Risk factors and a nomogram for predicting cognitive frailty in Chinese patients with lung cancer receiving drug therapy: A single-center cross-sectional study.

BACKGROUND: To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy. METHODS: In this cross-sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis. RESULTS: A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high- and low-risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility. CONCLUSIONS: The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.

A single-cell characterised signature integrating heterogeneity and microenvironment of lung adenocarcinoma for prognostic stratification.

BACKGROUND: The high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification. METHODS: Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation. FINDINGS: We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum. INTERPRETATION: This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection. FUNDING: Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).

Assessment of human leukocyte antigen-based neoantigen presentation to determine pan-cancer response to immunotherapy.

Despite the central role of human leukocyte antigen class I (HLA-I) in tumor neoantigen presentation, quantitative determination of presentation capacity remains elusive. Based on a pooled pan-cancer genomic dataset of 885 patients treated with immune checkpoint inhibitors (ICIs), we developed a score integrating the binding affinity of neoantigens to HLA-I, as well as HLA-I allele divergence, termed the HLA tumor-Antigen Presentation Score (HAPS). Patients with a high HAPS were more likely to experience survival benefit following ICI treatment. Analysis of the tumor microenvironment indicated that the antigen presentation pathway was enriched in patients with a high HAPS. Finally, we built a neural network incorporating factors associated with neoantigen production, presentation, and recognition, which exhibited potential for differentiating cancer patients likely to benefit from ICIs. Our findings highlight the clinical utility of evaluating HLA-I tumor antigen presentation capacity and describe how ICI response may depend on HLA-mediated immunity.

Emergence of brain-inspired small-world spiking neural network through neuroevolution.

Studies suggest that the brain's high efficiency and low energy consumption may be closely related to its small-world topology and critical dynamics. However, existing efforts on the performance-oriented structural evolution of spiking neural networks (SNNs) are time-consuming and ignore the core structural properties of the brain. Here, we introduce a multi-objective Evolutionary Liquid State Machine (ELSM), which blends the small-world coefficient and criticality to evolve models and guide the emergence of brain-inspired, efficient structures. Experiments reveal ELSM's consistent and comparable performance, achieving 97.23% on NMNIST and outperforming LSM models on MNIST and Fashion-MNIST with 98.12% and 88.81% accuracies, respectively. Further analysis shows its versatility and spontaneous evolution of topologies such as hub nodes, short paths, long-tailed degree distributions, and numerous communities. This study evolves recurrent spiking neural networks into brain-inspired energy-efficient structures, showcasing versatility in multiple tasks and potential for adaptive general artificial intelligence.

A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer.

BACKGROUND: Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high-risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management. METHODS: Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748). RESULTS: Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D-dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation. CONCLUSION: VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models.

Brain-inspired neural circuit evolution for spiking neural networks.

In biological neural systems, different neurons are capable of self-organizing to form different neural circuits for achieving a variety of cognitive functions. However, the current design paradigm of spiking neural networks is based on structures derived from deep learning. Such structures are dominated by feedforward connections without taking into account different types of neurons, which significantly prevent spiking neural networks from realizing their potential on complex tasks. It remains an open challenge to apply the rich dynamical properties of biological neural circuits to model the structure of current spiking neural networks. This paper provides a more biologically plausible evolutionary space by combining feedforward and feedback connections with excitatory and inhibitory neurons. We exploit the local spiking behavior of neurons to adaptively evolve neural circuits such as forward excitation, forward inhibition, feedback inhibition, and lateral inhibition by the local law of spike-timing-dependent plasticity and update the synaptic weights in combination with the global error signals. By using the evolved neural circuits, we construct spiking neural networks for image classification and reinforcement learning tasks. Using the brain-inspired Neural circuit Evolution strategy (NeuEvo) with rich neural circuit types, the evolved spiking neural network greatly enhances capability on perception and reinforcement learning tasks. NeuEvo achieves state-of-the-art performance on CIFAR10, DVS-CIFAR10, DVS-Gesture, and N-Caltech101 datasets and achieves advanced performance on ImageNet. Combined with on-policy and off-policy deep reinforcement learning algorithms, it achieves comparable performance with artificial neural networks. The evolved spiking neural circuits lay the foundation for the evolution of complex networks with functions.

Butyrate protects against MRSA pneumonia via regulating gut-lung microbiota and alveolar macrophage M2 polarization.

