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  • 1
    Online Resource
    Online Resource
    Dual-drug codelivery nanosystems: An emerging approach for overcoming cancer multidrug resistance
    Elsevier BV ; 2023
    In:  Biomedicine & Pharmacotherapy Vol. 161 ( 2023-05), p. 114505-
    Details
    In: Biomedicine & Pharmacotherapy, Elsevier BV, Vol. 161 ( 2023-05), p. 114505-
    Type of Medium: Online Resource
    ISSN: 0753-3322
    URL: Article
    DOI: 10.1016/j.biopha.2023.114505
    RVK:
    XA 10000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1501510-5
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  • 2
    Online Resource
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    Prevalence of nonsuicidal self-injury and its risk and protective factors among adolescents in Taiwan
    Elsevier BV ; 2017
    In:  Psychiatry Research Vol. 255 ( 2017-09), p. 119-127
    Details
    In: Psychiatry Research, Elsevier BV, Vol. 255 ( 2017-09), p. 119-127
    Type of Medium: Online Resource
    ISSN: 0165-1781
    URL: Article
    DOI: 10.1016/j.psychres.2017.05.028
    RVK:
    XA 10000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
    detail.hit.zdb_id: 1500675-X
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  • 3
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    Noninvasive detection of chromosomal instability in plasma circulating cell-free DNA for early pan-cancer diagnosis using low-pass whole-genome sequencing.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. e22509-e22509
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. e22509-e22509
    Abstract: e22509 Background: Genomic instability is a typical characteristic of the majority of cancers. Early non-invasive detection of cancer is the most effective way of improving the success of treatment and prognosis at present. Traditional tumor screening methods have limitations in terms of selection methods, sensitivity, specificity, cost, and comfortability. Furthermore, although traditional tumor screening is useful for common cancers, there is no available screening test for rare cancers. Here we developed a novel method for cancer detection with Low-Pass Whole Genome Sequencing (WGS) of cell-free DNA (cfDNA). Methods: The cfDNA samples from 52 healthy donors were first used to establish a blacklist of bins. Then the baseline was established to calculate the chromosomal instability score (CINscore). We optimized the parameters of our model using the following discovery datasets: healthy controls (n = 50), breast invasive carcinoma (BRCA) (n = 44), ovarian serous cystadenocarcinoma (OV) (n = 25), colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COREAD) (n = 52), hepatocellular carcinoma (HCC) (n = 43), Gastric adenocarcinoma (GAC) (n = 31); pancreatic adenocarcinoma (PAC) (n = 38), non-small cell lung cancer (NSCLC) (n = 45). Results: We further evaluated the performance of our method using the confirmation datasets of healthy controls (n = 30), BRCA (n = 20), prostate adenocarcinoma (PRAD) (n = 6), OV (n = 7), head and Neck squamous cell carcinoma (HNSC) (n = 10), Uterine Corpus Endometrial Carcinoma (UCEC) (n = 6), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (n = 5), COREAD (n = 75), HCC (n = 50), GAC (n = 31), cholangiocarcinoma (CHOL) (n = 8), PAC (n = 10), NSCLC (n = 98), esophageal carcinoma (ESCA) (n = 10), and nasopharyngeal carcinoma (NAC) (n = 4). Overall, the area under the curve (AUC) for pan-cancer is 88.7%, and for BRCA, COREAD, HCC, NSCLC, and GAC are 91.7%, 90.0%, 94.5%, 83.4%, and 88.6%, respectively. Moreover, our approach achieved good performance on the the early stage samples of pan-cancer (AUC = 84.3%) as well as COREAD (AUC = 89.3%) and NSCLC (AUC = 79.0%). Conclusions: Collectively, we show that the CINscore inferred from low-pass WGS could be applied in early non-invasive detection of different cancers with high accuracy. Our approach may aid the improvement of the cancer diagnosis.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.e22509
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
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  • 4
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    CCL18 signaling from tumor-associated macrophages activates fibroblasts to adopt a chemoresistance-inducing phenotype
    Springer Science and Business Media LLC ; 2023
    In:  Oncogene Vol. 42, No. 3 ( 2023-01-12), p. 224-237
    Details
    In: Oncogene, Springer Science and Business Media LLC, Vol. 42, No. 3 ( 2023-01-12), p. 224-237
