Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Unraveling the unfolded protein response signature: implications for tumor immune microenvironment heterogeneity and clinical prognosis in stomach cancer.

BACKGROUND: Stomach cancer is a leading cause of cancer-related deaths globally due to its high grade and poor response to treatment. Understanding the molecular network driving the rapid progression of stomach cancer is crucial for improving patient outcomes. METHODS: This study aimed to investigate the role of unfolded protein response (UPR) related genes in stomach cancer and their potential as prognostic biomarkers. RNA expression data and clinical follow-up information were obtained from the TCGA and GEO databases. An unsupervised clustering algorithm was used to identify UPR genomic subtypes in stomach cancer. Functional enrichment analysis, immune landscape analysis, and chemotherapy benefit prediction were conducted for each subtype. A prognostic model based on UPR-related genes was developed and validated using LASSO-Cox regression, and a multivariate nomogram was created. Key gene expression analyses in pan-cancer and in vitro experiments were performed to further investigate the role of the identified genes in cancer progression. RESULTS: A total of 375 stomach cancer patients were included in this study. Analysis of 113 UPR-related genes revealed their close functional correlation and significant enrichment in protein modification, transport, and RNA degradation pathways. Unsupervised clustering identified two molecular subtypes with significant differences in prognosis and gene expression profiles. Immune landscape analysis showed that UPR may influence the composition of the tumor immune microenvironment. Chemotherapy sensitivity analysis indicated that patients in the C2 molecular subtype were more responsive to chemotherapy compared to those in the C1 molecular subtype. A prognostic signature consisting of seven UPR-related genes was constructed and validated, and an independent prognostic nomogram was developed. The gene IGFBP1, which had the highest weight coefficient in the prognostic signature, was found to promote the malignant phenotype of stomach cancer cells, suggesting its potential as a therapeutic target. CONCLUSIONS: The study developed a UPR-related gene classifier and risk signature for predicting survival in stomach cancer, identifying IGFBP1 as a key factor promoting the disease's malignancy and a potential therapeutic target. IGFBP1's role in enhancing cancer cell adaptation to endoplasmic reticulum stress suggests its importance in stomach cancer prognosis and treatment.

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy.

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

Distinct ER and PR expression patterns significantly affect the clinical outcomes of early HER2-positive breast cancer: A real-world analysis of 871 patients treated with neoadjuvant therapy.

INTRODUCTION: The impact of distinct estrogen receptor (ER) and progesterone receptor (PR) expression patterns on tumor behavior and treatment outcomes within HER2-positive breast cancer is not fully explored. This study aimed to comprehensively examine the clinical differences among patients with HER2-positive breast cancer harboring distinct ER and PR expression patterns in the neoadjuvant setting. METHODS: This retrospective analysis included 871 HER2-positive breast patients treated with neoadjuvant therapy at our hospital between 2011 and 2022. Comparisons were performed across the three hormone receptor (HR)-specific subtypes, namely the ER-negative/PR-negative/HER2-positive (ER-/PR-/HER2+), the single HR-positive (HR+)/HER2+, and the triple-positive breast cancer (TPBC) subtypes. RESULTS: Of 871 patients, 21.0% had ER-/PR-/HER2+ tumors, 33.6% had single HR+/HER2+ disease, and 45.4% had TPBC. Individuals with single HR+/HER2+ tumors and TPBC cases demonstrated significantly lower pathological complete response (pCR) rates compared to those with ER-/PR-/HER2+ tumors (36.9% vs. 24.3% vs. 49.2%, p < 0.001). Multivariate analysis confirmed TPBC as significantly associated with decreased pCR likelihood (OR = 0.42, 95%CI 0.28-0.63, p < 0.001). Survival outcomes, including disease-free survival (DFS) and overall survival (OS), showed no significant differences across HR-specific subtypes in the overall patient population. However, within patients without anti-HER2 therapy, TPBC was linked to improved DFS and a trend towards better OS. CONCLUSIONS: HER2-positive breast cancer exhibited three distinct HR-specific subtypes with varying clinical manifestations and treatment responses. These findings suggest personalized treatment strategies considering ER and PR expression patterns, emphasizing the need for further investigations to unravel molecular traits underlying HER2-positive breast cancer with distinct HR expression patterns.

