Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all ...patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
While optical microscopy of single particle electrochemistry has proven insightful for future nanoparticle-based batteries, little is explored for micron-sized particles of more practical interest. ...This is largely hindered by the currently limited methodology. Accordingly, we report transmission optical microscopy using near-infrared light for accessing intra-particle electrochemistry in virtue of strong light penetration as compared to visible light. Using near-infrared (
λ
> 730 nm) bright-field microscopy, the redox electrochemistry of single LiCoO
2
microparticles can be readily measured based on the measurements of optical contrast changes during electrochemical cycling. Further using the established methodology, we discover that the solid-state diffusion inside most single microparticles is distinctly directional, instead of in an isotropic manner from outer to inner as observed for the other particles. This phenomenon is also observed using dark field scattering microscopy with near-infrared light, suggesting non-uniform crystal inner structures responsible for the geometrically asymmetric heterogeneity of charge transfer kinetics within each single particle. These results indicate potential opportunities offered by the near-infrared optical methodology for
operando
studying practical battery materials.
Owing to the stronger penetration of near-infrared light than visible light, opaque battery (
e.g.
LiCoO
2
and LiFePO
4
) microparticles turn transparent and thus their intraparticle electrochemical behaviour can be optically monitored quantitatively.
Mitochondria play an essential role in energy metabolism. Oxygen deprivation can poison cells and generate a chain reaction due to the free radical release. In patients with sepsis, the kidneys tend ...to be the organ primarily affected and the proximal renal tubules are highly susceptible to energy metabolism imbalances. Dynamin-related protein 1 (DRP1) is an essential regulator of mitochondrial fission. Few studies have confirmed the role and mechanism of DRP1 in acute kidney injury (AKI) caused by sepsis. We established animal and cell sepsis-induced AKI (S-AKI) models to keep DRP1 expression high. We found that Mdivi-1, a DRP1 inhibitor, can reduce the activation of the NOD-like receptor pyrin domain-3 (NLRP3) inflammasome-mediated pyroptosis pathway and improve mitochondrial function. Both S-AKI models showed that Mdivi-1 was able to prevent the mitochondrial content release and decrease the expression of NLRP3 inflammasome-related proteins. In addition, silencing NLRP3 gene expression further emphasized the pyroptosis importance in S-AKI occurrence. Our results indicate that the possible mechanism of action of Mdivi-1 is to inhibit mitochondrial fission and protect mitochondrial function, thereby reducing pyroptosis. These data can provide a potential theoretical basis for Mdivi-1 potential use in the S-AKI prevention.
To investigate the correlations of cognitive function with glycated albumin (GA), the ratio of GA to glycated hemoglobin (GA/HbA1c), and the concentrations of interleukin-6 (IL-6) and superoxide ...dismutase (SOD) in elderly patients with type 2 diabetes mellitus (T2DM).
A total of 44 elderly T2DM patients were evaluated for cognitive function using the mini-mental state examination (MMSE) and the Montreal cognitive assessment (MoCA). Patients were then divided into two groups based on the MMSE and MoCA scores: a cognitive dysfunction group and a normal cognitive function group. The correlations of the MMSE and MoCA scores with GA/HbA1c, GA, IL-6, and SOD were analyzed. Logistic regression analysis was used to identify independent influential factors for cognitive dysfunction. The predictive value of GA and GA/HbA1c for cognitive dysfunction in elderly T2DM patients was evaluated by receiver operating characteristic (ROC) curve analysis.
Among these patients, 28 had cognitive impairment. They had significantly higher GA/HbA1c, increased GA and IL-6 levels, and lower SOD concentrations than the normal cognitive function group (all P < 0.05). GA/HbA1c was negatively correlated with the MMSE (r = −0.430, P = 0.007) and MoCA (r = −0.432, P = 0.007) scores. SOD was positively correlated with the MMSE (r = 0.585, P=0.014) and MoCA (r = 0.635, P=0.006) scores. IL-6 was negatively correlated with the MoCA score (r = −0.421, P=0.015). Age and GA/HbA1c were independent factors contributing to cognitive dysfunction. The areas under the ROC curves of GA and GA/HbA1c for the diagnosis of cognitive dysfunction were 0.712 and 0.720, respectively.
GA and GA/HbA1c are related to cognitive dysfunction in elderly patients with T2DM.
