Metabolic Regulation-Mediated Reversion of the Tumor Immunosuppressive Microenvironment for Potentiating Cooperative Metabolic Therapy and Immunotherapy.

Tumor cells exhibit heightened glucose (Glu) consumption and increased lactic acid (LA) production, resulting in the formation of an immunosuppressive tumor microenvironment (TME) that facilitates malignant proliferation and metastasis. In this study, we meticulously engineer an antitumor nanoplatform, denoted as ZLGCR, by incorporating glucose oxidase, LA oxidase, and CpG oligodeoxynucleotide into zeolitic imidazolate framework-8 that is camouflaged with a red blood cell membrane. Significantly, ZLGCR-mediated consumption of Glu and LA not only amplifies the effectiveness of metabolic therapy but also reverses the immunosuppressive TME, thereby enhancing the therapeutic outcomes of CpG-mediated antitumor immunotherapy. It is particularly important that the synergistic effect of metabolic therapy and immunotherapy is further augmented when combined with immune checkpoint blockade therapy. Consequently, this engineered antitumor nanoplatform will achieve a cooperative tumor-suppressive outcome through the modulation of metabolism and immune responses within the TME.

Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues.

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators (e.g., glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers (e.g., glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy.

Fused Cytomembrane-Camouflaged Nanoparticles for Tumor-Specific Immunotherapy.

Tumor immunotherapy is commonly hindered by inefficient delivery and presentation of tumor antigens as well as immunosuppressive tumor microenvironment. To overcome these barriers, a tumor-specific nanovaccine capable of delivering tumor antigens and adjuvants to antigen-presenting cells and modulating the immune microenvironment to elicit strong antitumor immunity is reported. This nanovaccine, named FCM@4RM, is designed by coating the nanocore (FCM) with a bioreconstituted cytomembrane (4RM). The 4RM, which is derived from fused cells of tumorous 4T1 cells and RAW264.7 macrophages, enables effective antigen presentation and stimulation of effector T cells. FCM is self-assembled from Fe(II), unmethylated cytosine-phosphate-guanine oligodeoxynucleotide (CpG), and metformin (MET). CpG, as the stimulator of toll-like receptor 9, induces the production of pro-inflammatory cytokine and the maturation of cytotoxic T lymphocytes (CTLs), thereby enhancing antitumor immunity. Meanwhile, MET functions as the programmed cell death ligand 1 inhibitor and can restore the immune responses of T cells against tumor cells. Therefore, FCM@4RM exhibits high targeting capabilities toward homologous tumors that develop from 4T1 cells. This work offers a paradigm for developing a nanovaccine that systematically regulates multiple immune-related processes to achieve optimal antitumor immunotherapy.

Engineering metal-based hydrogel-mediated tertiary lymphoid structure formation via activation of the STING pathway for enhanced immunotherapy.

Tertiary lymphoid structures (TLSs) primarily constructed by multiple immune cells can effectively enhance tumor immune responses, but expediting the formation of TLSs is still an enormous challenge. Herein, a stimulator of interferon gene (STING)-activating hydrogel (ZCCG) was elaborately developed by coordinating Zn2+ with 4,5-imidazole dicarboxylic acid, and simultaneously integrating chitosan (a stimulant of STING pathway activation) and CpG (an agonist of toll-like receptor 9, TLR9) for initiating and activating cGAS-STING and TLR9 pathway-mediated immunotherapy. Moreover, the dual-pathway activation could effectively enhance the infiltration of immune cells and the expression of lymphocyte-recruiting chemokines in the tumor microenvironment (TME), thereby promoting the formation of TLSs and further strengthening tumoricidal immunity. Local administration of the hydrogel could prime systemic immune responses and long-term immune memory and improve the therapeutic effects of programmed death-1 antibody (αPD-1) to inhibit tumor progression, metastasis and recurrence. The engineered hydrogel lays the foundation for tumor immunotherapy strategies based on the enhanced formation of TLSs via the activation of the cGAS-STING and TLR9 pathways.

Immunostimulant Hydrogel-Guided Tumor Microenvironment Reprogramming to Efficiently Potentiate Macrophage-Mediated Cellular Phagocytosis for Systemic Cancer Immunotherapy.

