Noninvasive prediction of axillary lymph node status in breast cancer using promoter profiling of circulating cell-free DNA.

BACKGROUND: Lymph node metastasis (LNM) is one of the most important factors affecting the prognosis of breast cancer. The accurate evaluation of lymph node status is useful to predict the outcomes of patients and guide the choice of cancer treatment. However, there is still lack of a low-cost non-invasive method to assess the status of axillary lymph node (ALN). Gene expression signature has been used to assess lymph node metastasis status of breast cancer. In addition, nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of its original tissues, so it may be used to evaluate the axillary lymph node status in breast cancer. METHODS: In this study, we found that the cfDNA nucleosome footprints between the ALN-positive patients and ALN-negative patients showed different patterns by implementing whole-genome sequencing (WGS) to detect 15 ALN-positive and 15 ALN-negative patients. In order to further evaluate its potential for assessing ALN status, we developed a classifier with multiple machine learning models by using 330 WGS data of cfDNA from 162 ALN-positive and 168 ALN-negative samples to distinguish these two types of patients. RESULTS: We found that the promoter profiling between the ALN-positive patients and ALN-negative patients showed distinct patterns. In addition, we observed 1071 genes with differential promoter coverage and their functions were closely related to tumorigenesis. We found that the predictive classifier based on promoter profiling with a support vector machine model, named PPCNM, produced the largest area under the curve of 0.897 (95% confidence interval 0.86-0.93). CONCLUSIONS: These results indicate that promoter profiling can be used to distinguish ALN-positive patients from ALN-negative patients, which may be helpful to guide the choice of cancer treatment.

Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis.

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.

A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing.

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.

A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer.

BACKGROUND: According to the global cancer burden data released in 2020, breast cancer (BC) has become the most common cancer in the world. Similar to those of other cancers, the present methods used in clinic for diagnosing early BC are invasive, inaccurate, and insensitive. Hence, new non-invasive methods capable of early diagnosis are needed. METHODS: We applied next-generation sequencing and analyzed the messenger RNA (mRNA) profiles of plasma extracellular vesicles (EVs) derived from 14 BC patients and 6 patients with benign breast lesions. We used 3 regression models, namely support vector machine (SVM), linear discriminate analysis (LDA), and logistic regression (LR), to develop classifiers for use in making predictive BC diagnoses; and used 259 plasma samples, including those obtained from 144 patients with BC, 72 patients with benign breast lesions, and 43 healthy women, which were divided into training groups and validation groups to verify their performances as classifiers by quantitative reverse transcription polymerase chain reaction (RT-qPCR). The area under the curve (AUC) and accuracy, sensitivity, and specificity of the classifiers were cross-validated with the leave-1-out cross-validation (LOOCV) method. RESULTS: Among all combinations assessed with the 3 different regression models, an 8-mRNA combination, named EXOBmRNA, exhibited high performance [accuracy =71.9% and AUC =0.718, 95% confidence interval (CI): 0.652 to 0.784] in the training cohort after LOOCV was performed, showing the largest AUC in the SVM model. The mRNAs in EXOBmRNA were HLA-DRB1, HAVCR1, ENPEP, TIMP1, CD36, MARCKS, DAB2, and CXCL14. In the validation cohort, the AUC of EXOBmRNA was 0.737 (95% CI: 0.636 to 0.837). In addition, gene function and pathway analyses revealed that different levels of gene expression were associated with cancer. CONCLUSIONS: We developed a high-performing predictive classifiers including 8 mRNAs from plasma extracellular vesicles for diagnosing breast cancer.

Noninvasive discrimination of benign and malignant breast lesions using genome-wide nucleosome profiles of plasma cell-free DNA.

BACKGROUND: Breast malignancy is the most frequently diagnosed malignancy in women worldwide, and the diagnosis relies on invasive examinations. However, most clinical breast changes in women are benign, and invasive diagnostic approaches cause unnecessary suffering for the patients. Thus, a novel noninvasive approach for discriminating malignant breast lesions from benign lesions is needed. METHODS: We performed cell-free DNA (cfDNA) sequencing on plasma samples from 173 malignant breast lesion patients, 158 benign breast lesion patients, and 102 healthy women. We then analyzed the cfDNA-based nucleosome profiles, which reflect the various tissues of origin and transcription factor activities. Moreover, by using machine learning classifiers along with the cfDNA sequencing data, we built classifiers for discriminating benign from malignant breast lesions. Receiver operating characteristic curve analyses were used to evaluate the performance of the classifiers. RESULTS: cfDNA-based nucleosome profiles reflected the various tissues of origin and transcription factor activities in benign and malignant breast lesions. The cfDNA-based transcription factor activities and breast malignancy-specific transcription factor-binding site accessibility profiles could accurately distinguish benign and malignant breast lesions, with area under the curve values of 0.777 and 0.824, respectively. CONCLUSIONS: Our proof-of-principle study established a methodology for noninvasively discriminating benign from malignant breast lesions.

Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer.

Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

A Genotype Signature for Predicting Pathologic Complete Response in Locally Advanced Rectal Cancer.

PURPOSE: To construct and validate a predicting genotype signature for pathologic complete response (pCR) in locally advanced rectal cancer (PGS-LARC) after neoadjuvant chemoradiation. METHODS AND MATERIALS: Whole exome sequencing was performed in 15 LARC tissues. Mutation sites were selected according to the whole exome sequencing data and literature. Target sequencing was performed in a training cohort (n = 202) to build the PGS-LARC model using regression analysis, and internal (n = 76) and external validation cohorts (n = 69) were used for validating the results. Predictive performance of the PGS-LARC model was compared with clinical factors and between subgroups. The PGS-LARC model comprised 15 genes. RESULTS: The area under the curve (AUC) of the PGS model in the training, internal, and external validation cohorts was 0.776 (0.697-0.849), 0.760 (0.644-0.867), and 0.812 (0.690-0.915), respectively, and demonstrated higher AUC, accuracy, sensitivity, and specificity than cT stage, cN stage, carcinoembryonic antigen level, and CA19-9 level for pCR prediction. The predictive performance of the model was superior to clinical factors in all subgroups. For patients with clinical complete response (cCR), the positive prediction value was 94.7%. CONCLUSIONS: The PGS-LARC is a reliable predictive tool for pCR in patients with LARC and might be helpful to enable nonoperative management strategy in those patients who refuse surgery. It has the potential to guide treatment decisions for patients with different probability of tumor regression after neoadjuvant therapy, especially when combining cCR criteria and PGS-LARC.

Noninvasive inferring expressed genes and in vivo monitoring of the physiology and pathology of pregnancy using cell-free DNA.

BACKGROUND: Noninvasive monitoring of fetal development and the early detection of pregnancy-associated complications is challenging, largely because of the lack of information about the molecular spectrum during pregnancy. Recently, cell-free DNA in plasma was found to reflect the global nucleosome footprint and status of gene expression and showed potential for noninvasive health monitoring during pregnancy. OBJECTIVE: We aimed to test the relationships between plasma cell-free DNA profiles and pregnancy biology and evaluate the use of a cell-free DNA profile as a noninvasive method for physiological and pathologic status monitoring during pregnancy. STUDY DESIGN: We used genome cell-free DNA sequencing data generated from noninvasive prenatal testing in a total of 2937 pregnant women. For each physiological and pathologic condition, features of the cell-free DNA profile were identified using the discovery cohort, and support vector machine classifiers were built and evaluated using independent training and validation cohorts. RESULTS: We established nucleosome occupancy profiles at transcription start sites in different gestational trimesters, demonstrated the relationships between gene expression and cell-free DNA coverage at transcription start sites, and showed that the cell-free DNA profiles at transcription start sites represented the biological processes of pregnancy. In addition, using cell-free DNA data, nucleosome profiles of transcription factor binding sites were identified to reflect the transcription factor footprint, which may help to reveal the molecular mechanisms underlying pregnancy. Finally, by using machine-learning models on low-coverage noninvasive prenatal testing data, we evaluated the use of cell-free DNA nucleosome profiles for distinguishing gestational trimesters, fetal sex, and fetal trisomy 21 and highlighted its potential utility for predicting physiological and pathologic fetal conditions by using low-coverage noninvasive prenatal testing data. CONCLUSION: Our analyses profiled nucleosome footprints and regulatory networks during pregnancy and established a noninvasive proof-of-principle methodology for health monitoring during pregnancy.

Systematic Analyses of the Role of the Reader Protein of N 6-Methyladenosine RNA Methylation, YTH Domain Family 2, in Liver Hepatocellular Carcinoma.

