Precise Tumor Research Articles

B2R-Targeting Radiotracer for PET/MR Imaging of Hepatocellular Carcinoma and Guiding Anti-B2R Therapy.

The bradykinin B2 receptor (B2R) is overexpressed in a wide variety of tumors and is a well-defined target for tumor imaging and therapy. The hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) scanner is considered a noninvasive and advanced instrument for precise tumor imaging. In this work, we developed a novel B2R-targeting radiotracer, 68Ga-DOTA-icatibant, for quantifying B2R expression. 68Ga-DOTA-icatibant showed high stability, fast clearance and specific binding to B2R. PET/MR imaging revealed excellent tumor accumulation, and the uptake in tumors could be blocked by DOTA-icatibant. Icatibant-mediated anti-B2R therapy downregulated B2R expression in tumor cells and inhibited the growth of HepG2 tumors, and the decrease in tumor uptake was monitored by timely PET/MR imaging. Hematoxylin and eosin (H&E) and immunohistochemical staining results further demonstrated that the efficacy of anti-B2R could be accurately monitored with the developed PET/MR imaging radiotracer. 68Ga-DOTA-icatibant can be utilized to noninvasively determine B2R expression and dynamically and sensitively monitor the efficacy of anti-B2R therapy.

Iridium(III) carbene complexes as potent girdin inhibitors against metastatic cancers

Many cancer-driving protein targets remain undruggable due to a lack of binding molecular scaffolds. In this regard, octahedral metal complexes with unique and versatile three-dimensional structures have rarely been explored as inhibitors of undruggable protein targets. Here, we describe antitumor iridium(III) pyridinium-N-heterocyclic carbene complex 1a, which profoundly reduces the viability of lung and breast cancer cells as well as cancer patient-derived organoids at low micromolar concentrations. Compound 1a effectively inhibits the growth of non-small-cell lung cancer and triple-negative breast cancer xenograft tumors, impedes the metastatic spread of breast cancer cells, and can be modified into an antibody-drug conjugate payload to achieve precise tumor delivery in mice. Identified by thermal proteome profiling, an important molecular target of 1a in cellulo is Girdin, a multifunctional adaptor protein that is overexpressed in cancer cells and unequivocally serves as a signaling hub for multiple pivotal oncogenic pathways. However, specific small-molecule inhibitors of Girdin have not yet been developed. Notably, 1a exhibits high binding affinity to Girdin with a Kd of 1.3 μM and targets the Girdin-linked EGFR/AKT/mTOR/STAT3 cancer-driving pathway, inhibiting cancer cell proliferation and metastatic activity. Our study reveals a potent Girdin-targeting anticancer compound and demonstrates that octahedral metal complexes constitute an untapped library of small-molecule inhibitors that can fit into the ligand-binding pockets of key oncoproteins.

Ultrabright NIR-IIb Fluorescence Quantum Dots for Targeted Imaging-Guided Surgery.

Pioneering approaches for precise tumor removal involve fluorescence-guided surgery, while challenges persist, including the low fluorescence contrast observed at tumor boundaries and the potential for excessive damage to normal tissue at the edges. Lead/cadmium sulfide quantum dots (PbS@CdS QDs), boasting high quantum yields (QYs) and vivid fluorescence, have facilitated advancements in the second near-infrared window (NIR-II, 900-1700 nm). However, during fluorescent surgical navigation operations, hydrophilic coatings of these inorganic nanoparticles (NPs) guarantee biosafety; it also comes at the expense of losing a significant portion of QY and NIR-II fluorescence, causing heightened damage to normal tissues caused by cutting edges. Herein, we present hydrophilic core-shell PbS@CdS@PEG NPs with an exceptionally small diameter (∼8 nm) and a brilliant NIR-IIb (1500-1700 nm) emission at approximately 1600 nm. The mPEG-SH (MW: 2000) addresses the hydrophobicity and enhances the biosafety of PbS@CdS QDs. In vivo fluorescence-guided cervical tumor resection becomes achievable immediately upon injection of an aqueous solution of PbS@CdS@PEG NPs. Notably, this approach results in a significantly reduced thickness (100-500 μm) of damage to normal tissues at the margins of the resected tumors. With a high QY (∼30.2%) and robust resistance to photobleaching, NIR-IIb imaging is sustained throughout the imaging process.

Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.

Surgical decision-making for glioblastoma poses significant challenges due to its complexity and variability. This study investigates the potential of artificial intelligence (AI) tools in improving "decision-making processes" for glioblastoma surgery. A systematic review of literature identified 10 relevant studies, primarily focused on predicting resectability and surgery-related neurological outcomes. AI tools, especially rooted in radiomics and connectomics, exhibited promise in predicting resection extent through precise tumor segmentation and tumor-network relationships. However, they demonstrated limited effectiveness in predicting postoperative neurological due to dynamic and less quantifiable nature of patient-related factors. Recognizing these challenges, including limited datasets and the interpretability requirement in medical applications, underscores the need for standardization, algorithm optimization, and addressing variability in model performance and then further validation in clinical settings. While AI holds potential, it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.

Comparative analysis of non-invasive and invasive alternating electric fields therapy for malignant gliomas: a simulation study

Alternating electric fields (AEFs) at intermediate frequencies (100–300 kHz) and low intensities (1–3 V/cm) have shown promise as an effective approach for inhibiting cancer cell proliferation. However, a noticeable research gap exists in comparing the biophysical properties of invasive and non-invasive AEFs methods, and AEFs delivery strategies require further improvement. In this study, we constructed a realistic head model to simulate the effects of non-invasive and invasive AEFs on malignant gliomas. Additionally, a novel method was proposed involving the placement of a return electrode under the scalp. We simulated the electric field and temperature distributions in the brain tissue for each method. Our results underscore the advantages of invasive AEFs, showcasing their superior tumor-targeting abilities and reduced energy requirements. The analysis of brain tissue temperature changes reveals that non-invasive AEFs primarily generate heat at the scalp level, whereas invasive methods localize heat production within the tumor itself, thereby preserving surrounding healthy brain tissue. Our proposed invasive AEFs method also shows potential for selective electric field intervention. In conclusion, invasive AEFs demonstrate potential for precise and effective tumor treatment. Its enhanced targeting capabilities and limited impact on healthy tissue make it a promising avenue for further research in the realm of cancer treatment.

Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

Use of liquid biomarker thymidine kinase activity (TKa) to predict outcome and progression in patients with metastatic breast cancer (MBC) in the GEICAM/2014-12 FLIPPER trial.

1028 Background: There is a need for more precise blood-based tumor biomarkers in MBC. TKa is a liquid proliferation biomarker providing prognostic, predictive and monitoring information in MBC. TKa can be measured in blood using the DiviTum TKa assay. Here we report the results of TKa in a trial of endocrine therapy (ET) fulvestrant (F) + the CDK4/6 inhibitor (CDK4/6i) Palbociclib (Pa) vs. F + Placebo (Pl), the GEICAM FLIPPER trial (NCT02690480), a multicenter, double-blind, randomized (1:1) study. F+Pa showed 1-year PFS rates of 83.5% vs. 71.9% (p=0.064) and better median (m)PFS (31.8 vs. 22.0 months, p=0.001) than F+Pl in first line treatment of HR+/HER2- MBC patients. F+Pa also significantly improved ORR and CBR. ET+CDK4/6i are currently the cornerstone of treatment for HR+/HER2- MBC. However, 10-30% of these patients display primary resistance to treatment. Early identification of patient subsets with different risk of progression during treatment with CDK4/6i may help tailor clinical management and disease monitoring. Methods: Sequential plasma was collected from 187 patients (pts) at baseline (BL) and at 12, 24, 36, 48 weeks (w) on treatment, and at progression. 910 samples were analyzed for TKa with the FDA approved and CE labelled DiviTum TKa assay (Biovica, Sweden) using DuA (DiviTum unit of Activity) as measuring unit. The cutoff for categorizing in high vs low is 250 DuA. The Kaplan-Meier method was used to estimate mPFS and mOS. Unadjusted and adjusted hazard ratio (HR) and odds ratio (OR) with 95% confidence interval (CI) were calculated with the Cox proportional hazards regression model and with logistic regression respectively, considering relevant confounders. Results: Low BL TKa predicted better PFS (30.5 months mPFS in low vs 19.8 in high, HR 1.62; 95% CI 1.11-2.35, p=0.011) and OS (HR 1.81; 95% CI 1.15-2.85, p=0.0099). Higher TKa at BL and at 12w was detected in pts that progressed before 12 months. Conversely, low BL TKa, maintained throughout week 12-48, were associated with objective response (HR 2.58; 95% CI 1.38-4.81, p=0.0029) and OS (HR 5.74; 95%CI 2.06-16, p=0.0008). At progression, pts on F+Pa tended to have higher TKa levels than F+Pl (246 vs. 138 DuA), reflecting faster growing tumors, not achieving statistical significance (p=0.0585). TKa monitoring and dynamics from BL and on treatment timepoints (week 12-48) show that the group of pts with low TK at BL that remain low during treatment is enriched with non-progressors and pts that progress after 12 months (p=0.0108). High TKa at BL predicted shorter OS in the F+Pa arm (HR 4.39; 95% CI 2.15-8.99, p<0.0001). Conclusions: Measuring blood TKa levels can monitor and predict efficacy of F+/-Pa at BL and during therapy in MBC. TKa dynamics before and during therapy are associated with PFS and OS. Future studies will provide additional knowledge on the utility of TKa in MBC. Clinical trial information: NCT02690480 .

