Project description:Due to their role in tumorigenesis and remarkable stability in body fluids, microRNAs (miRNAs) are emerging as a promising diagnostic tool. The aim of this study was to identify tumor miRNA signatures for the discrimination of breast cancer and the intrinsic molecular subtypes, and the study in plasma of the status of the most significant ones in order to identify potential circulating biomarkers for breast cancer detection. MiRNA expression profiling of 1919 human miRNAs was conducted in 122 FFPE breast tumors (31 luminal A, 33 luminal B, 27 Her2 and 31 triple negative) and 11 normal breast tissues using LNA based miRNA microarrays. Breast tumors were divided into a training (n=61) and a test set (n=61). Both series comprised a similar number of samples from each molecular subtype. Differential expression analysis was performed and microarray classifiers were developed with samples from the training set and validated in samples from the test set. The most relevant miRNAs were validated by quantitative PCR and analyzed in plasma from 36 pretreated patients, 47 postreated patients and 26 healthy individuals. In addition, further validation in 114 pretreated patients and 116 healthy individuals was performed.
Project description:We report RNA-sequencing data of 805 blood platelet samples, including 240 tumor-educated platelet (TEP) samples collected from patients with glioblastoma and 126 TEP samples collected from patients with brain metastases. In addition, we report RNA-sequencing data of blood platelets isolated from 353 asymptomatic controls and 86 individuals with multiple sclerosis. This dataset highlights the ability of TEP RNA-based 'liquid biopsy' diagnostics for the detection and (pseudo)progression monitoring of glioblastoma.
Project description:We developed an enrichment-free, metabolic-based assay for rapid detection of tumor cells in the pleural effusion and peripheral blood samples. All nucleated cells are plated on microwell chips that contain 200,000 addressable microwells and then screened the chips. After candidate tumor cells were identified, retrieved single tumor cells with micromanipultor. To detection and analysis molecular characterization of these circulating tumor cells, we performed single cell whole genome amplification with multiple displacement amplification (MDA) technology and whole exome sequencing.
Project description:In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic workup for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models (random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), support vector machine (SVM)) that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF=83%, NN=88%, LOGREG=SVM=89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic workup of HNSC-CUP.
Project description:Breast cancer was one of the first cancer types where molecular subtyping led to explanation of interpersonal heterogeneity and resulted in improvement of treatment regimen. Several multigene classifiers have been developed and in particular those defining molecular signatures of early breast cancers possess significant prognostic information. Hence since 2014, molecular subtyping of primary breast cancers was implemented as a part of routine diagnostics with direct impact of therapy assignment. In this study, we evaluate direct and potential benefits of molecular subtyping in low-risk breast cancers as well as present the advantages of a robust molecular signature in regard to patient work-up among high-risk breast cancers.
Project description:We developed two panel successively, contain 68 and 136 genes respectively. Combination with ultrasound or mammography, it could be used for breast cancer early detection and avoided unnecessary surgery or other invasive detection.
Project description:Stratification of breast cancers into subtypes are generally based on immune assays on tumor cells and/or mRNA expression of tumor cell enriched tissues. Here, we have laser microdissected tumor epithelium and tumor stroma from 24 breast cancer biopsies (12 luminal-like and 12 basal-like). We hypothesized that the stromal proteome would separate patients with breast into groups independently of the traditional epithelial based subtypes.
Project description:We report RNA-sequencing data of 283 blood platelet samples, including 228 tumor-educated platelet (TEP) samples collected from patients with six different malignant tumors (non-small cell lung cancer, colorectal cancer, pancreatic cancer, glioblastoma, breast cancer and hepatobiliary carcinomas). In addition, we report RNA-sequencing data of blood platelets isolated from 55 healthy individuals. This dataset highlights the ability of TEP RNA-based 'liquid biopsies' in patients with several types with cancer, including the ability for pan-cancer, multiclass cancer and companion diagnostics.
2015-10-30 | GSE68086 | GEO
Project description:Tumor-educated platelets as a promising biomarker for blood-based detection of renal cell carcinoma
Project description:Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often 30 exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results 31 in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an Area Under the Curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 40 circRNA) enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.