Double- or Triple-Tiered Protection: Prospects for the Sustainable Application of Copper-Based Antimicrobial Compounds for Another Fourteen Decades.

Copper (Cu)-based antimicrobial compounds (CBACs) have been widely used to control phytopathogens for nearly fourteen decades. Since the first commercialized Bordeaux mixture was introduced, CBACs have been gradually developed from highly to slightly soluble reagents and from inorganic to synthetic organic, with nanomaterials being a recent development. Traditionally, slightly soluble CBACs form a physical film on the surface of plant tissues, separating the micro-organisms from the host, then release divalent or monovalent copper ions (Cu2+ or Cu+) to construct a secondary layer of protection which inhibits the growth of pathogens. Recent progress has demonstrated that the release of a low concentration of Cu2+ may elicit immune responses in plants. This supports a triple-tiered protection role of CBACs: break contact, inhibit microorganisms, and stimulate host immunity. This spatial defense system, which is integrated both inside and outside the plant cell, provides long-lasting and broad-spectrum protection, even against emergent copper-resistant strains. Here, we review recent findings and highlight the perspectives underlying mitigation strategies for the sustainable utilization of CBACs.

Safety and effectiveness of neoadjuvant PD-1 inhibitor (toripalimab) plus chemotherapy in stage II-III NSCLC (LungMate 002): an open-label, single-arm, phase 2 trial.

BACKGROUND: This trial aimed to analyse the safety, effectiveness and transcriptomic characteristics of neoadjuvant toripalimab plus chemotherapy in II-III non-small-cell lung cancer (NSCLC). METHODS: Patient eligibility mainly involved treatment-naive, clinical stage II-III and wild-type EGFR/ALK NSCLC. The patients received 2-4 cycles of toripalimab (240 mg q3w) plus carboplatin-based chemotherapy. After the second treatment cycle, all patients were re-evaluated by a multidisciplinary team. Candidates eligible for surgery underwent surgery; otherwise, patients received the remaining treatment cycles. The primary endpoints were safety and major pathological response (MPR). Secondary endpoints were R0 resection rate, progression-free survival (PFS) and overall survival (OS). RNA sequencing of baseline and post-treatment samples was conducted to explore the transcriptomic characteristics of the therapeutic response. RESULTS: In total, 50 eligible patients were enrolled, including 12 (24.0%) with resectable disease (RD) and 38 (76.0%) with potentially resectable disease (PRD). Treatment-related adverse events (TRAEs) were recorded in 48 cases (96.0%). Severe TRAEs occurred in 3 (6.0%) cases, including myelosuppression, drug-induced liver injury and death related to haemoptysis. The objective response rate (ORR) was 76.0%, with 8 (16.0%) patients having a complete response (CR), 30 (60.0%) partial response (PR), 10 (20.0%) stable disease (SD) and 2 (4.0%) progressive disease (PD). Surgery could be achieved in 12 (100%) patients with RD and 25 (65.8%) with PRD; 1 (2.0%) with PRD refused surgery. Therefore, R0 resection was performed for all 36 (100%) patients who underwent surgery; 20 (55.6%) achieved MPR, including 10 (27.8%) with a complete pathological response (pCR). The CHI3L1 (chitinase-3-like protein 1) immunohistochemistry (IHC) expression of baseline tumour samples could predict the therapeutic response (AUC=0.732), OS (P=0.017) and PFS (P=0.001). Increased PD-1 expression, T cell abundance and immune-related pathway enrichment were observed in post-treatment samples compared to baseline in the response group (CR+PR) but not in the non-response group (SD+PD). CONCLUSIONS: Neoadjuvant toripalimab plus chemotherapy was safe and effective, with a high MPR and manageable TRAEs for II-III NSCLC, even converting initially PRD to RD. Disparate transcriptomic characteristics of therapeutic efficiency were observed, and CHI3L1 expression predicted therapeutic response and survival. TRIAL REGISTRATION: ChiCTR1900024014, June 22, 2019.

Predictive role of ferroptosis-related long non-coding RNAs in bladder cancer and their association with immune microenvironment and immunotherapy response.

