scholarly journals Breast cancer identification via modeling of peripherally circulating miRNAs

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4551 ◽  
Author(s):  
Xiaomeng Cui ◽  
Zhangming Li ◽  
Yilei Zhao ◽  
Anqi Song ◽  
Yunbo Shi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Accuracy ◽  
Diagnostic Performance ◽  
Validation Cohort ◽  
Multiple Linear Regression Model ◽  
Circulating Mirnas ◽  
Independent Validation ◽  
Technical Possibility ◽  
Validation Set ◽  
Prolonged Life Expectancy

Prolonged life expectancy in humans has been accompanied by an increase in the prevalence of cancers. Breast cancer (BC) is the leading cause of cancer-related deaths. It accounts for one-fourth of all diagnosed cancers and affects one in eight females worldwide. Given the high BC prevalence, there is a practical need for demographic screening of the disease. In the present study, we re-analyzed a large microRNA (miRNA) expression dataset (GSE73002), with the goal of optimizing miRNA biomarker selection using neural network cascade (NNC) modeling. Our results identified numerous candidate miRNA biomarkers that are technically suitable for BC detection. We combined three miRNAs (miR-1246, miR-6756-5p, and miR-8073) into a single panel to generate an NNC model, which successfully detected BC with 97.1% accuracy in an independent validation cohort comprising 429 BC patients and 895 healthy controls. In contrast, at least seven miRNAs were merged in a multiple linear regression model to obtain equivalent diagnostic performance (96.4% accuracy in the independent validation set). Our findings suggested that suitable modeling can effectively reduce the number of miRNAs required in a biomarker panel without compromising prediction accuracy, thereby increasing the technical possibility of early detection of BC.

Increased Incidence of Visceral Metastases in Scottish Patients With BRCA1/2-Defective Ovarian Cancer: An Extension of the Ovarian BRCAness Phenotype

2010 ◽  
Vol 28 (15) ◽  
pp. 2505-2511 ◽  
Author(s):  
Charlie Gourley ◽  
Caroline O. Michie ◽  
Patricia Roxburgh ◽  
Timothy A. Yap ◽  
Sharon Harden ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Control Group ◽  
Primary Peritoneal Cancer ◽  
Peritoneal Cancer ◽  
Independent Validation ◽  
Visceral Metastases ◽  
Splenic Metastases ◽  
Validation Set

Purpose To compare the frequency of visceral relapse of BRCA1/2-deficient ovarian cancer to that of nonhereditary controls. Patients and Methods All patients diagnosed in Scotland with epithelial ovarian cancer (EOC) or primary peritoneal cancer (PPC) and a germline BRCA1/2 mutation were identified. Those with previous malignancy were excluded. Each remaining patient who experienced relapse was matched with two nonhereditary controls. Results Seventy-nine patients with EOC/PPC and germline BRCA1/2 mutations were identified. Fifteen had inadequate clinical data, two had carcinosarcoma, 27 had previous breast cancer, and 16 were in remission. Of the remaining 19 patients who were BRCA1/2 deficient, 14 patients (74%) developed visceral metastases compared with six (16%) of 38 patients in the control group. The percentages of liver, lung, and splenic metastases were 53%, 32%, and 32%, respectively, in the patients compared with 5%, 3%, and 5%, respectively, in the controls. When events occurring outside the matched follow-up period were omitted, the percentages of visceral, liver, lung, and splenic metastases were 58%, 42%, 16%, and 32% in the patients compared with 5%, 0%, 0%, and 3% in controls (P < .001, P < .001, P = .066, and P = .011, respectively). In an independent validation set, the corresponding percentages of visceral, liver, lung, and splenic metastases were 63%, 46%, 13%, and 17% in the patients compared with 11%, 4%, 2%, and 2% in controls (P < .001, P < .001, P = .153, and P = .052, respectively). Conclusion Although sporadic EOC commonly remains confined to the peritoneum, BRCA1/2-deficient ovarian cancer frequently metastasizes to viscera. These data extend the ovarian BRCAness phenotype, imply BRCA1/2-deficient ovarian cancer is biologically distinct, and suggest that patients with visceral metastases should be considered for BRCA1/2 sequencing.

scholarly journals Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set

2018 ◽  
Vol 173 (2) ◽  
pp. 455-463 ◽  
Author(s):  
Elizabeth Hope Cain ◽  
Ashirbani Saha ◽  
Michael R. Harowicz ◽  
Jeffrey R. Marks ◽  
P. Kelly Marcom ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Neoadjuvant Therapy ◽  
Pathologic Response ◽  
Learning Models ◽  
Independent Validation ◽  
Mri Features ◽  
Validation Set ◽  
Machine Learning Models

Noninvasive diagnostic test in breast cancer; utopia or option with serum proteomics?