Promptable foundation model for automatic whole body RECIST measurement.

e13643 Background: RECIST 1.1 is the gold standard for tumor evaluation in clinical trials and patient care. However, it faces challenges due to the subjective selection and measurement of lesions, notably because of inter-observer variability in the 20-30% range (1), which can potentially result in inaccuracies in therapeutic response classification (2). The main objective of this study is to assess the added value of a radiological foundation model to assist the radiologist to measure RECIST across different lesion topographies, using visual prompting. Methods: We use a promptable segmentation algorithm, based on a visual foundation model, pre-trained on various CT-scans and then trained on a subset of the Medical Segmentation Decathlon (MSD) dataset, covering 538 patients with pancreas, liver, colon and lung lesions. It outputs a 3D segmentation mask of the lesion, using a visual prompt, i.e. a 3D bounding box (BBox) as input. We then measure the capacity of the model to measure RECIST on the MSD validation set and on the KiTS validation set (99 samples), which features a new lesion type (kidney). To simulate inter-reader variability of up to 23%, we provide as inputs BBoxes centered on the lesions but with a varying error (15% variability: +10 pixels, 23% variability: +15 pixels). Results: The model is able to correct variability in BBox input: with all BBox error ranges, it beats the input variability (15% and 23% thresholds in each column) across many organ types, except in the pancreas when using large error BBoxes. Moreover, the model is able to generalize to new lesion types not seen during training, on an external dataset (KiTS - Kidney). Conclusions: Unlike existing supervised machine learning models dedicated to lesion detections in specific organs, our approach is organ and lesion agnostic and offers a more reliable, precise tumor assessment. This is a first step before allowing the longitudinal evaluation of tumors while safeguarding the clinical intuition necessary for selecting the right target lesions. Moreover, we believe that these methods will be crucial in advancing beyond RECIST 1.1, facilitating the identification of new prognostic biomarkers and proxies for tumor burden derived from previously underexplored radiomics features, ultimately refining the efficacy of clinical trial assessments and elevating the standard of patient care. 1. Yoon, S et al. 2015. 2. Kuhl et al. 2018. [Table: see text]

DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.

