MK-BMC: a Multi-Kernel framework with Boosted distance metrics for Microbiome data for Classification.

MOTIVATION: Research on human microbiome has suggested associations with human health, opening opportunities to predict health outcomes using microbiome. Studies have also suggested that diverse forms of taxa such as rare taxa that are evolutionally related and abundant taxa that are evolutionally unrelated could be associated with or predictive of a health outcome. Although prediction models were developed for microbiome data, no prediction models currently exist that use multiple forms of microbiome-outcome associations. RESULTS: We developed MK-BMC, a Multi-Kernel framework with Boosted distance Metrics for Classification using microbiome data. We propose to first boost widely used distance metrics for microbiome data using taxon-level association signal strengths to up-weight taxa that are potentially associated with an outcome of interest. We then propose a multi-kernel prediction model with one kernel capturing one form of association between taxa and the outcome, where a kernel measures similarities of microbiome compositions between pairs of samples being transformed from a proposed boosted distance metric. We demonstrated superior prediction performance of (i) boosted distance metrics for microbiome data over original ones and (ii) MK-BMC over competing methods through extensive simulations. We applied MK-BMC to predict thyroid, obesity, and inflammatory bowel disease status using gut microbiome data from the American Gut Project and observed much-improved prediction performance over that of competing methods. The learned kernel weights help us understand contributions of individual microbiome signal forms nicely. AVAILABILITY AND IMPLEMENTATION: Source code together with a sample input dataset is available at https://github.com/HXu06/MK-BMC.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping.

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance.

PURPOSE: To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. MATERIALS AND METHODS: Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. RESULTS: Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. CONCLUSION: Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.

Evaluating Prognostic Value of Dynamics of Circulating Lactate Dehydrogenase in Colorectal Cancer Using Modeling and Machine Learning.

Pretreatment serum lactate dehydrogenase (LDH) levels have been associated with poor prognosis in several types of cancer, including metastatic colorectal cancer (mCRC). However, very few models link survival to longitudinal LDH measured repeatedly over time during treatment. We investigated the prognostic value of on-treatment LDH dynamics in mCRC. Using data from two large phase III studies (2L and 3L+ mCRC settings, n = 824 and 210, respectively), we found that integrating longitudinal LDH data with baseline risk factors significantly improved survival prediction. Current LDH values performed best, enhancing discrimination ability (area under the receiver operating characteristic curve) by 4.5~15.4% and prediction accuracy (Brier score) by 3.9~15.0% compared with baseline variables. Combining all longitudinal LDH markers further improved predictive performance. After controlling for baseline covariates and other longitudinal LDH indicators, current LDH levels remained a significant risk factor in mCRC, increasing mortality risk by over 90% (P < 0.001) in 2L patients and 60-70% (P < 0.01) in 3L+ patients per unit increment in current log (LDH). Machine-learning techniques, like functional principal component analysis (FPCA), extracted informative features from longitudinal LDH data, capturing over 99% of variability and allowing prediction of survival. Unsupervised clustering based on the extracted FPCA features stratified patients into three groups with distinct LDH dynamics and survival outcomes. Hence, our approaches offer a valuable and cost-effective way for risk stratification and improves survival prediction in mCRC using LDH trajectories.

Immunotherapy as a later-line option for HER2-altered advanced non-small-cell lung cancer: taxane might be a favorable partner.

Aim: To assess the effectiveness of different types of taxanes, including nab-paclitaxel, paclitaxel and docetaxel, and further compare the effectiveness of taxane-based chemotherapy, taxane-based chemotherapy plus angiogenesis inhibitors or taxane-based chemotherapy plus immune checkpoint inhibitors in HER2-altered non-small-cell lung cancer in the second- or third-line setting. Materials & methods: A total of 52 patients were included in the study. Progression-free survival was compared between subgroups. Results: A clinically meaningful improvement in progression-free survival was observed among patients in the nab-paclitaxel group compared with the docetaxel group. Taxane-based chemotherapy plus immune checkpoint inhibitors achieved longer progression-free survival than taxane-based chemotherapy. There was no difference between taxane-based chemotherapy plus immune checkpoint inhibitors and taxane-based chemotherapy plus angiogenesis inhibitors. Conclusion: Nab-paclitaxel appears to be a reasonable alternative to docetaxel. Chemotherapy plus immune checkpoint inhibitors might yield more survival benefits than chemotherapy alone.

GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response.

Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.

The efficacy and safety of chemo-free therapy in epidermal growth factor receptor tyrosine kinase inhibitor-resistant advanced non-small cell lung cancer: A single-arm, phase II study.

OBJECTIVES: The purpose of this study was to explore the efficacy and safety of toripalimab combined with anlotinib in patients with advanced non-small cell lung cancer (NSCLC) who acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). MATERIALS AND METHODS: Patients who developed resistance after using first- or second-generation EGFR-TKIs as their first-line regimen without EGFR T790M mutation or had disease progression after being treated with third-generation EGFR-TKIs as first- or second-line therapy were enrolled. All patients received toripalimab (240 mg/day on Day 1, intravenously) combined with anlotinib (12 mg/day, Days 1-14, orally) once every 3 weeks. Treatment continued until disease progression, or if toxicity was intolerable. The primary endpoint was the objective response rate (ORR) assessed by the investigator. The secondary endpoint was the progression-free survival (PFS). RESULTS: In total, 19 patients were enrolled between May 2020 and October 2021.The ORR was 0%, and a median PFS was 2.1 months (95% CI 0.251-3.949). Grade ≥3 treatment-related adverse events (AEs) occurred in 11% patients. Common adverse events included hypothyroidism (12/19), fatigue (9/19), and hypertension (8/19). Patients in stable disease (SD) group had lower abundance of EGFR mutation allele frequency (AF) before enrollment than those in progressive disease (PD) group (p = 0.031). Patients without detectable EGFR mutation (EGFR-) had longer PFS compared to the ones with EGFR mutations (p = 0.059). Patients with high levels of soluble programmed cell death ligand 1 (PD-L1) at baseline also tended to have longer PFS (p = 0.160). CONCLUSION: Toripalimab combined with anlotinib was tolerable in EGFR-TKI-resistant advanced NSCLC patients not previously treated with chemotherapy. Patients without detectable EGFR mutation and high soluble PD-L1 levels may benefit from this chemotherapy-free treatment.

Pyrotinib plus antiangiogenic agents for HER2-altered advanced non-small cell lung cancer: A retrospective real-world study.

BACKGROUND: Although some targeted therapies have been shown to be effective in treating HER2-altered non-small cell lung cancer (NSCLC), the survival demands have not yet been met due to the high cost and limited availability. This study aimed to assess the effectiveness and safety of pyrotinib plus antiangiogenic agents, including apatinib, anlotinib, and bevacizumab, in previously treated patients with HER2-altered advanced NSCLC. METHODS: In this retrospective real-world study, patients with HER2-altered NSCLC who received pyrotinib plus antiangiogenic agents as a second- or later-line treatment between November 2015 and January 2022 were reviewed. The objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and safety profiles of patients were analyzed. RESULTS: A total of 107 patients were included in the analysis, of which 59 patients (55.1%) had received at least two lines of prior chemotherapy or tyrosine kinase inhibitors. Most of them (87.9%) were identified as harboring HER2 exon 20 insertions. At the data cutoff date (May 13, 2022), the ORR, DCR, median PFS, and median OS were 19.6% (21/107), 94.4% (101/107), 7.13 months (95% confidence interval [CI]: 6.26-8.01), and 19.50 months (95% CI: 12.83-26.17), respectively. There was no difference in the PFS between patients receiving apatinib or anlotinib/bevacizumab (median PFS, 7.13 vs. 6.27 months, hazard ratio [HR] = 1.49, 95% CI: 0.87-2.54, p = 0.15). The most frequent grade 3 or higher treatment-related adverse events was diarrhea (17.6%), followed by hypertension (11.0%) and nausea (3.3%). No treatment-related death occurred. CONCLUSION: In this study, pyrotinib plus antiangiogenic agents demonstrated promising efficacy and were tolerable in HER2-altered NSCLC patients.

