Precision Cancer Research Articles

Towards precision medicine: design considerations for nanozymes in tumor treatment.

Since the discovery of Fe3O4 nanoparticles with enzyme-like activity in 2007, nanozymes have emerged as a promising class of catalysts, offering advantages such as high catalytic efficiency, low cost, mild reaction conditions, and excellent stability. These properties make nanozymes highly suitable for large-scale production. In recent years, the convergence of nanomedicine and nanocatalysis has highlighted the potential of nanozymes in diagnostic and therapeutic applications, particularly in tumor therapy. Despite these advancements, the clinical translation of nanozymes remains hindered by the lack of designs tailored to specific tumor characteristics, limiting their effectiveness in targeted therapy. This review addresses the mechanisms by which nanozymes induce cell death in various tumor types and emphasizes the key design considerations needed to enhance their therapeutic potential. By identifying the challenges and opportunities in the field, this study aims to provide a foundation for future nanozyme development, ultimately contributing to more precise and effective cancer treatments.

A cascade X-ray energy converting approach toward radio-afterglow cancer theranostics.

Leveraging X-rays to initiate prolonged luminescence (radio-afterglow) and stimulate radiodynamic 1O2 production from optical agents provides opportunities for diagnosis and therapy at tissue depths inaccessible to light. However, X-ray-responsive organic luminescent materials are rare due to their intrinsic low X-ray conversion efficiency. Here we report a cascade X-ray energy converting approach to develop organic radio-afterglow nanoprobes (RANPs) for cancer theranostics. RANPs comprise a radiowave absorber that down-converts X-ray energy to emit radioluminescence, which is transferred to a radiosensitizer to produce singlet oxygen (1O2). 1O2 then reacts with a radio-afterglow substrate to generate an active intermediate that simultaneously decomposes to emit radio-afterglow. Through finetuning such a cascade, intraparticle radioluminescence energy transfer and the 1O2 transfer process, RANPs possess tunable wavelengths and long half-lives, and generate radio-afterglow and 1O2 at tissue depths of up to 15 cm. Moreover, we developed a biomarker-activatable nanoprobe (tRANP) that produces a tumour-specific radio-afterglow signal, leading to ultrasensitive detection and the possibility of surgical removal of diminutive tumours (1 mm3) under an X-ray dosage 20 times lower than inorganic materials. The efficient radiodynamic 1O2 generation of tRANP permits complete tumour eradication at an X-ray dosage lower than clinical radiotherapy and a drug dosage one to two orders of magnitude lower than most existing inorganic agents, leading to prolonged survival rates with minimized radiation-related adverse effects. Thus, our work reveals a generic approach to address the lack of organic radiotheranostic materials and provides molecular design towards precision cancer radiotherapy.

Performance of an AI-powered visualization software platform for precision surgery in breast cancer patients.

Surgery remains the primary treatment modality in the management of early-stage invasive breast cancer. Artificial intelligence (AI)-powered visualization platforms offer the compelling potential to aid surgeons in evaluating the tumor's location and morphology within the breast and accordingly optimize their surgical approach. We sought to validate an AI platform that employs dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to render three-dimensional (3D) representations of the tumor and 5 additional chest tissues, offering clear visualizations as well as functionalities for quantifying tumor morphology, tumor-to-landmark structure distances, excision volumes, and approximate surgical margins. This retrospective study assessed the visualization platform's performance on 100 cases with ground-truth labels vetted by 2 breast-specialized radiologists. We assessed features including automatic AI-generated clinical metrics (e.g., tumor dimensions) as well as visualization tools including convex hulls at desired margins around the tumor to help visualize lumpectomy volume. The statistical performance of the platform's automated features was robust and within the range of inter-radiologist variability. These detailed 3D tumor and surrounding multi-tissue depictions offer both qualitative and quantitative comprehension of cancer topology and may aid in formulating an optimal surgical approach for breast cancer treatment. We further establish the framework for broader data integration into the platform to enhance precision cancer care.