Breast cancer remains a critical global concern, underscoring the urgent need for early detection and accurate diagnosis to improve survival rates among women. Recent developments in deep learning have shown promising potential for computer-aided detection (CAD) systems to address this challenge. In this study, a novel segmentation method based on deep learning is designed to detect tumors in breast ultrasound images. Our proposed approach combines two powerful attention mechanisms: the novel Positional Convolutional Block Attention Module (PCBAM) and Shifted Window Attention (SWA), integrated into a Residual U-Net model. The PCBAM enhances the Convolutional Block Attention Module (CBAM) by incorporating the Positional Attention Module (PAM), thereby improving the contextual information captured by CBAM and enhancing the model's ability to capture spatial relationships within local features. Additionally, we employ SWA within the bottleneck layer of the Residual U-Net to further enhance the model's performance. To evaluate our approach, we perform experiments using two widely used datasets of breast ultrasound images and the obtained results demonstrate its capability in accurately detecting tumors. Our approach achieves state-of-the-art performance with dice score of 74.23% and 78.58% on BUSI and UDIAT datasets, respectively in segmenting the breast tumor region, showcasing its potential to help with precise tumor detection. By leveraging the power of deep learning and integrating innovative attention mechanisms, our study contributes to the ongoing efforts to improve breast cancer detection and ultimately enhance women's survival rates. The source code of our work can be found here: https://github.com/AyushRoy2001/DAUNet.

In situ forming an injectable hyaluronic acid hydrogel for drug delivery and synergistic tumor therapy

Stimulus-responsive injectable hydrogel has the key characteristics of in situ drug-loading ability and the controlled drug release, enabling efficient delivery and precise release of chemotherapy drugs at the tumor site, thereby being used as a local drug delivery system for sustained tumor treatment. This article designed a smart responsive injectable hydrogel loaded with anti-tumor drugs and nanoparticles to achieve efficient and specific synergistic treatment of tumors. Hyaluronic acid (HA) hydrogel obtained by cross-linking HA-SH (HS) and HA-Tyr (HT) through horseradish peroxidase (HRP), and doxorubicin hydrochloride (DOX) and folic acid-polyethylene glycol-amine (FA-PEG-NH2) modified PDA (denoted as PPF) were encapsulated to construct the HS/HT@PPF/D hydrogel. The hydrogel had good biocompatibility, injectability, and could respond to multiple stimuli at the tumor site, thereby achieving controlled drug release. At the same time, PPF gave it excellent photothermal efficiency, photothermal stability and tumor targeting. In vitro and in vivo experimental results showed that the HS/HT@PPF/D hydrogel combined with near-infrared laser irradiation could significantly improve its anti-tumor effect and could almost eliminate the entire tumor mass without obvious adverse reactions. The HS/HT@PPF/D hydrogel could achieve multi-stimulus-responsive drug delivery and be used for precise chemo-photothermal synergistic tumor treatment, thus providing a new platform for local synergistic tumor treatment.

The Loss of an Orphan Nuclear Receptor NR2E3 Augments Wnt/β-catenin Signaling via Epigenetic Dysregulation that Enhances Sp1-β catenin-p300 Interactions in Hepatocellular Carcinoma.

The orphan nuclear receptor NR2E3 (Nuclear receptor subfamily 2 group E, Member 3) is an epigenetic player that modulates chromatin accessibility to activate p53 during liver injury. Nonetheless, a precise tumor suppressive and epigenetic role of NR2E3 in hepatocellular carcinoma (HCC) development remains unclear. HCC patients expressing low NR2E3 exhibit unfavorable clinical outcomes, aligning with heightened activation of the Wnt/β-catenin signaling pathway. The murine HCC models utilizing NR2E3 knockout mice consistently exhibits accelerated liver tumor formation accompanied by enhanced activation of Wnt/β-catenin signaling pathway and inactivation of p53 signaling. At cellular level, the loss of NR2E3 increases the acquisition of aggressive cancer cell phenotype and tumorigenicity and upregulates key genes in the WNT/β-catenin pathway with increased chromatin accessibility. This event is mediated through increased formation of active transcription complex involving Sp1, β-catenin, and p300, a histone acetyltransferase, on the promoters of target genes. These findings demonstrate that the loss of NR2E3 activates Wnt/β-catenin signaling at cellular and organism levels and this dysregulation is associated with aggressive HCC development and poor clinical outcomes. In summary, NR2E3 is a novel tumor suppressor with a significant prognostic value, maintaining epigenetic homeostasis to suppress the Wnt/β-catenin signaling pathway that promotes HCC development.

