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.

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.

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.

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.

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study.

Background: Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. Methods: We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. Results: The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. Conclusions: The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

Circular RNA-mediated ceRNA network was identified in human lung adenocarcinoma by high-throughput sequencing.

OBJECTIVES: Aberrantly expressed circular RNAs (circRNAs) have been detected in many types of tumors. Hence, they are currently investigated as candidate biomarkers for diagnostic and potential targets for therapy in cancers. The objective of this study was to assess the expression profile of circRNA in lung adenocarcinoma (LUAD). METHODS: This study included 14 pairs of postoperative lung adenocarcinoma specimens, including cancer tissues and matched adjacent tissues. Second-generation sequencing was applied to the specimens to determine the circRNA expression in them among the 5242 distinct circRNAs detected. RESULTS: We identified a total of 18 significantly dysregulated circRNAs in the LUAD tissues: upregulation in four and downregulation in 14. ROC (The receiver operating characteristic curve) further suggested that hsa_circ_0120106, has_circ_0007342, has_circ_0005937, and circRNA_0000826 could potentially be used as biomarkers in the diagnosis of LUAD. Furthermore, study of the circRNA-microRNA (miRNA)-messenger RNA (mRNA) revealed interactions between the 18 dysregulated circRNA and several cancer-related miRNAs. Finally, a further Kyoto Encyclopedia of Genes and Genomes analysis showed that the cell cycle phase transition, p53 signaling pathway, AMP-activated protein kinase (AMPK) relative signaling pathway, and so on were key putative pathways in the process of LUAD. CONCLUSIONS: These findings demonstrated the correlation between abnormality in circRNA expression and LUAD, which lays the foundation of making CircRNAs candidate biomarkers in the diagnosis of LUAD.

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.

Genomic and immune characteristics of HER2-mutated non-small-cell lung cancer and response to immune checkpoint inhibitor-based therapy.

The efficacy of immunotherapy in advanced HER2-mutated non-small-cell lung cancer (NSCLC) remains incomprehensively studied. A total of 107 NSCLC patients with de novo HER2 mutations were retrospectively studied at Guangdong Lung Cancer Institute [GLCI cohort, exon 20 insertions (ex20ins): 71.0%] to compare clinical/molecular features and immune checkpoint inhibitor (ICI)-based therapy efficacy between patients with ex20ins and non-ex20ins. Two external cohorts (TCGA, n = 21; META-ICI, n = 30) were used for validation. In the GLCI cohort, 68.2% of patients displayed programmed death-ligand 1 (PD-L1) expression < 1%. Compared with ex20ins patients, non-ex20ins patients had more concurrent mutations in the GLCI cohort (P < 0.01) and a higher tumour mutation burden in the TCGA cohort (P = 0.03). Under ICI-based therapy, advanced NSCLC patients with non-ex20ins had potentially superior progression-free survival [median: 13.0 vs. 3.6 months, adjusted hazard ratio (HR): 0.31, 95% confidence interval (CI): 0.11-0.83] and overall survival (median: 27.5 vs. 8.1 months, adjusted HR: 0.39, 95% CI: 0.13-1.18) to ex20ins patients, consistent with findings in the META-ICI cohort. ICI-based therapy may serve as an option for advanced HER2-mutated NSCLC, with potentially better efficacy in non-ex20ins patients. Further investigations are warranted in clinical practice.

Impact of Homologous Recombination Deficiency on Outcomes in Patients With Triple-Negative Breast Cancer Treated With Carboplatin-Based Neoadjuvant Chemotherapy: Secondary Analysis of the NeoCART Randomized Clinical Trial.

