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:Gene expression profile of platelets. In this study, we try to address the knowledge gap regarding liquid biopsy markers for early detection of non-small cell lung cancer (NSCLC) and head and neck squamous cell carcinoma (HNSCC). For that blood samples were collected in two time points, in the presence and absence of NSCLC or HNSCC. Platelets were isolated and gene expression evaluated by microarray technique.
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:To achieve the best outcomes, breast cancer necessitates robust strategies for early detection. However, reliable blood-based tests for identifying early-stage disease remains elusive. Here we have employed plasma metabolomics and machine learning techniques to establish a non-invasive metabolic approach for early detection of breast cancer.
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: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: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:The diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for high-risk prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence status and the CAPRA-S to identify genes related to high-risk disease