Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics.

BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics. METHODS: Peripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions. RESULTS: The cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier's outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%. CONCLUSION: This study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.

Heterogeneity in the immune microenvironment of bone metastasis in driver-positive non-small cell lung cancer.

Mutations in epidermal growth factor receptor and anaplastic lymphoma kinase are common driver events in non-small cell lung cancer (NSCLC), which are associated with a high frequency of bone metastases (BMs). While the bone marrow represents a specialized immune microenvironment, the immune repertoire of BMs remains unknown. Considering the higher incidence of BMs in driver gene-positive NSCLCs, and the unique biology of the bone, herein, we assessed the infiltrating immune cells and T cell receptor (TCR) profile of BMs in driver-positive NSCLCs. Immune profile of BMs in driver gene-positive NSCLC were assessed in 10 patients, where 6 had driver gene-positive mutation. TCR and bulk RNA sequencing were performed on malignant bone samples. The diversity and clonality of the TCR repertoire were analyzed. The cellular components were inferred from bulk gene expression profiles computationally by CIBERSORT. Although BMs were generally regarded as immune-cold tumors, immune cell composition analyses showed co-existence of cytotoxic and suppressor immune cells in driver-positive BM samples, as compared to primary lung. Analysis of the TCR repertoire indicated a trend of higher diversity and similar clonality in the driver-positive compared with the driver-negative subsets. In addition, we identified two cases that showed the opposite response to immune checkpoint blockade. A comparison of these two patients' BM samples showed more highly amplified clones, fewer M2 macrophages and more activated natural killer cells in the responder. In summary, BMs in NSCLC are heterogeneous in their immune microenvironment, which might be related to differential clinical outcomes to immune checkpoint blockade.

Neutrophil to lymphocyte ratio is associated with the epilepsy after primary intracerebral hemorrhage.

BACKGROUND: Post-stroke epilepsy (PSE) is one of the major sequelae of stroke. Inflammation has been implicated in the development of stroke. The study aimed to explore the relationship between neutrophil-to-lymphocyte ratio (NLR) levels and epilepsy in patients with primary intracerebral hemorrhage (ICH). METHODS: A retrospective study was performed on 1132 patients with first-time ICH. Blood samples were obtained at admission after ICH. Patients included in the study were classified into three groups according to NLR tertiles. Logistic regression was used to analyze the relationship between NLR levels and the occurrence of PSE. RESULTS: The occurrence of PSE was significantly correlated with NLR levels (r = 0.118, P < 0.001). Patients with PSE had higher NLR levels than those without PSE. After adjusting for potential confounders, high NLR was independently associated with an increased risk of PSE (OR = 1.861, 95% CI 1.032-3.355, P = 0.039). Neutrophil-to-lymphocyte ratio levels were independently associated with the occurrence of PSE in the poor functional outcome group, while this association was not significant in the favorable functional outcome group. The model (cortical involvement + hematoma volume + early seizures + NLR) showed good prognostic performance. CONCLUSION: High NLR at admission is associated with an increased risk of PSE, which suggests that NLR may play a role in risk stratification in patients with ICH.

Genetic characterisation of sarcomatoid carcinomas reveals multiple novel actionable mutations and identifies KRAS mutation as a biomarker of poor prognosis.

BACKGROUND: Sarcomatoid component occurs in various epithelial malignancies and is associated with an aggressive disease course and poor clinical outcome. As it is largely rare, the molecular events underlying sarcomatoid carcinomas (SCs) remain poorly characterised. Here, we performed targeted next-generation sequencing (NGS) on patients with surgically resected SCs comprising distinct tissues of origin. METHODS: A total of 71 patients with pathological diagnosis of sarcomatoid carcinomas and underwent surgery were retrospectively enrolled in this study. Overall survival (OS) was defined as the time from surgery to death from any cause. Patients alive or lost to follow-up were censored. Genomic DNA from formalin-fixed paraffin-embedded samples was extracted for NGS and tumour mutation burden (TMB) analysis. RESULTS: In general, SCs occurred more commonly in males, except those of the gallbladder. SCs of the lung and the larynx were associated with a higher proportion of smokers (p=0.0015). Alterations in TP53, RB1, TERT and KRAS were highly frequent, with KRAS mutations being a biomarker of poor prognosis (median OS=8 vs 16 months, p=0.03). Multiple alterations in potentially actionable genes, including ROS1 and NTRK1 fusions and ERBB2 amplification, were detected in the extra-pulmonary cohort. A relatively high proportion (30%) of patients with extra-pulmonary SC had high TMB, with a median of 5.39 mutations per Mb. Lastly, copy number variations were common in SCs, and were non-overlapping between the primary and metastatic tumours. CONCLUSION: Taken together, our results suggest that comprehensive genetic testing may be necessary to inform treatment options and identify prognostic biomarkers.