BACKGROUND: YTH domain family (YTHDF) 2 acts as a "reader" protein for RNA methylation, which is important in tumor regulation. However, the effect of YTHDF2 in liver hepatocellular carcinoma (LIHC) has yet to be elucidated. METHODS: We explored the role of YTHDF2 in LIHC based on publicly available datasets [The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO)]. A bioinformatics approach was employed to analyze YTHDF2. Logistic regression analyses were applied to analyze the correlation between YTHDF2 expression and clinical characteristics. To evaluate the effect of YTHDF2 on the prognosis of LIHC patients, we used Kaplan-Meier (K-M) curves. Gene set enrichment analysis (GSEA) was undertaken using TCGA dataset. Univariate and multivariate Cox analyses were used to ascertain the correlations between YTHDF2 expression and clinicopathologic characteristics with survival. Genes co-expressed with YTHDF2 were identified and detected using publicly available datasets [LinkedOmics, University of California, Santa Cruz (UCSC), Gene Expression Profiling Interactive Analysis (GEPIA), and GEO]. Correlations between YTHDF2 and infiltration of immune cells were investigated by Tumor Immune Estimation Resource (TIMER) and GEPIA. RESULTS: mRNA and protein expression of YTHDF2 was significantly higher in LIHC tissues than in non-cancerous tissues. High YTHDF2 expression in LIHC was associated with poor prognostic clinical factors (high stage, grade, and T classification). K-M analyses indicated that high YTHDF2 expression was correlated with an unfavorable prognosis. Univariate and multivariate Cox analyses revealed that YTHDF2 was an independent factor for a poor prognosis in LIHC patients. GSEA revealed that the high-expression phenotype of YTHDF2 was consistent with the molecular pathways implicated in LIHC carcinogenesis. Analyses of receiver operating characteristic curves showed that YTHDF2 might have a diagnostic value in LIHC patients. YTHDF2 expression was associated positively with SF3A3 expression, which implied that they may cooperate in LIHC progression. YTHDF2 expression was associated with infiltration of immune cells and their marker genes. YTHDF2 had the potential to regulate polarization of tumor-associated macrophages, induce T-cell exhaustion, and activate T-regulatory cells. CONCLUSION: YTHDF2 may be a promising biomarker for the diagnosis and prognosis of LIHC and may provide new directions and strategies for LIHC treatment.

Prognostic Value and Potential Role of Alternative mRNA Splicing Events in Cervical Cancer.

BACKGROUND: Increasing evidence suggests that aberrant alternative splicing (AS) events are associated with progression of cancer. This study evaluated the prognostic value and clarify the role of AS events in cervical cancer (CC). METHODS: Based on RNA-seq AS event data and clinical information of CC patients in The Cancer Genome Atlas (TCGA) database, we sought to identify prognosis-related AS events in this setting. We selected several survival-associated AS events to construct a prognostic predictor for CC through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Moreover, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed on genes with prognosis-related AS events and constructed an AS-splicing factors (SFs) regulatory network. RESULTS: 2770 AS events were significantly correlated with overall survival (OS). The area under the curve (AUC) values of receiver-operator characteristic curve (ROC) for the final prognostic predictor were 0.926, 0.946 and 0.902 at 3, 5, and 10 years, respectively. These values indicated efficiency in prognostic risk stratification for patients with CC. The final prognostic predictor was an independent predictor of OS (HR: 1.24; 95% CI: 1.020-1.504; P < 0.05). The AS-SFs correlation network may reveal an underlying regulatory mechanism of AS events. CONCLUSION: AS events are essential participants in the prognosis of CC and hold great potentials for the prognostic stratification and development of treatment strategy.

Detection of chromosomal abnormalities in spontaneous miscarriage by low­coverage next­generation sequencing.