Exosomal integrins in tumor progression, treatment and clinical prediction (Review).

Integrins are a large family of cell adhesion molecules involved in tumor cell differentiation, migration, proliferation and neovascularization. Tumor cell‑derived exosomes carry a large number of integrins, which are closely associated with tumor progression. As crucial mediators of intercellular communication, exosomal integrins have gained attention in the field of cancer biology. The present review examined the regulatory mechanisms of exosomal integrins in tumor cell proliferation, migration and invasion, and emphasized their notable roles in tumor initiation and progression. The potential of exosomal integrins as drug delivery systems in cancer treatment was explored. Additionally, the potential of exosomal integrins in clinical tumor prediction was considered, while summarizing their applications in diagnosis, prognosis assessment and treatment response prediction. Thus, the present review aimed to provide guidance and insights for future basic research and the clinical translation of exosomal integrins. The study of exosomal integrins is poised to offer new perspectives and methods for precise cancer treatment and clinical prediction.

Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.

Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electronic health records (EHRs). Artificial intelligence (AI) models have been trained to extract outcomes from individual EHRs. However, patient privacy restrictions have historically precluded dissemination of these models beyond the centers at which they were trained. In this study, the vulnerability of text classification models trained directly on protected health information to membership inference attacks is confirmed. A teacher-student distillation approach is applied to develop shareable models for annotating outcomes from imaging reports and medical oncologist notes. 'Teacher' models trained on EHR data from Dana-Farber Cancer Institute (DFCI) are used to label imaging reports and discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. 'Student' models are trained to use these MIMIC documents to predict the labels assigned by teacher models and sent to Memorial Sloan Kettering (MSK) for evaluation. The student models exhibit high discrimination across outcomes in both the DFCI and MSK test sets. Leveraging private labeling of public datasets to distill publishable clinical AI models from academic centers could facilitate deployment of machine learning to accelerate precision oncology research.

Dual targeting and bioresponsive nano-PROTAC induced precise and effective lung cancer therapy

Epigenetic regulation has emerged as a promising therapeutic strategy for lung cancer treatment, which can facilitate the antitumor responses by modulating epigenetic dysregulation of target proteins in lung cancer. The proteolysis-targeting chimera (PROTAC) reagent, dBET6 shows effective inhibition of bromodomain-containing protein 4 (BRD4) that exerts antitumor efficacy by degrading BRD4 via the ubiquitin-proteasome system. Nevertheless, the low tissue specificity and bioavailability impede its therapeutic effects and clinical translation on lung cancer treatment. Herein, we developed a type of dual targeting and bioresponsive nano-PROTAC (c R GD/L LC membrane/D S-P LGA/d B ET6, named RLDPB), which was constructed by using the pH and glutathione (GSH)-responsive polymer, disulfide bond-linked poly(lactic-co-glycolic acid) (PLGA-S-S-PLGA, DS-PLGA) to load the PROTAC agent dBET6, and further camouflaged with the homotypic LLC cell membranes, followed by the conjugation with cRGD ligand to the surface of the nanoparticles. Notably, RLDPB showed enhanced celluar uptake by lung cancer cells in vitro and accumulation in the tumors via the dual targeting structure including cRGD and LLC membrane. The pH/GSH responsiveness improved the release of dBET6 from the DS-PLGA-based nanoparticles within the cells. RLDPB was demonstrated to facilitate tumor regression by inducing the apoptosis of lung cancer cells with the degradation of BRD4. Thus, RLDPB can be considered a powerful tool to suppress lung cancer, which opens a new avenue to treat lung cancer by PROTAC.Graphical abstract

Precision Cancer Therapy Enabled Anti-Epidermal Growth Factor Receptor-Conjugated Manganese Core Phthalocyanine Bismuth Nanocomposite for Dual Imaging-Guided Breast Cancer Treatment.

