scholarly journals DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

2020 ◽  
Author(s):  
Arjun Bhattacharya ◽  
Alina M. Hamilton ◽  
Melissa A. Troester ◽  
Michael I. Love
Keyword(s):  
Breast Cancer ◽  
Compressed Sensing ◽  
Mrna Expression ◽  
Expression Profiles ◽  
Low Cost ◽  
High Sensitivity ◽  
Microarray Dataset ◽  
Clinical Settings ◽  
Cell Type ◽  
Specific Expression

ABSTRACTTargeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

scholarly journals DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

2021 ◽  
Author(s):  
Arjun Bhattacharya ◽  
Alina M Hamilton ◽  
Melissa A Troester ◽  
Michael I Love
Keyword(s):  
Breast Cancer ◽  
Compressed Sensing ◽  
Mrna Expression ◽  
Expression Profiles ◽  
Low Cost ◽  
High Sensitivity ◽  
Microarray Dataset ◽  
Clinical Settings ◽  
Cell Type ◽  
Specific Expression

Abstract Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C–C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

Development of a high-performance, blood-based screening diagnostic to detect early-stage breast cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e22083-e22083
Author(s):  
Joseph Wagner ◽  
Karen Chapman ◽  
Maria Prendes-Garcia ◽  
Markus Lacher ◽  
Jennifer Kidd ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Performance ◽  
Early Stage ◽  
Low Cost ◽  
High Sensitivity ◽  
Screening Mammography ◽  
Early Stage Breast Cancer ◽  
Breast Cancer Patients ◽  
Elevated Expression ◽  
Sera Samples

e22083 Background: Limitations of current screening mammography, particularly in younger women, demonstrate the need for an alternative breast cancer screening strategy. A non-invasive, easily interpreted and low cost test should address this need. Methods: Gene expression microarray analysis was carried out on 128 individual tumor samples representing over 20 tumor types, 86 samples representing 31 diverse normal tissue types, 68 tumor cell lines and 97 diverse normal primary cell cultures. Genes were ranked for elevated expression in either: i) a large number and variety of tumors relative to normal tissues, or ii) in breast tumors. Elevated expression was verified for a subset of genes using qPCR in a set of independent RNA samples. Proteins coded by genes elevated in breast cancer samples were analyzed in a retrospective training set of breast cancer patient sera samples with cancer-free patient and benign pathology controls using ELISA or bead-based detection assay. Results: Based on availability of suitable reagents, 25 candidate biomarkers were assessed in patient sera samples (31-227 patient samples per biomarker) using ELISA or bead-based assays. Individually, the performance of individual markers varied (ROC AUC, 0.51 - 0.88); however, when expression levels of the best performing markers were combined, the multiplex test demonstrated high-sensitivity (>80%) and specificity (>90%) in identifying early-stage breast cancer patients. Conclusions: A multiplex, proteomic-based approach may provide for a high-performance, blood-based screening diagnostic for breast cancer.

scholarly journals PRISM: Recovering cell type specific expression profiles from composite RNA-seq data

2019 ◽  
Author(s):  
Antti Häkkinen ◽  
Kaiyang Zhang ◽  
Amjad Alkodsi ◽  
Noora Andersson ◽  
Erdogan Pekcan Erkan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Expression Profiles ◽  
Cell Type ◽  
Specific Expression ◽  
Patient Response ◽  
Statistical Framework ◽  
Sample Composition ◽  
Cell Type Specific Expression ◽  
Treatment Naïve ◽  
Cell Type Specific

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

scholarly journals A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders

2020 ◽  
Author(s):  
Abolfazl Doostparast Torshizi ◽  
Jubao Duan ◽  
Kai Wang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Complex Diseases ◽  
Specific Gene ◽  
Cellular Composition ◽  
Rna Seq ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

AbstractThe importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, the vast majority of gene expression studies are conducted on bulk tissues, necessitating computational approaches to infer biological insights on cell type-specific contribution to diseases. Several computational methods are available for cell type deconvolution (that is, inference of cellular composition) from bulk RNA-Seq data, but cannot impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq (scRNA-seq) and population-wide expression profiles, it can be a computationally tractable and identifiable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations by employing genome-wide tissue-wise expression signatures from GTEx to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations, and uses a multi-variate stochastic search algorithm to estimate the expression level of each gene in each cell type. Extensive analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease, and type 2 diabetes validated efficiency of CellR, while revealing how specific cell types contribute to different diseases. We conducted numerical simulations on human cerebellum to generate pseudo-bulk RNA-seq data and demonstrated its efficiency in inferring cell-specific expression profiles. Moreover, we inferred cell-specific expression levels from bulk RNA-seq data on schizophrenia and computed differentially expressed genes within certain cell types. Using predicted gene expression profile on excitatory neurons, we were able to reproduce our recently published findings on TCF4 being a master regulator in schizophrenia and showed how this gene and its targets are enriched in excitatory neurons. In summary, CellR compares favorably (both accuracy and stability of inference) against competing approaches on inferring cellular composition from bulk RNA-seq data, but also allows direct imputation of cell type-specific gene expression, opening new doors to re-analyze gene expression data on bulk tissues in complex diseases.

scholarly journals The Advent of Salivary Breast Cancer Biomarker Detection Using Affinity Sensors

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2373 ◽  
Author(s):  
Imad Abrao Nemeir ◽  
Joseph Saab ◽  
Walid Hleihel ◽  
Abdelhamid Errachid ◽  
Nicole Jafferzic-Renault ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Low Cost ◽  
Current Trend ◽  
High Sensitivity ◽  
Breast Cancer Diagnosis ◽  
Current Status ◽  
Attractive Alternative ◽  
Early Screening ◽  
Screening Programs ◽  
Sensitivity And Selectivity

