Regular use of aspirin and statins reduces the risk of cancer in individuals with systemic inflammatory diseases.

Aspirin has shown potential for cancer prevention, but a recent large randomized controlled trial found no evidence for a reduction in cancer risk. Given the anti-inflammatory effects of aspirin, systemic inflammatory diseases (SIDs), such as osteoporosis, cardiovascular diseases, and metabolic diseases, could potentially modify the aspirin-cancer link. To investigate the impact of aspirin in people with SIDs, we conducted an observational study on a prospective cohort of 478,615 UK Biobank participants. Individuals with at least one of the 41 SIDs displayed a higher cancer risk than those without SIDs. Regular aspirin use showed protective effects exclusively in patients with SID, contrasting an elevated risk among their non-SID counterparts. Nonetheless, aspirin use demonstrated preventative potential only for 9 of 21 SID-associated cancer subtypes. Cholesterol emerged as another key mediator linking SIDs to cancer risk. Notably, regular statin use displayed protective properties in patients with SID but not in their non-SID counterparts. Concurrent use of aspirin and statins exhibited a stronger protective association in patients with SID, covering 14 common cancer subtypes. In summary, patients with SIDs may represent a population particularly responsive to regular aspirin and statin use. Promoting either combined or individual use of these medications within the context of SIDs could offer a promising chemoprevention strategy.

Progression of Gastrointestinal Injury During Antiplatelet Therapy After Percutaneous Coronary Intervention: A Secondary Analysis of the OPT-PEACE Randomized Clinical Trial.

Importance: Gastrointestinal injury progression induced by antiplatelet therapy in patients after percutaneous coronary intervention (PCI) has not been well studied. Objective: To assess the association of aspirin, clopidogrel, and their combination with gastrointestinal injury progression among patients without high bleeding risk after PCI. Design, Setting, and Participants: This secondary analysis assessed data from the Optimal Antiplatelet Therapy for Prevention of Gastrointestinal Injury Evaluated by ANKON Magnetically Controlled Capsule Endoscopy (OPT-PEACE) double-masked, placebo-controlled, multicenter randomized clinical trial. The OPT-PEACE trial was conducted at 28 centers in China, and recruitment took place from July 13, 2017, to July 13, 2019. The trial included patients with stable coronary artery disease or acute coronary syndromes without ST-segment elevation after PCI. Statistical analysis was conducted from September 13, 2022, to January 23, 2023. Interventions: Patients underwent magnetically controlled capsule endoscopy (MCE) at baseline and after 6 months of dual antiplatelet therapy (DAPT) with aspirin (100 mg/d) plus clopidogrel (75 mg/d). Those with no evidence of gastrointestinal ulcers or bleeding (ie, the intention-to-treat [ITT] cohort) were randomized (1:1:1) to aspirin (100 mg/d) plus matching placebo (aspirin alone), clopidogrel (75 mg/d) plus matching placebo (clopidogrel alone), or DAPT for an additional 6 months. A third MCE was performed 12 months after PCI. Main Outcomes and Measures: The primary outcome was the rate of gastric injury progression as assessed with the results of the 3 MCEs (at baseline, 6 months, and 12 months) in the modified intention-to-treat (mITT) population. The key secondary outcome was the rate of small-intestinal injury progression. Gastric or small-intestinal injury progression was defined as a quantitative increase in erosions or ulcers between the second and third MCEs (at 6 and 12 months, respectively). Results: This study included the 394 patients in the mITT cohort. Their mean (SD) age was 56.9 (8.7) years, and most were men (296 [75.1%]). A total of 132 patients were randomized to aspirin alone, 132 to clopidogrel alone, and 130 to DAPT. Gastric injury progression occurred in 49 aspirin users (37.1%), 64 clopidogrel users (48.5%), and 69 DAPT users (53.1%) (P = .02), reflecting a lower rate of gastric injury progression among aspirin users vs DAPT users (risk ratio [RR], 0.70 [95% CI, 0.49-0.99]; P = .009). No significant difference was observed between clopidogrel alone and DAPT (48.5% vs 53.1%; P = .46) or between aspirin alone and clopidogrel alone (37.1% vs 48.5%; P = .06). A total of 51 aspirin users (38.6%), 65 clopidogrel users (49.2%), and 71 DAPT users (54.6%) (P = .03) developed progressive small-intestinal injury, reflecting a lower rate of small-intestinal injury among aspirin users vs DAPT users (RR, 0.71 [95% CI, 0.50-0.99]; P = .01). No difference was observed between patients treated with clopidogrel vs DAPT (49.2% vs 54.6%; P = .38) or with aspirin vs clopidogrel (38.6% vs 49.2%; P = .08). Conclusions and Relevance: In this secondary analysis of a randomized clinical trial, ongoing use of aspirin, clopidogrel, or their combination between 6 and 12 months after PCI was associated with progressive gastric and small-intestinal injury in a substantial proportion of patients, more so with DAPT than with monotherapy. Clopidogrel was at least as likely as aspirin to induce gastrointestinal injury progression. Future research is warranted to determine what impact the findings from MCEs would have on decision-making of antiplatelet therapy. Trial Registration: ClinicalTrials.gov Identifier: NCT03198741.