Cancer remains a formidable global health challenge, demanding the exploration of innovative treatment modalities with minimized side effects. One promising avenue involves the synergistic integration of targeted photothermal/photodynamic therapy (PTT/PDT), utilizing specially designed functional nanomaterials for precise cancer diagnosis and treatment. This study introduces a composite biomaterial, anti-epidermal growth factor receptor-conjugated manganese core phthalocyanine bismuth (anti-EGFR-MPB), synthesized for precise cancer imaging and treatment. The biomaterial, synthesized via a solvothermal process, effectively treats and images breast cancer in mouse models. Its biomimetic design targets cancer cells precisely, with dual imaging for real-time monitoring. The biomimetic design of the composite enables precise targeting of cancer cells, whereas the dual imaging allows for real-time visualization and monitoring of the treatment. Invivo examinations confirm substantial damage to tumor tissues with no recurrence following 808-nm laser irradiation. The composite shows strong fluorescence/photoacoustic imaging (PAI) contrast, aiding malignancy detection. Biological assays and histological analyses confirmed the efficacy of the nanocomposite in inducing apoptosis in cancer cells. The integrated targeted dual image-guided phototherapy offered by this composite substantially enhances the precision and efficacy of cancer therapy, achieving an impressive photothermal efficiency of ~33.8%. Our findings demonstrate the utility of the anti-EGFR-MPB nanocomposite for both invitro and invivo photoacoustic image-guided PTT and PDT. The optimal treatment strategy for triple-negative breast cancer is found to be the use of 250 μg/ml of nanocomposite irradiated with 1.0 W/cm2 808-nm laser for 7 min.

AI-powered precision: breast carcinoma diagnosis through digital proliferation index (Ki-67) assessment in pathological anatomy

PurposeThe primary objective of the study is to enhance the accuracy and efficiency of assessing the proliferation index in cancer cells, specifically focusing on the role of Ki-67. The purpose is to address the limitations of traditional visual assessments conducted by pathologists by integrating AI technologies, particularly deep learning. By accurately computing the percentage of Ki-67-labeled cells, the research aims to streamline the diagnostic process, reduce subjectivity and contribute to the advancement of diagnostic precision in pathological anatomy.Design/methodology/approachThe research employs a methodological approach that integrates Ki-67, a non-histone nuclear protein, as a vital biomarker for assessing the proliferative status of cancer cells. Given the challenges associated with traditional visual assessments by pathologists, including inter- and intra-observer variability and time-consuming efforts, the study adopts a novel methodology leveraging artificial intelligence (AI) solutions. Deep learning is applied to precisely calculate the percentage of Ki-67-labeled cells. The process involves pathologists delineating the tumor area at x40 magnification, enabling the segmentation of various cell types (positive, negative and tumor-infiltrating lymphocytes). The subsequent percentage calculation enhances efficiency and minimizes subjectivity in the diagnostic process.FindingsDespite inherent errors, the research findings indicate that the model surpasses existing benchmarks, showcasing superior accuracy in terms of average error measurement. The comparison with diverse datasets and benchmarking against pathologists’ diagnoses contributes empirical evidence to support the effectiveness of the AI-based model in accurately computing the percentage of Ki-67-labeled cells. These findings signify a noteworthy advancement in diagnostic methodologies and reinforce the potential of AI technologies in improving the precision of cancer diagnostics within the realm of pathological anatomy.Originality/valueThe research contributes to the field by introducing an innovative approach that combines Ki-67 as a biomarker and AI technologies for improved diagnostic precision. The originality lies in the utilization of deep learning to calculate the percentage of labeled cells, mitigating the challenges associated with manual assessments. The validation of the model against diverse datasets and benchmarking against pathologists’ diagnoses demonstrates its superior accuracy, highlighting the value of integrating AI in pathological anatomy for enhanced diagnostic outcomes. The study represents a significant stride in original research, offering novel insights and methodologies in the pursuit of more precise cancer diagnostics.