Dynamic monitoring of cerebrospinal fluid circulating tumor DNA to identify unique genetic profiles of brain metastatic tumors and better predict intracranial tumor responses in non-small cell lung cancer patients with brain metastases: a prospective cohort study (GASTO 1028).

BACKGROUND: Due to the blood-brain barrier, plasma is not an ideal source to evaluate the genetic characteristics of central nervous system tumors. Thus, cerebrospinal fluid (CSF) is becoming an alternative biopsy type to evaluate the genetic landscape of intracranial tumors. We aimed to explore the genetic profiles of CSF-derived circulating tumor DNA (ctDNA) to predict intracranial tumor responses and monitor mutational evolution during the treatment of non-small cell lung cancer (NSCLC) patients with brain metastases. METHODS: We conducted a prospective study of 92 newly diagnosed NSCLC patients with brain metastases. Paired CSF and plasma samples were collected at baseline, 8 weeks after treatment initiation, and disease progression. All samples underwent next-generation sequencing of 425 cancer-related genes. RESULTS: At baseline, the positive detection rates of ctDNA in CSF, plasma, and extracranial tumors were 63.7% (58/91), 91.1% (82/90), and 100% (58/58), respectively. A high level of genetic heterogeneity was observed between paired CSF and plasma, while concordance in driver mutations was also observed. A higher number of unique copy number variations was detected in CSF-ctDNA than in plasma. ctDNA positivity of CSF samples at baseline was associated with poor outcomes (HR=2.565, P=0.003). Moreover, patients with ≥ 50% reductions in the concentrations of CSF ctDNA after 8 weeks of treatment had significantly longer intracranial progression-free survivals (PFS) than patients with < 50% reductions in CSF ctDNA concentrations (13.27 months vs 6.13 months, HR=0.308, P=0.017). A ≥ 50% reduction in CSF ctDNA concentrations had better concordance with radiographic intracranial tumor responses than plasma. A ≥ 50% reduction in plasma ctDNA concentrations was also associated with longer extracranial PFS (11.57 months vs 6.20 months, HR=0.406, P=0.033). Based on clonal evolution analyses, the accumulation of subclonal mutations in CSF ctDNA was observed after 8 weeks of treatment. The clonal mutations that remained in more than 80% in CSF after 8 weeks also predicted shorter intracranial PFS (HR=3.785, P=0.039). CONCLUSIONS: CSF ctDNA exhibited unique genetic profiles of brain metastases, and dynamic changes in CSF ctDNA could better predict intracranial tumor responses and track clonal evolution during treatment in NSCLC patients with brain metastases. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03257735.

Moderate-Severe White Matter Lesion Predicts Delayed Intraventricular Hemorrhage in Intracerebral Hemorrhage.