Influence of PD-L1 expression on the efficacy of EGFR-TKIs in EGFR-mutant non-small cell lung cancer.

BACKGROUND: Evidence on the influence of programmed death-ligand 1 (PD-L1) expression on the efficacy of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in EGFR-mutant non-small cell lung cancer (NSCLC) patients is at variance. METHODS: A single-center retrospective study was conducted to evaluate the influence of PD-L1 expression on the efficacy of EGFR-TKIs for NSCLC patients with EGFR mutation. Clinical information was retrieved from electronic medical records. The patients were divided into three subgroups according to PD-L1 expression level: PD-L1 < 1% (negative), PD-L1 1%-49% and PD-L1 ≥ 50%. The clinicopathological features, overall response rate (ORR), progression-free survival (PFS) and comutation information were collected and compared between the three subgroups. RESULTS: A total of 117 patients were included. For PD-L1 < 1%, PD-L1 1%-49% and PD-L1 ≥ 50% group, there were 39 (33.3%), 51 (43.5%) and 27 (23.0%) patients respectively, and the ORR was 43.2%, 64.0%, and 51.9%, respectively (p = 0.162), and the median progression-free survival (mPFS) was 22.0 months (95% CI: 14.0-29.9 months), 15.4 months (95% CI: 8.9-21.8 months) and 13.0 months (95% CI: 10.6-15.3 months), respectively (log-rank, p = 0.01). The mPFS was negatively correlated with PD-L1 expression level (r = -0.264, p = 0.041) and PD-L1 expression was an independent risk factor for worse PFS of EGFR-TKIs in multivariate Cox regression. Patients with concurrent TP53 mutation had shorter PFS (p = 0.039) and the patients harboring both mutant TP53 and positive PD-L1 had the shortest PFS (p = 0.006). CONCLUSIONS: The efficacy of EGFR-TKIs was influenced by the baseline PD-L1 expression. Higher PD-L1 expression was associated with shorter PFS. The combined indicators of TP53 and PD-L1 identified subgroups showing divergent benefits from EGFR-TKIs.

Pd sub-nanolayer on Au core for enhanced catalytic hydrogenation reduction of oxyanions pollutants: Synergistic effect of Pd and Au.

Oxyanion pollutants in industrial wasterwater, such as (Cr(VI)), BrO3- (Br(V)) and SeO32- (Se(IV)) have detrimental or toxic effects on individual health when their concentrations accumulated to a certain level. The conversion of these oxyanions into harmless/industrial-valuable products or removal from wastewater is of significance. Herein, we designed Pd sub-nanolayer on Au core catalysts supported on Al2O3 (sub-Pd-Au/Al2O3) for highly effective catalytic hydrogenation reduction of oxyanions under ambient conditions. The sub-Pd(0.049)-Au(0.927)/Al2O3 catalyst exhibited the highest catalytic activity and TOF value for Cr(VI), Br(V) and Se(IV) reduction, respectively, by optimizing the Pd loading amount. The synergistic effect between Pd sub-nanolayer and Au core enhanced catalytic activity by regulating the Pd dispersion and site property, according to thorough characterizations that included high-angle annular dark-field transmission electron microscopy (HAADF-TEM) image, in-situ CO-IR adsorption, CO chemisorption, and X-ray photoelectron spectroscopy (XPS). This work might provide some new lights on design of highly efficient catalysts for the elimination of oxyanion pollutants.

Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events.

COVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHR d and csHR r ). The eventual mortality/release outcome is also analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations: (1) The magnitude of OR is an upper limit of the csHR d : |log(OR)| ≥ |log(csHR d )|. This relationship between OR and HR might be understood from the definition of the two quantities; (2) csHR d and csHR r point in opposite directions: log(csHR d ) ⋅ log(csHR r ) < 0; This relation is a direct consequence of the nature of the two events; and (3) there is a tendency for a reciprocal relation between csHR d and csHR r : csHR d ∼ 1/csHR r . Though an approximate reciprocal trend between the two hazard ratios is in indication that the same factor causing faster death also lead to slow recovery by a similar mechanism, and vice versa, a quantitative relation between csHR d and csHR r in this context is not obvious. These results may help future analyses of data from COVID-19 or other similar diseases, in particular if the deceased patients are lacking, whereas surviving patients are abundant.

Remarkably efficient Pt/CeO2-Al2O3 catalyst for catalytic hydrodeiodination of monoiodoacetic acid: Synergistic effect of Al2O3 and CeO2.

Monoiodoacetic acid (MIAA) is one of the highly toxic halogenated disinfection by-products, which is formed during water disinfection processes. Catalytic hydrogenation with supported noble metal catalyst is a green and effective technique for the transformation of halogenated pollutant, but its activity still needs to be identified. In this study, Pt nanoparticles were supported on CeO2 modified γ-Al2O3 (Pt/CeO2-Al2O3) by chemical deposition method and the synergistic effect of Al2O3 and CeO2 on catalytic hydrodeiodination (HDI) of MIAA was systematically studied. Characterizations revealed that Pt dispersion could be improved by the introduced CeO2 through the formation of Ce-O-Pt bond and MIAA adsorption could be facilitated by high Zeta potential of Al2O3 component. Furthermore, optimal Ptn+/Pt0 could be obtained by adjusting CeO2 deposition amount on Al2O3, which could effectively facilitate the activation of C-I bond. Therefore, Pt/CeO2-Al2O3 exhibited remarkable catalytic activities and TOF values compared with those of Pt/CeO2 and Pt/Al2O3. Through detailed kinetic experiments and characterization, the extraordinary catalytic performance of Pt/CeO2-Al2O3 can be attributed to the abundant Pt sites as well as the synergistic effect between CeO2 and Al2O3.

First-line immunotherapy or angiogenesis inhibitor combined with chemotherapy for advanced non-small cell lung cancer with EGFR exon 20 insertions: Real-world evidence from China.

BACKGROUND: Currently, survival benefit of immunotherapy in advanced non-small cell lung cancer (NSCLC) with EGFR exon 20 insertions (ex20ins) is controversial, though it generally indicates poor response and activity. Compared with standard chemotherapy in combination with bevacizumab, first-line chemotherapy plus immune checkpoint inhibitor (ICI) in advanced NSCLC with EGFR ex20ins remains elusive and lacks real-world evidence. PATIENTS AND METHODS: A retrospective real-world study was conducted to evaluate clinical outcomes of chemotherapy alone (C), chemotherapy plus ICI (C + I), or chemotherapy plus angiogenesis inhibitors (C + A) as first-line strategies for advanced NSCLC patients with EGFR ex20ins. Investigator-assessed response and survival outcomes were compared between subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted to reveal concomitant alterations and explore the molecular landscape of ex20ins. RESULTS: A total of 164 patients were screened, identifying 35 kinds of ex20ins, and 122 cases treated with C, C + I, and C + A were finally included in the first-line analysis. C + A achieved much better objective response rate (ORR, 38.1% vs. 18.2%) and significant progression-free survival (PFS) benefit compared with C (median, 7.73 vs.5.93 months, HR = 0.60, 95% CI: 0.40-0.90, p = 0.014), and it showed similar ORR (38.1% vs. 40.0%), but higher disease control rate (DCR, 96.8% vs. 80.0%) and numerically longer median PFS (7.73 vs. 6.53 months, HR = 0.83, 95% CI: 0.44-1.56, p = 0.30) than C + I. There was no PFS difference between C + I and C, despite of PD-L1 expression or tumor mutational burden. KEGG analysis revealed concomitant upregulation of PI3K/AKT signaling might mediate intrinsic resistance to ICI in ex20ins. CONCLUSION: First-line chemotherapy plus angiogenesis inhibitors might yield more survival benefits than chemotherapy alone for NSCLC with EGFR ex20ins, whereas, it suggests that chemotherapy in combination with ICI might not obtain a better survival benefit for this subset of patients. Activation of PI3K/AKT signaling might mediate intrinsic immunosuppression in NSCLC with EGFR ex20ins.

CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network.

Recent advancements of artificial intelligence based on deep learning algorithms have made it possible to computationally predict compound-protein interaction (CPI) without conducting laboratory experiments. In this manuscript, we integrated a graph attention network (GAT) for compounds and a long short-term memory neural network (LSTM) for proteins, used end-to-end representation learning for both compounds and proteins, and proposed a deep learning algorithm, CPGL (CPI with GAT and LSTM) to optimize the feature extraction from compounds and proteins and to improve the model robustness and generalizability. CPGL demonstrated an excellent predictive performance and outperforms recently reported deep learning models. Based on 3 public CPI datasets, C.elegans, Human and BindingDB, CPGL represented 1 - 5% improvement compared to existing deep-learning models. Our method also achieves excellent results on datasets with imbalanced positive and negative proportions constructed based on the C.elegans and Human datasets. More importantly, using 2 label reversal datasets, GPCR and Kinase, CPGL showed superior performance compared to other existing deep learning models. The AUC were substantially improved by 20% on the Kinase dataset, indicative of the robustness and generalizability of CPGL.

Targeting exon 20 insertion mutations in lung cancer.

PURPOSE OF REVIEW: The application of tyrosine kinase inhibitor (TKI) has successfully changed the standard of care in epidermal growth factor receptor ( EGFR ) positive non-small cell lung cancer. However, clinical survivals for patients with EGFR exon 20 insertions have failed to improve over the long period and the mutation appeared resistant to EGFR -TKIs. This overview focused on the current treatment strategies, summarized the emerging regimens for patients with EGFR exon 20 insertions, and demonstrated historical challenges and future development. RECENT FINDING: Current clinical trials suggested that several regimens selectively-targeted EGFR exon 20 insertions presented potent antitumor activity, like mobocertinib and the bispecific anti- EGFR-MET monoclonal antibody amivantamab and were approved by Food and Drug Administration (FDA) in patients progressed beyond first-line treatment. Novel treatments, including DZD9008, CLN-081, revealed modest clinical efficacy as well and clinical trials are underway, which may lead to improvement of survival outcomes. SUMMARY: Recent clinical evidence indicates that targeted therapies could improve survival benefits to some extent. More efforts on drug development are underway to bring higher response rates both extracranial and intracranial, sustained clinical remission, and better survival benefits.

EGFR exon 20 insertion variants A763_Y764insFQEA and D770delinsGY confer favorable sensitivity to currently approved EGFR-specific tyrosine kinase inhibitors.