A Rationally Designed Azobenzene Photoswitch for DNA G‐Quadruplex Regulation in Live Cells

AbstractG‐quadruplex (G4) DNA structures are increasingly acknowledged as promising targets in cancer research, and the development of G4‐specific stabilizing compounds may lay a fundamental foundation in precision medicine for cancer treatment. Here, we propose a light‐responsive G4‐binder for precise modulation of drug activation, providing dynamic and spatiotemporal control over G4‐associated biological processes contributing to cancer cell death. We developed a specialized fluorinated azobenzene (AB) switch equipped with a quinoline unit and a positively charged carboxamide side chain, Q‐Azo4F‐C, designed for targeted binding to G4 structures within cells. Biophysical studies, combined with molecular dynamics simulations, provide insights into the unique coordination modes of the photoswitchable ligand in its trans and cis configurations when interacting with G4s. The observed variations in complexation processes between the two isomeric states in different cancer cell lines manifest in more than 25‐fold reversible cytotoxic activity. Immunostaining conducted with the structure‐specific G4 antibody (BG4), establishes a direct correlation between cytotoxicity and the varying extent of G4 induction regulated by the two isoforms. Finally, we demonstrate the photo‐driven reversible regulation of G4 structures in lung cancer cells by Q‐Azo4F‐C. Our findings highlight the potential of light‐responsive G4‐binders in advancing precision cancer therapy through dynamic control of G4‐mediated pathways.

Reactive oxygen species augmented polydopamine-chlorin e6 nanosystem for enhanced chemo/photothermal/photodynamic therapy: A synergistic trimodal combination approach in vitro & in vivo

Amalgamation of near-infrared laser phototherapies with chemotherapy in multi-modal synergistic therapy holds great promise for future precision cancer nanomedicine due to its minimal invasiveness, reduced adverse reactions, and high anticancer efficacy. Herein, CuO nanoparticles were functionalized with photosensitizer molecule, chlorin e6 (Ce6) and coated with polydopamine (PDA) to achieve a drug delivery system (CuO@Ce6-PDA) with photothermal/photodynamic therapy (PTT/PDT). Subsequently, chemical drug PTX was loaded for chemotherapy, and folic acid (FA) serving as cancer-targeting exterior material. Prepared FA@CuO@Ce6-PDA/PTX nanoparticles were nano-sized with favorable biocompatibility, colloidal stability, optimal surface charge, effective PTX loading, and controllable PTX release. In vitro studies on 4 T1 cells showed that FA@CuO@Ce6-PDA/PTX had noteworthy synergistic therapeutic antitumour effects featuring chemo/PTT/PDT with IC50 of 50 μg/mL lower than that FA@CuO@Ce6-PDA/PTX without NIR laser irradiation (225 μg/mL). Additionally, FA@CuO@Ce6-PDA/PTX produced intracellular high reactive oxygen species (ROS) in presence of 660 nm laser, altering mitochondrial membrane potential and promoting tumour cell death. In vivo results indicate nanoplatform could accumulate in tumour spots enabling thermal imaging capabilities and exhibit synergistic therapeutic effect if irradiated with NIR laser (808 and 660 nm), evident from in vitro antitumour assay. Therefore, in vitro finding postulates FA@CuO@Ce6-PDA/PTX could be an intriguing nanoplatform for Chemo/PTT/PDT-based combination therapy.

X-ray-triggered fenton-like nanocomposites for passive-activation and non-spontaneous chemodynamic therapy

Currently, many studies focused on the TME-regulating strategies to address the unsatisfactory therapeutic effect of single chemodynamic therapy (CDT). However, most of TME-regulating strategies were nonspecific, the CDT agents with spontaneous CDT activity would result in augmented killing effect not only in tumor but also in normal tissues. Furthermore, CDT efficiency was subject to attenuation due to the gradually exhausted active sites in CDT agents. Herein, the passive-activation and non-spontaneous CDT strategy was proposed by constructing a completely X-ray-triggered CDT agent (F-SCNPs) to target the therapeutic dilemma between the augmented CDT efficacy and high tumor specificity. In this study, the designed F-SCNPs can launch Fe2+ sites generation to initiate CDT activity only when they were exposed to X-ray, which were totally different from the traditional CDT agents including other stimuli-responsive CDT agents that always spontaneously exert CDT activity no matter they had accepted the stimuli or not. The X-ray-activated feature of F-SCNPs endowed their tumor specificity with less affect by nonspecific TME-regulating strategies. Meanwhile, X-ray can promote the Fe2+/Fe3+ cycling of F-SCNPs to enhance the •OH yield. This X-ray-mediated nanosystem with remarkable synergistic therapy of CDT and radiotherapy provided a promising avenue for high-efficiency and precise cancer treatments.