Chromosomal abnormalities (CAs) can cause spontaneous miscarriage and increase the incidence of subsequent pregnancy loss and other complications. Presently, CAs are detected mainly by array comparative genomic hybridization (CGH) and single nucleotide polymorphism microarrays. The present study developed a low­coverage next­generation sequencing method to detect CAs in spontaneous miscarriage and assess its clinical performance. In total, 1,401 patients who had experienced an abortion were enrolled in the present study and divided into two groups. In group I, 437 samples that had been previously validated by array CGH were used to establish a method to detect CAs using a semiconductor sequencing platform. In group II, 964 samples, which were not verified, were assessed using established methods with respect to clinical significance. Copy number variant (CNV)­positive and euploidy samples were verified by array CGH and short tandem repeat profiling, respectively, based on quantitative fluorescent PCR. The low­coverage sequencing method detected CNVs >1 Mb in length and a total of 3.5 million unique reads. Similar results to array CGH were obtained in group I, except for six CNVs <1 Mb long. In group II, there were 341 aneuploidies, 195 CNVs, 25 mosaicisms and 403 euploidies. Overall, among the 1,401 abortion samples, there were 536 aneuploidies, 263 CNVs, 34 mosaicisms, and 568 euploidies. Trisomies were present in all autosomal chromosomes. The most common aneuploidies were T16, monosomy X, T22, T15, T21 and T13. Furthermore, one tetrasomy 21, one CNV associated with Wolf­Hirschhorn syndrome, one associated with DiGeorge syndrome and one associated with both Prader­Willi and Angelman syndromes were identified. These four cases were confirmed by short tandem repeat profiling and array CGH. Quantitative fluorescent PCR revealed nine polyploidy samples. The present method demonstrated equivalent efficacy to that of array CGH in detecting CNVs >1 Mb, with advantages of requiring less input DNA and lower cost.

RBPTD: a database of cancer-related RNA-binding proteins in humans.

RNA-binding proteins (RBPs) play important roles in regulating the expression of genes involved in human physiological and pathological processes, especially in cancers. Many RBPs have been found to be dysregulated in cancers; however, there was no tool to incorporate high-throughput data from different dimensions to systematically identify cancer-related RBPs and to explore their causes of abnormality and their potential functions. Therefore, we developed a database named RBPTD to identify cancer-related RBPs in humans and systematically explore their functions and abnormalities by integrating different types of data, including gene expression profiles, prognosis data and DNA copy number variation (CNV), among 28 cancers. We found a total of 454 significantly differentially expressed RBPs, 1970 RBPs with significant prognostic value, and 53 dysregulated RBPs correlated with CNV abnormality. Functions of 26 cancer-related RBPs were explored by analysing high-throughput RNA sequencing data obtained by crosslinking immunoprecipitation, and the remaining RBP functions were predicted by calculating their correlation coefficient with other genes. Finally, we developed the RBPTD for users to explore functions and abnormalities of cancer-related RBPs to improve our understanding of their roles in tumorigenesis. Database URL: http: //www.rbptd.com.

Translated Long Non-Coding Ribonucleic Acid ZFAS1 Promotes Cancer Cell Migration by Elevating Reactive Oxygen Species Production in Hepatocellular Carcinoma.

Micropeptides (≤100 amino acids) are essential regulators of physiological and pathological processes, which can be encoded by small open reading frames (smORFs) derived from long non-coding RNAs (lncRNAs). Recently, lncRNA-encoded micropeptides have been shown to have essential roles in tumorigenesis. Since translated smORF identification remains technically challenging, little is known of their pathological functions in cancer. Therefore, we created classifiers to identify translated smORFs derived from lncRNAs based on ribosome-protected fragment sequencing and machine learning methods. In total, 537 putative translated smORFs were identified and the coding potential of five smORFs was experimentally validated via green fluorescent protein-tagged protein generation and mass spectrometry. After analyzing 11 lncRNA expression profiles of seven cancer types, we identified one validated translated lncRNA, ZFAS1, which was significantly up-regulated in hepatocellular carcinoma (HCC). Functional studies revealed that ZFAS1 can promote cancer cell migration by elevating intracellular reactive oxygen species production by inhibiting nicotinamide adenine dinucleotide dehydrogenase expression, indicating that translated ZFAS1 may be an essential oncogene in the progression of HCC. In this study, we systematically identified translated smORFs derived from lncRNAs and explored their potential pathological functions in cancer to improve our comprehensive understanding of the building blocks of living systems.

SELER: a database of super-enhancer-associated lncRNA- directed transcriptional regulation in human cancers.

