Noninvasively Deciphering the Immunosuppressive Tumor Microenvironment Using Galectin-1 PET to Inform Immunotherapy Responses.

Immune checkpoint blockade (ICB) has achieved groundbreaking results in clinical cancer therapy; however, only a subset of patients experience durable benefits. The aim of this study was to explore strategies for predicting tumor responses to optimize the intervention approach using ICB therapy. Methods: We used a bilateral mouse model for proteomics analysis to identify new imaging biomarkers for tumor responses to ICB therapy. A PET radiotracer was synthesized by radiolabeling the identified biomarker-targeting antibody with 124I. The radiotracer was then tested for PET prediction of tumor responses to ICB therapy. Results: We identified galectin-1 (Gal-1), a member of the carbohydrate-binding lectin family, as a potential negative biomarker for ICB efficacy. We established that Gal-1 inhibition promotes a sensitive immune phenotype within the tumor microenvironment (TME) for ICB therapy. To assess the pre-ICB treatment status of the TME, a Gal-1-targeted PET radiotracer, 124I-αGal-1, was developed. PET imaging with 124I-αGal-1 showed the pretreatment immunosuppressive status of the TME before the initiation of therapy, thus enabling the prediction of ICB resistance in advance. Moreover, the use of hydrogel scaffolds loaded with a Gal-1 inhibitor, thiodigalactoside, demonstrated that a single dose of thiodigalactoside-hydrogel significantly potentiated ICB and adoptive cell transfer immunotherapies by remodeling the immunosuppressive TME. Conclusion: Our study underscores the potential of Gal-1-targeted PET imaging as a valuable strategy for early-stage monitoring of tumor responses to ICB therapy. Additionally, Gal-1 inhibition effectively counteracts the immunosuppressive TME, resulting in enhanced immunotherapy efficacy.

Screening and early warning system for chronic obstructive pulmonary disease with obstructive sleep apnoea based on the medical Internet of Things in three levels of healthcare: protocol for a prospective, multicentre, observational cohort study.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) and obstructive sleep apnoea (OSA) are prevalent respiratory diseases in China and impose significant burdens on the healthcare system. Moreover, the co-occurrence of COPD and OSA exacerbates clinical outcomes significantly. However, comprehensive epidemiological investigations in China remain scarce, and the defining characteristics of the population affected by COPD and OSA, alongside their intrinsic relationship, remain ambiguous. METHODS AND ANALYSIS: We present a protocol for a prospective, multicentre, observational cohort study based on a digital health management platform across three different healthcare tiers in five sites among Chinese patients with COPD. The study aims to establish predicative models to identify OSA among patients with COPD and to predict the prognosis of overlap syndrome (OS) and acute exacerbations of COPD through the Internet of Things (IoT). Moreover, it aims to evaluate the feasibility, effectiveness and cost-effectiveness of IoT in managing chronic diseases within clinical settings. Participants will undergo baseline assessment, physical examination and nocturnal oxygen saturation measuring. Specific questionnaires screening for OSA will also be administered. Diagnostic lung function tests and polysomnography will be performed to confirm COPD and OSA, respectively. All patients will undergo scheduled follow-ups for 12 months to record the changes in symptoms, lung functions and quality of life. Primary outcomes include the prevalence and characteristics of OS, while secondary outcomes encompass OS prognosis and the feasibility of the management model in clinical contexts. A total of 682 patients with COPD will be recruited over 12-24 months. ETHICS AND DISSEMINATION: The study has been approved by Peking University Third Hospital, and all study participants will provide written informed consent. Study results will be published in an appropriate journal and presented at national and international conferences, as well as relevant social media and various stakeholder engagement activities. TRIAL REGISTRATION NUMBER: NCT04833725.

Annotation of CD8+ T-cell function via ICAM-1 imaging identifies FAK inhibition as an adjuvant to augment the antitumor immunity of radiotherapy.