BACKGROUND: We have previously reported that ferroptosis has an important role in bladder cancer development. In this study, we aimed to further explore the possible predictive ability of ferroptosis-related long non-coding RNAs (lncRNAs) in bladder cancer and their relation with immune microenvironment and immunotherapy response. MATERIALS AND METHODS: The ferroptosis-related lncRNAs were identified by Pearson's correlation analysis. The predictive lncRNA signature was developed by univariate and multivariate regression analyses. Only the main effects of independent variables in multivariate analysis were included in this signature. The TCGA dataset was defined as the training cohort and GEO was the validation cohort in this study. All samples were grouped into a high- or low-risk group depending on risk signature. The prognostic role of lncRNA signature was explored through survival analysis and receiver operating characteristic curve (ROC) analysis in both TCGA and GEO cohorts. Additionally, the independent prognostic ability of the lncRNA signature was confirmed by multivariate independent analysis. Furthermore, the relationship between lncRNAs and immune microenvironment as well as immunotherapy response in bladder cancers was studied. RESULTS: The Kaplan-Meier curves identified significantly poorer overall survival outcomes for high-risk groups in both TCGA (p < 0.001) and GEO (p < 0.001) cohorts. The area under the curve (AUC) during ROC analysis of 1, 3, and 5 years was 0.781 ± 0.046, 0.784 ± 0.027, and 0.817 ± 0.025, respectively, in the TCGA cohort and 0.665 ± 0.177, 0.719 ± 0.068, and 0.791 ± 0.055, respectively, in the GEO cohort. The multivariate independent analysis in TCGA cohort identified age (p = 0.003), stage (p < 0.001), and signature risk score (p < 0.001) as independent risk factors for overall survival. Furthermore, this study demonstrated a significant difference in infiltration levels of various immune cells between high- and low-risk groups. The high risk group tended to have a lower expression of proteins including PD1 (p < 0.01), PD-L1 (p < 0.01), CTLA-4 (p < 0.05), etc. corresponding to various immune checkpoints. Additionally, the immunotherapy trial confirmed that the high-risk group tended to have a poorer treatment response than the low-risk group (p < 0.001). CONCLUSIONS: The ferroptosis-related lncRNAs exhibited a good predictive capacity for overall survival in bladder cancer. Additionally, they could be utilized to reveal tumour-immune microenvironment and immunotherapy responses.

Identification of a prognostic signature based on immune-related genes in bladder cancer.

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.

Epigenetic modifications potentially controlling the allelic expression of imprinted genes in sunflower endosperm.

BACKGROUND: Genomic imprinting is an epigenetic phenomenon mainly occurs in endosperm of flowering plants. Genome-wide identification of imprinted genes have been completed in several dicot Cruciferous plant and monocot crops. RESULTS: Here, we analyzed global patterns of allelic gene expression in developing endosperm of sunflower which belongs to the composite family. Totally, 691 imprinted loci candidates were identified in 12 day-after-pollination sunflower endosperm including 79 maternally expressed genes (MEG) and 596 paternally expressed genes (PEG), 6 maternally expressed noncoding RNAs (MNC) and 10 paternally expressed noncoding RNAs (PNC). And a clear clustering of imprinted genes throughout the rapeseed genome was identified. Generally, imprinting in sunflower is conserved within a species, but intraspecific variation also was detected. Limited loci in sunflower are imprinted in other several different species. The DNA methylation pattern around imprinted genes were investigated in embryo and endosperm tissues. In CG context, the imprinted genes were significantly associated with differential methylated regions exhibiting hypomethylation in endosperm and hypermethylation in embryo, which indicated that the maternal demethylation in CG context potentially induce the genomic imprinting in endosperm. CONCLUSION: Our study would be helpful for understanding of genomic imprinting in plants and provide potential basis for further research in imprinting in sunflower.

Non-Preheating Processed Quasi-2D Perovskites for Efficient and Stable Solar Cells.