P.103 Automated pituitary adenoma segmentation for radiosurgery with deep learning-based model

Background: Pituitary adenomas are treated with endoscopic surgery, while stereotactic radiosurgery addresses complex cases. Our study highlights AI’s role in accurate segmentation, improving treatment planning workflow efficiency Methods: In a retrospective study at Na Homolce Hospital (January 2010 to October 2022), SRS for pituitary adenomas was analyzed. Data were split 80:20 for training and validation. Using nnU-net, a medical image segmentation tool, a model predicted precise tumor, optic nerve, and pituitary gland segmentation. Accuracy was evaluated quantitatively with Dice similarity coefficient and qualitatively by human experts. The study explored the impact of tumor volume and hormonal activity status on segmentation accuracy. Results: The study comprised 582 and 146 patients in training and validation sets, respectively. The model achieved Dice similarity coefficients of 83.1% (tumor), 62.9% (normal gland), and 78.0% (optic nerve). Expert assessments deemed 41% directly applicable, 31.5% needing minor adjustments, and 27.4% unsuitable for clinical use. Larger tumor volume and non-functioning adenomas correlated with higher accuracy. Including T2 weighted scans improved DSC for optic nerve and normal gland. Conclusions: The study showcases deep learning’s potential in automating pituitary adenoma segmentation from MRI data, particularly excelling in large, hormonally inactive macroadenomas. Encourages collaborative use with clinicians for improved neurosurgical patient care.

Novel Near-Infrared Fluorescent Probe for Hepatocyte Growth Factor in Vivo Imaging in Surgical Navigation of Colorectal Cancer.

Despite recent advancements in colorectal cancer (CRC) treatment, the prognosis remains unfavorable primarily due to high recurrence and liver metastasis rates. Fluorescence molecular imaging technologies, combined with specific probes, have gained prominence in facilitating real-time tumor resection guided by fluorescence. Hepatocyte growth factor (HGF) is overexpressed in CRC, but the advancement of HGF fluorescent probes has been impeded by the absence of effective HGF-targeting small-molecular ligands. Herein, we present the targeted capabilities of the novel V-1-GGGK-MPA probe labeled with a near-infrared fluorescent dye, which targets HGF in CRC. The V-1-GGGK peptide exhibits high specificity and selectivity for HGF-positive in vitro tumor cells and in vivo tumors. Biodistribution analysis of V-1-GGGK-MPA revealed tumor-specific accumulation with low background uptake, yielding signal-to-noise ratio (SNR) values of tumor-to-colorectal >6 in multiple subcutaneous CRC models 12 h postinjection. Quantitative analysis confirmed the probe's high uptake in SW480 and HT29 orthotopic and liver metastatic models, with SNR values of tumor-to-colorectal and -liver being 5.6 ± 0.4, 4.6 ± 0.5, and 2.1 ± 0.3, 2.0 ± 0.5, respectively, enabling precise tumor visualization for surgical navigation. Pathological analysis demonstrated the excellent tumor boundaries discrimination capacity of the V-1-GGGK-MPA probe at the molecular level. With its rapid tumor targeting, sustained tumor retention, and precise tumor boundary delineation, V-1-GGGK-MPA merges as a promising HGF imaging agent, enriching the toolbox of intraoperative navigational fluorescent probes for CRC.

Emerging Chemodynamic Nanotherapeutics for Cancer Treatment.

Chemodynamic therapy (CDT) has emerged as a transformative paradigm in the realm of reactive oxygen species -mediated cancer therapies, exhibiting its potential as a sophisticated strategy for precise and effective tumor treatment. CDT primarily relies on metal ions and hydrogen peroxide to initiate Fenton or Fenton-like reactions, generating cytotoxic hydroxyl radicals. Its notable advantages in cancer treatment are demonstrated, including tumor specificity, autonomy from external triggers, and a favorable side-effect profile. Recent advancements in nanomedicine are devoted to enhancing CDT, promising a comprehensive optimization of CDT efficacy. This review systematically elucidates cutting-edge achievements in chemodynamic nanotherapeutics, exploring strategies for enhanced Fenton or Fenton-like reactions, improved tumor microenvironment modulation, and precise regulation in energy metabolism. Moreover, a detailed analysis of diverse CDT-mediated combination therapies is provided. Finally, the review concludes with a comprehensive discussion of the prospects and intrinsic challenges to the application of chemodynamic nanotherapeutics in the domain of cancer treatment.

