Unidentified
Ontology highlight
ABSTRACT:
PROVIDER: PRJDB19217 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| DRR669166_1.fastq.gz | Fastqsanger.gz | |||
| DRR669166_2.fastq.gz | Fastqsanger.gz | |||
| DRR669167_1.fastq.gz | Fastqsanger.gz | |||
| DRR669167_2.fastq.gz | Fastqsanger.gz | |||
| DRR669168_1.fastq.gz | Fastqsanger.gz |
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Project description:A sensitive assay to identify biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. In this study, we have conducted a prospective sample collection and retrospective blinded validation (PRoBE design) to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. The Affymetrix HG U133 Plus 2.0 Array and 2D-DIGE were used to profile transcriptomes and proteomes in saliva supernatants respectively. Significant variations of salivary transcriptomic and proteomic profiles were observed between breast cancer patients and healthy controls. Eleven transcriptomic biomarker candidates and two proteomic biomarker candidates were selected for a preclinical validation using an independent sample set. Transcriptomic biomarkers were validated by RT-qPCR and proteomic biomarkers were validated by quantitative protein immunoblot. Eight mRNA biomarkers and one protein biomarker have been validated for breast cancer detection, yielding ROC-plot AUC values between 0.665 and 0.959. This report provides proof of concept of salivary biomarkers for the non-invasive detection of breast cancer. The salivary biomarkersâ discriminatory power paves the way for a PRoBE-design definitive validation study. Keywords: Salivary biomarker, Breast cancer, Early detection, Salivary transcriptome, Salivary proteome This study, which was approved by the UCLA and Cedars-Sinai Medical Center Institutional Review Boards (#06-07-043 and #3870, respectively), started sample collection in February 2007. The sample collection followed the PRoBE principle (prospective specimen collection). The saliva bank for breast cancer project at the UCLA Dental Research Institute in collaboration with Cedars Sinai Medical Center has collected 200 saliva samples since 2007 with all subjects recruited from the Saul and Joyce Brandman Breast Cancer Center. Of these, 113 samples, including 41 breast cancer patients and 72 healthy control individuals, were selected for the discovery and validation phase of this study. Inclusion criteria of cancer patients consisted of a confirmed diagnosis of breast cancer. Exclusion criteria of cancer patients included therapy/surgery and/or a diagnosis of other malignancies within 5 years prior to saliva collection. Exclusion criteria of control patients included a diagnosis of any malignancies within 5 years prior to saliva collection. Written informed consents and questionnaire data sheets were obtained from all patients who agreed to serve as saliva donors. Unstimulated saliva samples were consistently collected, stabilized, and preserved as previously described. The sample supernatants were reserved at -80 C prior to assay. This study consisted of a discovery phase, followed by an independent preclinical validation phase. Of the 113 samples, 10 breast cancer samples and 10 healthy control samples were chosen for the discovery phase. All breast cancer cases are invasive ductal carcinoma (IDC), the most common type of breast cancer. Biomarkers identified from the discovery studies were first verified using the discovery sample set. An independent sample set, including 31 breast cancer patients and 62 healthy control subjects, was used for the biomarker validation phase.
2010-02-21 | E-GEOD-20266 | biostudies-arrayexpress
Project description:This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.
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Project description:This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication:
Creemers JHA, Lesterhuis WJ, Mehra N, et al.
"A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery."
Journal for ImmunoTherapy of Cancer 2021;9:e002032.
doi:10.1136/jitc-2020-002032
Comment:
Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used:
Reproduction of Fig. 1(C), xi = 0.0005
Reproduction of Fig. 1(D), xi = 0.00025
Abstract:
Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.
Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.
Results: Our model shows that a tipping point—a sharp state transition between immune control and immune evasion—induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.
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Project description:Three-Dimensional Organotypic Cultures Reshape the microRNAs Transcriptional Program in Breast Cancer Cells Three-dimensional (3D) cell cultures have several advantages over conventional monolayer two-dimensional (2D) cultures as they can better mimic tumor biology. This study delineated the changes in microRNA (miRNA) expression patterns of breast cancer cells cultured in 3D and 2D conditions. 3D organotypic cultures showed morphological changes such as cell–cell and cell–extracellular matrix interactions associated with a loss of polarity and reorganization on bulk structures in both basal Hs578T and luminal T47D breast cancer cells. Data indicate that down-regulated miRNAs in Hs578T 3D cultures, relative to the 2D condition, contribute to a positive regulation of biological processes such as response to hypoxia and focal adhesion, whereas over-expressed miRNAs were related to negative regulation of the cell cycle. Remarkably, the repro-gramming of miRNAs’ transcriptional profiles was accompanied by changes in the expression of key miRNA/mRNA coregulation networks, such as miR-935/HIF-1A, which correlated with the expression found in clinical breast tumors and predicted poor patient outcomes. These data have implications in our understanding of cancer biology and impact the miRNA/mRNA regulatory axes of cells grown in 3D cultures. Our data represent a guide for novel miRNA candidates for functional analysis, including the response to therapy and biomarker discovery in breast cancer.
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