Although the hot-casting (HC) technique is prevalent in developing preferred crystal orientation of quasi-2D perovskite films, the difficulty of accurately controlling the thermal homogeneity of substrate is unfavorable for the reproducibility of device fabrication. Herein, a facile and effective non-preheating (NP) film-casting method is proposed to realize highly oriented quasi-2D perovskite films by replacing the butylammonium (BA+ ) spacer partially with methylammonium (MA+ ) cation as (BA)2- x (MA)3+ x Pb4 I13 (x = 0, 0.2, 0.4, and 0.6). At the optimal x-value of 0.4, the resultant quasi-2D perovskite film possesses highly orientated crystals, associated with a dense morphology and uniform grain-size distribution. Consequently, the (BA)1.6 (MA)3.4 Pb4 I13 -based solar cells yield champion efficiencies of 15.44% with NP processing and 16.29% with HC processing, respectively. As expected, the HC-processed device shows a poor performance reproducibility compared with that of the NP film-casting method. Moreover, the unsealed device (x = 0.4) displays a better moisture stability with respect to the x = 0 stored in a 65% ± 5% relative humility chamber.

A surface modifier enhances the performance of the all-inorganic CsPbI2Br perovskite solar cells with efficiencies approaching 15.

All-inorganic perovskite solar cells (PSCs) are attracting considerable attention due to their promising thermal stability, but their inferior power-conversion efficiencies (PCE) hinder their realistic application. Here, we propose an approach through surface modification based on methyl ammonium halide (MAX) for inorganic CsPbI2Br solar cells processed at a low temperature. The combined benefits of the introduced MAX modifier enable the boosting of the power conversion efficiency to 14.8% with an impressive FF of 82.2% in CsPbI2Br PSCs. Our experimental analyses coupled with optical modeling indicate that the incorporated MAX leads to an effective passivation of the surface traps originating from Pb2+ and I- ions in CsPbI2Br and simultaneously mediates the crystallization of CsPbI2Br with slightly enlarged grains and reduced numbers of structural defects and pinhole. As a result, the interfacial trap-assisted recombination is suppressed and the charge extraction is promoted. Mechanistically, we show that in the presence of MAX, the deep-level traps in the perovskites are passivated, leaving the energy of the trapping centers to become shallower. In this situation, the negative impacts of the traps on carrier transport and recombination are mitigated.

A New Nomogram Allowing Physicians to Predict Patients at High Risk of Fever Occurring After Prostate Biopsy.

BACKGROUND To facilitate early treatment, we constructed a nomogram to predict risk of postoperative fever before prostate biopsy in patients with high risk of fever. MATERIAL AND METHODS We collected information on patients undergoing prostate biopsy from January 2015 to December 2018 from their medical records, including clinical characteristics and laboratory test results. Finally, after strict screening, the prediction model was established in 440 patients who underwent a transrectal prostate biopsy (TRPB). We divided these patients into a training group and validation group at a ratio of 7: 3, respectively. Univariate analysis and multivariate logistic regression analysis were used to select the predictors and to develop the model. Calibration curve and C-index were used to evaluate the accuracy of the nomogram, while DCA was used to assess the clinical value. RESULTS The individualized predictive nomogram contained 3 clinical features - Biopsy-positive rate (BPR), Hematuria, and Urine WBC - significantly associated with post-biopsy fever. The nomogram had good discriminating ability in both the training group and validation group - the C-index was 0.774 (95% CI=0.717-0.832) in the training group and 0.808 (95% CI=0.706-0.909) in the validation group. Hosmer-Lemeshow test proved a good calibration curve fit. The DCA curve suggested that the nomogram would have good clinical utility. CONCLUSIONS This is the first study to develop a nomogram to predict fever after prostate biopsy via Biopsy-positive rate (BPR), Hematuria, and Urine WBC. Use of this nomogram might help prevent fever and infection, and could facilitate individualized medical treatment after prostate biopsy.

Visual Tracking via Deep Feature Fusion and Correlation Filters.

Visual tracking is a fundamental vision task that tries to figure out instances of several object classes from videos and images. It has attracted much attention for providing the basic semantic information for numerous applications. Over the past 10 years, visual tracking has made a great progress, but huge challenges still exist in many real-world applications. The facade of a target can be transformed significantly by pose changing, occlusion, and sudden movement, which possibly leads to a sudden target loss. This paper builds a hybrid tracker combining the deep feature method and correlation filter to solve this challenge, and verifies its powerful characteristics. Specifically, an effective visual tracking method is proposed to address the problem of low tracking accuracy due to the limitations of traditional artificial feature models, then rich hiearchical features of Convolutional Neural Networks are used to make the multi-layer features fusion improve the tracker learning accuracy. Finally, a large number of experiments are conducted on benchmark data sets OBT-100 and OBT-50, and show that our proposed algorithm is effective.