Perceptions of Cancer Through the Ages—From Hippocratic Oncology to Precision Cancer Medicine

The unravelling of the human genome created new perceptions of the origin and evolution of diseases, and for cancer in particular, it established the notion that neoplasia has been a companion of life since its appearance on Earth. It is not surprising that neoplasms, in various forms, develop in numerous species of animals and even in plants. Unmistakable accounts of cancer with clinical features as are understood today begin in the 5th c. B.C. The principles and practice of the Hippocratic and Galenic tradition dominated cancer care virtually into the 20th century. Advanced sequencing technologies at the dawn of the 21st century generated new therapeutic opportunities with immunotherapy, oncolytic virotherapy, and gene transfer, with the latter especially being used in cases of hereditary cancer.

Nuclear imaging of PD-L1 expression promotes the synergistic antitumor efficacy of targeted radionuclide therapy and immune checkpoint blockade.

In order to maximize synergistic effect of targeted radionuclide therapy (TRT) and immune checkpoint blockade (ICB) as well as reduce the toxicity, we pioneered a strategy guided by PD-L1-targeted nuclear medicine imaging for the combination of TRT and ICB towards precision cancer therapy. As a novel targeted radiotherapeutic agent, 177Lu-AB-3PRGD2 targeting integrin αvβ3 was developed to achieve sustained antitumor effect by introducing an albumin binder (AB) into the structure of 3PRGD2. The 177Lu-AB-3PRGD2 TRT as well as different types of combination therapies of 177Lu-AB-3PRGD2 TRT and anti-PD-L1 ICB were performed in animal models. The changes of PD-L1 expression in tumors after TRT were evaluated in vitro and in vivo by PD-L1-specific SPECT/CT imaging of 99mTc-MY1523. 177Lu-AB-3PRGD2 showed improved tumor uptake and prolonged tumor retention, leading to significantly enhanced tumor growth suppression. Moreover, 177Lu-AB-3PRGD2 TRT remodeled the tumor immune microenvironment by upregulating PD-L1 expression and increasing tumor-infiltrating CD8+ T cells, facilitating immunotherapy. We found that the anti-PD-L1 treatment was more effective during the upregulation of tumor PD-L1 expression, and the time window could be determined by 99mTc-MY1523 SPECT/CT. We developed a novel and long-acting radiotherapeutic agent 177Lu-AB-3PRGD2, and pioneered a strategy guided by PD-L1-targeted nuclear medicine imaging for the combination of TRT and ICB towards precision cancer therapy, optimizing the therapeutic efficacy and reducing the cost and potential toxicity risks. This strategy could also be adapted for clinical practice, combining conventional radiotherapy or chemotherapy with ICB to enhance therapeutic efficacy.

Omics-Enhanced Nanomedicine for Cancer Therapy.

Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high-throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine-mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics-enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

A Hybrid Alloying Nanozyme-Glutathione Inhibitor Co-Delivery System Initiates a Dual-Disruption on Cancer Redox Homeostasis.