Macrophage-mediated cellular phagocytosis (MMCP) plays a critical role in conducting antitumor immunotherapy but is usually impaired by the intrinsic phagocytosis evading ability of tumor cells and the immunosuppressive tumor microenvironment (TME). Herein, a MMCP-boosting hydrogel (TCCaGM) was elaborately engineered by encapsulating granulocyte-macrophage colony-stimulating factor (GM-CSF) and a therapeutic nanoplatform (TCCaN) that preloaded with the tunicamycin (Tuni) and catalase (CAT) with the assistance of CaCO3 nanoparticles (NPs). Strikingly, the hypoxic/acidic TME was efficiently alleviated by the engineered hydrogel, "eat me" signal calreticulin (CRT) was upregulated, while the "don't eat me" signal CD47 was downregulated on tumor cells, and the infiltrated DCs were recruited and activated, all of which contributed to boosting the macrophage-mediated phagocytosis and initiating tumor-specific CD8+ T cells responses. Meanwhile, the remodeled TME was beneficial to accelerate the polarization of tumor-associated macrophages (TAMs) to the antitumoral M1-like phenotype, further heightening tumoricidal immunity. With the combination of PD-1 antibody (αPD-1), the designed hydrogel significantly heightened systemic antitumor immune responses and long-term immunological effects to control the development of primary and distant tumors as well as suppress tumor metastasis and recurrence, which established an optimal strategy for high-performance antitumor immunotherapy.

Specific activation of cGAS-STING pathway by nanotherapeutics-mediated ferroptosis evoked endogenous signaling for boosting systemic tumor immunotherapy.

Activation of the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway could effectively initiate antitumor immunity, but specific activation of STING pathway is still an enormous challenge. Herein, a ferroptosis-induced mitochondrial DNA (mtDNA)-guided tumor immunotherapy nanoplatform (designated as HBMn-FA) was elaborately developed for activating and boosting STING-based immunotherapy. On the one hand, the high-levels of reactive oxygen species (ROS) in tumor cells induced by HBMn-FA-mediated ferroptosis elicited mitochondrial stress to cause the release of endogenous signaling mtDNA, which specifically initiate cGAS-STING pathway with the cooperation of Mn2+. On the other hand, the tumor-derived cytosolic double-stranded DNA (dsDNA) from debris of death cells caused by HBMn-FA further activated the cGAS-STING pathway in antigen-presenting cells (e.g., DCs). This bridging of ferroptosis and cGAS-STING pathway could expeditiously prime systemic antitumor immunity and enhance the therapeutic efficacy of checkpoint blockade to suppress tumor growth in both localized and metastatic tumor models. The designed nanotherapeutic platform paves the way for novel tumor immunotherapy strategies that are based on specific activation of STING pathway.

Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer.

Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.

Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.

OBJECTIVE: In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murine lungs with the goal of setting the foundation for a predictive disease model. METHODS: Analysis of Raman tissue data is achieved through a combination of techniques. We first distinguish between tissue measurements and air pockets in the lung by using group and basis restricted non-negative matrix factorization. We then analyze the tissue spectra using sparse multinomial logistic regression to discriminate between fibrotic gradings. Model validation is achieved by splitting the data into a training set containing 70% of the data and a test set with the remaining 30%; classification accuracy is used as the performance metric. We also explore several other potential classification tasks wherein the response considered is the grade of pneumonitis and fibrosis sickness. RESULTS: A classification accuracy of 91.6% is achieved on the test set of fibrotic gradings, illustrating the ability of Raman measurements to detect differing levels of fibrotic disease among the murine lungs. It is also shown via further modeling that coarser consideration of fibrotic grading via binning (ie. 'Low', 'Medium', 'High') does not degrade performance. Finally, we consider preliminary models for pneumonitis discrimination using the same methodologies.

Complementary Memtransistor-Based Multilayer Neural Networks for Online Supervised Learning Through (Anti-)Spike-Timing-Dependent Plasticity.