Real-world progression in the survival of de novo Metastatic prostate cancer over the past decade.

BACKGROUND: The impact of evolving treatment strategies for metastatic prostate cancer (mPCa) on real-world survival is not well understood. We analyzed changes in mPCa survival over the past decade and discussed the potential driving factors behind these changes. METHODS: Our study involved 43,228 mPCa patients (2004-2020) from the SEER database, divided into 4 diagnostic periods. We used a multivariate Cox proportional hazards model to evaluate diagnostic periods' influence on overall mortality (OM) and prostate cancer-specific mortality (PSM), and calculated relative median survival improvements between adjacent periods. Subgroup analyses based on age and distant metastasis sites were conducted. RESULTS: Patients diagnosed in 2016 to 2020 experienced significantly reduced mortality risk compared to those in 2004 to 2007 (HR 0.64 for OM, HR 0.62 for CSM, both P < 0.001). The study period witnessed an absolute improvement in median overall survival (OS) and prostate cancer-specific survival (PCSS), 17 months (54.8%) and 25 months (67.6%) respectively. The most rapid relative survival improvement occurred post-2016, with a 29.7% increase in median OS and a 37.8% increase in PCSS compared to 2012 to 2015. There was a significant reduction in mortality risk throughout the study period in both age groups (age <75 and ≥75), but absolute survival gains were smaller in the older group (24 months [68.6%] vs. 8 months [32%] for OS, 36 months [90.0%] vs. 11 months [33.3%] for PCSS), with lower relative survival improvements after 2016 (37.2% vs. 17.9% for OS, 49% vs. 22.2% for PCSS). All metastasis site subgroups (except M1a) exhibited a significant reduction in mortality risk (all P < 0.001). Absolute survival improvements were 58 months (134.9%) for M1a, 16 months (50.0%) for M1b, and 17 months (54.8%) for M1c. CONCLUSION: The survival of mPCa have significantly improved over the past decade, although the progress is slower in elderly patients. Investigating the underlying reasons for survival differences among various patient profiles can further refine mPCa treatment strategies.

Neoadjuvant camrelizumab (an anti-PD-1 antibody) plus chemotherapy or apatinib (a VEGFR-2 inhibitor) for initially unresectable stage II-III non-small-cell lung cancer: a multicentre, two-arm, phase 2 exploratory study.

This multicentre, two-arm, phase 2 study aimed to explore the efficacy and safety of neoadjuvant camrelizumab plus chemotherapy or apatinib in patients with initially unresectable stage II-III non-small-cell lung cancer (NSCLC). Eligible patients regardless of PD-L1 expression received neoadjuvant camrelizumab 200 mg and platinum-doublet chemotherapy every 3 weeks (arm A) or those with PD-L1-positive tumors received neoadjuvant camrelizumab and apatinib 250 mg once daily (arm B), for 2-4 cycles, followed by surgery. The primary endpoint was major pathological response (MPR) rate. Thirty patients in arm A and 21 in arm B were enrolled. Surgery rates were 50.0% (15/30) in arm A and 42.9% (9/21) in arm B, with all patients achieving R0 resections. Of these patients, the MPR and pathological complete response rates were both 20.0% (95% CI 4.3-48.1) in arm A and were 55.6% (95% CI 21.2-86.3) and 11.1% (95% CI 0.3-48.2) in arm B, respectively. The corresponding objective response rates were 33.3% (95% CI 11.8-61.6) and 55.6% (95% CI 21.2-86.3). With a median follow-up of 22.4 months (95% CI 19.0-26.0), the median event-free survival was not reached (NR; 95% CI 13.6-NR) in arm A and 16.8 months (95% CI 8.6-NR) in arm B. Grade 3 or above treatment-related adverse events occurred in eight (26.7%) patients in arm A and three (14.3%) in arm B. Biomarker analysis showed baseline TYROBP expression was predictive of treatment response in arm B. Neoadjuvant camrelizumab plus chemotherapy or apatinib exhibits preliminary efficacy and manageable toxicity in patients with initially unresectable stage II-III NSCLC.