Liver kinase B1 (LKB1) mutation is prevalent and a driver of resistance to immune checkpoint blockade (ICB) therapy for lung adenocarcinoma. Here leveraging single cell RNA sequencing data, we ...demonstrate that trafficking and adhesion process of activated T cells are defected in genetically engineered Kras-driven mouse model with Lkb1 conditional knockout. LKB1 mutant cancer cells result in marked suppression of intercellular adhesion molecule-1 (ICAM1). Ectopic expression of Icam1 in Lkb1-deficient tumor increases homing and activation of adoptively transferred SIINFEKL-specific CD8
T cells, reactivates tumor-effector cell interactions and re-sensitises tumors to ICB. Further discovery proves that CDK4/6 inhibitors upregulate ICAM1 transcription by inhibiting phosphorylation of retinoblastoma protein RB in LKB1 deficient cancer cells. Finally, a tailored combination strategy using CDK4/6 inhibitors and anti-PD-1 antibodies promotes ICAM1-triggered immune response in multiple Lkb1-deficient murine models. Our findings renovate that ICAM1 on tumor cells orchestrates anti-tumor immune response, especially for adaptive immunity.
To search for both high-rate capacity and high stability Li-ion battery anodes, here we design a new strategy of high-throughput material screening based on novel global potential energy surface ...exploration techniques. This permits us to quantify the structural evolution thermodynamics for 11 candidate materials after lithiation. When reaching the high capacity (∼300 mA h/g) in lithiation, all bulk materials studied are shown to experience solid-to-solid phase transition with appreciable volume expansion. Among 11 candidates, we screen out a new three-dimensional (3-D) tunnel TiO2 (TiO2-S) phase to be a high-performance anode material. This TiO2-S phase has a spinel-like structure, achieving only 5.5% volume expansion at 335.5 mA h/g capacity (Li + TiO2 → LiTiO2) and also possessing a fast charging kinetics as seen from the low Li diffusion barrier in a wide range of Li concentrations. Because all common stable forms of materials are not good anode materials at high-rate condition, our results indicate that metastable phases with 3-D open structures could be the choice for the next-generation Li-ion battery anode. A natural database for these candidates could be the delithiated crystal phases with open skeletons that are stable enough to survive in subsequent (dis)charging cycles.
It is not a rare clinical scenario to have patients presenting with coexisting malignant tumor and tuberculosis. Whether it is feasible to conduct programmed death-(ligand) 1 PD-(L)1 inhibitors to ...these patients, especially those with active tuberculosis treated with concurrent anti-tuberculosis, is still unknown.
This study enrolled patients with coexisting malignancy and tuberculosis and treated with anti-PD-(L)1 from Jan 2018 to July 2021 in 2 institutions. The progression-free survival (PFS), objective response rate (ORR), and safety of anti-PD-(L)1 therapy, as well as response to anti-tuberculosis treatment, were evaluated.
A total of 98 patients were screened from this cohort study, with 45 (45.9%), 21 (21.4%), and 32 (32.7%) patients diagnosed with active, latent, and obsolete tuberculosis, respectively. The overall ORR was 36.0% for anti-PD-(L)1 therapy, with 34.2%, 35.5%, and 41.2% for each subgroup. Median PFS was 8.0 vs 6.0 vs 6.0 months (P=0.685) for each subgroup at the time of this analysis. For patients with active tuberculosis treated with concurrent anti-tuberculosis, median duration of anti-tuberculosis therapy was 10.0 (95% CI, 8.01-11.99) months. There were 83.3% (20/24) and 93.3% (42/45) patients showing sputum conversion and radiographic response, respectively, after anti-tuberculosis therapy, and two patients experienced tuberculosis relapse. Notably, none of the patients in latent and only one patient in obsolete subgroups showed tuberculosis induction or relapse after anti-PD-(L)1 therapy. Treatment-related adverse events (TRAEs) occurred in 33 patients (73.3%) when treated with concurrent anti-PD-(L)1 and anti-tuberculosis. Grade 3 or higher TRAEs were hematotoxicity (n = 5, 11.1%), and one patient suffered grade 3 pneumonitis leading to the discontinuation of immunotherapy.
This study demonstrated that patients with coexisting malignant tumor and tuberculosis benefited equally from anti-PD-(L)1 therapy, and anti-tuberculosis response was unimpaired for those with active tuberculosis. Notably, the combination of anti-PD-(L)1 and anti-tuberculosis therapy was well-tolerated without significant unexpected toxic effects.