CDK4/6 inhibition triggers ICAM1-driven immune response and sensitizes LKB1 mutant lung cancer to immunotherapy.

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.

Interstitial pneumonitis associated with combined regimen of immunotherapy and conventional therapies-pharmacovigilance database analysis with real-world data validation.

BACKGROUND: Immune checkpoint inhibitor (ICI) therapy combined with conventional therapies is being broadly applied in non-small cell lung cancer (NSCLC) patients. However, the risk of interstitial pneumonitis (IP) following a combined regimen is incompletely characterized. METHODS: A total of 46,127 NSCLC patients were extracted for disproportionality analyses of IP from the Food and Drug Administration's Adverse Event Reporting System (FAERS) database. A total of 1108 NSCLC patients who received ICI treatment at Nanfang Hospital of Southern Medical University were collected and utilized for real-world validation. RESULTS: Of the 46,127 patients with NSCLC, 3830 cases (8.3%; 95% confidence interval [CI], 8.05-8.56) developed IP. Multivariable logistic regression analyses revealed that the adjusted ROR of ICI combined with radiation (RT) was the highest (121.69; 95% CI, 83.60-184.96; P < 0.0001) among all therapies, while that of ICI combined with chemotherapy (CHEMO) or targeted therapy (TARGET) was 0.90 (95% CI, 0.78-1.04; P = 0.160) and 1.49 (95% CI, 0.95-2.23; P = 0.065), respectively, using ICI monotherapy as reference. Furthermore, analyses from our validation cohort of 1108 cases showed that the adjusted odds ratio of ICI combined with RT was the highest (12.25; 95% CI, 3.34-50.22; P < 0.01) among all the therapies, while that of ICI combined with CHEMO or TARGET was 2.32 (95% CI, 0.89-7.92; P = 0.12) and 0.66 (95% CI, 0.03-4.55; P = 0.71), respectively, using ICI monotherapy as reference. CONCLUSIONS: Compared with ICI monotherapy, ICI combined with RT, rather than with CHEMO or TARGET, is associated with a higher risk of IP in NSCLC patients. Hence, patients receiving these treatments should be carefully monitored for IP.

PARP Inhibition Induces Synthetic Lethality and Adaptive Immunity in LKB1-Mutant Lung Cancer.

Contradictory characteristics of elevated mutational burden and a "cold" tumor microenvironment (TME) coexist in liver kinase B1 (LKB1)-mutant non-small cell lung cancers (NSCLC). The molecular basis underlying this paradox and strategies tailored to these historically difficult to treat cancers are lacking. Here, by mapping the single-cell transcriptomic landscape of genetically engineered mouse models with Kras versus Kras/Lkb1-driven lung tumors, we detected impaired tumor-intrinsic IFNγ signaling in Kras/Lkb1-driven tumors that explains the inert immune context. Mechanistic analysis showed that mutant LKB1 led to deficiency in the DNA damage repair process and abnormally activated PARP1. Hyperactivated PARP1 attenuated the IFNγ pathway by physically interacting with and enhancing the poly(ADP-ribosyl)ation of STAT1, compromising its phosphorylation and activation. Abrogation of the PARP1-driven program triggered synthetic lethality in NSCLC on the basis of the LKB1 mutation-mediated DNA repair defect, while also restoring phosphorylated STAT1 to favor an immunologically "hot" TME. Accordingly, PARP1 inhibition restored the disrupted IFNγ signaling and thus mounted an adaptive immune response to synergize with PD-1 blockade in multiple LKB1-deficient murine tumor models. Overall, this study reveals an unexplored interplay between the DNA repair process and adaptive immune response, providing a molecular basis for dual PARP1 and PD-1 inhibition in treating LKB1-mutant NSCLC. SIGNIFICANCE: Targeting PARP exerts dual effects to overcome LKB1 loss-driven immunotherapy resistance through triggering DNA damage and adaptive immunity, providing a rationale for dual PARP and PD-1 inhibition in treating LKB1-mutant lung cancers.

Organ-specific metastatic landscape dissects PD-(L)1 blockade efficacy in advanced non-small cell lung cancer: applicability from clinical trials to real-world practice.

BACKGROUND: 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). METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Assessment of anti-PD-(L)1 for patients with coexisting malignant tumor and tuberculosis classified by active, latent, and obsolete stage.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

De Novo Mutation in Non-Tyrosine Kinase Domain of ROS1 as a Potential Predictor of Immune Checkpoint Inhibitors in Melanoma.