    Abstract: The heterogeneity of cancer-associated fibroblasts (CAFs) might be ascribed to differences in origin. CD10 and GPR77 have been reported to identify a chemoresistance-inducing CAF subset in breast cancer. However, the precise mechanism for the formation of the CD10 + GPR77 + CAFs remains unknown. In this study, we found that CCL18 expression was positively correlated with the density of CD10 + GPR77 + CAFs in breast cancer and associated with a poor response to chemotherapy. Moreover, CCL18 secreted by tumor-associated macrophages (TAMs) activated a CD10 + GPR77 + CAF phenotype in normal breast-resident fibroblasts (NBFs), which could then enrich cancer stem cells (CSCs) and induce chemoresistance in breast cancer cells. Mechanistically, CCL18 activated NF-κB signaling via PITPNM3 and thus enhanced the production of IL-6 and IL-8. Furthermore, intratumoral CCL18 injection significantly induced the activation of NBFs and the chemoresistance of xenografts in vivo. In addition, targeting CCL18 by anti-CCL18 antibody could inhibit the formation of CD10 + GPR77 + CAFs and recover the chemosensitivity in vivo, leading to effective tumor control. Collectively, these findings reveal that inflammatory signaling crosstalk between TAMs and fibroblasts is responsible for the formation of the CD10 + GPR77 + CAFs, suggesting CCL18–PITPNM3 signaling is a potential therapeutic target to block the activation of this specific CAF subtype and tumor chemoresistance.
    Type of Medium: Online Resource
    ISSN: 0950-9232 , 1476-5594
    URL: Article
    DOI: 10.1038/s41388-022-02540-2
    RVK:
    XA 10000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2008404-3
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  • 5
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    A unique molecular identifier-based and clonal hematopoiesis-aware approach for accurate mutation calling in cell-free DNA assays.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. e22515-e22515
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. e22515-e22515
    Abstract: e22515 Background: Given peripheral blood cells (PBCs) matched cell-free DNA (cfDNA), accurate mutation calling in next generation sequencing (NGS)-based assays relies on discriminating artifacts and clonal hematopoiesis mutations from tissue originated somatic mutations. Although clonal hematopoiesis has been considered in previous overall error modeling, it has not been adapted to PBCs without using unique molecular identifiers (UMIs). Moreover, previous studies on background error profiling were mainly based on healthy controls without matched PBC gDNA, which may lead to potential overestimation of the error rates on those sites with clonal hematopoiesis mutations. Additionally, the fraction of tissue cells is also an important influencing factor but is usually ignored. Methods: We performed UMI-assisted capture-based DNA assays on cfDNA samples, matched PBCs, and oral epithelium cells from 150 healthy donors. A site-specific and subtype-specific background error model was first built for PBCs using the SNVs called from PBCs with matched oral epithelium cells to exclude potential clonal hematopoiesis mutations. Then a similar background model was established for cfDNA with the SNVs inferred from cfDNA to exclude clonal hematopoiesis. The SNVs identified in cfDNA and matched PBCs were separately filtered with the cfDNA and PBC background error models. In this study, we used the ultrasensitive liquid biopsy approach to evaluate paired with tissue and blood samples from 56 early-stage NSCLC patients. All samples are sequenced using NGS target-capture panels covering 29 genes. Results: The mutations were detected in 91.1% of tissue and 67.9% were discovered in plasma. The coincidence rate between tissue and plasma of the 56 early-stage NSCLC patients was 66.1%. Conclusions: We have developed a novel method tailored for UMI-assisted capture-based targeted DNA assays, which outperforms currently available methods in terms of modeling background errors and filtering clonal hematopoietic mutations.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.e22515
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
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  • 6
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    Accurately estimating the homologous recombination deficiency based on low-pass whole-genome sequencing and target sequencing.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. e15077-e15077
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. e15077-e15077