Breast Cancer is one of the world’s most notorious diseases affecting two million women in 2018 worldwide. It is a highly heterogeneous disease, making it difficult to treat. However, its linear progression makes it a candidate for early screening programs, and the earlier its detection the higher the chance of recovery. However, one key hurdle for breast cancer screening is the fact that most screening techniques are expensive, time-consuming, and cumbersome, making them impractical for use in several parts of the world. One current trend in breast cancer detection has pointed to a possible solution, the use of salivary breast cancer biomarkers. Saliva is an attractive medium for diagnosis because it is readily available in large quantities, easy to obtain at low cost, and contains all the biomarkers present in blood, albeit in lower quantities. Affinity sensors are devices that detect molecules through their interactions with biological recognition molecules. Their low cost, high sensitivity, and selectivity, as well as rapid detection time make them an attractive alternative to traditional means of detection. In this review article, we discuss the current status of breast cancer diagnosis, its salivary biomarkers, as well as the current trends in the development of affinity sensors for their detection.

scholarly journals Druggable Nucleolin Identifies Breast Tumours Associated with Poor Prognosis That Exhibit Different Biological Processes

Cancers ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 390 ◽  
Author(s):  
Flora Nguyen Van Long ◽  
Audrey Lardy-Cleaud ◽  
Susan Bray ◽  
Sylvie Chabaud ◽  
Thierry Dubois ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mrna Expression ◽  
Expression Profiles ◽  
Disease Free Survival ◽  
Mrna Levels ◽  
Breast Cancers ◽  
Breast Tumours ◽  
Expression Levels ◽  
Cancer Management ◽  
Mrna Expression Levels

Background: Nucleolin (NCL) is a multifunctional protein with oncogenic properties. Anti-NCL drugs show strong cytotoxic effects, including in triple-negative breast cancer (TNBC) models, and are currently being evaluated in phase II clinical trials. However, few studies have investigated the clinical value of NCL and whether NCL stratified cancer patients. Here, we have investigated for the first time the association of NCL with clinical characteristics in breast cancers independently of the different subtypes. Methods: Using two independent series (n = 216; n = 661), we evaluated the prognostic value of NCL in non-metastatic breast cancers using univariate and/or multivariate Cox-regression analyses. Results: We reported that NCL mRNA expression levels are markers of poor survivals independently of tumour size and lymph node invasion status (n = 216). In addition, an association of NCL expression levels with poor survival was observed in TNBC (n = 40, overall survival (OS) p = 0.0287, disease-free survival (DFS) p = 0.0194). Transcriptomic analyses issued from The Cancer Genome Atlas (TCGA) database (n = 661) revealed that breast tumours expressing either low or high NCL mRNA expression levels exhibit different gene expression profiles. These data suggest that tumours expressing high NCL mRNA levels are different from those expressing low NCL mRNA levels. Conclusions: NCL is an independent marker of prognosis in breast cancers. We anticipated that anti-NCL is a promising therapeutic strategy that could rapidly be evaluated in high NCL-expressing tumours to improve breast cancer management.

scholarly journals Sensei: How many samples to tell evolution in single-cell studies?

2020 ◽  
Author(s):  
Shaoheng Liang ◽  
Jason Willis ◽  
Jinzhuang Dou ◽  
Vakul Mohanty ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Expression Profiles ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Controlled Study ◽  
Cancer Evolution ◽  
Cell Type ◽  
Specific Expression ◽  
Mathematical Accuracy

1AbstractCellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

scholarly journals Sensei: how many samples to tell a change in cell type abundance?

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Shaoheng Liang ◽  
Jason Willis ◽  
Jinzhuang Dou ◽  
Vakul Mohanty ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Expression Profiles ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Controlled Study ◽  
Cancer Evolution ◽  
Cell Type ◽  
Specific Expression ◽  
Mathematical Accuracy

AbstractCellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

scholarly journals Hereditary Breast and Ovarian Cancer Screening Syndrome Profile in Women Diagnosed with Breast Cancer from Paraná State Southwest

2021 ◽  
Vol 43 (08) ◽  
pp. 616-621
Author(s):  
Juliana Batista de Moura ◽  
Carla Camila Ghedin ◽  
Érika Tomie Takakura ◽  
Thalita Basso Scandolara ◽  
Daniel Rech ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Low Cost ◽  
High Sensitivity ◽  
High Grade ◽  
Breast And Ovarian Cancer ◽  
Ovarian Cancer Screening ◽  
Unified Health System ◽  
The Family ◽  
Patients At Risk

Abstract Objective This study evaluated the risk of the hereditary breast and ovarian cancer (HBOC) syndrome in patients with breast cancer by using the Family History Screening 7 (FHS-7) tool, a validated low-cost questionnaire with high sensitivity able to screen the HBOC risk in the population. Methods Women diagnosed with breast cancer (n = 101) assisted by the Unified Health System at the 8th Regional Health Municipal Office of the state of Paraná answered the FHS-7, and the results were analyzed using IBM SPSS Statistics for Windows, Version 25.0. software (IBM Corp., Armonk, NY, USA). Results The risk of HBOC was 19.80% (n = 20). Patients at risk exhibited aggressive tumor characteristics, such as high-grade tumors (30%), presence of angiolymphatic emboli (35%), and premenopausal at diagnosis (50%). Significant associations between the prevalence of high-grade tumors were observed in women younger than 50 years at diagnosis with HBOC (p = 0.003). Conclusion Our findings suggest a possible family inheritance associated with worse clinical features in women with breast cancer in this population, indicating that HBOC investigation can be initially performed with low-cost instruments such as FHS-7.

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