Near-Infrared-II Nanomaterials for Activatable Photodiagnosis and Phototherapy.

Near-Infrared-II (NIR-II) spans wavelengths between 1,000 to 1,700 nanometers, featuring deep tissue penetration and reduced tissue scattering and absorption characteristics, providing robust support for cancer treatment and tumor imaging research. This review explores the utilization of activatable NIR-II photodiagnosis and phototherapy based on tumor microenvironments (e. g., reactive oxygen species, pH, glutathione, hypoxia) and external stimulation (e. g., laser, ultrasound, photothermal) for precise tumor treatment and imaging. Special emphasis is placed on the advancements and advantages of activatable NIR-II nanomedicines in novel therapeutic modalities like photodynamic therapy, photothermal therapy, and photoacoustic imaging. This encompasses achieving deep tumor penetration, real-time monitoring of the treatment process, and obtaining high-resolution, high signal-to-noise ratio images even at low material concentrations. Lastly, from a clinical perspective, the challenges faced by activatable NIR-II phototherapy are discussed, alongside potential strategies to overcome these hurdles.

Novel locally nebulized indocyanine green for simultaneous identification of tumor margin and intersegmental plane.

Segmentectomy, recommended for early-stage lung cancer or compromised lung function, demands precise tumor detection and intersegmental plane identification. While indocyanine green (ICG) commonly aids in these aspects using near-infrared imaging, its separate administrations through different routes and times can lead to complications and patient anxiety. This study aims to develop a lung-specific delivery method by nebulizing low-dose ICG to targeted lung segments, allowing simultaneous detection of lung tumors and intersegmental planes across diverse animal models. To optimizing the dose of ICG for lung tumor and interlobar fissure detection, different doses of ICG (0.25, 0.1, and 0.05mg/kg) were nebulized to rabbit lung tumor models. The distribution of locally nebulized ICG in targeted segments was studied to evaluate the feasibility of detecting lung tumor and intersegmental planes in canine lung pseudotumor models. Near-infrared fluorescence imaging demonstrated clear visualization of lung tumor margin and interlobar fissure using local nebulization of 0.1mg/kg ICG for only 4min during surgery in the rabbit models. In the canine model, the local nebulization of 0.05mg/kg of ICG into the target segment enabled clear visualization of pseudotumor and intersegmental planes for 30min. This innovative approach achieves a reduction in ICG dose and prolonged the visualization time of the intersegmental plane and effectively eliminates the need for the hurried marking of tumors and intersegmental planes. The authors anticipate that lung-specific delivery of ICG will prove valuable for image-guided limited resection of lung tumors in clinical practice.

Autonomous Feedback-Driven Engineered DNAzyme-Coated Trojan Horse-like Nanocapsules for On-Demand CRISPR/Cas9 Delivery.

Manipulating the expression of cellular genes through efficient CRISPR/Cas9 delivery is rapidly evolving into a desirable tumor therapeutics. The exposure of CRISPR/Cas9 to a complex external environment poses challenges for conventional delivery carriers in achieving responsive and accurate release. Here, we report a Trojan horse-like nanocapsule for the on-demand delivery of CRISPR/Cas9 in a microRNA-responsive manner, enabling precise tumor therapy. The nanocapsule comprises a nanoassembled, engineered DNAzyme shell encasing a Cas9/sgRNA complex core. The DNAzyme, functioning as a catalytic unit, undergoes a conformational change in the presence of tumor-associated microRNA, followed by activating a positive feedback-driven autonomous catabolic cycle of the nanocapsule shell. This catabolic cycle is accomplished through chain reactions of DNAzyme "cleavage-hybridization-cleavage", which ensures sensitivity in microRNA recognition and effective release of Cas9/sgRNA. Utilizing this Trojan horse-like nanocapsule, as low as 1.7 pM microRNA-21 can trigger the on-demand release of Cas9/sgRNA, enabling the specific editing of the protumorigenic microRNA coding gene. The resulting upregulation of tumor suppressor genes induces apoptosis in tumor cells, leading to significant inhibition of tumor growth by up to 75.94%. The Trojan horse-like nanocapsule, with superior programmability and biocompatibility, is anticipated to serve as a promising carrier for tailoring responsive gene editing systems, achieving enhanced antitumor specificity and efficacy.