Altered redox homeostasis has long been observed in cancer cells, which can be exploited for therapeutic benefits. However, reactive oxygen species (ROS) pleiotropy coupling with reductive adaptation in cancer cells poses a formidable challenge for redox dyshomeostasis-based cancer therapy. Herein, a AuPd alloying nanozyme-glutathione (GSH) biosynthesis inhibitor co-delivery system (B-BMES) is developed using dendritic SiO2 as a matrix to target cancer redox homeostasis. By optimizing element composition, the alloying nanozyme in B-BMES exhibits a potent peroxidase (POD)-like activity to trigger ROS insults-mediated redox dyshomeostasis. Such a POD functionality is attributed to the optimized electronic structure and catalytic activity. Simultaneously, the B-BMES abrogates the reductive adaptation by exerting its molecule-targeted GSH suppression, thereby achieving a dual-disruption on cancer redox homeostasis. Camouflaging B-BMES with tumor-homologous cytomembrane, a hybrid nanosystem with biological stability and tumor-targeting ability is further fabricated, which initiates a safe, precise redox disruption-based cancer therapy and sensibilizes standard chemotherapy.

A Knowledge-Based Planning model for IMRT in breast and lung cancer

The advent of Knowledge-Based Planning (KBP) models has introduced a transformative approach to Intensity-Modulated Radiation Therapy (IMRT) treatment planning in breast cancer and lung cancer cases. This paper explores the application of KBP models to these specific cancer types, highlighting their potential to enhance treatment accuracy, efficiency, and patient outcomes. By leveraging historical treatment data and machine learning techniques, KBP-IMRT offers a data-driven framework for optimizing dose distributions, minimizing radiation exposure to healthy tissues, and improving overall treatment plan quality. Through a comprehensive review of the literature and clinical case studies, this paper underscores the advantages of KBP-IMRT, such as streamlined planning processes and improved plan consistency, while acknowledging the challenges associated with model development and implementation. As the field of radiotherapy continues to evolve, KBP models hold the promise of shaping the future of personalized and precise cancer treatment strategies.

Nanocarriers Targeting Circular RNA ADARB1 Boost Radiosensitivity of Nasopharyngeal Carcinoma through Synergically Promoting Ferroptosis.

Nasopharyngeal carcinoma (NPC) is a common malignant tumor of the head and neck, prevalent in regions such as Southern China and Southeast Asia. Radiotherapy serves as the primary clinical treatment for this carcinoma. However, resistance to radiotherapy is a fundamental cause of treatment failure and patient mortality, with the underlying mechanisms yet to be fully elucidated. We identified a recently characterized circular RNA, circADARB1, which is markedly upregulated in NPC tissues and closely associated with poor prognosis and radiotherapy resistance. Both in vitro and in vivo experiments demonstrated that circADARB1 inhibited ferroptosis, thereby inducing radiotherapy resistance in NPC cells. Building on these findings, we synthesized a biomimetic nanomaterial consisting of semiconducting polymer nanoparticles wrapped in cell membranes, designed to deliver both siRNA targeting circADARB1 and iron ions. The application of this nanomaterial not only efficiently suppressed the expression of circADARB1 and boosted intracellular iron concentrations, but also enhanced ferroptosis induced by radiotherapy, improving the radiosensitivity of NPC cells. Furthermore, our study revealed that circADARB1 upregulated the expression of heat shock protein HSP90B1, which repaired misfolded SLC7A11 and GPX4 proteins triggered by radiotherapy, thereby preserving their stability and biological functions. Mechanistically, SLC7A11 facilitated cysteine transportation into cells and glutathione synthesis, while GPX4 employed glutathione to mitigate intracellular lipid peroxidation induced by radiotherapy, shielding cells from oxidative damage and inhibiting ferroptosis, and ultimately leading to radiotherapy resistance in NPC cells. Our investigation elucidates molecular mechanisms with substantial clinical relevance, highlights the promising application prospects of nanotechnology in precision cancer therapy.

Oncogenic H-RAS induces metformin resistance in head and neck cancer by promoting glycolytic metabolism.