PURPOSE: Pathologic complete response (pCR) rates of patients with triple-negative breast cancer who were administered docetaxel plus carboplatin were significantly higher than those of patients administered epirubicin/cyclophosphamide followed by docetaxel in the neoadjuvant NeoCART trial. Here, we performed a preplanned secondary analysis of the homologous recombination deficiency (HRD) score as a predictor of the pCR in patients with triple-negative breast cancer from the NeoCART cohort. METHODS: Pretherapeutic tumor tissues were assessed retrospectively by DNA extraction and sequencing. BRCA1/2 mutations were evaluated in both somatic and germline forms. HRD scores were calculated from genome-wide allele-specific copy number results and comprised telomeric allelic imbalance, loss of heterozygosity, and large-scale state transitions. High HRD scores were defined as ≥ 38, and HRD was defined as either a high HRD score or a deleterious BRCA1/2 mutation. RESULTS: HRD testing was completed for 43 (79.6%) of 54 NeoCART cohort patients. Thirty of 43 (69.8%) tumors had high HRD scores, and eight patients had BRCA-mutated tumors. No significant association between BRCA1/2 mutation status and pCR was observed either in the general population or in the two treatment arms. Docetaxel plus carboplatin group patients who achieved pCR had higher HRD scores than non-pCR patients, and this difference approached significance (61.69 ± 24.26 v 39.44 ± 22.83, P = .061). No significant correlations between HRD scores and pCR (61.29 ± 24.02 v 53.21 ± 24.31, P = .480) or residual cancer burden 0/1 (62.50 ± 22.50 v 51.85 ± 24.74, P = .324) were observed in the epirubicin/cyclophosphamide followed by docetaxel group. CONCLUSION: HRD is a potential predictive biomarker for clinical benefit from neoadjuvant carboplatin-based chemotherapy and provides a possibility for screening the optimum chemotherapy backbone to combine with immunotherapy.

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.

An FDA-Approved Antifungal, Ketoconazole, and Its Novel Derivative Suppress tGLI1-Mediated Breast Cancer Brain Metastasis by Inhibiting the DNA-Binding Activity of Brain Metastasis-Promoting Transcription Factor tGLI1.

The goal of this study is to identify pharmacological inhibitors that target a recently identified novel mediator of breast cancer brain metastasis (BCBM), truncated glioma-associated oncogene homolog 1 (tGLI1). Inhibitors of tGLI1 are not yet available. To identify compounds that selectively kill tGLI1-expressing breast cancer, we screened 1527 compounds using two sets of isogenic breast cancer and brain-tropic breast cancer cell lines engineered to stably express the control, GLI1, or tGLI1 vector, and identified the FDA-approved antifungal ketoconazole (KCZ) to selectively target tGLI1-positive breast cancer cells and breast cancer stem cells, but not tGLI1-negative breast cancer and normal cells. KCZ's effects are dependent on tGLI1. Two experimental mouse metastasis studies have demonstrated that systemic KCZ administration prevented the preferential brain metastasis of tGLI1-positive breast cancer and suppressed the progression of established tGLI1-positive BCBM without liver toxicities. We further developed six KCZ derivatives, two of which (KCZ-5 and KCZ-7) retained tGLI1-selectivity in vitro. KCZ-7 exhibited higher blood-brain barrier penetration than KCZ/KCZ-5 and more effectively reduced the BCBM frequency. In contrast, itraconazole, another FDA-approved antifungal, failed to suppress BCBM. The mechanistic studies suggest that KCZ and KCZ-7 inhibit tGLI1's ability to bind to DNA, activate its target stemness genes Nanog and OCT4, and promote tumor proliferation and angiogenesis. Our study establishes the rationale for using KCZ and KCZ-7 for treating and preventing BCBM and identifies their mechanism of action.

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.

Characteristics of and Treatment Strategies for Advanced EGFR-Mutant NSCLC With Concomitant BRAF Variations.

Introduction: BRAF variants were reported resistant mechanisms to EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutant NSCLC. Nevertheless, characteristics and subsequent treatment strategies of such patients remain unclear. Methods: From October 2016 to May 2020, patients with advanced NSCLC for whom next-generation sequencing detected mutations of both BRAF and EGFR were retrospectively included. From June 2020 to January 2021, patients with EGFR-mutant NSCLC who acquired the BRAF V600E mutation after progression on osimertinib were prospectively enrolled to explore the efficacy and safety of EGFR plus BARF co-inhibition. Results: A total of 58 patients were retrospectively identified and five prospectively included. BRAF variants were acquired after a median time of 22.7 months from initial diagnosis. The frequency of variations in TP53, PIK3CA, RB1, MET, LRP1B, APC, CDKN2A, MYC, ERBB2, and SMAD4 was all more than 10%; these mutations affected the cell cycle or p53 pathway and the EGFR downstream and bypass pathways. The median progression-free survival was 5.0 months for patients on chemotherapy and 2.1 months for those on TKIs not targeting both of EGFR and BRAF (p = 0.019). The median PFS was 7.8 months in five patients who received EGFR plus BRAF co-inhibitory drugs. RAS signaling was activated on disease progression. Conclusions: Variations in the EGFR downstream and bypass pathways were frequent in patients with dual mutations of EGFR and BRAF. The efficacies of TKIs not targeting both EGFR and BRAF were inferior to chemotherapy. EGFR plus BRAF co-inhibition improved efficacy. Such treatment strategies should be further explored.