IMPORTANCE: Pneumonia caused by methicillin-resistant Staphylococcus aureus (MRSA) continues to carry a high burden in terms of mortality. With the roles of gut microbiota in mediating lung diseases being gradually uncovered, the details of the molecular mechanism of the "gut-lung axis" mediated by beneficial microorganisms and small-molecule metabolites have gradually attracted the attention of researchers. However, further studies are still necessary to determine the efficacy of microbial-based interventions. Our findings indicate that sodium butyrate (NaB) alleviates MRSA-induced pulmonary inflammation by improving gut-lung microbiota and promoting M2 polarization of alveolar macrophages. Therefore, the preventive administration of NaB might be explored as an effective strategy to control MRSA pneumonia.

Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy.

Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.

BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.

Spiking neural networks (SNNs) serve as a promising computational framework for integrating insights from the brain into artificial intelligence (AI). Existing software infrastructures based on SNNs exclusively support brain simulation or brain-inspired AI, but not both simultaneously. To decode the nature of biological intelligence and create AI, we present the brain-inspired cognitive intelligence engine (BrainCog). This SNN-based platform provides essential infrastructure support for developing brain-inspired AI and brain simulation. BrainCog integrates different biological neurons, encoding strategies, learning rules, brain areas, and hardware-software co-design as essential components. Leveraging these user-friendly components, BrainCog incorporates various cognitive functions, including perception and learning, decision-making, knowledge representation and reasoning, motor control, social cognition, and brain structure and function simulations across multiple scales. BORN is an AI engine developed by BrainCog, showcasing seamless integration of BrainCog's components and cognitive functions to build advanced AI models and applications.

An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections.

The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn. The human brain can quickly learn various conceptual knowledge in a self-organized and unsupervised manner, accomplished through coordinating various learning rules and structures in the human brain. Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. In this paper, taking inspiration from short-term synaptic plasticity, we design an adaptive synaptic filter and introduce the adaptive spiking threshold as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to adjust the spikes balance dynamically to help the network learn richer features. To speed up and stabilize the training of unsupervised spiking neural networks, we design a samples temporal batch STDP (STB-STDP), which updates weights based on multiple samples and moments. By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets. Further, we tested on the more complex CIFAR10 dataset, and the results fully illustrate the superiority of our algorithm. Our model is also the first work to apply unsupervised STDP-based SNNs to CIFAR10. At the same time, in the small-sample learning scenario, it will far exceed the supervised ANN using the same structure.

N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.

Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for biologically plausible spiking neural networks (SNNs). Datasets for traditional few-shot learning domains provide few amounts of temporal information. And the absence of neuromorphic datasets has hindered the development of few-shot learning for SNNs. Here, to the best of our knowledge, we provide the first neuromorphic dataset for few-shot learning using SNNs: N-Omniglot, based on the Dynamic Vision Sensor. It contains 1,623 categories of handwritten characters, with only 20 samples per class. N-Omniglot eliminates the need for a neuromorphic dataset for SNNs with high spareness and tremendous temporal coherence. Additionally, the dataset provides a powerful challenge and a suitable benchmark for developing SNNs algorithms in the few-shot learning domain due to the chronological information of strokes. We also provide the improved nearest neighbor, convolutional network, SiameseNet, and meta-learning algorithm in the spiking version for verification.

Maximum Somatic Allele Frequency-Adjusted Blood-Based Tumor Mutational Burden Predicts the Efficacy of Immune Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer.

INTRODUCTION: Recent studies exhibited the unstable prediction ability of blood-based tumor mutational burden (bTMB) when predicting the response of immune checkpoint inhibitors (ICIs) therapy in patients with non-small cell lung cancer (NSCLC). Circulating tumor DNA (ctDNA) abundance, usually represented by maximum somatic allele frequency (MSAF), was one possible confounding factor influencing bTMB ability in ICIs response prediction. METHODS: MSAF-adjusted bTMB (Ma-bTMB) was established and validated in patients with advanced NSCLC among Geneplus Cancer Genome Database (GCGD, n = 1679), Zhuo (n = 35), Wang (n = 45), POPLAR (NCT01903993, n = 211) and OAK (NCT02008227, n = 642) cohorts. RESULTS: MSAF demonstrated a modest positive correlation with bTMB and a negative one with survival benefit. Improved survival outcomes of ICIs therapy have been observed among patients with high-Ma-bTMB compared to those with low-Ma-bTMB in Zhuo and Wang cohorts. In addition, compared to low-Ma-bTMB, high-Ma-bTMB was associated with more positive clinical benefits from ICIs therapy than chemotherapy both in POPLAR and OAK cohorts. Further exploration suggested that Ma-bTMB could precisely identify more potential ICIs beneficiaries compared to bTMB and LAF-bTMB, complementary to PD-L1 expression. CONCLUSIONS: We developed Ma-bTMB, a convenient, readily available, non-invasive predictive biomarker effectively differentiates beneficiaries of ICIs therapy in advanced NSCLC, warranting future clinical trials.