Super-enhancers (SEs) are enriched with a cluster of mediator binding sites, which are major contributors to cell-type-specific gene expression. Currently, a large quantity of long non-coding RNAs has been found to be transcribed from or to interact with SEs, which constitute super-enhancer associated long non-coding RNAs (SE-lncRNAs). These SE-lncRNAs play essential roles in transcriptional regulation through controlling SEs activity to regulate a broad range of physiological and pathological processes, especially tumorigenesis. However, the pathological functions of SE-lncRNAs in tumorigenesis are still obscure. In this paper, we characterized 5056 SE-lncRNAs and their associated genes by analysing 102 SE data sets. Then, we analysed their expression profiles and prognostic information derived from 19 cancer types to identify cancer-related SE-lncRNAs and to explore their potential functions. In total, 436 significantly differentially expressed SE-lncRNAs and 2035 SE-lncRNAs with high prognostic values were identified. Additionally, 3935 significant correlations between SE-lncRNAs and their regulatory genes were further validated by calculating their correlation coefficients in each cancer type. Finally, the SELER database incorporating the aforementioned data was provided for users to explore their physiological and pathological functions to comprehensively understand the blocks of living systems.

Dexmedetomidine Attenuates Acute Lung Injury Induced by Heatstroke and Improve Outcome.

INTRODUCTION: Dexmedetomidine (DEX) has been demonstrated to inhibit inflammatory response and protect against multiorgan injury in various scenarios. The objectives of the present study were to ascertain whether DEX is able to attenuate acute lung injury (ALI) under heatstroke (HS), and to explore the underlying mechanism. METHODS: Male C57BL/6 mice were exposed to ambient temperature of 39.5 ±â€Š0.2°C until core temperature reach 43°C. DEX or 0.9% saline was injected i.p. immediately. At the end of the experiment, bronchoalveolar lavage fluid (BALF) and lung tissue were harvested. RESULTS: HS induce ALI and pulmonary dysfunction, while DEX treatment could significantly inhibit lung injury and improve respiratory dysfunction under HS. The overall effect was beneficial and improved the 72 h cumulative survival rate of mice with HS. Furthermore, HS significantly elevated the levels of cytokines in BALF, as well as increased the activity of toll-like receptor 4 (TLR4)/MyD88/nuclear factor-κB (NFκB) signaling pathway in lung tissue, while DEX treatment could inhibit such effects. Finally, DEX could upregulate the expression of caveolin 1 downregulated by HS, which may contribute to the inhibition of TLR4/MyD88/NFκB signaling pathway. DISCUSSION: In conclusion, the present results indicated that DEX may protect against lung inflammatory response and injury under HS via TLR4/MyD88/NFκB signaling pathway, and caveolin-1 may participate in the effects.

Oncogene mutational analysis in Chinese gastrointestinal stromal tumor patients.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors and exhibit a high frequency of oncogenic KIT or PDGFRA mutations. Tyrosine kinase inhibitors (TKIs) have been mainly used in the treatment of GISTs bearing KIT/PDGFRA mutations. However, other mutation profiles have been found to affect the sensitivity to and effectiveness of TKIs in the treatment of GISTs. PURPOSE: The aim of the present study was to describe the mutational status of multiple genes in GIST samples and to provide information for finding potential predictive markers of therapeutic targets in Chinese GIST patients. PATIENTS AND METHODS: MassARRAY spectrometry was used to test 40 Chinese GIST patients for 238 mutations affecting 19 oncogenes. RESULTS: A total of 14 oncogenes with 43 mutations were detected in 38 samples, with a mutation frequency of 95%. Among these mutation samples, 26 GISTs were found for KIT or PDGFRA mutations, while 12 were KIT/PDGFRA wild-type. Approximately half of the GIST samples harbored multiple mutations. The most frequent mutations were found in KIT (62.5%), CDK4 (17.5%), NRAS (15%) and EGFR (12.5%). Other mutations included PIK3CA and AKT1 (10%), BRAF and ABL1 (7.5%), PDGFRA, ERBB2 and HRAS (5%), and AKT2, FLT3 and KRAS (2.5%). New mutated genes (CDK4, AKT2, FLT3, ERBB2, ABL1 and AKT1), a higher BRAF mutation frequency (7.5%) and new BRAF mutation sites (G464E) were found in Chinese GIST patients. CONCLUSION: This study demonstrated useful mutations in a small fraction of Chinese GIST, but targeted therapeutics on these potential predictive markers need to be investigated in depth especially in Oriental populations.

Noninvasive Prenatal Testing of Rare Autosomal Aneuploidies by Semiconductor Sequencing.