Breast cancer extracellular vesicles-derived miR-1290 activates astrocytes in the brain metastatic microenvironment via the FOXA2→CNTF axis to promote progression of brain metastases.

Mechanisms underlying breast cancer brain metastasis (BCBM) are still unclear. In this study, we observed that extracellular vesicles (EVs) secreted from breast cancer cells with increased expression of tGLI1, a BCBM-promoting transcription factor, strongly activated astrocytes. EV-derived microRNA/miRNA microarray revealed tGLI1-positive breast cancer cells highly secreted miR-1290 and miR-1246 encapsulated in EVs. Genetic knockin/knockout studies established a direct link between tGLI1 and both miRNAs. Datamining and analysis of patient samples revealed that BCBM patients had more circulating EV-miRs-1290/1246 than those without metastasis. Ectopic expression of miR-1290 or miR-1246 strongly activated astrocytes whereas their inhibitors abrogated the effect. Conditioned media from miR-1290- or miR-1246-overexpressing astrocytes promoted mammospheres. Furthermore, miRs-1290/1246 suppressed expression of FOXA2 transcription repressor, leading to CNTF cytokine secretion and subsequent activation of astrocytes. Finally, we conducted a mouse study to demonstrate that astrocytes overexpressing miR-1290, but not miR-1246, enhanced intracranial colonization and growth of breast cancer cells. Collectively, our findings demonstrate, for the first time, that breast cancer EV-derived miR-1290 and miR-1246 activate astrocytes in the brain metastatic microenvironment and that EV-derived miR-1290 promotes progression of brain metastases through the novel EV-miR-1290→FOXA2→CNTF signaling axis.

A Genomic Signature Reflecting Fibroblast Infiltration Into Gastric Cancer Is Associated With Prognosis and Treatment Outcomes of Immune Checkpoint Inhibitors.

Background: The immunotherapy efficacy in gastric cancer (GC) is limited. Cancer-associated fibroblasts (CAFs) induce primary resistance to immunotherapy. However, CAF infiltration in tumors is difficult to evaluate due to the lack of validated and standardized quantified methods. This study aimed to investigate the impact of infiltrating CAFs alternatively using fibroblast-associated mutation scoring (FAMscore). Methods: In a GC cohort from Affiliated Hospital of Jiangsu University (AHJU), whole exon sequencing of genomic mutations, whole transcriptome sequencing of mRNA expression profiles, and immunofluorescence staining of tumor-infiltrating immune cells were performed. GC data from The Cancer Genome Atlas were used to identify genetic mutations which were associated with overall survival (OS) and impacted infiltrating CAF abundance determined by transcriptome-based estimation. FAMscore was then constructed through a least absolute shrinkage and selection operator Cox regression model and further validated in AHJU. The predictive role of FAMscore for immunotherapy outcomes was tested in 1 GC, one melanoma, and two non-small-cell lung cancer (NSCLC-1 and -2) cohorts wherein participants were treated by immune checkpoint inhibitors. Results: FAMscore was calculated based on a mutation signature consisting of 16 genes. In both TCGA and AHJU, a high FAMscore was an independent predictor for poor OS of GC patients. FAMscore was associated with immune-associated genome biomarkers, immune cell infiltration, and signaling pathways of abnormal immunity. Importantly, patients with high FAMscore presented inferiority in the objective response rate of immunotherapy compared to those with low FAMscore, with 14.6% vs. 66.7% (p<0.001) in GC, 19.6% vs. 68.2% (p<0.001) in NSCLC-1, 23.1% vs 75% (p = 0.007) in NSCLC-2, and 40.9% vs 75% (p = 0.037) in melanoma. For available survival data, a high FAMscore was also an independent predictor of poor progression-free survival in NSCLC-1 (HR = 2.55, 95% CI: 1.16-5.62, p = 0.02) and NSCLC-2 (HR = 5.0, 95% CI: 1.13-22.19, p = 0.034) and poor OS in melanoma (HR = 3.48, 95% CI: 1.27-9.55, p = 0.015). Conclusions: Alternative evaluation of CAF infiltration in GC by determining the FAMscore could independently predict prognosis and immunotherapy outcomes. The FAMscore may be used to optimize patient selection for immunotherapy.