Construction and validation of N6-methyladenosine long non-coding RNAs signature of prognostic value for early biochemical recurrence of prostate cancer.

PURPOSE: Early biochemical recurrence (eBCR) indicated a high risk for potential recurrence and metastasis in prostate cancer. The N6-methyladenosine (m6A) methylation modification played an important role in prostate cancer progression. This study aimed to develop a m6A lncRNA signature to accurately predict eBCR in prostate cancer. METHODS: Pearson correlation analysis was first conducted to explore m6A lncRNAs and univariate Cox regression analysis was further performed to identify m6A lncRNAs of prognostic roles for predicting eBCR in prostate cancer. The m6A lncRNA signature was constructed by least absolute shrinkage and selection operator analysis (LASSO) in training cohort and further validated in test cohort. Furthermore, half maximal inhibitory concentration (IC50) values were utilized to explore potential effective drugs for high-risk group in this study. RESULTS: Five hundred and thirty-eighth m6A lncRNAs were searched out through Pearson correlation analysis and 25 out of 538 m6A lncRNAs were identified to pose prediction roles for eBCR in prostate cancers. An m6A lncRNA signature including 5 lncRNAs was successfully built in training cohort. The high-risk group derived from m6A lncRNA signature could efficiently predict eBCR occurrence in both training (p < 0.001) and test cohort (p = 0.002). ROC analysis also confirmed that lncRNA signature in this study posed more accurate prediction roles for eBCR occurrence when compared with PSA, TNM stages and Gleason scores. Drug sensitivity analysis further discovered that various drugs could be potentially utilized to treat high-risk samples in this study. CONCLUSIONS: The m6A lncRNA signature in this study could be utilized to efficiently predict eBCR occurrence, various clinical characteristic and immune microenvironment for prostate cancer.

Biochemical recurrence related metabolic novel signature associates with immunity and ADT treatment responses in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is a unique cancer from a metabolic perspective. Androgen receptor assumes a vital part in normal and malignant prostate cells regarding almost all aspects of cell metabolism, such as glucose, fat, amino acids, nucleotides, and so on. METHODS: We used The Cancer Genome Atlas database as training set, Memorial Sloan-Kettering Cancer Center cohort as validation set, and Gene Expression Omnibus database (GSE70769) as test set to identify the optimal prognostic signature. We evaluated the signature in terms of biochemical progression-free survival (bPFS), ROC curve, clinicopathological features, independent prognostic indicators, tumor microenvironment, and infiltrating immune cells. Nomogram was built dependent on the results of cox regression analyses. GSEA algorithm was used to evaluate differences in metabolism. The signature's prediction of androgen deprivation therapy (ADT) response was validated based on two groups of basic cytological experiments treat with ADT (GSE143408 and GSE120343) and the transcriptional information of pre-ADT/post-ADT of six local PCa patients. RESULTS: We finally input four screened genes into the stepwise regression model to construct metabolism-related signature. The signature shows good prediction performance in training set, verification set, and test set. A nomogram based on the PSA, Gleason score, T staging, and the signature risk score could predict 1-, 3-, and 5-year bPFS with the high area under curve values. Based on gene-set enrichment analysis, the characteristics of four genes signature could influence some important metabolic biological processes of PCa and were serendipitously found to be significantly related to androgen response. Subsequently, two cytological experimental data sets and our local patient sequencing data set verified that the signature may be helpful to evaluate the therapeutic response of PCa to ADT. CONCLUSIONS: Our systematic study definite a metabolism-related gene signature to foresee prognosis of PCa patients which might add to individual prevention and treatment.

Cuproptosis-related long noncoding RNAs predicts overall survival and reveal immune microenvironment of bladder cancer.

