherapy, personalized medicine, and intelligent self-adaptive systems capable of real-time tumor response promises to accelerate clinical translation. The development of these adaptive biomaterials, alongside advancements in nanotechnology and AI-driven platforms, represents the next frontier in precision oncology. This review highlights the potential of multifunctional biomaterials to revolutionize cancer therapy by addressing multidrug resistance at cellular, genetic, and microenvironmental levels, offering a roadmap to improve therapeutic outcomes and reshape oncology practice.

Multi-omic profiling in breast cancer: utility for advancing diagnostics and clinical care

INTRODUCTION

Breast cancer remains a major global health challenge. While advances in precision oncology have contributed to improvements in patient outcomes and provided a deeper understanding of the biological mechanisms that drive the disease, historically, research and patients' allocation to treatment have heavily relied on single-omic approaches, analyzing individual molecular dimensions such as genomics, transcriptomics, or proteomics. While these have provided deep insights into breast cancer biology, they often fail to offer a complete understanding of the disease's complex molecular landscape.

AREAS COVERED

In this review, the authors explore the recent advancements in multi-omic research in the realm of breast cancer and use clinical data to show how multi-omic integration can offer a more holistic understanding of the molecular alterations and their functional consequences underlying breast cancer.

EXPERT OPINION

The overall developments in multi-omic research and AI are expected to complement precision diagnostics through potentially refining prognostic models, and treatment selection. Overcoming challenges such as cost, data complexity, and lack of standardization is crucial for unlocking the full potential of multi-omics and AI in breast cancer patient care to enable the advancement of personalized treatments and improve patient outcomes.

Osteosarcoma Cell-Derived Migrasomes Promote Macrophage M2 Polarization to Aggravate Osteosarcoma Proliferation and Metastasis

The local tumor microenvironment (TME) of osteosarcoma (OS) includes several tumor niches that control tumor growth and cell extravasation. Migrasomes are recently discovered extracellular vesicles produced during cell migration. Herein, the results show OS cell production of migrasomes in vivo and in vitro. Osteosarcoma cell-derived migrasomes (OCDMs) aggravate OS proliferation and metastasis, and impeding OCDM formation alleviates the malignant progression of OS. Further studies revealed that migrasome-associated nanoparticles (MANPs) are the functional unit of OCDMs and that OCDMs promote M2 polarization of macrophages in the TME in a MANPs-dependent manner. Moreover, milk fat globule-EGF factor 8 (MFGE8) in OCDMs is identified as a key protein that enhances phagocytosis to promote the M2 polarization of macrophages. Overall, the results reveal that OCDMs enhance the M2 polarization of macrophages in the TME to aggravate OS progression via MFGE8. These findings may guide the development of OCDM-modulating OS therapies.

Gut microbiota and their influence in brain cancer milieu

Microbial communities are not simply remnants of the past but dynamic entities that continuously evolve under the selective pressures of nature, reflecting the intricate and adaptive processes of evolution. The microbiota residing in the various regions of the human body has numerous roles in different physiological processes such as nutrition, metabolism, immune regulation, etc. In the zeal of achieving empirical insights into the ambit of the gut microbiome, the research over the years led to the revelation of reciprocal interaction between the gut microbiome and the cognitive functioning of the human body. Dysbiosis in the gut microbial composition disturbs the homeostatic cognitive functioning of the human body. This dysbiosis has been associated with various chronic diseases, including brain cancer, such as glioma, glioblastoma, etc. This review explores the mechanistic role of dysbiosis-mediated progression of brain cancers and their subtypes. Moreover, it demonstrates the regulatory role of microbial metabolites produced by the gut microbiota, such as short-chain fatty acids, amino acids, lipids, etc., in the tumour progression. Further, we also provide valuable insights into the microbiota mediating the efficiency of therapeutic regimens, thereby leveraging gut microbiota as potential biomarkers and targets for improved treatment outcomes.

Radiotherapy-induced alterations in tumor microenvironment: metabolism and immunity

Tumor metabolism plays a pivotal role in shaping immune responses within the tumor microenvironment influencing tumor progression, immune evasion, and the efficacy of cancer therapies. Radiotherapy has been shown to impact both tumor metabolism and immune modulation, often inducing immune activation through damage-associated molecular patterns and the STING pathway. In this study, we analyse the particular characteristics of the tumour metabolic microenvironment and its effect on the immune microenvironment. We also review the changes in the metabolic and immune microenvironment that are induced by radiotherapy, with a focus on metabolic sensitisation to the effects of radiotherapy. Our aim is to contribute to the development of research ideas in the field of radiotherapy metabolic-immunological studies.