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing.

The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C-X-C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single-cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune-inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune-related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune-related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G-related genes was developed using multi-level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune-active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

Synergistic role of activated CD4+ memory T cells and CXCL13 in augmenting cancer immunotherapy efficacy.

The development of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, their efficacy is not consistent across all patients, underscoring the need for personalized approaches. In this study, we examined the relationship between activated CD4+ memory T cell expression and ICI responsiveness. A notable correlation was observed between increased activated CD4+ memory T cell expression and better patient survival in various cohorts. Additionally, the chemokine CXCL13 was identified as a potential prognostic biomarker, with higher expression levels associated with improved outcomes. Further analysis highlighted CXCL13's role in influencing the Tumor Microenvironment, emphasizing its relevance in tumor immunity. Using these findings, we developed a deep learning model by the Multi-Layer Aggregation Graph Neural Network method. This model exhibited promise in predicting ICI treatment efficacy, suggesting its potential application in clinical practice.

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

OBJECTIVES: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts. METHODS: A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only. RESULTS: The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05). CONCLUSIONS: The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.

Efficacy, Safety, and Population Pharmacokinetics of MW032 Compared With Denosumab for Solid Tumor-Related Bone Metastases: A Randomized, Double-Blind, Phase 3 Equivalence Trial.

Importance: The bioequivalence of denosumab biosimilar has yet to be studied in a 53-week, multicenter, large-scale, and head-to-head trial. A clinically effective biosimilar may help increase access to denosumab in patients with solid tumor-related bone metastases. Objectives: To establish the biosimilarity of MW032 to denosumab in patients with solid tumor-related bone metastases based on a large-scale head-to-head study. Design, Setting, and Participants: In this 53-week, randomized, double-blind, phase 3 equivalence trial, patients with solid tumors with bone metastasis were recruited from 46 clinical sites in China. Overall, 856 patients were screened and 708 eligible patients were randomly allocated to receive either MW032 or denosumab. Interventions: Patients were randomly assigned (1:1) to receive MW032 or reference denosumab subcutaneously every 4 weeks until week 49. Main Outcomes and Measures: The primary end point was percentage change from baseline to week 13 of natural logarithmic transformed urinary N-telopeptide/creatinine ratio (uNTx/uCr). Results: Among the 701 evaluable patients (350 in the MW032 group and 351 in the denosumab group), the mean (range) age was 56.1 (22.0-86.0) years and 460 patients were women (65.6%). The mean change of uNTx/uCr from baseline to week 13 was -72.0% (95% CI, -73.5% to -70.4%) in the MW032 group and -72.7% (95% CI, -74.2% to -71.2%) in the denosumab group. These percent changes corresponded to mean logarithmic ratios of -1.27 and -1.30, or a difference of 0.02. The 90% CI for the difference (-0.04 to 0.09) was within the equivalence margin (-0.13 to 0.13); the mean changes of uNTx/uCr and bone-specific alkaline phosphatase (s-BALP) at each time point were also similar during 53 weeks. The differences of uNTx/uCr change were 0.015 (95% CI, -0.06 to 0.09), -0.02 (95% CI, -0.09 to 0.06), -0.05 (95% CI, -0.13 to 0.03) and 0.001 (95% CI, -0.10 to 0.10) at weeks 5, 25, 37, and 53, respectively. The differences of s-BALP change were -0.006 (95% CI, 0.06 to 0.05), 0.00 (95% CI, -0.07 to 0.07), -0.085 (95% CI, -0.18 to 0.01), -0.09 (95% CI, -0.20 to 0.02), and -0.13 (95% CI, -0.27 to 0.004) at weeks 5, 13, 25, 37 and 53, respectively. No significant differences were observed in the incidence of skeletal-related events (-1.4%; 95% CI, -5.8% to 3.0%) or time to first on-study skeletal-related events (unadjusted HR, 0.86; P = .53; multiplicity adjusted HR, 0.87; P = .55) in the 2 groups. Conclusions and Relevance: MW032 and denosumab were biosimilar in efficacy, population pharmacokinetics, and safety profile. Availability of denosumab biosimilars may broaden the access to denosumab and reduce the drug burden for patients with advanced tumors. Trial Registration: ClinicalTrials.gov Identifier: NCT04812509.