Biophysical heterogeneity of myeloid-derived microenvironment to regulate resistance to cancer immunotherapy.

Despite the advances in immunotherapy for cancer treatment, patients still obtain limited benefits, mostly owing to unrestrained tumour self-expansion and immune evasion that exploits immunoregulatory mechanisms. Traditionally, myeloid cells have a dominantly immunosuppressive role. However, the complicated populations of the myeloid cells and their multilateral interactions with tumour/stromal/lymphoid cells and physical abnormalities in the tumour microenvironment (TME) determine their heterogeneous functions in tumour development and immune response. Tumour-associated myeloid cells (TAMCs) include monocytes, tumour-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), dendritic cells (DCs), and granulocytes. Single-cell profiling revealed heterogeneous TAMCs composition, sub-types, and transcriptomic signatures across 15 human cancer types. We systematically reviewed the biophysical heterogeneity of TAMC composition and pro/anti-tumoral and immuno-suppressive/stimulating properties of myeloid-derived microenvironments. We also summarised comprehensive clinical strategies to overcome resistance to immunotherapy from three dimensions: targeting TAMCs, reversing physical abnormalities, utilising nanomedicines, and finally, put forward futuristic perspectives for scientific and clinical research.

Blockade of STAT3/IL-4 overcomes EGFR T790M-cis-L792F-induced resistance to osimertinib via suppressing M2 macrophages polarization.

BACKGROUND: The mechanism of missense alteration at EGFR L792F in patients with non-small cell lung cancer resistant to osimertinib has not been sufficiently clarified. We aimed to explore the critical molecular events and coping strategies in osimertinib resistance due to acquired L792F mutation. METHODS: Circulating tumor DNA-based sequencing data of 1153 patients with osimertinib resistance were collected to illustrate the prevalence of EGFR L792F mutation. Sensitivity to osimertinib was tested with constructed EGFR 19Del/T790M-cis-L792F cell lines in vitro and in vivo. The correlation and linked pathways between M2 macrophage polarization and EGFR L792Fcis-induced osimertinib resistance were investigated. Possible interventions to suppress osimertinib resistance by targeting IL-4 or STAT3 were explored. FINDINGS: The concomitant EGFR L792F was identified as an independent mutation following the acquisition of T790M after osimertinib resistance, in that 5 of the 946 patients with osimertinib resistance harbored EGFR T790M-cis-L792F mutation. Transfected EGFR 19Del/T790M-cis-L792F in cell lines had decreased sensitivity to osimertinib and enhanced infiltrating macrophage with M2 polarization. Silico analyses confirmed the role of M2 polarization in osimertinib resistance induced by EGFR T790M-cis-L792F mutation. EGFR T790M-cis-L792F mutation upregulated phosphorylation of STAT3 Tyr705 and promoted its specific binding to IL4 promoter, enhancing IL-4 expression and secretion and inducing macrophage M2 polarization. Furthermore, blockade of STAT3/IL-4 (SH-4-54 or dupilumab) suppressed macrophage M2 polarization and regressed tumor sensitivity to osimertinib. INTERPRETATION: Our results proved that targeting EGFR T790M-cis-L792F/STAT3 Tyr705/IL-4 pathway could be a potential strategy to suppress osimertinib resistance in NSCLC. FUNDING: This work was supported by the National Natural Science Foundation of China (81871889, 82072586, 81902910), Beijing Natural Science Foundation (7212084, 7214249), the China National Natural Science Foundation Key Program (81630071), the National Key Research and Development Project (2019YFC1315704), CAMS Innovation Fund for Medical Sciences (CIFMS 2021-1-I2M-012), Aiyou Foundation (KY201701) and CAMS Key Laboratory of translational research on lung cancer (2018PT31035).

PAPPA2 mutation as a novel indicator stratifying beneficiaries of immune checkpoint inhibitors in skin cutaneous melanoma and non-small cell lung cancer.