Smart on-demand drug release strategies for cancer combination therapy

In cancer therapy, enhancing therapeutic indices and patient compliance has been a central focus in recent drug delivery technology development. However, achieving a delicate balance between improving anti-tumor efficacy and minimizing toxicity to normal tissues remains a significant challenge. With the advent of smart on-demand drug release strategies, new opportunities have emerged. These strategies represent a promising approach to drug delivery, enabling precise control over the release of therapeutic agents in a programmed and spatiotemporal manner. Recent studies have focused on designing delivery systems capable of releasing multiple therapeutic agents sequentially, while achieving spatial resolution in vivo. Smart on-demand drug release strategies have demonstrated considerable potential in tumor combination therapy for achieving precision drug delivery and controlled release by responding to specific physiological signals or external physical stimuli in the tumor microenvironment. These strategies not only improve tumor targeting and reduce toxicity to healthy tissues but also enable sequential release in combination therapy, allowing multiple drugs to be released in a specific spatiotemporal order to enhance synergistic treatment effects. In this paper, we systematically reviewed the current research progress of smart on-demand drug release drug delivery strategies in anti-tumor combination therapy. We highlighted representative integrated drug delivery systems and discussed the challenges associated with their clinical application. Additionally, potential future research directions are proposed to further advance this promising field.

Bidirectional Mendelian randomization and potential mechanistic insights into the causal relationship between gut microbiota and malignant mesothelioma

Malignant mesothelioma (MM) is a rare but aggressive cancer originating from mesothelial cells, which presents significant challenges to patients' physical and psychological well-being. The gut-lung axis underscores the connection between gut microbiota and respiratory diseases, with emerging evidence suggesting a strong association between gut microbiota and the development of MM. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between gut microbiota and MM, while also exploring the underlying mechanisms through bioinformatics approaches. Gut microbiota summary data were obtained from the MiBioGen consortium, while MM data were sourced from the FinnGen R11 dataset. Causality was examined using the inverse variance weighted method as the primary analysis. Additional methods, including the weighted median, simple mode, MR-Egger, and weighted mode, were also employed. The robustness of the findings was validated through sensitivity analyses, and reverse causality was considered to further strengthen the MR results. Moreover, bioinformatics analyses were conducted on genetic loci associated with both gut microbiota and MM to explore potential underlying mechanisms. Our study suggests that genetically predicted increases in class.Bacilli, family.Rikenellaceae, genus.Clostridium innocuum group, and order.Lactobacillales were suggestively associated with a higher risk of MM, whereas increases in genus.Ruminococcaceae UCG004, genus.Flavonifractor, phylum.Firmicutes, genus.Anaerofilum, genus.Clostridium sensu stricto 1, and genus.Lactobacillus appeared to confer protective effects. Bioinformatics analysis indicated that differentially expressed genes near loci associated with gut microbiota might affect MM by modulating pathways and the tumor microenvironment. The results of this study point to a potential genetic predisposition linking gut microbiota to MM. Further experimental validation is crucial to confirm these candidate microbes, establish causality, and elucidate the underlying mechanisms.

Fibroblast Activation Protein (FAP)+ cancer-associated fibroblasts induce macrophage M2-like polarization via the Fibronectin 1-Integrin α5β1 axis in breast cancer

Cancer-associated fibroblasts expressing fibroblast activation protein (FAP+ CAFs) are critical modulators of the breast cancer microenvironment, yet their immunoregulatory mechanisms remain poorly understood. Through integrated analysis of single-cell RNA sequencing data, clinical specimens, and in vivo and in vitro experiments, we identified FAP+ CAFs as the predominant stromal population associated with poor clinical outcomes and immunosuppressive features. Mechanistically, FAP+ CAFs secrete high levels of fibronectin 1 (FN1), which engages integrin α5β1 on macrophages to trigger FAK-AKT-STAT3 signaling, driving their polarization toward an immunosuppressive M2-like phenotype. Importantly, pharmacological disruption of FN1-integrin α5β1 signaling using Cilengitide effectively reprogrammed the tumor immune landscape and suppressed tumor growth in mice models. These findings establish FAP+ CAF-derived FN1 as a critical orchestrator of tumor immunosuppression and identify the FN1-integrin α5β1 axis as a promising therapeutic target in breast cancer.