Leritrelvir for the treatment of mild or moderate COVID-19 without co-administered ritonavir: a multicentre randomised, double-blind, placebo-controlled phase 3 trial.

Background: Leritrelvir is a novel α-ketoamide based peptidomimetic inhibitor of SARS-CoV-2 main protease. A preclinical study has demonstrated leritrelvir poses similar antiviral activities towards different SARS-CoV-2 variants compared with nirmatrelvir. A phase 2 clinical trial has shown a comparable antiviral efficacy and safety between leritrelvir with and without ritonavir co-administration. This trial aims to test efficacy and safety of leritrelvir monotherapy in adults with mild-to-moderate COVID-19. Methods: This was a randomised, double-blind, placebo-controlled, multicentre phase 3 trial at 29 clinical sites in China. Enrolled patients were from 18 to 75 years old, diagnosed with mild or moderate COVID-19 and not requiring hospitalization. Patients had a positive SARS-CoV-2 nucleic acid test (NAT) and at least one of the COVID-19 symptoms within 48 h before randomization, and the interval between the first positive SARS-CoV-2 NAT and randomization was ≤120 h (5 days). Patients were randomly assigned in a 1:1 ratio to receive a 5-day course of either oral leritrelvir 400 mg TID or placebo. The primary efficacy endpoint was the time from the first dose to sustained clinical recovery of all 11 symptoms (stuffy or runny nose, sore throat, shortness of breath or dyspnea, cough, muscle or body aches, headache, chills, fever ≥37 °C, nausea, vomiting, and diarrhea). The safety endpoint was the incidence of adverse events (AE). Primary and safety analyses were performed in the intention-to-treat (ITT) population. This study is registered with ClinicalTrials.gov, NCT05620160. Findings: Between Nov 12 and Dec 30, 2022 when the zero COVID policy was abolished nationwide, a total of 1359 patients underwent randomization, 680 were assigned to leritrelvir group and 679 to placebo group. The median time to sustained clinical recovery in leritrelvir group was significantly shorter (251.02 h [IQR 188.95-428.68 h]) than that of Placebo (271.33 h [IQR 219.00-529.63 h], P = 0.0022, hazard ratio [HR] 1.20, 95% confidence interval [CI], 1.07-1.35). Further analysis of subgroups for the median time to sustained clinical recovery revealed that (1) subgroup with positive viral nucleic acid tested ≤72 h had a 33.9 h difference in leritrelvir group than that of placebo; (2) the subgroup with baseline viral load >8 log 10 Copies/mL in leritrelvir group had 51.3 h difference than that of placebo. Leritrelvir reduced viral load by 0.82 log10 on day 4 compared to placebo. No participants in either group progressed to severe COVID-19 by day 29. Adverse events were reported in two groups: leritrelvir 315 (46.46%) compared with placebo 292 (43.52%). Treatment-relevant AEs were similar 218 (32.15%) in the leritrelvir group and 186 (27.72%) in placebo. Two cases of COVID-19 pneumonia were reported in placebo group, and one case in leritrelvir group, none of them were considered by the investigators to be leritrelvir related. The most frequently reported AEs (occurring in ≥5% of participants in at least one group) were laboratory finding: hypertriglyceridemia (leritrelvir 79 [11.7%] vs. placebo 70 [10.4%]) and hyperlipidemia (60 [8.8%] vs. 52 [7.7%]); all of them were nonserious. Interpretation: Leritrelvir monotherapy has good efficacy for mild-to-moderate COVID-19 and without serious safety concerns. Funding: This study was funded by the National Multidisciplinary Innovation Team Project of Traditional Chinese Medicine, Guangdong Science and Technology Foundation, Guangzhou Science and Technology Planning Project and R&D Program of Guangzhou Laboratory.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.

BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

Assessing real-world safety concerns of Sacituzumab govitecan: a disproportionality analysis using spontaneous reports in the FDA adverse event reporting system.

Aim: The aim of this study was to identify potential safety concerns associated with Sacituzumab Govitecan (SG), an antibody-drug conjugate targeting trophoblastic cell-surface antigen-2, by analyzing real-world safety data from the largest publicly available worldwide pharmacovigilance database. Methods: All data obtained from the FDA Adverse Event Reporting System (FAERS) database from the second quarter of 2020 to the fourth quarter of 2022 underwent disproportionality analysis and Bayesian analysis to detect and assess the adverse event signals of SG, considering statistical significance when the lower limit of the 95% CI >1, based on at least 3 reports. Results: Total of 1072 cases were included. The main safety signals were blood and lymphatic system disorders [ROR(95CI)=7.23 (6.43-8.14)], gastrointestinal disorders [ROR(95CI)=2.01 (1.81-2.22)], and relative infection adverse events, such as neutropenic sepsis [ROR(95CI)=46.02 (27.15-77.99)] and neutropenic colitis [ROR(95CI)=188.02 (120.09-294.37)]. We also noted unexpected serious safety signals, including large intestine perforation [ROR(95CI)=10.77 (3.47-33.45)] and hepatic failure [ROR(95CI)=3.87 (1.45-10.31)], as well as a high signal for pneumonitis [ROR(95CI)=9.93 (5.75-17.12)]. Additionally, age sub-group analysis revealed that geriatric patients (>65 years old) were at an increased risk of neutropenic colitis [ROR(95CI)=282.05 (116.36-683.66)], neutropenic sepsis [ROR(95CI)=101.11 (41.83-244.43)], acute kidney injury [ROR(95CI)=3.29 (1.36-7.94)], and atrial fibrillation [ROR(95CI)=6.91 (2.86-16.69)]. Conclusion: This study provides crucial real-world safety data on SG, complementing existing clinical trial information. Practitioners should identify contributing factors, employ monitoring and intervention strategies, and focus on adverse events like neutropenic sepsis, large intestine perforation, and hepatic failure. Further prospective studies are needed to address these safety concerns for a comprehensive understanding and effective management of associated risks.

LncRNA EILA promotes CDK4/6 inhibitor resistance in breast cancer by stabilizing cyclin E1 protein.

CDK4/6 inhibitors (CDK4/6i) plus endocrine therapy are now standard first-line therapy for advanced HR+/HER2- breast cancer, but developing resistance is just a matter of time in these patients. Here, we report that a cyclin E1-interacting lncRNA (EILA) is up-regulated in CDK4/6i-resistant breast cancer cells and contributes to CDK4/6i resistance by stabilizing cyclin E1 protein. EILA overexpression correlates with accelerated cell cycle progression and poor prognosis in breast cancer. Silencing EILA reduces cyclin E1 protein and restores CDK4/6i sensitivity both in vitro and in vivo. Mechanistically, hairpin A of EILA binds to the carboxyl terminus of cyclin E1 protein and hinders its binding to FBXW7, thereby blocking its ubiquitination and degradation. EILA is transcriptionally regulated by CTCF/CDK8/TFII-I complexes and can be inhibited by CDK8 inhibitors. This study unveils the role of EILA in regulating cyclin E1 stability and CDK4/6i resistance, which may serve as a biomarker to predict therapy response and a potential therapeutic target to overcome resistance.

Establishing an evaluation index system of nursing quality for clinical drug trials: A Delphi study.