Background: Radiotherapy (RT) may trigger systemic antitumor immunity, manifesting as regression of non-irradiated lesions (abscopal effect). Intracellular adhesion molecule-1 (ICAM-1) is a key molecule involved in the abscopal effect of RT. However, the specific function of ICAM-1 in CD8+ T cells during antitumor immune responses remains unclear. Herein, we investigated whether noninvasive imaging of ICAM-1 can be used to annotate CD8+ T-cell function, thereby better selecting combinational therapy to enhance the antitumor immunity induced by RT. Methods: Using knockout mouse models, we investigated the role of ICAM-1 expressed on CD8+ T cells in the antitumor immunity of RT and conducted drug screening guided by ICAM-1-targeted noninvasive imaging. Results: The systemic antitumor effect of RT relies on the expression of ICAM-1 on CD8+ T cells. ICAM-1 expression is essential for CD8+ T-cell activation, proliferation, and effector function. Noninvasive annotation of the proliferation and effector function of CD8+ T cells by ICAM-1-targeted imaging identified VS-6063, a focal adhesion kinase inhibitor, as a new adjuvant to augment systemic antitumor immunity of RT in an immunologically "cold" tumor model. Mechanistically, VS-6063 overcomes the physical barriers in tumors and promotes the migration and infiltration of CD8+ T cells primed by RT into distant tumors. Conclusion: Our findings highlight that molecular imaging of ICAM-1 levels provides a dynamic readout of the proliferation and effector function of tumor-infiltrating CD8+ T cells, which facilitates the high-throughput exploitation of new combinational drugs to maximize the systemic antitumor effect of RT.

Colorectal cancer lymph node metastasis prediction with weakly supervised transformer-based multi-instance learning.

Lymph node metastasis examined by the resected lymph nodes is considered one of the most important prognostic factors for colorectal cancer (CRC). However, it requires careful and comprehensive inspection by expert pathologists. To relieve the pathologists' burden and speed up the diagnostic process, in this paper, we develop a deep learning system with the binary positive/negative labels of the lymph nodes to solve the CRC lymph node classification task. The multi-instance learning (MIL) framework is adopted in our method to handle the whole slide images (WSIs) of gigapixels in size at once and get rid of the labor-intensive and time-consuming detailed annotations. First, a transformer-based MIL model, DT-DSMIL, is proposed in this paper based on the deformable transformer backbone and the dual-stream MIL (DSMIL) framework. The local-level image features are extracted and aggregated with the deformable transformer, and the global-level image features are obtained with the DSMIL aggregator. The final classification decision is made based on both the local and the global-level features. After the effectiveness of our proposed DT-DSMIL model is demonstrated by comparing its performance with its predecessors, a diagnostic system is developed to detect, crop, and finally identify the single lymph nodes within the slides based on the DT-DSMIL and the Faster R-CNN model. The developed diagnostic model is trained and tested on a clinically collected CRC lymph node metastasis dataset composed of 843 slides (864 metastasis lymph nodes and 1415 non-metastatic lymph nodes), achieving the accuracy of 95.3% and the area under the receiver operating characteristic curve (AUC) of 0.9762 (95% confidence interval [CI]: 0.9607-0.9891) for the single lymph node classification. As for the lymph nodes with micro-metastasis and macro-metastasis, our diagnostic system achieves the AUC of 0.9816 (95% CI: 0.9659-0.9935) and 0.9902 (95% CI: 0.9787-0.9983), respectively. Moreover, the system shows reliable diagnostic region localizing performance: the model can always identify the most likely metastases, no matter the model's predictions or manual labels, showing great potential in avoiding false negatives and discovering incorrectly labeled slides in actual clinical use.

Noninvasive interrogation of CD8+ T cell effector function for monitoring early tumor responses to immunotherapy.

Accurately identifying patients who respond to immunotherapy remains clinically challenging. A noninvasive method that can longitudinally capture information about immune cell function and assist in the early assessment of tumor responses is highly desirable for precision immunotherapy. Here, we show that PET imaging using a granzyme B-targeted radiotracer named 68Ga-grazytracer, could noninvasively and effectively predict tumor responses to immune checkpoint inhibitors and adoptive T cell transfer therapy in multiple tumor models. 68Ga-grazytracer was designed and selected from several radiotracers based on non-aldehyde peptidomimetics, and exhibited excellent in vivo metabolic stability and favorable targeting efficiency to granzyme B secreted by effector CD8+ T cells during immune responses. 68Ga-grazytracer permitted more sensitive discrimination of responders and nonresponders than did 18F-fluorodeoxyglucose, distinguishing between tumor pseudoprogression and true progression upon immune checkpoint blockade therapy in mouse models with varying immunogenicity. In a preliminary clinical trial with 5 patients, no adverse events were observed after 68Ga-grazytracer injection, and clinical responses in cancer patients undergoing immunotherapy were favorably correlated with 68Ga-grazytracer PET results. These results highlight the potential of 68Ga-grazytracer PET to enhance the clinical effectiveness of granzyme B secretion-related immunotherapies by supporting early response assessment and precise patient stratification in a noninvasive and longitudinal manner.