Background: Recently, a newly programmed cell death has been discovered, namely cuproptosis. It is considered a novel copper-dependent cell death model. Long non-coding RNA (lncRNA) influence the prognosis of bladder cancer. In this study, we established a scoring system based on 7 cuproptosis-related lncRNA to predict the prognosis and immune landscape of bladder cancer (BCa). Method: Gene expression and clinical data of 431 tissues were downloaded from The Cancer Genome Atlas (TCGA), including 19 normal samples and 419 cancer samples. All samples were randomly categorized into train and test cohorts. Cuproptosis-related lncRNA were distinguished. Then we conduct univariate COX and multivariate COX regression, paralleled with LASSO regression to cultivate a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, scatter diagram, C-index, ROC curves, nomogram, PCA analysis and univariate and multivariate Cox regression were used to test the accuracy of risk model and to predict patient survival. Additional, gene mutation status between high- and low-risk groups was calculated.GO and KEGG were used to access the DEGs (different expression genes)-related pathway.The ssGSEA and ESTIMATE algorithms were used to assess the immune function in different tumor samples. Besides, patient's response to immunotherapy and drug susceptibility were also been estimated. Results: 7 cuproptosis-related lncRNA (LINC01184, LINC00513, LINC02443, SMARCA5-AS1, BDNF-AS, SOD2-OT1, HYI-AS1) were selected to construct the risk model in the train cohort. This model can well predict the overall survival (OS) in test group and entire cohort with different stage. Despite no significant different is observed in gene mutation between high- and low-risk group, different immune infiltration, different survival and sensitivity to drugs are discovered. Conclusion: We established a novel cuproptosis-related lncRNA risk model which can predict the outcome and immunotherapy response with satisfactory predictive effects. This risk model can provide a new insight into prognostic evaluation and may have potential to guide comprehensive treatment in bladder cancer.

Identification and application of species-specific peptide biomarkers from soft-shelled turtles (Pelodiscus sinensis) using post-translational modification detection-based peptidomics analysis.

The soft-shelled turtle is a commercially aquatic species in Asian countries, which serves as an important source of collagen with high nutritional and medicinal value, so it is of great significance to distinguish soft-shelled turtle derived collagen from others or adulterations. In this work, peptidomics analysis based on post-translational modification (PTM) assay was used to discover specific peptide biomarkers of soft-shelled turtle gelatin (STG). In total eight specific sequences and 74 peptides with different PTM types were screened out, and seven peptides with good signal responses and STG specificity were selected and validated as STG-specific peptide biomarkers. These peptide biomarkers could be used for distinguishing STG from other animal gelatins, and applied for ensuring the quality of collagens or gelatins from soft-shelled turtle with authenticity and traceability.

Selective androgen receptor degrader (SARD) to overcome antiandrogen resistance in castration-resistant prostate cancer.

In patients with castration-resistant prostate cancer (CRPC), clinical resistances such as androgen receptor (AR) mutation, AR overexpression, and AR splice variants (ARVs) limit the effectiveness of second-generation antiandrogens (SGAs). Several strategies have been implemented to develop novel antiandrogens to circumvent the occurring resistance. Here, we found and identified a bifunctional small molecule Z15, which is both an effective AR antagonist and a selective AR degrader. Z15 could directly interact with the ligand-binding domain (LBD) and activation function-1 region of AR, and promote AR degradation through the proteasome pathway. In vitro and in vivo studies showed that Z15 efficiently suppressed AR, AR mutants and ARVs transcription activity, downregulated mRNA and protein levels of AR downstream target genes, thereby overcoming AR LBD mutations, AR amplification, and ARVs-induced SGAs resistance in CRPC. In conclusion, our data illustrate the synergistic importance of AR antagonism and degradation in advanced prostate cancer treatment.

Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing.