Metformin administration has recently emerged as a candidate strategy for the prevention of head and neck squamous cell carcinoma (HNSCC). However, the intricate relationship between genetic alterations in HNSCC and metformin sensitivity is still poorly understood, which prevents the stratifications of patients harboring oral premalignant lesions that may benefit from the chemopreventive activity of metformin. In this study, we investigate the impact of prevalent mutations in HNSCC in response to metformin. Notably, we found that the expression of oncogenic HRAS mutants confers resistance to metformin in isogenic HNSCC cell systems and that HNSCC cells harboring endogenous HRAS mutations display limited sensitivity to metformin. Remarkably, we found that metformin fails to reduce activation of the mTOR pathway in HRAS oncogene expressing HNSCC cells in vitro and in vivo, correlating with reduced tumor suppressive activity. Mechanistically, we found that this process depends on the ability of HRAS to enhance glycolytic metabolism, thereby suppressing the requirement of oxidative phosphorylation to maintain the cellular energetic balance. Overall, our study revealed that HNSCC cells with oncogenic HRAS mutations exhibit diminished metformin sensitivity, thus shedding light on a potential mechanism of treatment resistance. This finding may also help explain the limited clinical responses to metformin in cancers with RAS mutations. Ultimately, our study underscores the importance of understanding the impact of the genetic landscape in tailoring precision cancer preventive approaches in the context of HNSCC and other cancers that are characterized by the presence of a defined premalignant state and, therefore, amenable for cancer interception strategies.

Size Matters: Curvature and Antigen-Mediated Dual Recognition of Size-Specific Tumor-Derived Exosomes.

Accurate identification of tumor-derived exosomes is crucial for advancing cancer diagnosis and therapies. However, distinguishing tumor-derived exosomes is challenging due to the heterogeneity of exosomes, which reflect different sizes and cells of origin. To address this challenge, we introduce the curvature and antigen-mediated proximity ligation assay for tumor-derived exosomes (CAPTURE) strategy, which leverages the size-selective properties of curvature-sensing peptides and specific antigen binding of aptamers. CAPTURE enables highly specific identification and precise quantification of the PD-L1+ exosomes in plasma samples. CAPTURE is proven to be simple, homogeneous, rapid, and highly selective, achieving a 100% specificity in discriminating colorectal cancer (CRC) patients from healthy donors. Overall, the CAPTURE strategy presents a promising avenue for precise and noninvasive cancer diagnosis.

Machine learning-assisted identification of potential cancer inhibitors: Multifunctional biomineralized CaCO3-polyethyleneimine nanoparticle carriers and their application in lung cancer therapy

Cancer, as a prevalent phenomenon among malignant tumors, has a late-stage diagnosis rate approaching half, significantly limiting treatment efficacy and imposing substantial physiological and psychological burdens on patients. Therefore, developing innovative therapeutic platforms that simultaneously optimize diagnostic accuracy, enhance therapeutic efficacy, and minimize side effects is a critical scientific challenge in the field of precision cancer medicine. In this study, we employed a simple and efficient one-pot synthesis method combined with polyethyleneimine (PEI)-mediated biomineralization technology to successfully prepare calcium carbonate-polyethyleneimine (CaCO3-PEI) composite nanoparticles. These nanoparticles exhibit high pH sensitivity and can rapidly degrade under the mildly acidic conditions that mimic the tumor microenvironment, providing potential for targeted tumor therapy. Subsequently, we encapsulated compound 1, which has potential for lung cancer treatment, within the CaCO3-PEI nanoparticles, successfully constructing the CaCO3-PEI@1 composite system. Structural analysis of this composite system was conducted through characterization techniques. In vitro cell experiments demonstrated that the CaCO3-PEI@1 composite system exhibited significant anticancer effects by effectively inhibiting cancer cell proliferation through the regulation of apoptosis-related protein Bax expression. This provides new insights and experimental evidence for the development of cancer treatment strategies. Based on the experiment and molecular docking identified activity against lung cancer, the compound 1 was used as the initiator for the machine learning model, and up to 10,000 episodes were generated, after thorough examination, it was found about two-thirds of the generated episodes were entirely different from each other, which demonstrated that the machine learning model was a powerful tool for designing of potential inhibitors against lung cancer.