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning

As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of breast cancer patients. Lactylation-associated subtypes were obtained by unsupervised consensus clustering analysis. Lactylation-related gene signature (LRS) was constructed by 15 machine learning algorithms, and the relationship between LRS and tumor microenvironment (TME) as well as drug sensitivity was analyzed. In addition, the expression of genes in the LRS in different cells was explored by single-cell analysis and spatial transcriptome. The expression levels of genes in LRS in clinical tissues were verified by RT-PCR. Finally, the potential small-molecule compounds were analyzed by CMap, and the molecular docking model of proteins and small-molecule compounds was constructed. LRS was composed of 6 key genes (SHCBP1, SIM2, VGF, GABRQ, SUSD3, and CLIC6). BC patients in the high LRS group had a poorer prognosis and had a TME that promoted tumor progression. Single-cell analysis and spatial transcriptome revealed differential expression of the key genes in different cells. The results of PCR showed that SHCBP1, SIM2, VGF, GABRQ, and SUSD3 were up-regulated in the cancer tissues, whereas CLIC6 was down-regulated in the cancer tissues. Arachidonyltrifluoromethane, AH-6809, W-13, and clofibrate can be used as potential target drugs for SHCBP1, VGF, GABRQ, and SUSD3, respectively. The gene signature we constructed can well predict the prognosis as well as the treatment response of BC patients. In addition, our predicted small-molecule complexes provide an important reference for personalized treatment of breast cancer patients.

Exploring the in silico adaptation of the Nephroblastoma Oncosimulator to MRI scans, treatment data, and histological profiles of patients from different risk groups

Introduction

Nephroblastoma or Wilms' tumor is the most prevalent type of renal tumor in pediatric oncology. Although the overall survival rate for this condition is excellent today (∼90%), there have been no significant improvements over the past two decades. In silico models aim to simulate tumor progression and treatment responses over time; they hold immense potential for enhancing the predictive accuracy and optimizing treatment protocols as they are inspired by the digital twin paradigm.

Methods

The present study uses T2-weighted magnetic resonance images, chemotherapy treatment plans, and post-surgical histological profiles from three patients enrolled in the SIOP 2001/GPOH clinical trial, where each patient represents a distinct clinically assessed risk group. We investigated the clinical adaptation of the Nephroblastoma Oncosimulator to the datasets from these patients with the goal of deriving appropriate value distributions of the model input parameters that enable accurate prediction of tumor volume reduction in response to preoperative chemotherapy.

Results

Our primary focus was on the total cell kill ratio as a parameter reflecting treatment effectiveness. We derived the distribution of this parameter for one patient from each risk group: low (Mdn = 0.875, IQR [0.750, 0.875], n = 178), intermediate (Mdn = 0.875, IQR [0.750, 0.875], n = 175), and high (Mdn = 0.485, IQR [0.438, 0.532], n = 103). Statistically significant differences were observed between the high-risk group and both the low- and intermediate-risk groups (p < 0.001).

Discussion

The present work establishes a foundation for further studies using available retrospective datasets and additional patients per risk group. These efforts are expected to help validate the findings, advance model development, and extend this mechanistic multiscale discretized cancer model. However, clinical validation is ultimately required to assess the potential uses of the model in clinical decision-support systems.

p53 Deficiency in Colon Cancer Cells Promotes Tumor Progression Through the Modulation of Meflin in Fibroblasts

Cancer-associated fibroblasts (CAFs), a major component of the tumor microenvironment, play an important role in tumor progression. Colon cancer cells deficient in p53 activate fibroblasts and enhance fibroblast-mediated tumor growth. Meflin is a CAF marker capable of inhibiting tumor growth. In this study, we investigated the role of Meflin in fibroblasts using human cell lines (colon cancer HCT116 and fibroblasts CCD-18Co) and clinical specimens. TP53-suppressed HCT116 (HCT116sh p53) cells cocultured with CCD-18Co cells showed significantly faster proliferation than HCT116sh control cells. In xenograft experiments, the volume of tumors induced by coinoculation with HCT116sh p53 and CCD-18Co cells was significantly larger than that induced by HCT116sh control cells co-inoculated with CCD-18Co cells. HCT116sh p53 cells increased the levels of CAF-like phenotypic markers in CCD-18Co cells. Moreover, Meflin expression was significantly reduced in CCD-18Co cells cocultured with HCT116sh p53 cells compared to that in CCD-18Co cells cocultured with HCT116sh control cells. si-RNA-mediated inhibition of Meflin activated CCD-18Co cells into tumor-promoting CAF-like cells, which significantly promoted xenograft tumor growth. Overexpression of Meflin in CCD-18Co cells using lentivirus suppressed fibroblast-mediated growth of HCT116sh p53 tumor xenografts. The expression of Meflin in CCD-18Co cells was suppressed by TGF-β and enhanced by vitamin D. These results indicate that colon cancer cells deficient in p53 suppress Meflin expression in fibroblasts, which affects tumor growth by altering the properties of tumor growth-promoting CAFs. Our results suggest that targeting Meflin in fibroblasts may be a novel therapeutic strategy for colorectal cancer.