We propose a complete hardware-based architecture of multilayer neural networks (MNNs), including electronic synapses, neurons, and periphery circuitry to implement supervised learning (SL) algorithm of extended remote supervised method (ReSuMe). In this system, complementary (a pair of n- and p-type) memtransistors (C-MTs) are used as an electrical synapse. By applying the learning rule of spike-timing-dependent plasticity (STDP) to the memtransistor connecting presynaptic neuron to the output one whereas the contrary anti-STDP rule to the other memtransistor connecting presynaptic neuron to the teacher one, extended ReSuMe with multiple layers is realized without the usage of those complicated supervising modules in previous approaches. In this way, both the C-MT-based chip area and power consumption of the learning circuit for weight updating operation are drastically decreased comparing with the conventional single memtransistor (S-MT)-based designs. Two typical benchmarks, the linearly nonseparable benchmark XOR problem and Mixed National Institute of Standards and Technology database (MNIST) recognition have been successfully tackled using the proposed MNN system while impact of the nonideal factors of realistic devices has been evaluated.

Encapsulating Electron-Rich Pd NPs with Lewis Acidic MOF: Reconciling the Electron-Preference Conflict of the Catalyst for Cascade Condensation via Nitro Reduction.

Cascade reactions take advantage of step-saving and facile operation for obtaining chemicals. Herein, catalytic hydrogenation of nitroarene coupled condensation with ß-diketone to afford ß-ketoenamines is achieved by an integrated nanocatalyst, Pd-e@UiO-66. The catalyst has the structure of an acid-rich metal-organic framework (MOF), UiO-66-encapsulated electron-rich Pd nanoparticles, and it reconciles the electron-effect contradiction of cascade catalytic reactions: catalytic hydrogenation requires an electron-rich catalyst, while condensation requires electron-deficient Lewis acid sites. The catalyst showed good activity, high chemoselectivity, and universal applicability for the synthesis of ß-ketoenamines using nitroarenes. More than 30 ß-ketoenamines have been successfully prepared with up to 99% yield via the methodology of relay catalysis. The catalyst exhibited excellent stability to maintain its catalytic performance for more than five cycles. Furthermore, we conducted an in-depth exploration of the reaction mechanism with theoretical calculations.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer.

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Raman spectroscopy and group and basis-restricted non negative matrix factorisation identifies radiation induced metabolic changes in human cancer cells.

This work combines single cell Raman spectroscopy (RS) with group and basis restricted non-negative matrix factorisation (GBR-NMF) to identify individual biochemical changes associated with radiation exposure in three human cancer cell lines. The cell lines analysed were derived from lung (H460), breast (MCF7) and prostate (LNCaP) tissue and are known to display varying degrees of radio sensitivity due to the inherent properties of each cell type. The GBR-NMF approach involves the deconstruction of Raman spectra into component biochemical bases using a library of Raman spectra of known biochemicals present in the cells. Subsequently, scores are obtained on each of these bases which can be directly correlated with the contribution of each chemical to the overall Raman spectrum. We validated GBR-NMF through the correlation of GBR-NMF-derived glycogen scores with scores that were previously observed using principal component analysis (PCA). Phosphatidylcholine, glucose, arginine and asparagine showed a distinct differential score pattern between radio-resistant and radio-sensitive cell types. In summary, the GBR-NMF approach allows for the monitoring of individual biochemical radiation-response dynamics previously unattainable with more traditional PCA-based approaches.

Monitor Ionizing Radiation-Induced Cellular Responses with Raman Spectroscopy, Non-Negative Matrix Factorization, and Non-Negative Least Squares.

