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:Tumor-derived extracellular vesicles (tdEVs) have been emerging as potential biomarkers for cancer diagnosis because the tdEVs precisely reflect tumor cell alterations with significantly increased production. The proteomic profiling study of tdEVs represents a promising approach in a non-invasive manner to novel biomarker discovery for early detection and targeted therapy of cancer. Previously, we have developed a novel microfluidic chip for rapid and selective isolation of tdEVs. This microfluidic chip enables the selection of two types of EVs by using breast tumor-derived proteins (EpiCAM & CD49f) within two minutes. Using a microfluidic chip capable of selectively isolating tdEVs, the study compared the proteomes of EVs isolated from the serum of 100 breast cancer patients with different subtypes and 30 healthy individuals.
Project description:Metastatic triple-negative breast cancer (TNBC) is highly aggressive and lacks targeted therapies. Circulating tumor cells (CTCs) are invaluable for monitoring metastatic tumor progression and treatment response but are difficult to capture due to their rarity and heterogeneity. Surface-based staining for live CTCs is essential to preserve RNA quality in single cells, but current markers tend to perform poorly on more mesenchymal tumor cells such as TNBCs. To enhance live TNBC CTC detection, we developed a workflow for live CTC capture and single-cell RNA sequencing (scRNAseq). Using a mouse model of metastatic TNBC, we identified four new CTC surface markers, AHNAK2, CAVIN1, ODR4, and TRIML2, that specifically stain tumor cells. Combining antibodies against these markers improved CTC detection rates in multiple TNBC mouse models and patient samples. Also, combining these new markers with traditional CTC surface markers enhanced detection sensitivity, achieving the highest CTC coverage. This approach identifies diverse CTC populations, while preserving RNA quality for scRNAseq, which is essential for understanding and therapeutically targeting metastatic breast cancer.
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:Abstract: Background: With amyloid-β (Aβ) therapies for Alzheimer’s disease (AD) under active debate, there is a need for diagnostic tools that reflect disease biology beyond Aβ and tau for early detection and for patient stratification. The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that uses standardized cells as biosensors to transduce complex circulating signals into gene-expression readouts, which are then used to train multivariate disease classifiers for precision medicine. We developed an iCAP for early detection of Alzheimer’s disease (AD-iCAP). Methods: In a retrospective study, AD-iCAP was developed by incubating banked plasma samples from patients with early-stage AD (mild cognitive impairment or mild dementia) and age-matched controls with standardized neurons; whole-transcriptome responses were measured and used train disease classifiers by machine learning. The assay was optimized and analytically validated. Patient AD-iCAP data were separated into a training set, external validation set and an independent test set and used to parameterize and test models. To minimize bias, modeling features were selected from a predefined (a priori) 84-gene panel derived from an independent AD-iCAP dataset generated using plasma from 5xFAD mice. Results: We developed AD-iCAP using 191 banked plasma samples across three cohorts. Performance was assessed in two held-out sets: an external validation set (n=82; AUC 0.64, 95% CI 0.51–0.78) and an independent test set (n=23, AUC 0.77, 95% CI 0.57–0.96). Systems biology analyses of differential response profiles showed concordance with postmortem AD brain transcriptomes and enrichment of AD-relevant pathways beyond amyloid, including cholesterol biosynthesis, synaptic structure/neurotransmission and NGF/TrkA signaling (FDR < 0.05). The final model’s features included AD-linked genes AKT3, GPR50, PALLD and RGMA, related to neuronal signaling and cytoskeletal/axon-guidance processes. Conclusions: AD-iCAP is a blood-based diagnostic for early-stage detection of AD. It outputs cell-based transcriptional profiles with disease-relevance that are induced by circulating factors. In retrospective, multi-cohort testing, it showed modest-to-good discrimination, supporting prospective confirmation in larger cohorts. Because its readout captures biology beyond amyloid and tau, it may provide complementary information for combined testing. The multivariate readout supports further development for patient stratification as Aβ-targeted therapies and alternatives evolve.
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.