A Nexus of Biomolecular Complexities in Pituitary Neuroendocrine Tumors: Insights into Key Molecular Drivers

Approximately 90% of the lesions of hypophyseal origins are represented by pituitary neuroendocrine tumors, which further account for up to 22.5% of the intracranial tumors in the adult population. Although the intricacy of this pathology is yet to be fully understood on a biomolecular level, it is well known that these lesions develop within a microenvironment that supports their evolution and existence. The role of the tumoral microenvironment in pituitary lesions is pivotal, mainly due to this gland's distinct anatomical, histological, and physiological structure and function. Each component of the tumoral microenvironment is specifically involved in tumorigenesis, angiogenesis, tumoral growth, progression, and dissemination. By recognizing and understanding how these elements are involved in such processes, targeted treatments can emerge, and better future management of pituitary lesions can be provided. This article aims to summarize the role of each component of the tumoral microenvironment in pituitary lesions while assessing their association with biomolecular mechanisms.

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

BACKGROUND

Advancements in RNA sequencing have expanded our ability to study gene expression profiles of biological samples in bulk tissue and single cells. Deconvolution of bulk data with single-cell references provides the ability to study relative cell-type proportions, but most methods assume a reference is present for every cell type in bulk data. This is not true in all circumstances-cell types can be missing in single-cell profiles for many reasons. In this study, we examine the impact of missing cell types on deconvolution methods.

RESULTS

Using paired single-cell and single-nucleus data, we simulate realistic scenarios where cell types are missing since single-nucleus RNA sequencing is able to capture cell types that would otherwise be missing in a single-cell counterpart. Single-nucleus sequencing captures cell types absent in single-cell counterparts, allowing us to study their effects on deconvolution. We evaluate three different methods and find that performance is influenced by both the number and similarity of missing cell types. Additionally, missing cell-type profiles can be recovered from residuals using a simple non-negative matrix factorization strategy. We also analyzed real bulk data of cancerous and non-cancerous samples. We observe results consistent with simulation, namely that expression patterns from cell types likely to be missing appear present in residuals.

CONCLUSIONS

We expect our results to provide a starting point for those developing new deconvolution methods and help improve their to better account for the presence of missing cell types. Our results suggest that deconvolution methods should consider the possibility of missing cell types.

Rare Subset of T Cells Form Heterotypic Clusters with Circulating Tumor Cells to Foster Cancer Metastasis

The immune ecosystem is central to maintaining effective defensive responses. However, how immune cells in the periphery blood interact with circulating tumor cells (CTCs) - seeds of metastasis - remains largely understudied. Here, our analysis of the blood specimens (N=1,529) from patients with advanced breast cancer revealed that over 75% of the CTC-positive blood specimens contained heterotypic CTC clusters with CD45 + white blood cells (WBCs). Detection of CTC-WBC clusters correlates with breast cancer subtypes (triple negative and luminal B), racial groups (Black), and decreased survival rates. Flow cytometry and ImageStream analyses revealed diverse WBC composition of heterotypic CTC-WBC clusters, including overrepresented T cells and underrepresented neutrophils. Most strikingly, a rare subset of CD4 and CD8 double positive T (DPT) cells showed an up to 140-fold enrichment in the CTC clusters versus its frequency in WBCs. DPT cells shared part of the profiles with CD4 + T cells and others with CD8 + T cells but exhibited unique features of T cell exhaustion and immune suppression with higher expression of TIM-3 and PD-1. Single-cell RNA sequencing and genetic perturbation studies further pinpointed the integrin VLA4 (α4β1) in DPT cells and its ligand VCAM1 in tumor cells as essential mediators of heterotypic WBC-CTC clusters. Neoadjuvant administration of anti-α4 (VLA4) neutralizing antibodies markedly blocked CTC-DPT cell clustering and inhibited metastasis for extended survival in preclinical mouse models in vivo . These findings uncover a pivotal role of rare DPT cells with immune suppressive features in fostering cancer dissemination through direct interactive clustering with CTCs. It lays a foundation for developing innovative biomarkers and therapeutic strategies to prevent and target cancer metastasis, ultimately benefiting cancer care.

Brief summary

Our findings uncover a fostering role of immune-suppressive T cells in contact with circulating tumor cells and identify therapeutic approaches to eliminate devastating cancer metastasis.

The Impact of T-cell Exhaustion Dynamics on Tumour-Immune Interactions and Tumour Growth

Tumours evade immune surveillance through a number of different immunosuppressive mechanisms. One such mechanism causes cytotoxic T-cells, a major driving force of the immune system, to differentiate to a state of 'exhaustion', rendering them less effective at killing tumour cells. We present a structured mathematical model that focuses on T-cell exhaustion and its effect on tumour growth. We compartmentalise cytotoxic T-cells into discrete subgroups based on their exhaustion level, which affects their ability to kill tumour cells. We show that the model reduces to a simpler system of ordinary differential equations (ODEs) that describes the time evolution of the total number of T-cells, their mean exhaustion level and the total number of tumour cells. Numerical simulations of the model equations reveal how the exhaustion distribution of T-cells changes over time and how it influences the tumour's growth dynamics. Complementary bifurcation analysis shows how altering key parameters significantly reduces the tumour burden, highlighting exhaustion as a promising target for immunotherapy. Finally, we derive a continuum approximation of the discrete ODE model, which admits analytical solutions that provide complementary insight into T-cell exhaustion dynamics and their effect on tumour growth.