AIMS: To construct a quality evaluation index system for clinical drug trials nursing management and obtain the weight of all indicators. DESIGN: A mixed-method research design with a quantitative component was used, primarily qualitative. METHODS: Through a literature review and semi-structured interview, an expert consultation questionnaire on the quality of nursing evaluation indicators for clinical drug trials was developed in April 2021. Eighteen experts in clinical drug trial nursing, medical, and pharmacy conducted 2 rounds of consultation according to the Delphi method to determine the indicators for evaluating the quality of clinical drug trial nursing. The weights of each indicator were determined using analytic hierarchical analysis. RESULTS: The established quality evaluation system of clinical drug trial nursing mainly includes 3 first-level indicators, 12 second-level indicators, and 59 third-level indicators. The positive coefficients of the two rounds of expert consultation were 90%-100%, and the authority coefficients were 0.831 and 0.885, respectively; the coordination coefficients were 0.189 and 0.214, respectively. The consulting results and weight settings are reliable. The evaluation index system we constructed is in line with the characteristics of the clinical drug trial nursing profession, with clear index levels and strong clinical operability, which can provide a reference for the assessment, monitoring and improvement of nursing quality in clinical drug trials. It will also clarify how nurses contribute to subjects' safety.

Nanoparticles (NPs)-mediated lncBCMA silencing to promote eEF1A1 ubiquitination and suppress breast cancer growth and metastasis.

Long non-coding RNAs (lncRNAs) play an important role in cancer metastasis. Exploring metastasis-associated lncRNAs and developing effective strategy for targeted regulation of lncRNA function in vivo are of utmost importance for the treatment of metastatic cancer, which however remains a big challenge. Herein, we identified a new functional lncRNA (denoted lncBCMA), which could stabilize the expression of eukaryotic translation elongation factor 1A1 (eEF1A1) via antagonizing its ubiquitination to promote triple-negative breast cancer (TNBC) growth and metastasis. Based on this regulatory mechanism, an endosomal pH-responsive nanoparticle (NP) platform was engineered for systemic lncBCMA siRNA (siBCMA) delivery. This NPs-mediated siBCMA delivery could effectively silence lncBCMA expression and promote eEF1A1 ubiquitination, thereby leading to a significant inhibition of TNBC tumor growth and metastasis. These findings show that lncBCMA could be used as a potential biomarker to predict the prognosis of TNBC patients and NPs-mediated lncBCMA silencing could be an effective strategy for metastatic TNBC treatment.

Effective of metastasis-directed therapy for de novo metastatic nasopharyngeal carcinoma: A propensity score matched analysis.

OBJECTIVE: Our objective was to establish a prognostic model for patients with de novo metastatic nasopharyngeal carcinoma (NPC) who received chemotherapy followed by locoregional radiotherapy (LRRT) to identify candidates for metastasis-directed therapy (MDT). METHODS: De novo metastatic NPC patients who received chemotherapy followed by LRRT were enrolled. Propensity score matching (PSM) method was used to compare overall survival (OS) for patients receiving LRRT alone and MDT plus LRRT. We developed a predictive model to predict survival and estimate the outcome of stratified therapy and identify suitable candidates for MDT. RESULTS: A total of 107 patients received MDT plus LRRT and 178 received LRRT alone were enrolled. PSM analysis identified 107 patients in each cohort and showed that MDT plus LRRT was associated with a significant survival benefit (HR: 0.640; 95% CI, 0.29-0.956; p = 0.027). Based on five independent prognostic factors, including metastases number, serum lactate dehydrogenase, liver metastasis, C-reactive protein, and tumor response, a prognostic model was established. All patients were stratified according to the prognostic score obtained by the prognostic model. In the low-risk group, MDT plus LRRT group revealed a significant improvement for OS compared with LRRT alone group (5-year OS, 69.9% vs. 57.8%, p = 0.020). However, no significant difference was observed between MDT plus LRRT group and LRRT alone in the high-risk group (p = 0.75). CONCLUSION: MDT plus LRRT was associated with improved OS in patients with de novo metastatic NPC, especially low-risk patients identified with a newly developed prognostic model.

Association of PIK3CA mutation with outcomes in HER2-positive breast cancer treated with anti-HER2 therapy: A meta-analysis and bioinformatic analysis of TCGA­BRCA data.