BACKGROUND: Most existing studies have focused on the correlation between white matter lesion (WML) and baseline intraventricular hemorrhage (IVH) in patients with intracerebral hemorrhage (ICH), whereas few studies have investigated the relationship between WML severity and delayed IVH after admission. This study aimed to investigate the correlation between WML severity and delayed IVH and to verify the association between WML and baseline IVH. METHODS: A total of 480 patients with spontaneous ICH from February 2018 to October 2020 were selected. WML was scored using the Van Swieten Scale, with scores of 0-2 representing nonslight WML and scores of 3-4 representing moderate-severe WML. We determined the presence of IVH on baseline (< 6 h) and follow-up computed tomography (< 72 h) images. Univariate analysis and multiple logistic regression were used to analyze the influencing factors of baseline and delayed IVH. RESULTS: Among 480 patients with ICH, 172 (35.8%) had baseline IVH, and there was a higher proportion of moderate-severe WML in patients with baseline IVH (20.3%) than in those without baseline IVH (12.7%) (P = 0.025). Among 308 patients without baseline IVH, delayed IVH was found in 40 patients (12.9%), whose proportion of moderate-severe WML (25.0%) was higher than that in patients without delayed IVH (10.8%) (P = 0.012). Multiple logistic regression results showed that moderate-severe WML was independently correlated with baseline IVH (P = 0.006, odds ratio = 2.266, 95% confidence interval = 1.270-4.042) and delayed IVH (P = 0.002, odds ratio = 7.009, 95% confidence interval = 12.086-23.552). CONCLUSIONS: Moderate-severe WML was an independent risk factor for delayed IVH as well as baseline IVH.

Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with 18F-PSMA-1007 PET: comparison among different volume segmentation thresholds.

BACKGROUND: PET-based radiomics features could predict the biological characteristics of primary prostate cancer (PCa). However, the optimal thresholds to predict the biological characteristics of PCa are unknown. This study aimed to compare the predictive power of 18F-PSMA-1007 PET radiomics features at different thresholds for predicting multiple biological characteristics. METHODS: One hundred and seventy-three PCa patients with complete preoperative 18F-PSMA-1007 PET examination and clinical data before surgery were collected. The prostate lesions' volumes of interest were semi-automatically sketched with thresholds of 30%, 40%, 50%, and 60% maximum standardized uptake value (SUVmax). The radiomics features were respectively extracted. The prediction models of Gleason score (GS), extracapsular extension (ECE), and vascular invasion (VI) were established using the support vector machine. The performance of models from different thresholding regions was assessed using receiver operating characteristic curve and confusion matrix-derived indexes. RESULTS: For predicting GS, the 50% SUVmax model showed the best predictive performance in training (AUC, 0.82 [95%CI 0.74-0.88]) and testing cohorts (AUC, 0.80 [95%CI 0.66-0.90]). For predicting ECE, the 40% SUVmax model exhibit the best predictive performance (AUC, 0.77 [95%CI 0.68-0.84] and 0.77 [95%CI 0.63-0.88]). As for VI, the 50% SUVmax model had the best predictive performance (AUC, 0.74 [95%CI 0.65-0.82] and 0.74 [95%CI 0.56-0.82]). CONCLUSION: The 18F-1007-PSMA PET-based radiomics features at 40-50% SUVmax showed the best predictive performance for multiple PCa biological characteristics evaluation. Compared to the single PSA model, radiomics features may provide additional benefits in predicting the biological characteristics of PCa.

Tumor mutation burden as a biomarker in resected gastric cancer via its association with immune infiltration and hypoxia.

BACKGROUND: Tumor mutation burden (TMB) predicts immunotherapy efficacy in solid tumors. However, the biomarker role of TMB is still conflicting in resected tumors. We aimed to examine the association of TMB with prognosis and postoperative chemotherapy (CT) or radiochemotherapy (RCT) efficacy in resected gastric cancer (GC). METHODS: Whole-exome sequencing (WES) was performed in 73 resected GC specimens. Validation cohorts included 352 patients from The Cancer Genome Atlas (TCGA) and 222 patients from the Asian Cancer Research Group (ACRG). Immune infiltration and hypoxia were evaluated by transcriptome data and immunohistochemistry assay. RESULTS: TMB-high GC had favorable overall survival (OS) and disease-free survival (DFS), but the OS and DFS benefits with postoperative CT/RCT were more pronounced in TMB-low GC. These findings were consistent among all three cohorts and were maintained in the pooled cohort. Stratified by stages in the pooled cohort, stage III GC benefited from postoperative CT/RCT regardless of TMB level while stage Ib/II GC benefited from postoperative CT/RCT in TMB-low but not in TMB-high subgroup. TMB positively correlated with immune infiltration which was characterized by NK cell rather than CD8 + T cell enrichment. TMB-high GC was more hypoxic than TMB-low GC, and TMB-high stage Ib/II GC was the most hypoxic. CONCLUSIONS: High TMB may predict favorable prognosis in resected GC but poor response to postoperative CT/RCT in stage Ib/II subgroup, which may be determined by TMB-associated immune infiltration and hypoxia, respectively.