Background: The EGFR exon 20 insertions (ex20ins) D770_N771insSVD and V769_D770insASV are most frequent in non-small-cell lung cancer (NSCLC) and are associated with intrinsic resistance to currently approved EGFR tyrosine kinase inhibitors (TKIs). A763_Y764insFQEA and D770delinsGY, respectively, account for 3%-8% and 2.0%-4.8% of EGFR ex20ins in NSCLC and are associated with a more favorable response to EGFR-specific TKIs as per case reports. The aim of this study was to elucidate the molecular structures of these mutants and their binding affinities to diverse EGFR TKIs and compare the clinical outcomes in NSCLC patients harboring these mutations. Methods: A real-world cohort study was conducted to evaluate and compare the clinical outcomes of EGFR TKIs among NSCLC patients with different EGFR ex20ins mutants in response to EGFR TKIs. The structures of A763_Y764insFQEA and D770delinsGY were also analyzed and drug binding simulations were performed. Results: With a median follow-up of 24.0 months, the first-line objective response rate (ORR), disease control rate (DCR), and median progression-free survival (PFS) were, respectively, 0 (0/16), 50.0% (8/16), and 2.07 months (95%CI, 0-6.25) in patients harboring D770_N771insSVD and V769_D770insASV variants and 33.3% (4/12), 83.3% (10/12), and 9.97 months (95%CI, 4.75-15.19) in patients with A763_Y764insFQEA and D770delinsGY variants. There was a significant difference between the PFS of these two subgroups (median, 9.97 vs.2.07 months, HR = 0.33, 95%CI, 0.13-0.85, p = 0.02). Similarly, the PFS was significantly longer after second-line treatment with EGFR TKIs in patients harboring A763_Y764insFQEA and D770delinsGY compared to those with other insertions (median, 6.77 vs.2.23 months, HR = 0.14, p < 0.001). Computational simulations indicated that A763_Y764insFQEA and D770delinsGY mutants were structurally similar to wild-type EGFR. In contrast, the C-helix and phosphate-binding loop of D770_N771insSVD and V769_D770insASV had shifted into the drug-binding pocket, resulting in significant steric hindrance and a lack of affinity for the currently approved EGFR inhibitors. Conclusion: NSCLC patients harboring A763_Y764insFQEA and D770delinsGY insertions of EGFR are responsive to the currently approved EGFR TKIs as opposed to patients with the D770_N771insSVD and V769_D770insASV variants. Therefore, A763_Y764insFQEA and D770delinsGY should be classified as active mutations among heterogeneous EGFR ex20ins subtypes and the carriers can be treated with the suitable EGFR TKIs.

An exploration of LAF-bTMB as a predictor for the efficacy of immunotherapy combined with chemotherapy in non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitor (ICI) combined with chemotherapy is one of the standards of care for advanced non-small cell lung cancer (NSCLC) without driver mutations. However, the biomarker of combination therapy is still unknown. Although previous studies have confirmed that low allele frequency adjusted blood-based tumor mutational burden (LAF-bTMB) is associated with the efficacy of ICI monotherapy, there has been no report on the correlation between the efficacy of LAF-bTMB and ICI combined chemotherapy. This study aimed to explore whether LAF-bTMB can be used as a predictive biomarker for the efficacy of immunotherapy combined with chemotherapy in advanced NSCLC. METHODS: This study enrolled patients diagnosed with advanced NSCLC and who received ICI combined with chemotherapy for first-line therapy from May 2020 to December 2021 at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. Clinical information, treatment information, survival data, and peripheral blood samples of every patient before treatment were collected. Next-generation sequencing was performed on plasma samples to estimate bTMB and LAF-bTMB. RESULTS: A total of 42 patients with NSCLC were enrolled. In this cohort, 19 patients achieved partial response (PR), and the objective response rate (ORR) was 45.2%. The median progression-free survival (PFS) of all patients was 13.4 months (95% CI, 7.49-19.72). Both PFS and the overall survival (OS) were significantly longer in the responder (R) group than in the non-responder (NR) group (median PFS, 16.4 months vs. 7.2 months, p = 0.028; median OS, NE vs. 9.3 months, p = 0.016). There was no significant difference in bTMB and LAF-BTMB between the R and NR group. The ORR of patients with LAF-bTMB≤8muts/Mb was significantly higher than that of patients with LAF-bTMB >8muts/Mb (ORR, 61% vs. 26%, respectively, p = 0.033). When LAF-bTMB ≤8muts/Mb or > 20muts/Mb, ORR was significantly higher than that of patients with LAF-bTMB between 8 and 20muts/Mb (ORR were 57% and 21%, p = 0.047). No correlation has been found between LAF-bTMB and PFS or OS. CONCLUSIONS: This study confirmed that neither bTMB nor LAF-bTMB is feasible as a potential predictor of first-line immunochemotherapy for advanced NSCLC. More suitable biomarkers need to be explored to screen patients with better efficacy of immunotherapy combined with chemotherapy in the future.