BACKGROUND: This study aimed to comprehensively explore the clinical significance of PIK3CA mutation in human epidermal growth factor receptor 2 (HER2)-positive breast cancer treated with anti-HER2 therapy. METHODS: We systematically searched PubMed, Embase, and the Cochrane databases for eligible studies assessing the association between PIK3CA mutation and outcomes in patients with HER2-positive breast cancer receiving anti-HER2 therapy. The main outcomes included: (1) pathological complete response (pCR) or disease-free survival (DFS) for the neoadjuvant setting; (2) DFS or invasive DFS for the adjuvant setting; (3) objective response rate (ORR), progression-free survival (PFS), time-to-progression (TTP), or overall survival (OS) for the metastatic setting. The mutational landscape of HER2-positive breast cancer according to PIK3CA mutation status was examined based on TCGA breast cancer dataset. RESULTS: Totally, 43 eligible studies, covering 11,099 patients with available data on PIK3CA mutation status, were identified. In the neoadjuvant setting, PIK3CA mutation was significantly associated with a lower pCR rate (OR=0.23, 95% CI 0.19-0.27, p<0.001). This association remained significant irrespective of the type of anti-HER2 therapy (single-agent or dual-agent) and hormone receptor status. There were no significant differences in DFS between PIK3CA mutated and wild-type patients in either the neoadjuvant or adjuvant settings. In the metastatic setting, PIK3CA mutation predicted worse ORR (OR=0.26, 95%CI 0.17-0.40, p<0.001), PFS (HR=1.28, 95%CI 1.03-1.59, p = 0.024) and TTP (HR=2.27, 95%CI 1.54-3.34, p<0.001). However, no significant association was observed between PIK3CA mutation status and OS. Distinct mutational landscapes were observed in HER2-positive breast cancer between individuals with PIK3CA mutations and those with wild-type PIK3CA. CONCLUSIONS: PIK3CA mutation was significantly associated with a lower pCR rate in HER2-positive breast cancer treated with neoadjuvant anti-HER2 therapy. In the metastatic setting, PIK3CA mutation was predictive of worse ORR, PFS and TTP. These results suggest the potential for developing PI3K inhibitors as a therapeutic option for these patients.

Impact of body mass index and its change on survival outcomes in patients with early breast cancer: A pooled analysis of individual-level data from BCIRG-001 and BCIRG-005 trials.

INTRODUCTION: The relationships between body mass index (BMI) and survival rates are complex, and have not been thoroughly investigated in breast cancer patients who received adjuvant chemotherapy. METHODS: We collected data on 2394 patients from two randomized, phase III clinical trials that investigated adjuvant chemotherapy in breast cancer identified in Project Data Sphere. The objective was to examine the effect of baseline BMI, BMI after adjuvant chemotherapy, and BMI change from baseline to post-adjuvant chemotherapy on disease-free survival (DFS) and overall survival (OS). Restricted cubic splines were used to examine potential non-linear associations between continuous BMI value and survival. Stratified analyses involved chemotherapy regimens. RESULTS: Severe obesity (BMI≥40.0 kg/m2) at baseline was independently associated with worse DFS (hazard ration [HR] = 1.48, 95% confidence interval [CI] 1.02-2.16, P = 0.04) and OS (HR = 1.79, 95%CI 1.17-2.74, P = 0.007) compared with underweight/normal weight (BMI≤24.9 kg/m2). A BMI loss >10% was also an independent prognostic factor for adverse OS (HR = 2.14, 95%CI 1.17-3.93, P = 0.014). Stratified analyses revealed that severe obesity adversely affected DFS (HR = 2.38, 95%CI 1.26-4.34, P = 0.007) and OS (HR = 2.90, 95%CI 1.46-5.76, P = 0.002) in the docetaxel-based group, but not in the non-docetaxel-based group. Restricted cubic splines revealed a "J-shaped" association of baseline BMI with risk of recurrence or all-cause death, and this relationship was more pronounced in the docetaxel-based group. CONCLUSIONS: In early breast cancer patients treated with adjuvant chemotherapy, baseline severe obesity was significantly linked to worse DFS and OS, and a BMI loss over 10% from baseline to post-adjuvant chemotherapy also negatively affected OS. Moreover, the prognostic role of BMI might differ between docetaxel-based and non-docetaxel-based groups.

Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study.

BACKGROUND: Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC). METHODS: Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis. RESULTS: Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p<0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype. CONCLUSIONS: This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.