BACKGROUND: Immunotherapy has revolutionized cancer treatment, but most patients are refractory to immunotherapy or acquire resistance, with the underlying mechanisms remaining to be explored. METHODS: We characterized the transcriptomes of ~92,000 single cells from 3 pre-treatment and 12 post-treatment patients with non-small cell lung cancer (NSCLC) who received neoadjuvant PD-1 blockade combined with chemotherapy. The 12 post-treatment samples were categorized into two groups based on pathologic response: major pathologic response (MPR; n = 4) and non-MPR (NMPR; n = 8). RESULTS: Distinct therapy-induced cancer cell transcriptomes were associated with clinical response. Cancer cells from MPR patients exhibited a signature of activated antigen presentation via major histocompatibility complex class II (MHC-II). Further, the transcriptional signatures of FCRL4+FCRL5+ memory B cells and CD16+CX3CR1+ monocytes were enriched in MPR patients and are predictors of immunotherapy response. Cancer cells from NMPR patients exhibited overexpression of estrogen metabolism enzymes and elevated serum estradiol. In all patients, therapy promoted expansion and activation of cytotoxic T cells and CD16+ NK cells, reduction of immunosuppressive Tregs, and activation of memory CD8+T cells into an effector phenotype. Tissue-resident macrophages were expanded after therapy, and tumor-associated macrophages (TAMs) were remodeled into a neutral instead of an anti-tumor phenotype. We revealed the heterogeneity of neutrophils during immunotherapy and identified an aged CCL3+ neutrophil subset was decreased in MPR patients. The aged CCL3+ neutrophils were predicted to interact with SPP1+ TAMs through a positive feedback loop to contribute to a poor therapy response. CONCLUSIONS: Neoadjuvant PD-1 blockade combined with chemotherapy led to distinct NSCLC tumor microenvironment transcriptomes that correlated with therapy response. Although limited by a small patient sample size subjected to combination therapy, this study provides novel biomarkers to predict therapy response and suggests potential strategies to overcome immunotherapy resistance.

Neoadjuvant Afatinib for stage III EGFR-mutant non-small cell lung cancer: a phase II study.

Afatinib, an irreversible ErbB-family blocker, could improve the survival of advanced epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer patients (NSCLCm+). This phase II trial (NCT04201756) aimed to assess the feasibility of neoadjuvant Afatinib treatment for stage III NSCLCm+. Forty-seven patients received neoadjuvant Afatinib treatment (40 mg daily). The primary endpoint was objective response rate (ORR). Secondary endpoints included pathological complete response (pCR) rate, pathological downstaging rate, margin-free resection (R0) rate, event-free survival, disease-free survival, progression-free survival, overall survival, treatment-related adverse events (TRAEs). The ORR was 70.2% (95% CI: 56.5% to 84.0%), meeting the pre-specified endpoint. The major pathological response (MPR), pCR, pathological downstaging, and R0 rates were 9.1%, 3.0%, 57.6%, and 87.9%, respectively. The median survivals were not reached. The most common TRAEs were diarrhea (78.7%) and rash (78.7%). Only three patients experienced grade 3/4 TRAEs. Biomarker analysis and tumor microenvironment dynamics by bulk RNA sequencing were included as predefined exploratory endpoints. CISH expression was a promising marker for Afatinib response (AUC = 0.918). In responders, compared to baseline samples, increasing T-cell- and B-cell-related features were observed in post-treatment tumor and lymph-node samples, respectively. Neoadjuvant Afatinib is feasible for stage III NSCLC+ patients and leads to dynamic changes in the tumor microenvironment.

A Novel Prognostic Model for Acute Myeloid Leukemia Based on Gene Set Variation Analysis.

Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 (P < 0.001). The median overall survival (mOS) of subgroups 1-3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort (P < 0.001) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.

A Novel Gene Signature Associated With "E2F Target" Pathway for Predicting the Prognosis of Prostate Cancer.

Background: The effect of the adenoviral early region 2 binding factors (E2Fs) target pathway on prostate cancer is not clear. It is necessary to establish an E2F target-related gene signature to predict prognosis and facilitate clinical decision-making. Methods: An E2F target-related gene signature was established by univariate and LASSO Cox regression analyses, and its predictive ability was verified in multiple cohorts. Moreover, the enrichment pathway, immune microenvironment, and drug sensitivity of the activated E2F target pathway were also explored. Results: The E2F target-related gene signature consisted of MXD3, PLK1, EPHA10, and KIF4A. The patients with high-risk scores showed poor prognosis, therapeutic resistance, and immunosuppression, along with abnormal growth characteristics of cells. Tinib drugs showed high sensitivity to the expression of MXD3 and EPHA10 genes. Conclusion: Our research established an E2F target-related signature for predicting the prognosis of prostate cancer. This study provides insights into formulating individualized detection and treatment as well as provides a theoretical basis for future research.