Treatment patterns and outcomes of immunotherapy in extensive-stage small-cell lung cancer based on real-world practice.

BACKGROUND: The application of immune checkpoint inhibitors (ICIs) represents a breakthrough in the current landscape for the treatment of extensive-stage small-cell lung cancer (ES-SCLC), but the real-world outcome is limited. This study aimed to investigate the treatment options and efficacy evaluation of first-line, second-line, and subsequent-line immunotherapy in routine practice. METHODS: A retrospective analysis of ES-SCLC patients treated with ICIs was conducted between May 2016 and September 2021. Objective response rate, disease control rate, progression-free survival (PFS) and overall survival were assessed between groups to explore the value of ICIs at different treatment time periods. PFS1 and PFS2 were defined as the duration from initial therapy to disease progression or death in first-line or second-line treatment. RESULTS: Ninety-six patients with ES-SCLC were included. PFS1 was prolonged in patients treated with first-line ICIs-combined therapy (median PFS1 7.20 months vs. 5.30 months, hazard ratio [HR] 0.55, 95% confidence interval [CI] 0.36-087, p = 0.0085). For patients who progressed after first-line ICIs treatment (N = 22), PFS1 + PFS2 was longer in the second-line ICIs continuation group with no significant difference (median PFS1 + PFS2 11.27 months vs. 7.20 months, HR 0.45, 95% CI 0.14-1.51, p = 0.19). For patients who experienced a progression event after first-line chemotherapy (N = 50), PFS2 and PFS1 + PFS2 were prolonged in patients who accepted second-line ICIs-combined therapy without significant difference (median PFS2 4.00 months vs. 2.43 months, HR 0.59, 95% CI 0.33-1.05, p = 0.070; median PFS1 + PFS2 11.30 months vs. 8.70 months, HR 0.53, 95% CI 0.29-0.98, p = 0.056). CONCLUSION: First-line ICIs plus chemotherapy should be applied in the clinical practice of ES-SCLC. If patients did not receive ICIs plus chemotherapy in first-line treatment, therapies that include ICIs in second-line treatment should be considered.

Supplementation with Queen Bee Larva Powder Extended the Longevity of Caenorhabditis elegans.

Queen bee larva (QBL) is one kind of important edible insect that is harvested during royal jelly production process. QBL has many physiological functions; however, limited information is available regarding its antiaging effects. In this study, the antiaging function of freeze-dried QBL powder (QBLP) was investigated by combining the Caenorhabditis elegans (C. elegans) model and transcriptomics. The administration of QBLP to C. elegans was shown to improve lifespan parameters. Additionally, QBLP improved the mobility of nematodes. Transcriptome analysis showed the differentially expressed genes (DEGs) were significantly enriched in Gene Ontology (GO) terms that were almost all related to the biological functions of cell metabolism and stress, which are associated with lifespan. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that the lifespan of C. elegans was related to the longevity regulating pathway-worm. The expression levels of the key genes sod-3, gst-6, hsp-12.6, lips-7, ins-8, and lips-17 were upregulated. sod-3, hsp-12.6, lips-7, and lips-17 are downstream targets of DAF-16, which is an important transcription factor related to lifespan extension. CF1038 (daf-16(mu86)) supplemented with QBLP did not show a life-prolonging. This indicates that the antiaging function of QBLP is closely related to daf-16. Thus, QBLP is a component that could potentially be used as a functional material to ameliorate aging and aging-related symptoms.