A nomogram to predict rupture risk of middle cerebral artery aneurysm.

BACKGROUND: Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique. METHODS: We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model. RESULTS: Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it. CONCLUSION: Our model can be used to predict the rupture risk of MCA aneurysm.

The predicting role of circulating tumor DNA landscape in gastric cancer patients treated with immune checkpoint inhibitors.

A more common and noninvasive predicting biomarker for programmed cell death 1 (PD-1) antibody remains to be explored. We assessed 46 patients with advanced gastric cancer who received PD-1 antibody immunotherapy and 425-genes next-generation sequencing (NGS) testing. Patients who had a > 25% decline in maximal somatic variant allelic frequency (maxVAF) had a longer progression free survival (PFS) and higher response rate than those who did not (7.3 months vs 3.6 months, p = 0.0011; 53.3% vs 13.3%, p = 0.06). The median PFS of patients with undetectable and detectable post-treatment circulating tumor DNA (ctDNA) was 7.4 months vs. 4.9 months (p = 0.025). Mutation status of TGFBR2, RHOA, and PREX2 in baseline ctDNA influenced the PFS of immunotherapy (p < 0.05). Patients with alterations in CEBPA, FGFR4, MET or KMT2B (p = 0.09) gene had greater likelihood of immune-related adverse events (irAEs). ctDNA can serve as a potential biomarker of the response to immunotherapy in advanced gastric cancers, and its potential role in predicting irAEs worth further exploration.

SMAD4 and NF1 mutations as potential biomarkers for poor prognosis to cetuximab-based therapy in Chinese metastatic colorectal cancer patients.

BACKGROUND: Cetuximab, an anti-EGFR monoclonal antibody, is used in combination with chemotherapy in clinic to enhance the outcome in metastatic colorectal cancer (mCRC) patients with only ~ 20% response rate. To date only activating mutations in KRAS and NRAS have been identified as poor prognosis biomarkers in cetuximab-based treatment, which makes an urgent need for identification of novel prognosis biomarkers to precisely predict patients' response in order to maximize the benefit. METHODS: In this study, we analysed the mutation profiles of 33 Chinese mCRC patients using comprehensive next-generation sequencing (NGS) targeting 416 cancer-relevant genes before cetuximab treatment. Upon receiving cetuximab-based therapy, patients were evaluated for drug response, and the progression-free survival (PFS) was monitored. The association of specific genetic alterations and cetuximab efficacy was analyzed. RESULTS: Patients carrying SMAD4 mutations (SMAD4mut, n = 8) or NF1 mutations (NF1mut, n = 4) had significantly shorter PFS comparing to those carrying wildtype SMAD4 (SMAD4wt, n = 25) (P = 0.0081) or wildtype NF1 (NF1wt, n = 29) (P = 0.0028), respectively. None of the SMAD4mut or NF1mut patients showed response to cetuximab when assessed at 12-week post-treatment. Interestingly, two patients carrying both SMAD4mut and NF1mut showed the shortest PFS among all the patients. CONCLUSIONS: Our results demonstrated that SMAD4 and NF1 mutations can serve as potential biomarkers for poor prognosis to cetuximab-based therapy in Chinese mCRC patients.

Compressing recognition network of cotton disease with spot-adaptive knowledge distillation.