MDSC checkpoint blockade therapy: a new breakthrough point overcoming immunosuppression in cancer immunotherapy

Despite the success of cancer immunotherapy in treating hematologic malignancies, their efficacy in solid tumors remains limited due to the immunosuppressive tumor microenvironment (TME), which is mainly formed by myeloid-derived suppressor cells (MDSCs). MDSCs not only exert potent immunosuppressive effects that hinder the success of immune checkpoint inhibitors (ICIs) and adaptive cellular therapies, but they also promote tumor advancement through non-immunological pathways, including promoting angiogenesis, driving epithelial-mesenchymal transition (EMT), and contributing to the establishment of pre-metastatic environments. While targeting MDSCs alone or in combination with conventional therapies has shown limited success, emerging evidence suggests that MDSC checkpoint blockade in combination with other immunotherapies holds great promise in overcoming both immunological and non-immunological barriers. In this review, we discussed the dual roles of MDSCs, with a particular emphasis on their underexplored checkpoints blockade strategies. We discussed the rationale behind combination strategies, their potential advantages in overcoming MDSC-mediated immunosuppression, and the challenges associated with their development. Additionally, we highlight future research directions aimed at optimizing combination immunotherapies to enhance cancer therapeutic effectiveness, particularly in solid tumor therapies where MDSCs are highly prevalent.

Emerging magic bullet: subcellular organelle-targeted cancer therapy

The therapeutic efficacy of anticancer drugs heavily relies on their concentration and retention at the corresponding target site. Hence, merely increasing the cellular concentration of drugs is insufficient to achieve satisfactory therapeutic outcomes, especially for the drugs that target specific intracellular sites. This necessitates the implementation of more precise targeting strategies to overcome the limitations posed by diffusion distribution and nonspecific interactions within cells. Consequently, subcellular organelle-targeted cancer therapy, characterized by its exceptional precision, have emerged as a promising approach to eradicate cancer cells through the specific disruption of subcellular organelles. Owing to several advantages including minimized dosage and side effect, optimized efficacy, and reversal of multidrug resistance, subcellular organelle-targeted therapies have garnered significant research interest in recent years. In this review, we comprehensively summarize the distribution of drug targets, targeted delivery strategies at various levels, and sophisticated strategies for targeting specific subcellular organelles. Additionally, we highlight the significance of subcellular targeting in cancer therapy and present essential considerations for its clinical translation.

Targeting cardiac fibrosis with Chimeric Antigen Receptor-Engineered Cells

Cardiac fibrosis poses a significant challenge in cardiovascular diseases due to its intricate pathogenesis, and there is currently no standardized and effective treatment approach. The fibrotic process entails the involvement of various cell types and molecular mechanisms, such as fibroblast activation and proliferation, increased collagen synthesis, and extracellular matrix rearrangement. Traditional therapies often fall short in efficacy or carry substantial side effects. However, recent studies have shown that Chimeric Antigen Receptor T (CAR-T) cells can selectively target and eliminate activated cardiac fibroblasts (CFs) in mice, leading to reduced cardiac fibrosis and improved myocardial tissue compliance. This breakthrough presents a new and promising avenue for treating cardiac fibrosis. Currently, CAR-T cell-based therapy for cardiac fibrosis is undergoing animal experimentation, indicating ample scope for enhancement. Future investigations could explore the application of CAR cell therapy in cardiac fibrosis treatment, including the potential of CAR-natural killer (CAR-NK) cells and CAR macrophages (CAR-M), offering novel insights and strategies for combating cardiac fibrosis.

Challenges and Emerging Strategies of Immunotherapy for Glioblastoma

Glioblastoma (GBM) is recognized as the most lethal primary malignant tumor of the central nervous system. Although traditional treatments can somewhat prolong patient survival, the overall prognosis remains grim. Immunotherapy has become an effective method for GBM treatment. Oncolytic virus, checkpoint inhibitors, CAR T cells and tumor vaccines have all been applied in this field. Moreover, the combining of immunotherapy with traditional radiotherapy, chemotherapy, or gene therapy can further improve the treatment outcome. This review systematically summarizes the features of GBM, the recent progress of immunotherapy in overcoming GBM.