    Abstract: e15077 Background: Homologous recombination deficiency (HRD) is a promising biomarker for poly ADP-ribose polymerase (PARP) inhibitors. In the past, the HRD status was mainly estimated by summing the loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transition (LST) based on target sequencing. However, the heterozygous loci coverage and probe number are closely correlated to B allele frequency and copy number variations (CNVs). Here we developed a novel method to combine the data from low-pass whole-genome sequencing (WGS) and target sequencing to accurately estimate the genome-wide CNVs and allelic imbalance. Methods: We first performed low-pass WGS and target sequencing on 30 healthy controls, formalin-fixed and paraffin-embedded (FFPE) samples, and matched blood cell samples for 29 patients with breast cancer or ovarian cancer. Two HRD positive samples were diluted to 40%, 30%, 20%, and 10% tumor purity with matched blood cell samples, respectively. Each diluted sample was sequenced 20 times. The baseline for CNVs was built based on the sequencing data of healthy donors. To eliminate the artifacts resulted from the sequencing platform, models for each candidate single nucleotide polymorphism (SNP) in target regions were trained using the data from healthy donors. The mutations of BRCA1/BRCA2 of 29 patients were used to define the threshold of the HRD score. The numbers of LOH, LST, and TAI were evaluated by CNVs and B allele frequency. Results: We defined a cutoff of 42 for the HRD status based on the low-pass WGS and target sequencing data of 29 patients. Specifically, the original HRD scores for two selected HRD positive samples were 68 and 89. The median HRD scores for 40%, 30%, 20%, and 10% tumor purity samples were 69 and 88, 66.5 and 90, 61 and 68, 52 and 34.5, respectively. Conclusions: We developed a novel method for robustly inferring the HRD status using low-pass WGS and target sequencing with a limit of detection of 20%.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.e15077
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
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  • 7
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    Online Resource
    DNA methylation profile in unknown cancer origin identification: A multi-class classification model.
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 15_suppl ( 2020-05-20), p. e13554-e13554
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e13554-e13554
    Abstract: e13554 Background: Approximately 3-5% of human cancers are cancer of unknown primary (CUP). Treatment of a cancer patient is largely dependent on the tumor origin. Therefore, identification of the tumor origin can improve the survival of patients with CUP. We developed a multi-class classification model using DNA methylation profile as biomarker to determine the primary site of CUP. Methods: We split 7,082 primary tumor samples of 19 cancers and 679 normal samples of 15 tissues from TCGA into a 75% training set and a 25% testing set to develop the classification model. We started with multiple support vector machine (SVM) models, and then combined them into an optimal multi-class ensemble model. Predictors included tumor-specific markers and tissue-specific markers, which were filtered by comparing between groups. Only the training samples were used for feature selection and model development. A validation dataset consisting of 150 primary tissues, 54 metastasis tissues, 105 plasma samples with known cancer site origins from 12 classes was generated in house by a self-designed panel. Performance was measured by area under the curve (AUC) using the one-vs-all approach. Results: 7,453 tumor-specific and 1,533 tissue-specific markers were selected for model construction. AUCs of all cancer types were high in TCGA training and testing set (AUC≥0.96 for all classes). In our validation tissues, esophageal cancer, pancreatic cancer, colorectal cancer, lung adenocarcinoma, breast cancer and liver cancer achieved high AUC in both primary (0.83, 0.83, 0.82, 0.82, 0.80 and 0.79 respectively) and metastasis (0.74, 0.92, 0.86, 0.61, 0.92 and 0.65 respectively). Lung adenocarcinoma, colorectal cancer, liver cancer, breast cancer and esophageal cancer even achieved high AUC in the plasmas. Conclusions: Performance of our model in tissue and plasma samples indicated the potential clinical application of DNA methylation profile in unknown cancer origin identification.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2020.38.15_suppl.e13554
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 8
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    An ultrasensitive method for noninvasive pan-cancer early detection based on targeted methylation sequencing of cell-free DNA.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. 10544-10544
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. 10544-10544