BACKGROUND: Pappalysin 2 (PAPPA2) mutation, occurring most frequently in skin cutaneous melanoma (SKCM) and non-small cell lung cancer (NSCLC), is found to be related to anti-tumour immune response. However, the association between PAPPA2 and the efficacy of immune checkpoint inhibitors (ICIs) therapy remains unknown. METHODS: To analyse the performance of PAPPA2 mutation as an indicator stratifying beneficiaries of ICIs, seven public cohorts with whole-exome sequencing (WES) data were divided into the NSCLC set (n = 165) and the SKCM set (n = 210). For further validation, 41 NSCLC patients receiving anti-PD-(L)1 treatment were enrolled in China cohort (n = 41). The mechanism was explored based on The Cancer Genome Atlas database (n = 1467). RESULTS: In the NSCLC set, patients with PAPPA2 mutation (PAPPA2-Mut) demonstrated a significantly superior progress free survival (PFS, hazard ratio [HR], 0.28 [95% CI, 0.14-0.53]; p < 0.001) and objective response rate (ORR, 77.8% vs. 23.2%; p < 0.001) compared to those with wide-type PAPPA2 (PAPPA2-WT), consistent in the SKCM set (overall survival, HR, 0.49 [95% CI: 0.31-0.78], p < 0.001; ORR, 34.1% vs. 16.9%, p = 0.039) and China cohort. Similar results were observed in multivariable models. Accordingly, PAPPA2 mutation exhibited superior performance in predicting ICIs efficacy compared with other published ICIs-related gene mutations, such as EPHA family, MUC16, LRP1B and TTN, etc. In addition, combined utilization of PAPPA2 mutation and tumour mutational burden (TMB) could expand the identification of potential responders to ICIs therapy in both NSCLC set (HR, 0.36 [95% CI: 0.23-0.57], p < 0.001) and SKCM set (HR, 0.51 [95% CI: 0.34-0.76], p < 0.001). Moreover, PAPPA2 mutation was correlated with enhanced anti-tumour immunity including higher activated CD4 memory T cells level, lower Treg cells level, and upregulated DNA damage repair pathways. CONCLUSIONS: Our findings indicated that PAPPA2 mutation could serve as a novel indicator to stratify beneficiaries from ICIs therapy in NSCLC and SKCM, warranting further prospective studies.

Current status and progress in immunotherapy for malignant pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignant disease. Currently, the platinum doublet of pemetrexed and cisplatin is the standard first-line treatment for unresectable MPM. However, recent promising results of immunotherapy have markedly changed the landscape of MPM treatment. Further, the ongoing innovative therapeutic strategies are expected to expand the range of treatment options; however, several questions remain unanswered. First, establishing predictive biomarkers with high potency is urgently needed to optimize the patient selection process. Second, further exploration of the combination algorithm is expected to unveil more effective and safe regimens. Moreover, other dilemmas, such as the resistance mechanism of immunotherapy and the role of immunotherapy in perioperative settings, still warrant further exploration.

Potential of Polyethyleneimine as an Adjuvant To Prepare Long-Term and Potent Antifungal Nanovaccine.

Background: Candida albicans infections are particularly prevalent in immunocompromised patients. Even with appropriate treatment with current antifungal drugs, the mortality rate of invasive candidiasis remains high. Many positive results have been achieved in the current vaccine development. There are also issues such as the vaccine's protective effect is not persistent. Considering the functionality and cost of the vaccine, it is important to develop safe and efficient new vaccines with long-term effects. In this paper, an antifungal nanovaccine with Polyethyleneimine (PEI) as adjuvant was constructed, which could elicit more effective and long-term immunity via stimulating B cells to differentiate into long-lived plasma cells. Materials and Methods: Hsp90-CTD is an important target for protective antibodies during disseminated candidiasis. Hsp90-CTD was used as the antigen, then introduced SDS to "charge" the protein and added PEI to form the nanovaccine. Dynamic light scattering and transmission electron microscope were conducted to identify the size distribution, zeta potential, and morphology of nanovaccine. The antibody titers in mice immunized with the nanovaccine were measured by ELISA. The activation and maturation of long-lived plasma cells in bone marrow by nanovaccine were also investigated via flow cytometry. Finally, the kidney of mice infected with Candida albicans was stained with H&E and PAS to evaluate the protective effect of antibody in serum produced by immunized mice. Results: Nanoparticles (NP) formed by Hsp90-CTD and PEI are small, uniform, and stable. NP had an average size of 116.2 nm with a PDI of 0.13. After immunizing mice with the nanovaccine, it was found that the nano-group produced antibodies faster and for a longer time. After 12 months of immunization, mice still had high and low levels of antibodies in their bodies. Results showed that the nanovaccine could promote the differentiation of B cells into long-lived plasma cells and maintain the long-term existence of antibodies in vivo. After immunization, the antibodies in mice could protect the mice infected by C. albicans. Conclusion: As an adjuvant, PEI can promote the differentiation of B cells into long-lived plasma cells to maintain long-term antibodies in vivo. This strategy can be adapted for the future design of vaccines.