CAR-T therapy dilemma and innovative design strategies for next generation

Chimeric antigen receptor (CAR)-T-cell therapy has shown remarkable curative effects on hematological tumors, driving the exponential growth in CAR-T-related research. Although CD19-targeting CAR-T-cell therapy has displayed remarkable promise in clinical trials, many obstacles are arising that limit its therapeutic efficacy in tumor immunotherapy. The "dilemma" of CAR-T cell-based tumor therapy includes lethal cytotoxicity, restricted trafficking, limited tumor infiltration, an immunosuppressive microenvironment, immune resistance and limited potency. The solution to CAR-T-cell therapy's dilemma requires interdisciplinary strategies, including synthetic biology-based ON/OFF switch, bioinstructive scaffolds, nanomaterials, oncolytic viruses, CRISPR screening, intestinal microbiota and its metabolites. In this review, we will introduce and summarize these interdisciplinary-based innovative technologies for the next generation CAR-T-cell design and delivery to overcome the key barriers of current CAR-T cells.

Connecting the dots: Epigenetic regulation of extrachromosomal and inherited DNA amplifications

DNA amplification has intrigued scientists for decades. Since its discovery, significant progress has been made in understanding the mechanisms promoting DNA amplification and their associated function(s). While DNA copy gains were once thought to be regulated purely by stochastic processes, recent findings have revealed the important role of epigenetic modifications in driving these amplifications and their integration into the genome. Furthermore, advances in genomic technology have enabled detailed characterization of these genomic events in terms of size, structure, formation, and regulation. This review highlights how our understanding of DNA amplifications has evolved over time, tracing its trajectory from initial discovery to the contemporary landscape. We describe how recent discoveries have started to uncover how these genomic events occur by controlled biological processes rather than stochastic mechanisms, presenting opportunities for therapeutic modulation.

Apelin/APJ: Another Player in the Cancer Biology Network

The apelinergic system exerts multiple biological activities in human pathologies, including cancer. Overactivation of apelin/APJ, which has been detected in many malignant tumors, and the strong correlation with progression-free and overall survival, suggested the role of an oncogene for the apelin gene. Emerging evidence sheds new light on the effects of apelin on cellular functions and homeostasis in cancer cells and supports a direct role for this pathway on different hallmarks of cancer: "sustaining proliferative signaling", "resisting cell death", "activating invasion and metastasis", "inducing/accessing vasculature", "reprogramming cellular metabolism", "avoiding immune destruction" and "tumor-promoting inflammation", and "enabling replicative immortality". This article reviews the currently available literature on the intracellular processes regulated by apelin/APJ, focusing on those pathways correlated with tumor development and progression. Furthermore, the association between the activity of the apelinergic axis and the resistance of cancer cells to oncologic treatments (chemotherapy, immunotherapy, radiation) suggests apelin/APJ as a possible target to potentiate traditional therapies, as well as to develop diagnostic and prognostic applications. This issue will be also covered in the review.

Cancer‑associated fibroblasts: a pivotal regulator of tumor microenvironment in the context of radiotherapy

BACKGROUND

In the course of tumor treatment, radiation therapy (RT) not only kills cancer cells, but also induces complex biological effects in non-malignant cells around cancer cells. These biological effects such as angiogenesis, changes in stromal composition and immune cell infiltration remodel the tumor microenvironment (TME). As one of the major components of the TME, Cancer‑associated fibroblasts (CAFs) are not only involved in tumorigenesis, progression, recurrence, and metastasis but also regulate the tumor-associated immune microenvironment. CAFs and tumor cells or immune cells have complex intercellular communication in the context of tumor radiation.

MAIN CONTENT

Different cellular precursors, spatial location differences, absence of specific markers, and advances in single-cell sequencing technology have gradually made the abundant heterogeneity of CAFs well known. Due to unique radioresistance properties, CAFs can survive under high doses of ionizing radiation. However, radiation can induce phenotypic and functional changes in CAFs and further act on tumor cells and immune cells to promote or inhibit tumor progression. To date, the effect of RT on CAFs and the effect of irradiated CAFs on tumor progression and TME are still not well defined.

CONCLUSION

In this review, we review the origin, phenotypic, and functional heterogeneity of CAFs and describe the effects of RT on CAFs, focusing on the mutual crosstalk between CAFs and tumor or immune cells after radiation. We also discuss emerging strategies for targeted CAFs therapy.