PURPOSE: Despite the success of targeted therapy in c-ros oncogene 1 (ROS1)-rearranged cancers, especially non-small cell lung cancer (NSCLC), the clinical significance of ROS1 de novo mutation has not yet been understood. We sought to elucidate the predictive effect of ROS1 mutation for immune checkpoint inhibitor (ICI) therapy in melanoma. METHODS: The Cancer Genome Atlas [TCGA (n = 10967)] and Memorial Sloan Kettering Cancer Center [MSK (n = 10,945)] datasets, as well as two clinical cohorts of melanoma received ICI [CA209-038 (n = 73) and MEL-IPI (n = 110)], were included to explore the prevalence, prognostic effect, and immunotherapeutic predictive effect of ROS1 mutation in melanoma. Overall survival (OS) was defined as the primary outcome. RESULTS: Overall, melanoma accounted for the highest proportion of ROS1 mutation (~20%) which made up the majority (~95%) of the ROS1-alterated cases. Remarkably, ROS1 mutation yielded longer OS from ICI than the wild-type counterpart in the MSK melanoma population [hazard ratio (HR) 0.47, 95% confidence interval (CI) 0.30-0.74], and two external melanoma cohorts (CA209-038: HR 0.42, 95% CI 0.20-0.89; MEL-IPI: HR 0.55, 95% CI 0.34-0.91), without affecting the prognosis of patients. Elevated tumor mutational burden and enrichment of DNA damage repair was observed in ROS1 mutated patients, providing an explanation for the favorable responses to ICI therapy. Precisely, ROS1 mutation in non-protein tyrosine kinase (PTK) domain but not PTK mutation was responsible for the immunotherapy-specific responses of the ROS1 mutated patients in melanoma. CONCLUSIONS: Collectively, ROS1 mutation, specifically the non-PTK mutation, is a potential predictor of ICI therapy in melanoma, which is distinct from the well-established role of ROS1 rearrangement for targeted therapy in NSCLC.

Efficacy and Treatment Strategies in Advanced Cancers with Liver Metastasis Receiving Atezolizumab Therapy.

BACKGROUND: Atezolizumab has been used to treat patients with liver metastasis (LM). However, whether atezolizumab is superior to standard of care therapy in an all-comer or selective population with LM is still uncertain. METHODS: A pooled analysis based on 10 randomized controlled trials was conducted to evaluate the clinical benefit of atezolizumab versus standard therapy in patients stratified by liver metastatic status, followed by biomarker-based individual analyses of the non-small cell lung cancer (NSCLC) cohort (OAK and POPLAR studies) and urothelial cancer cohort (IMvigor210 study). RESULTS: The pooled analysis demonstrated an overall survival (OS) improvement using atezolizumab treatment versus standard therapy across cancer types and treatment lines regardless of liver metastatic status. However, the efficacy of atezolizumab in patients with LM from the second-line setting was limited, based on the individual analysis of NSCLC cohorts (P = 0.053). PD-L1 strong expression emerged as a predominant biomarker (P = 0.015) to screen atezolizumab-advantageous patients with LM. Notably, the combination of PD-L1 and LM improved the predictive power for atezolizumab therapy in both NSCLC and urothelial cancer cohorts. Exploratory translational analysis revealed that strong expression of PD-L1 might have reversed the non-inflamed immune phenotype of liver metastasis, thus sensitizing these patients to immunotherapy. CONCLUSION: Our study demonstrated a preferable efficacy of atezolizumab in patients with LM as first-line therapy over standard of care therapy, while sensitive patients should be selected in second-line settings. PD-L1 was demonstrated as the most effective biomarker for screening atezolizumab-advantageous patients with LM.

Integrative evaluation of primary and metastatic lesion spectrum to guide anti-PD-L1 therapy of non-small cell lung cancer: results from two randomized studies.

Objectives: Clinical benefits of immune-checkpoint blockade (ICB) versus standard chemotherapy have been established in unselected non-small cell lung cancer (NSCLC). However, the response to ICB therapy among patients is heterogeneous in clinical practice. Materials and Methods: We retrospectively assessed the predicitive effect of the primary and metastatic lesion spectrum (baseline sum of the longest diameters [SLD], number of metastatic sites and specific organ metastases) on the efficacy of atezolizumab over docetaxel in OAK and POPLAR trial cohorts. A decision model, termed DSO (Diameter-Site-Organ), based on the spectrum was developed and validated for guiding ICB. Results: Higher SLD (>38 mm) and more metastatic sites (≥2) were characterized with pronounced overall survival (OS) benefits from atezolizumab versus docetaxel. Specifically, adrenal gland and brain metastases were identified as favorable predictors of atezolizumab treatment. The DSO model was developed in the discovery cohort to integrate the directive effect of the primary and metastatic lesion spectrum. Remarkably, a general pattern of enhanced efficacy of atezolizumab versus docetaxel was observed along with the increase of the DSO score. For patients with DSO score > 0, atezolizumab yielded a significantly prolonged OS than docetaxel, whereas OS was generally similar between two treatments in patients with DSO score ≤ 0. Equivalent findings were also seen in the internal and external validation cohorts. Conclusions: The response to anti-PD-L1 therapy among patients varied with the primary and metastatic lesion spectrum. The DSO-based system might provide promising medication guidance for ICB treatment in NSCLC patients.