Radiation therapy (RT) is one of the most commonly prescribed cancer treatments. New tools that can accurately monitor and evaluate individual patient responses would be a major advantage and lend to the implementation of personalized treatment plans. In this study, Raman spectroscopy (RS) was applied to examine radiation-induced cellular responses in H460, MCF7, and LNCaP cancer cell lines across different dose levels and times post-irradiation. Previous Raman data analysis was conducted using principal component analysis (PCA), which showed the ability to extract biological information of glycogen. In the current studies, the use of non-negative matrix factorization (NMF) allowed for the discovery of multiplexed biological information, specifically uncovering glycogen-like and lipid-like component bases. The corresponding scores of glycogen and previously unidentified lipids revealed the content variations of these two chemicals in the cellular data. The NMF decomposed glycogen and lipid-like bases were able to separate the cancer cell lines into radiosensitive and radioresistant groups. A further lipid phenotype investigation was also attempted by applying non-negative least squares (NNLS) to the lipid-like bases decomposed individually from three cell lines. Qualitative differences found in lipid weights for each lipid-like basis suggest the lipid phenotype differences in the three tested cancer cell lines. Collectively, this study demonstrates that the application of NMF and NNLS on RS data analysis to monitor ionizing radiation-induced cellular responses can yield multiplexed biological information on bio-response to RT not revealed by conventional chemometric approaches.

Image acquisition optimization of a limited-angle intrafraction verification (LIVE) system for lung radiotherapy.

PURPOSE: Limited-angle intrafraction verification (LIVE) has been previously developed for four-dimensional (4D) intrafraction target verification either during arc delivery or between three-dimensional (3D)/IMRT beams. Preliminary studies showed that LIVE can accurately estimate the target volume using kV/MV projections acquired over orthogonal view 30° scan angles. Currently, the LIVE imaging acquisition requires slow gantry rotation and is not clinically optimized. The goal of this study is to optimize the image acquisition parameters of LIVE for different patient respiratory periods and gantry rotation speeds for the effective clinical implementation of the system. METHOD: Limited-angle intrafraction verification imaging acquisition was optimized using a digital anthropomorphic phantom (XCAT) with simulated respiratory periods varying from 3 s to 6 s and gantry rotation speeds varying from 1°/s to 6°/s. LIVE scanning time was optimized by minimizing the number of respiratory cycles needed for the four-dimensional scan, and imaging dose was optimized by minimizing the number of kV and MV projections needed for four-dimensional estimation. The estimation accuracy was evaluated by calculating both the center-of-mass-shift (COMS) and three-dimensional volume-percentage-difference (VPD) between the tumor in estimated images and the ground truth images. The robustness of LIVE was evaluated with varied respiratory patterns, tumor sizes, and tumor locations in XCAT simulation. A dynamic thoracic phantom (CIRS) was used to further validate the optimized imaging schemes from XCAT study with changes of respiratory patterns, tumor sizes, and imaging scanning directions. RESULTS: Respiratory periods, gantry rotation speeds, number of respiratory cycles scanned and number of kV/MV projections acquired were all positively correlated with the estimation accuracy of LIVE. Faster gantry rotation speed or longer respiratory period allowed less respiratory cycles to be scanned and less kV/MV projections to be acquired to estimate the target volume accurately. Regarding the scanning time minimization, for patient respiratory periods of 3-4 s, gantry rotation speeds of 1°/s, 2°/s, 3-6°/s required scanning of five, four, and three respiratory cycles, respectively. For patient respiratory periods of 5-6 s, the corresponding respiratory cycles required in the scan changed to four, three, and two cycles, respectively. Regarding the imaging dose minimization, for patient respiratory periods of 3-4 s, gantry rotation speeds of 1°/s, 2-4°/s, 5-6°/s required acquiring of 7, 5, 4 kV and MV projections, respectively. For patient respiratory periods of 5-6 s, 5 kV and 5 MV projections are sufficient for all gantry rotation speeds. The optimized LIVE system was robust against breathing pattern, tumor size and tumor location changes. In the CIRS study, the optimized LIVE system achieved the average center-of-mass-shift (COMS)/volume-percentage-difference (VPD) of 0.3 ± 0.1 mm/7.7 ± 2.0% for the scanning time priority case, 0.2 ± 0.1 mm/6.1 ± 1.2% for the imaging dose priority case, respectively, among all gantry rotation speeds tested. LIVE was robust against different scanning directions investigated. CONCLUSION: The LIVE system has been preliminarily optimized for different patient respiratory periods and treatment gantry rotation speeds using digital and physical phantoms. The optimized imaging parameters, including number of respiratory cycles scanned and kV/MV projection numbers acquired, provide guidelines for optimizing the scanning time and imaging dose of the LIVE system for its future evaluations and clinical implementations through patient studies.