Intratumoral heterogeneity and drug resistance in cancer

Intratumoral heterogeneity is the main cause of tumor treatment failure, varying across disease sites (spatial heterogeneity) and polyclonal properties of tumors that evolve over time (temporal heterogeneity). As our understanding of intratumoral heterogeneity, the formation of which is mainly related to the genomic instability, epigenetic modifications, plastic gene expression, and different microenvironments, plays a substantial role in drug-resistant as far as tumor metastasis and recurrence. Understanding the role of intratumoral heterogeneity, it becomes clear that a single therapeutic agent or regimen may only be effective for subsets of cells with certain features, but not for others. This necessitates a shift from our current, unchanging treatment approach to one that is tailored against the killing patterns of cancer cells in different clones. In this review, we discuss recent evidence concerning global perturbations of intratumoral heterogeneity, associations of specific intratumoral heterogeneity in lung cancer, the underlying mechanisms of intratumoral heterogeneity potentially leading to formation, and how it drives drug resistance. Our findings highlight the most up-to-date progress in intratumoral heterogeneity and its role in mediating tumor drug resistance, which could support the development of future treatment strategies.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Unveiling the oncogenic role of SLC25A13: a multi-omics pan-cancer analysis reveals its impact on glioma progression

SLC25A13, a pivotal component of the mitochondrial aspartate-glutamate carrier, is integral to cellular metabolism and has been linked to various diseases. However, its role in cancer biology remains largely unexplored. In this study, we employed multi-omics data to elucidate the genetic landscape, expression profile, and prognostic value of SLC25A13 in a pan-cancer context. Additionally, we examined the correlation between SLC25A13 and the immune microenvironment across various cancers. By applying multiple machine learning methods, we identified seven core SLC25A13 co-expressed genes and developed a nomogram to predict the prognosis of glioma patients, validating its efficacy across multiple independent datasets. Furthermore, in vitro and in vivo experiments demonstrated that SLC25A13 is significantly overexpressed in glioblastoma tissues compared to paraneoplastic tissues, promoting glioblastoma cell proliferation and migration while inhibiting apoptosis. Collectively, our study positions SLC25A13 as a promising biomarker for cancer prognosis and a potential therapeutic target, particularly in glioma, thereby laying the groundwork for future research into its therapeutic exploitation in cancer.

The Association Between Tumor Radiomic Analysis and Peritumor Habitat-Derived Radiomic Analysis on Gadoxetate Disodium-Enhanced MRI With Microvascular Invasion in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment.

PURPOSE

To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis.

STUDY TYPE

Retrospective.

SUBJECTS

Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94).

FIELD STRENGTH/SEQUENCE

1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence.

ASSESSMENT

Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews.

STATISTICAL TESTS

Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance.

RESULTS

Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival.

DATA CONCLUSION

The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

Cancer stem cells: Masters of all traits

Cancer is a heterogeneous disease, which contributes to its rapid progression and therapeutic failure. Besides interpatient tumor heterogeneity, tumors within a single patient can present with a heterogeneous mix of genetically and phenotypically distinct subclones. These unique subclones can significantly impact the traits of cancer. With the plasticity that intratumoral heterogeneity provides, cancers can easily adapt to changes in their microenvironment and therapeutic exposure. Indeed, tumor cells dynamically shift between a more differentiated, rapidly proliferating state with limited tumorigenic potential and a cancer stem cell (CSC)-like state that resembles undifferentiated cellular precursors and is associated with high tumorigenicity. In this context, CSCs are functionally located at the apex of the tumor hierarchy, contributing to the initiation, maintenance, and progression of tumors, as they also represent the subpopulation of tumor cells most resistant to conventional anti-cancer therapies. Although the CSC model is well established, it is constantly evolving and being reshaped by advancing knowledge on the roles of CSCs in different cancer types. Here, we review the current evidence of how CSCs play a pivotal role in providing the many traits of aggressive tumors while simultaneously evading immunosurveillance and anti-cancer therapy in several cancer types. We discuss the key traits and characteristics of CSCs to provide updated insights into CSC biology and highlight its implications for therapeutic development and improved treatment of aggressive cancers.

Controlled Inflammation Drives Neutrophil-Mediated Precision Drug Delivery in Heterogeneous Tumors

Tumor heterogeneity remains a formidable obstacle in targeted cancer therapy, often leading to suboptimal treatment outcomes. This study presents an innovative approach that harnesses controlled inflammation to guide neutrophil-mediated drug delivery, effectively overcoming the limitations imposed by tumor heterogeneity. By inducing localized inflammation within tumors using lipopolysaccharide, it significantly amplify the recruitment of drug-laden neutrophils to tumor sites, irrespective of specific tumor markers. This strategy not only enhances targeted drug delivery but also triggers the release of neutrophil extracellular traps, further potentiating the anti-tumor effect. Crucially, this study demonstrates that potential systemic inflammatory responses can be effectively mitigated through neutrophil transfusion, ensuring the safety and clinical viability of this approach. In a murine breast cancer model, the method significantly impedes tumor growth compared to conventional treatments. This work offers a versatile strategy for precise drug delivery across diverse tumor types. The findings pave the way for more effective and broadly applicable cancer treatments, potentially addressing the long-standing challenge of tumor heterogeneity.