Visible-light-promoted and chlorophyll-catalyzed aerobic desulfurization of thioamides to amides.

A novel method for the metal-free synthesis of amides from thioamides based on visible-light photoredox catalysis and in an air atmosphere is reported. Natural pigment chlorophyll is used as a photosensitizer to generate singlet molecular oxygen 1O2, which is involved in the aerobic desulfurization of thioamides. The protocol provides amides in good yields at room temperature under mild conditions. On the basis of experimental results, a plausible photoredox mechanism is proposed.

Galectin expression detected by 68Ga-galectracer PET as a predictive biomarker of radiotherapy resistance.

PURPOSE: Hypoxia is a hallmark of solid tumors that is related to radiotherapy resistance. As galectin members, such as galectin-1 and galectin-3, are associated with tumor hypoxia, herein we aimed to investigate whether positron emission tomography (PET) imaging of galectin expression can be employed to effectively pinpoint tumor hypoxia, and to predict radiotherapy resistance. METHODS: We synthesized a galectin-targeting radiotracer, designated 68Ga-galectracer, by radiolabeling a thiodigalactoside derivative. The properties of 68Ga-galectracer for PET imaging of tumor hypoxia were characterized in three tumor hypoxia mouse models. Additionally, preliminary PET/CT was performed in two patients with lung cancer to investigate the potential application of 68Ga-galectracer for clinical imaging. RESULTS: High-contrast imaging was achieved in the murine acute hypoxia tumor model, A549 natural hypoxia model, and sorafenib treatment-induced hypoxic 4T1 tumor model by PET using 68Ga-galectracer. In fact, 68Ga-galectracer exhibited superior hypoxia detection to that of 18F-misonidazole in the 4T1 tumors. Moreover, tumors with high galectin expression levels, as detected by 68Ga-galectracer PET, exhibited significantly lower responses to subsequent radiotherapy compared to those with low galectin expression levels. In patients with lung cancer, PET imaging using 68Ga-galectracer provided data that were complementary to that of the glucose metabolic PET radiotracer 18F-fluorodeoxyglucose. CONCLUSION: 68Ga-galectracer is a promising radiotracer for PET-based imaging of tumor hypoxia in vivo. Thus, hypoxia PET with 68Ga-galectracer could provide a noninvasive approach to proactively predict radiotherapy efficacy. TRIAL REGISTRATION: Chictr.org.cn (ChiCTR2000029522). Registered 03 February 2020.

ICAM-1 orchestrates the abscopal effect of tumor radiotherapy.

Compelling evidence indicates that radiotherapy (RT) has a systemic inhibitory effect on nonirradiated lesions (abscopal effect) in addition to the ablation of irradiated tumors. However, this effect occurs only in rare circumstances in clinical practice, and mechanisms underlying the abscopal effect of RT are neither fully understood nor therapeutically utilized. Here we identified that intercellular adhesion molecule-1 (ICAM-1), an inducible glycoprotein of the immunoglobulin superfamily, is up-regulated in nonirradiated tumors responsive to RT. ICAM-1 expression in preclinical animal models can be noninvasively detected by optical imaging and positron emission tomography (PET) using near-infrared fluorescence dye- and 64Cu-labeled imaging probes that we synthesized, respectively. Importantly, the expression levels of ICAM-1 determined by quantitative PET imaging showed a strong negative linear correlation with the growth of nonirradiated tumors. Moreover, genetic or pharmacologic up-regulation of ICAM-1 expression by either an intratumoral injection of engineered recombinant adenovirus or systemic administration of a Toll-like receptor 7 agonist-capsulated nanodrug could induce markedly increased abscopal responses to local RT in animal models. Mechanistic investigation revealed that ICAM-1 expression can enhance both the activation and tumor infiltration of CD8+ T cells to improve the responses of the nonirradiated tumors to RT. Together, our findings suggest that noninvasive PET imaging of ICAM-1 expression could be a powerful means to predict the responses of nonirradiated tumors to RT, which could facilitate the exploration of new combination RT strategies for effective ablation of primary and disseminated lesions.