Rare autosomal aneuploidies (RAAs) can cause miscarriage or other pregnancy complications and lead to inconsistent results of noninvasive prenatal testing (NIPT), but many NIPT providers have not yet started to provide related services. Our aim was to develop a semiconductor sequencing platform (SSP)-based method for detecting RAAs when pregnant women performed NIPT. Fifty-three aneuploidy samples with verified karyotyping or array comparative genomic hybridization (aCGH) results were collected and subjected to RAAs detection using an SSP to develop a method by genomic sequencing. Various trisomies on all chromosomes other than chromosomes 17 and 19, four multiple aneusomies, one monosomy and five sex chromosome abnormalities were got by our method which can directly identify RAAs via a z-score. Then, artificial mixtures of 10% and 5% DNA were created by adding fragmented fifty-three tissue samples and used in an NIPT simulation to develop a bioinformatics analysis method which can use in NIPT. And the results were in accordance with those of karyotyping and aCGH. Therefore, our method has potential for use in NIPT. Finally, 23,823 clinical plasma samples were tested to verify the performance of our approach. Karyotyping or aCGH was performed on the positive clinical samples. In total, 188 of 23,823 clinical samples were positive (T2, n = 1; T7, n = 1; T13, n = 15; T18, n = 45; T21, n = 125; and multiple aneusomies, n = 1) and verified by karyotyping or aCGH; no sample was a false negative. Several false positives were detected, one of which showed maternal copy number variation (CNV). One case of multiple aneusomies was caused by a maternal tumor. The method developed enables detection of RAAs without increasing costs.

Development of a Competitive Time-Resolved Fluoroimmunoassay for Paclitaxel.

INTRODUCTION: Paclitaxel (Tax) is a diterpene alkaloid isolated from Taxus species and has proved clinically effective in treating a number of malignancies. Current quantitative analytical methods for Tax such as high-performance liquid chromatography (HPLC) often involve complicated sample preparation procedures with low recovery rates. OBJECTIVE: To establish a rapid and sensitive time-resolved fluoroimmunoassay (TRFIA) for measuring Tax in Taxus materials with convenient sample preparation and a high recovery rate. METHODS: Rabbit anti-mouse IgG was coated onto a 96-well microplate, which was then incubated with standard solutions of Tax and anti-Tax monoclonal antibody 3A3. A Eu3+ -labelled conjugate of Tax and human serum albumin was used as the tracer. The luminescent system was enhanced with a solution containing 2-naphthoyltrifluoroacetone. RESULTS: The established TRFIA showed a linear response within the Tax concentration range of 3.2 to 80 ng/mL, with a limit of detection of 1.4 ng/mL. The intra- and inter-assay coefficient of variation of the assay was 9.6% and 9.7%, respectively, with an average recovery rate from spiked samples of 108.5%. Tax contents in Taxus samples were determined using both the established TRFIA system and a previously established enzyme-linked immunosorbent (ELISA), and the results of two assays were well correlated. CONCLUSION: This TRFIA system shows a high sensitivity, precision and accuracy for detection of Tax. This assay, which is convenient and less time-consuming, allows rapid analysis of Tax and provides another option for Tax measurement for quality control of Taxus materials and products. Copyright © 2017 John Wiley & Sons, Ltd.

Development of a high-throughput and sensitive assay of fusion genes in lung cancer by array-based MALDI-TOFMS.

ALK (anaplastic lymphoma kinase gene), ROS1 (ros proto-oncogene 1) and RET (ret proto-oncogene) fusions are oncogenic drivers in non-small cell lung cancer (NSCLC). Methods like fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC) are highly sensitive but subjectively analyzed, labor intensive, expensive and unsuitable for multiple fusion gene screening. This study aimed to establish a high-throughput, sensitive and cost-effective screening method (array-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, array-based MALDI-TOFMS) for ALK, ROS1 and RET fusion detection. This method was established with three fusion gene positive cell lines (H2228, ALK positive; HCC78, ROS1 positive; LC-2/AD, RET positive) and negative samples. Then, 34 clinical samples were selected and detected by Sanger sequencing, next generation sequencing (NGS) and array-based MALDI-TOFMS. The results were compared and analyzed and Sanger sequencing was considered the standard. 7 cases showed ALK fusions, 1 case showed ROS1 fusions, no case showed RET fusions and 4 cases were both ALK and ROS1 fusions. Results showed that array-based MALDI-TOFMS was 100% concordant with Sanger sequencing and NGS 82.3%. In this study, we reported the utility of array-based MALDI-TOFMS in the assessment of ALK, ROS1 and RET fusions in routine lung biopsies of FFPE and fresh tissue specimens. Besides, this method may also be applied to the diagnosis, monitoring and prognosis of illness.