Inferring Homologous Recombination Deficiency of Ovarian Cancer From the Landscape of Copy Number Variation at Subchromosomal and Genetic Resolutions.

BACKGROUND: Homologous recombination deficiency (HRD) is characterized by overall genomic instability and has emerged as an indispensable therapeutic target across various tumor types, particularly in ovarian cancer (OV). Unfortunately, current detection assays are far from perfect for identifying every HRD patient. The purpose of this study was to infer HRD from the landscape of copy number variation (CNV). METHODS: Genome-wide CNV landscape was measured in OV patients from the Australian Ovarian Cancer Study (AOCS) clinical cohort and >10,000 patients across 33 tumor types from The Cancer Genome Atlas (TCGA). HRD-predictive CNVs at subchromosomal resolution were identified through exploratory analysis depicting the CNV landscape of HRD versus non-HRD OV patients and independently validated using TCGA and AOCS cohorts. Gene-level CNVs were further analyzed to explore their potential predictive significance for HRD across tumor types at genetic resolution. RESULTS: At subchromosomal resolution, 8q24.2 amplification and 5q13.2 deletion were predominantly witnessed in HRD patients (both p < 0.0001), whereas 19q12 amplification occurred mainly in non-HRD patients (p < 0.0001), compared with their corresponding counterparts within TCGA-OV. The predictive significance of 8q24.2 amplification (p < 0.0001), 5q13.2 deletion (p = 0.0056), and 19q12 amplification (p = 0.0034) was externally validated within AOCS. Remarkably, pan-cancer analysis confirmed a cross-tumor predictive role of 8q24.2 amplification for HRD (p < 0.0001). Further analysis of CNV in 8q24.2 at genetic resolution revealed that amplifications of the oncogenes, MYC (p = 0.0001) and NDRG1 (p = 0.0004), located on this fragment were also associated with HRD in a pan-cancer manner. CONCLUSIONS: The CNV landscape serves as a generalized predictor of HRD in cancer patients not limited to OV. The detection of CNV at subchromosomal or genetic resolution could aid in the personalized treatment of HRD patients.

Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer.

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.

Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019.

OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study

BACKGROUND: Genetic 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. METHODS: In 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. RESULTS: A 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. CONCLUSIONS: Our 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.

Liquid chromatography mass spectrometry-based profiling of phosphatidylcholine and phosphatidylethanolamine in the plasma and liver of acetaminophen-induced liver injured mice.

BACKGROUND: Acetaminophen (APAP) overdose is one of the most common causes of acute liver failure in many countries. The aim of the study was to describe the profiling of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) in the plasma and liver of Acetaminophen -induced liver injured mice. METHODS: A time course study was carried out using C57BL/6 mice after intraperitoneal administration of 300 mg/kg Acetaminophen 1 h, 3 h, 6 h, 12 h and 24 h. A high-throughput liquid chromatography mass spectrometry (LC-MS) lipidomic method was utilized to detect phosphatidylcholine and phosphatidylethanolamine species in the plasma and liver. The expressions of phosphatidylcholine and phosphatidylethanolamine metabolism related genes in liver were detected by quantitative Reverse transcription polymerase chain reaction (qRT-PCR) and Western-blot. RESULTS: Following Acetaminophen treatment, the content of many PC and PE species in plasma increased from 1 h time point, peaked at 3 h or 6 h, and tended to return to baseline at 24 h time point. The relative contents of almost all PC species in liver decreased from 1 h, appeared to be lowest at 6 h, and then return to normality at 24 h, which might be partly explained by the suppression of phospholipases mRNA expressions and the induction of choline kinase (Chka) expression. Inconsistent with PC profile, the relative contents of many PE species in liver increased upon Acetaminophen treatment, which might be caused by the down-regulation of phosphatidylethanolamine N-methyltransferase (Pemt). CONCLUSIONS: Acetaminophen overdose induced dramatic change of many PC and PE species in plasma and liver, which might be caused by damaging hepatocytes and interfering the phospholipid metabolism in Acetaminophen -injured liver.