Deep networks play a crucial role in the recognition of agricultural diseases. However, these networks often come with numerous parameters and large sizes, posing a challenge for direct deployment on resource-limited edge computing devices for plant protection robots. To tackle this challenge for recognizing cotton diseases on the edge device, we adopt knowledge distillation to compress the big networks, aiming to reduce the number of parameters and the computational complexity of the networks. In order to get excellent performance, we conduct combined comparison experiments from three aspects: teacher network, student network and distillation algorithm. The teacher networks contain three classical convolutional neural networks, while the student networks include six lightweight networks in two categories of homogeneous and heterogeneous structures. In addition, we investigate nine distillation algorithms using spot-adaptive strategy. The results demonstrate that the combination of DenseNet40 as the teacher and ShuffleNetV2 as the student show best performance when using NST algorithm, yielding a recognition accuracy of 90.59% and reducing FLOPs from 0.29 G to 0.045 G. The proposed method can facilitate the lightweighting of the model for recognizing cotton diseases while maintaining high recognition accuracy and offer a practical solution for deploying deep models on edge computing devices.

Association of follow-up neutrophil-to-lymphocyte ratio and systemic inflammation response index with stroke-associated pneumonia and functional outcomes in cerebral hemorrhage patients: a case-controlled study.

BACKGROUND: The neutrophil-to-lymphocyte ratio (NLR) and systemic inflammation response index (SIRI) at admission are independent diagnostic biomarkers in stroke-associated pneumonia (SAP). Our study aimed to investigate the association between NLR, SIRI, specifically follow-up NLR and SIRI, and SAP, as well as their relationship with functional outcomes. PATIENTS AND METHODS: We retrospectively included 451 consecutive intracerebral hemorrhage patients from May 2017 to May 2019. We conducted univariate and multivariable analyses to identify the factors independently associated with SAP and poor functional outcomes. RESULTS: Compared to 127 (28.16%) patients diagnosed with SAP, those without SAP had both lower baseline and follow-up NLR and SIRI values ( P <0.001). After adjustments, we found that baseline NLR [OR, 1.039 (95% CI, 1.003-1.077); P =0.036] and follow-up NLR [OR, 1.054 (95% CI, 1.011-1.098); P =0.012] were independently associated with SAP. The follow-up NLR was also associated with a higher mRS [OR, 1.124 (95% CI, 1.025-1.233); P =0.013] and lower ADL-MBI score [OR, 1.167 (95% CI, 1.057-1.289); P =0.002] at discharge. Multivariable analysis indicated that advanced age and nasogastric tube feeding were independently associated with SAP ( P <0.05). We constructed a dynamic nomogram to identify SAP risk. Further subgroup analysis revealed that baseline NLR [OR, 1.062 (95% CI, 1.007-1.120); P =0.026] is independently associated with SAP in the nasogastric feeding group, while follow-up NLR [OR, 1.080 (95% CI, 1.024-1.139); P =0.005] was associated with the occurrence of SAP in non-nasogastric feeding patients. CONCLUSIONS: We found elevated baseline and follow-up NLR values were associated with SAP occurrence, and increasing follow-up NLR indicated poor functional outcomes. Inflammatory markers at different stages may offer individualized guidance for patients receiving various treatments.

An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage.

OBJECTIVE: Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it. METHODS: This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram. RESULTS: Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically. CONCLUSIONS: Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH.

Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction.

OBJECTIVES: To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE. METHODS: We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix. RESULTS: AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05). CONCLUSION: The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions. ADVANCES IN KNOWLEDGE: This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.

Modulation of neural gene networks by estradiol in old rhesus macaque females.

The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent subcutaneous bioidentical E2 chronic treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p = 1.6 × 10-51) and upregulation (p = 3.8 × 10-3) of UBE2M across both brain regions provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (p = 1.9 × 10-4; interaction p = 3.5 × 10-2) of LTBR in the PFC provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2's potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step toward understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.

Correction: Regua et al. TrkA Interacts with and Phosphorylates STAT3 to Enhance Gene Transcription and Promote Breast Cancer Stem Cells in Triple-Negative and HER2-Enriched Breast Cancers. Cancers 2021, 13, 2340.

In the original publication [...].

Multidimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early colorectal cancer detection and broad patient benefit. Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128.

Modulation of neural gene networks by estradiol in old rhesus macaque females.

The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent E2 treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p=1.6×10-51) and upregulation (p=3.8×10-3) of UBE2M across both brain regions, provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (p=1.9×10-4; interaction p=3.5×10-2) of LTBR in the PFC, provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2's potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step towards understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.