NEDD4 degrades TUSC2 to promote glioblastoma progression.

Whether tumor suppressor candidate 2 (TUSC2) plays an important role in glioblastoma (GBM) progression is largely unknown. Whether TUSC2 undergoes polyubiquitination is unknown. Herein, we report that TUSC2 protein expression is reduced/lost in GBM compared to normal brain due to protein destabilization; TUSC2 mRNA is equally expressed in both tissues. NEDD4 E3 ubiquitin ligase polyubiquitinates TUSC2 at residue K71, and the TUSC2-K71R mutant is resistant to NEDD4-mediated proteasomal degradation. Analysis of GBM specimens showed NEDD4 protein is highly expressed in GBM and the level is inversely correlated with TUSC2 protein levels. Furthermore, TUSC2 restoration induces apoptosis and inhibits patient-derived glioma stem cells (PD-GSCs) in vitro and in vivo. Conversely, TUSC2-knockout promotes PD-GSCs in vitro and in vivo. RNA-Seq analysis and subsequent validations showed GBM cells with TUSC2-knockout expressed increased Bcl-xL and were more resistant to apoptosis induced by a Bcl-xL-specific BH3 mimetic. A TUSC2-knockout gene signature created from the RNA-seq data predicts poor patient survival. Together, these findings establish that NEDD4-mediated polyubiquitination is a novel mechanism for TUSC2 degradation in GBM and that TUSC2 loss promotes GBM progression in part through Bcl-xL upregulation.

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.

Genetic Profiling of Cell-Free DNA From Pleural Effusion in Advanced Lung Cancer as a Surrogate for Tumor Tissue and Revealed Additional Clinical Actionable Targets.

BACKGROUND: Pleural effusion (PE) has been one of the promising sources of liquid biopsy in advanced lung cancer patients. However, its clinical utility is not widely accepted due to the lack of full estimation of its potential versus routine clinical samples. METHOD: A total of 164 advanced lung cancer patients were enrolled with 164 matched tumor tissue and PE-cfDNA, 153 accompanied plasma and 63 1PE-sDNA. RESULT: PE-cfDNA displayed significantly higher median mutant allele frequency and an overall mutation concordance rate of 65% to tissue, which was higher than PE-sDNA (43%) and plasma-cfDNA (43%). The discrepancies between PE-cfDNA and tumor tissue were high in several genes, including SMARCA4, PIK3CA, ERBB2, KM T2A, ALK and NF1. For clinically actionable mutations, the concordance rate between PE-cfDNA and tumor tissue is 87%. Eleven patients were identified with actionable mutations in PE-cfDNA and four patients benefited from PE-cfDNA-guided targeted. Meanwhile, PE-cfDNA recapitulated mutations of diverse tissue origins and provided more mutational information under the circumstance that tumor tissue or tumor tissue of different origins were unavailable. The combination of tumor tissue and PE-cfDNA profiling increased positive detection rates of patients compared to tumor tissue alone. Our finding highlighted the importance of PE-cfDNA in the optimal selection of patients for targeted therapy. CONCLUSION: The PE-cfDNA-based liquid biopsy displays better performance in the characterization of gene alterations than PE-sDNA and plasma-cfDNA. PE-cfDNA together with tumor tissue profiling optimizes comprehensively genomic profiling of lung cancer patients, which might be important for selecting patients for better treatment management.