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning.

Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

A Customized Biohybrid Presenting Cascade Responses to Tumor Microenvironment.

Intrinsic characteristics of microorganisms, including non-specific metabolism sites, toxic byproducts, and uncontrolled proliferation constrain their exploitation in medical applications such as tumor therapy. Here, the authors report an engineered biohybrid that can efficiently target cancerous sites through a pre-determined metabolic pathway to enable precise tumor ablation. In this system, DH5α Escherichia coli is engineered by the introduction of hypoxia-inducible promoters and lactate oxidase genes, and further surface-armored with iron-doped ZIF-8 nanoparticles. This bioengineered E. coli can produce and secrete lactate oxidase to reduce lactate concentration in response to hypoxic tumor microenvironment, as well as triggering immune activation. The peroxidase-like functionality of the nanoparticles extends the end product of the lactate metabolism, enabling the conversion of hydrogen peroxide (H2O2) into highly cytotoxic hydroxyl radicals. This, coupled with the transformation of tirapazamine loaded on nanoparticles to toxic benzotriazinyl, culminates in severe tumor cell ferroptosis. Intravenous injection of this biohybrid significantly inhibits tumor growth and metastasis.

Role of TORS as De-Escalation Strategy in HPV-Related Oropharyngeal Cancer, What We Need to Know.

Human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (OPSCC) presents unique challenges and opportunities for treatment, particularly regarding de-escalation strategies to reduce treatment morbidity without compromising oncological outcomes. This paper examines the role of Transoral Robotic Surgery (TORS) as a de-escalation strategy in managing HPV-related OPSCC. We conducted a comprehensive literature review from January 2010 to June 2023, focusing on studies exploring TORS outcomes in patients with HPV-positive OPSCC. These findings highlight TORS's potential to reduce the need for adjuvant therapy, thereby minimizing treatment-related side effects while maintaining high rates of oncological control. TORS offers advantages such as precise tumor resection and the ability to obtain accurate pathological staging, which can guide the tailoring of adjuvant treatments. Some clinical trials provide evidence supporting the use of TORS in specific patient populations. The MC1273 trial demonstrated promising outcomes with lower doses of adjuvant radiotherapy (RT) following TORS, showing high locoregional tumor control rates and favorable survival outcomes with minimal side effects. ECOG 3311 evaluated upfront TORS followed by histopathologically directed adjuvant therapy, revealing good oncological and functional outcomes, particularly in intermediate-risk patients. The SIRS trial emphasized the benefits of upfront surgery with neck dissection followed by de-escalated RT in patients with favorable survival and excellent functional outcomes. At the same time, the PATHOS trial examined the impact of risk-adapted adjuvant treatment on functional outcomes and survival. The ongoing ADEPT trial investigates reduced-dose adjuvant RT, and the DART-HPV study aims to compare standard adjuvant chemoradiotherapy (CRT) with a reduced dose of adjuvant RT in HPV-positive OPSCC patients. These trials collectively underscore the potential of TORS in facilitating treatment de-escalation while maintaining favorable oncological and functional outcomes in selected patients with HPV-related OPSCC. The aim of this scoping review is to discuss the challenges of risk stratification, the importance of HPV status determination, and the implications of smoking on treatment outcomes. It also explores the evolving criteria for adjuvant therapy following TORS, focusing on reducing radiation dosage and volume without compromising treatment efficacy. In conclusion, TORS emerges as a viable upfront treatment option for carefully selected patients with HPV-positive OPSCC, offering a pathway toward treatment de-escalation. However, selecting the optimal candidate for TORS-based de-escalation strategies is crucial to fully leverage the benefits of treatment de-intensification.