MET-Targeted Therapies and Clinical Outcomes: A Systematic Literature Review.

INTRODUCTION: Numerous therapeutic agents specifically targeting the mesenchymal-epithelial transition (MET) oncogene are being developed. OBJECTIVE: The aim of the current review was to systematically identify and analyze clinical trials that have evaluated MET inhibitors in various cancer types and to provide an overview of their clinical outcomes. METHODS: An electronic literature search was carried out in the PubMed and Embase databases to identify published clinical trials related to MET inhibitors. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement was followed for the systematic appraisal of the literature. Data related to clinical outcomes, including progression-free survival, overall survival, objective response rate, and overall tumor response, were extracted. RESULTS: In total, 49 publications were included. Among these, 51.02% were phase II studies, 14.28% were randomized controlled trials, three were phase III studies, two were prospective observational studies, and the remainder were either phase I or Ib studies. The majority (44.89%) of articles reported the clinical outcomes of MET inhibitors, including small molecules, monoclonal antibodies, and other agents, in patients with non-small-cell lung cancer (NSCLC) harboring MET alterations. MET amplification, overexpression, and MET exon 14 skipping mutations were the major MET alteration types reported across the included studies. Clinical responses/outcomes varied considerably. CONCLUSION: This systematic literature review provides an overview of the literature available in Embase and PubMed regarding MET-targeted therapies. MET-selective tyrosine kinase inhibitors (TKIs) (capmatinib, tepotinib, and savolitinib) may become a new standard of care in NSCLC, specifically with MET exon 14 skipping mutations. A combination of MET TKIs with epidermal growth factor receptor (EGFR) TKIs (osimertinib + savolitinib, tepotinib + gefitinib) may be a potential solution for MET-driven EGFR TKI resistance. Further, MET alteration (MET amplification/overexpression) may be an actionable target in gastric cancer and papillary renal cell carcinoma.

Therapeutic guidance of tumor mutation burden on immune checkpoint inhibitors in advanced non-small cell lung cancer: a systematic review and comprehensive meta-analysis.

Background: Tumor mutation burden (TMB) remains a promising but ambiguous predictive biomarker for the efficacy of immune checkpoint inhibitors (ICIs). We investigated the predictive value of TMB in patients with advanced non-small cell lung cancer (NSCLC) treated by ICI-containing therapies under strictly matched clinical settings. Methods: PubMed, Embase, Cochrane Central, ClinicalTrials.gov, and bioRxiv databases were searched till October 16, 2021. All randomized controlled trials (RCTs) that compared patients with high TMB (TMB-H) and low TMB (TMB-L) and provided hazard ratio (HR) and corresponding 95% confidence interval (CI) in advanced NSCLC patients receiving ICIs were included, and mirror-based meta-analysis was performed (Part1). Bayesian network meta-analysis was conducted to investigate the efficacy of distinct first-line regimens in TMB-H and TMB-L groups (Part2). Public cohorts were used for validation and further exploration (Part3). Results: Twelve RCTs (n=5527) and 5 public cohorts (n=573) were included. In Part1, TMB-H patients generally exhibited a more significant progression-free survival (PFS) benefit from ICI-containing therapies compared to TMB-L patients (HR=0.58, 95% CI: 0.49-0.67, P < 0.0001). In Part2, anti-PD-1 plus chemotherapy ranked best for PFS in both TMB-H and TMB-L groups. Anti-PD-L1 plus anti-CTLA-4 therapies indicated better PFS and overall survival (OS) benefit than single ICI and chemotherapy in the TMB-H group, but ranked worst in the TMB-L group. Finally, TMB was validated to be an independent predictive biomarker from programmed cell death-ligand 1 (PD-L1) expression in Part3, which could further distinguish beneficiaries of ICI-containing therapies with PD-L1 < 50%. Conclusion: TMB-H could be a predictive biomarker independent of PD-L1 expression to identify beneficiaries of ICI-containing therapy in advanced NSCLC patients.