Integrative Bioinformatics Analysis and Experimental Study of NLRP12 Reveal Its Prognostic Value and Potential Functions in Ovarian Cancer

NLRP12 plays a significant role in cellular functional behavior and immune homeostasis, influencing inflammation, tumorigenesis, and prognosis. This study aimed to explore its specific effects on the tumor microenvironment (TME) and its contribution to heterogeneity in ovarian cancer (OV) through bioinformatics analysis and experimental verification. Utilizing various bioinformatics databases and clinical specimens, we investigated NLRP12 expression and its relationship with OV prognosis and immune infiltration. In vitro assays were conducted to assess the impact of NLRP12 on the proliferation and invasion of OV cells. Our findings indicate that NLRP12 is upregulated in OV, with high expression correlating with a negative prognosis. Furthermore, NLRP12 expression demonstrated a positive correlation with the infiltration of various immune cells and the expression of immune checkpoint molecules in OV. Analysis of The Cancer Immunome Atlas (TCIA) database revealed that OV patients with lower NLRP12 expression may exhibit an enhanced response to immunotherapy, particularly CTLA4 blockers, a finding validated in animal experiments. Additionally, the study emphasized the role of NLRP12 in influencing the prognosis of OV patients by promoting epithelial-mesenchymal transition (EMT) in ovarian cancer cells. Finally, we identified a potential therapeutic compound, Schisandrin B (Schi B), which decreases NLRP12 expression in ovarian cancer cells by binding to the transcription factor SPI1 associated with NLRP12. Our findings suggest that NLRP12 serves as a crucial immune-related biomarker predicting poor outcomes in OV, and targeting NLRP12 may represent a promising therapeutic approach for OV patients in the future.

The multi-faceted roles of cancer-associated fibroblasts in pancreatic cancer

The tumor microenvironment (TME) has been linked with the pathogenesis of pancreatic ductal adenocarcinoma (PDAC), the most common histological subtype of pancreatic cancer. A central component of the TME are cancer-associated fibroblasts (CAFs), which can either suppress or promote tumor growth in a context-dependent manner. In this review, we will discuss the multi-faceted roles of CAFs in tumor-stroma interactions influencing cancer initiation, progression and therapeutic response.

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model

OBJECTIVE

The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical features to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

Between January 2018 and June 2023, 312 patients with histologically confirmed breast cancer were enrolled and randomly assigned to a training cohort (n = 219) and a test cohort (n = 93) in a 7:3 ratio. Next, pre-NAC and post-treatment 2-cycle ultrasound images were collected, and radiomics and deep learning features were extracted from NAC pre-treatment (Pre), post-treatment 2 cycle (Post), and Delta (pre-NAC-NAC 2 cycle) images. In the training cohort, to filter features, the intraclass correlation coefficient test, the Boruta algorithm, and the least absolute shrinkage and selection operator (LASSO) logistic regression were used. Single-modality models (Pre, Post, and Delta) were constructed based on five machine-learning classifiers. Finally, based on the classifier with the optimal predictive performance, the DLR model was constructed by combining Pre, Post, and Delta ultrasound features and was subsequently combined with clinical features to develop a combined model (Integrated). The discriminative power, predictive performance, and clinical utility of the models were further evaluated in the test cohort. Furthermore, patients were assigned into three subgroups, including the HR+/HER2-, HER2+, and TNBC subgroups, according to molecular typing to validate the predictability of the model across the different subgroups.

RESULTS

After feature screening, 16, 13, and 10 features were selected to construct the Pre model, Post model, and Delta model based on the five machine learning classifiers, respectively. The three single-modality models based on the XGBoost classifier displayed optimal predictive performance. Meanwhile, the DLR model (AUC of 0.827) was superior to the single-modality model (Pre, Post, and Delta AUCs of 0.726, 0.776, and 0.710, respectively) in terms of prediction performance. Moreover, multivariate logistic regression analysis identified Her-2 status and histological grade as independent risk factors for NAC response in breast cancer. In both the training and test cohorts, the Integrated model, which included Pre, Post, and Delta ultrasound features and clinical features, exhibited the highest predictive ability, with AUC values of 0.924 and 0.875, respectively. Likewise, the Integrated model displayed the highest predictive performance across the different subgroups.

CONCLUSION

The Integrated model, which incorporated pre-NAC treatment and early treatment ultrasound data and clinical features, accurately predicted pCR after NAC in breast cancer patients and provided valuable insights for personalized treatment strategies, allowing for timely adjustment of chemotherapy regimens.