Organ-specific metastatic context has not been incorporated into the clinical practice of guiding programmed death-(ligand) 1 PD-(L)1 blockade, due to a lack of understanding of its predictive versus ...prognostic value. We aim at delineating and then incorporating both the predictive and prognostic effects of the metastatic-organ landscape to dissect PD-(L)1 blockade efficacy in non-small cell lung cancer (NSCLC).
A total of 2062 NSCLC patients from a double-arm randomized trial (OAK), two immunotherapy trials (FIR, BIRCH), and a real-world cohort (NFyy) were included. The metastatic organs were stratified into two categories based on their treatment-dependent predictive significance versus treatment-independent prognosis. A metastasis-based scoring system (METscore) was developed and validated for guiding PD-(L)1 blockade in clinical trials and real-world practice.
Patients harboring various organ-specific metastases presented significantly different responses to immunotherapy, and those with brain and adrenal gland metastases survived longer than others overall survival (OS), p = 0.0105; progression-free survival (PFS), p = 0.0167. In contrast, survival outcomes were similar in chemotherapy-treated patients regardless of metastatic sites (OS, p = 0.3742; PFS, p = 0.8242). Intriguingly, the immunotherapeutic predictive significance of the metastatic-organ landscape was specifically presented in PD-L1-positive populations (PD-L1 > 1%). Among them, a paradoxical coexistence of a favorable predictive effect coupled with an unfavorable prognostic effect was observed in metastases to adrenal glands, brain, and liver (category I organs), whereas metastases to bone, pleura, pleural effusion, and mediastinum yielded consistent unfavorable predictive and prognostic effects (category II organs). METscore was capable of integrating both predictive and prognostic effects of the entire landscape and dissected OS outcome of NSCLC patients received PD-(L)1 blockade (p < 0.0001) but not chemotherapy (p = 0.0805) in the OAK training cohort. Meanwhile, general performance of METscore was first validated in FIR (p = 0.0350) and BIRCH (p < 0.0001), and then in the real-world NFyy cohort (p = 0.0181). Notably, METscore was also applicable to patients received PD-(L)1 blockade as first-line treatment both in the clinical trials (OS, p = 0.0087; PFS, p = 0.0290) and in the real-world practice (OS, p = 0.0182; PFS, p = 0.0045).
Organ-specific metastatic landscape served as a potential predictor of immunotherapy, and METscore might enable noninvasive forecast of PD-(L)1 blockade efficacy using baseline radiologic assessments in advanced NSCLC.
Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early ...identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19.
Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively.
The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval CI: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the model achieved AUCs of 0.955 (0.884-1.00) and 0.923 (0.864-0.983) and accuracies of 97.0 and 81.6% in the Honghu and Nanchang subgroups, respectively.
Our deep learning-based model can accurately predict disease severity as well as disease progression in COVID-19 patients using CT imaging, offering promise for guiding clinical treatment.
BackgroundGenetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed ...cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis to develop a genomic mutation signature (GMS) and predict the response to anti-PD-(L)1 therapy.MethodsIn this multicohort analysis, 316 patients with non-squamous NSCLC treated with anti-PD-(L)1 from three independent cohorts were included in our study. Tumor samples from the patients were molecularly profiled by MSK-IMPACT or whole exome sequencing. We developed a risk model named GMS based on the MSK training cohort (n=123). The predictive model was first validated in the separate internal MSK cohort (n=82) and then validated in an external cohort containing 111 patients from previously published clinical trials.ResultsA GMS risk model consisting of eight genes (TP53, KRAS, STK11, EGFR, PTPRD, KMT2C, SMAD4, and HGF) was generated to classify patients into high and low GMS groups in the training cohort. Patients with high GMS in the training cohort had longer progression-free survival (hazard ratio (HR) 0.41, 0.28–0.61, p<0.0001) and overall survival (HR 0.53, 0.32–0.89, p=0.0275) compared with low GMS. We noted equivalent findings in the internal validation cohort and in the external validation cohort. The GMS was demonstrated as an independent predictive factor for anti-PD-(L)1 therapy comparing with tumor mutational burden. Meanwhile, GMS showed undifferentiated predictive value in patients with different clinicopathological features. Notably, both GMS and PD-L1 were independent predictors and demonstrated poorly correlated; inclusion of PD-L1 with GMS further improved the predictive capacity for PD-1 blockade immunotherapy.ConclusionsOur study highlights the potential predictive value of GMS for immunotherapeutic benefit in non-squamous NSCLC. Besides, the combination of GMS and PD-L1 may serve as an optimal partner in guiding treatment decisions for anti-PD-(L)1 based therapy.
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