    Abstract: 10544 Background: Screening the biomarkers from the cell-free DNA (cfDNA) of peripheral blood is a non-invasive and promising method for cancer diagnosis. Among diverse types of biomarkers, epigenetic biomarkers have been reported to be one of the most promising ones. Epigenetic modifications are widespread on the human genome and generally have strong signals due to the similar methylation patterns shared by adjacent CpG sites. Although some epigenetic diagnostic methods have been developed based on cfDNAs, few of them could be applied to pan-cancer and their sensitivities are barely satisfactory for early cancer detection. Methods: Targeted methylation sequencing was performed using our in-house-designed panel targeting regions with abundant cancer-specific methylation CpGs. The cfDNA samples from 80 healthy individuals and 549 cancer patients of 14 cancer types were separately sequenced. The dataset was randomly split into one discovery dataset and one validation dataset. Moreover, cfDNA samples from four cancer patients were diluted with the healthy cfDNAs to generate 12 in vitro simulated samples with low circulating tumor DNA (ctDNA) fraction. Additionally, DNAs extracted from 130 unmatched tumor formalin fixation and paraffin embedding (FFPE) samples of 10 cancer types were sequenced to screen the diagnostic biomarkers. Adjacent CpG sites were first merged into methylation-correlated blocks (MCB) according to their correlations of methylation levels in tumor DNAs. The MCBs with higher methylation levels in tumor DNAs than that of healthy cfDNAs (from the discovery dataset) were defined as our hypermethylation biomarkers. For each cfDNA sample, a hypermethylation score (HM-score) was computed to measure the overall methylation level difference of selected biomarkers. The performance of our method was evaluated with the real-world dataset, while the limit of detection was estimated using the simulated low-ctDNA samples. Results: Our model based on 37 hypermethylation MCB biomarkers achieved an area under the curve (AUC) of 0.89 and 0.86 in the real-world pan-cancer discovery and validation cfDNA datasets, respectively. Furthermore, the overall specificity and sensitivity are 100% and 76.19% in the discovery dataset, and 96.67% and 72.86% in the validation dataset. In the validation dataset, 28/40 (70%) of early-stage colorectal cancer patients and 10/20 (50%) of non-small-cell lung cancer patients were successfully diagnosed. Additionally, all the simulated samples with theoretical ctDNA factions over 0.5% were predicted as diseased, demonstrating the ability of our method to detect tumor signals at early stages. Conclusions: Our cfDNA-based epigenetic method outperforms currently available methods in various cancer types, and is promising to be applied to early-stage cancer detection and samples with low ctDNA fractions.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.10544
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
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  • 9
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    Online Resource
    Early cancer detection using low-coverage whole-genome sequencing of cell-free DNA fragments.
    American Society of Clinical Oncology (ASCO) ; 2021
    In:  Journal of Clinical Oncology Vol. 39, No. 15_suppl ( 2021-05-20), p. e22510-e22510
    Details
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 39, No. 15_suppl ( 2021-05-20), p. e22510-e22510
    Abstract: e22510 Background: Recent advances in circulating cell-free DNA (cfDNA) of plasma have shown that tumor diagnosis based on tumor-specific genetic and epigenetic changes (e.g., somatic mutations, copy number variations, and DNA methylation) is a promising non-invasive method. However, the number of tumor-specific genomic variants identified by whole-genome sequencing (WGS) in early cancer patients is very limited. Moreover, the mutations generated by clonal hematopoiesis in cfDNA can further confound the detection of cancer-specific mutations. It has been shown that ctDNA and cfDNA fragments have differences in length distribution. Compared with a limited number of genomic mutations, cfDNA fragment size index (FSI) is more abundant and easier to be detected. Methods: We designed a novel method for fragment detection of plasma cfDNA based on low-coverage WGS. The fragment length differences between healthy individuals and tumor patients were systematically analyzed. The training dataset includes 50 healthy individuals and 354 patients from eight different cancers. After the data preprocessing, we calculated the weight of fragmental bins and built a model for distinguishing healthy individuals from cancer patients. An independent dataset involving 22 healthy controls and 340 cancer patients was used to validate the model. The performance of our method was measured by the area under the curve (AUC) using the one-versus-all approach. Results: In our analysis, a total of 504 markers were selected from the dataset for model construction. Our model performed well for all cancer types on both training (AUC = 0.804) and validation (AUC = 0.837) datasets. Conclusions: The good performance of our model in large-scale plasma samples demonstrates the potential clinical application of cfDNA fragment analysis in early cancer detection based on low-coverage WGS.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    URL: Article
    DOI: 10.1200/JCO.2021.39.15_suppl.e22510
    RVK:
    XA 52760
    RVK:
    XA 10000
    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2021
    detail.hit.zdb_id: 2005181-5
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