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21118-21118
Author(s):  
M. de Noo ◽  
M. van der Werff ◽  
H. Gelderblom ◽  
A. Deelder ◽  
R. Tollenaar
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Block Design ◽  
Recognition Rate ◽  
Control Group ◽  
Protein Profiles ◽  
Breast Cancer Patients ◽  
Serum Samples ◽  
Independent Validation ◽  
Validation Set

21118 Background: With a lifetime risk currently estimated 1 in 9, breast cancer is among the most common diagnosed malignancies. Proteomic expression profiling generated by mass spectrometry has been suggested as a potential tool for the early diagnosis of cancer. The objective of our study was to assess and validate the feasibility of this approach for the detection of breast cancer. Methods: In a randomized block design pre-operative serum samples obtained from 63 breast cancer patients and 73 controls were used to generate high-resolution MALDI-TOF protein profiles as a calibration set. The median age of the patient and control group was respectively, 52 (20–81) and 57 years (39–87). The MALDI-TOF spectra generated using WCX magnetic beads assisted mass spectrometry (Ultraflex) were smoothed, binned and normalized after baseline correction. After pre-processing of the spectra, linear discriminant analysis with double cross-validation, based on principal component analysis, was used to classify the protein profiles. Consequently, the classifier constructed on the first 2 plates was applied on the spectra of an independent validation set. This validation set consisted of serum samples from 29 breast cancer patients and 38 controls. The median age was 59 years (26–87) and 57 years (24–71) for the patient and control group respectively. Results: Double cross-validatory analysis carried out on the protein spectra of the calibration set yielded a total recognition rate of 86%, a sensitivity of 88% and a specificity of 84% for the detection of breast cancer within the calibration set. The AUC of this classifier was 90.3%. When this classifier was applied on the spectra of the independent validation set a total recognition rate of 80.9%, a sensitivity of 72% and a specificity of 89% were found. Conclusions: The use of a randomized block design, but mainly an independent validation set proves that discriminating protein profiles can be detected between breast cancer patients and healthy controls. Although further validation in larger series and identification of the discriminating proteins must be achieved, the high sensitivity and specificity indicate that serum protein profiles could be an option for the detection of breast cancer. No significant financial relationships to disclose.

scholarly journals Clinical Identification of Dysregulated Circulating microRNAs and Their Implication in Drug Response in Triple Negative Breast Cancer (TNBC) by Target Gene Network and Meta-Analysis

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 549
Author(s):  
Amal Qattan ◽  
Taher Al-Tweigeri ◽  
Wafa Alkhayal ◽  
Kausar Suleman ◽  
Asma Tulbah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Resistance ◽  
Triple Negative Breast Cancer ◽  
Drug Response ◽  
Triple Negative ◽  
Meta Analysis ◽  
Integrated Analysis ◽  
Circulating Mirnas ◽  
Persistent Problem ◽  
Network Analyses

Resistance to therapy is a persistent problem that leads to mortality in breast cancer, particularly triple-negative breast cancer (TNBC). MiRNAs have become a focus of investigation as tissue-specific regulators of gene networks related to drug resistance. Circulating miRNAs are readily accessible non-invasive potential biomarkers for TNBC diagnosis, prognosis, and drug-response. Our aim was to use systems biology, meta-analysis, and network approaches to delineate the drug resistance pathways and clinical outcomes associated with circulating miRNAs in TNBC patients. MiRNA expression analysis was used to investigate differentially regulated circulating miRNAs in TNBC patients, and integrated pathway regulation, gene ontology, and pharmacogenomic network analyses were used to identify target genes, miRNAs, and drug interaction networks. Herein, we identified significant differentially expressed circulating miRNAs in TNBC patients (miR-19a/b-3p, miR-25-3p, miR-22-3p, miR-210-3p, miR-93-5p, and miR-199a-3p) that regulate several molecular pathways (PAM (PI3K/Akt/mTOR), HIF-1, TNF, FoxO, Wnt, and JAK/STAT, PD-1/PD-L1 pathways and EGFR tyrosine kinase inhibitor resistance (TKIs)) involved in drug resistance. Through meta-analysis, we demonstrated an association of upregulated miR-93, miR-210, miR-19a, and miR-19b with poor overall survival outcomes in TNBC patients. These results identify miRNA-regulated mechanisms of drug resistance and potential targets for combination with chemotherapy to overcome drug resistance in TNBC. We demonstrate that integrated analysis of multi-dimensional data can unravel mechanisms of drug-resistance related to circulating miRNAs, particularly in TNBC. These circulating miRNAs may be useful as markers of drug response and resistance in the guidance of personalized medicine for TNBC.

scholarly journals Overexpression of circulating MiR-30b-5p identifies advanced breast cancer

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Helena Estevão-Pereira ◽  
João Lobo ◽  
Sofia Salta ◽  
Maria Amorim ◽  
Paula Lopes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Validation Cohort ◽  
Advanced Disease ◽  
Clinical Tool ◽  
Liquid Biopsies ◽  
Primary Tumors ◽  
Bodily Fluids ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Formalin Fixed

Abstract Background Breast cancer (BrC) remains the leading cause of cancer-related death in women, mainly due to recurrent and/or metastatic events, entailing the need for biomarkers predictive of progression to advanced disease. MicroRNAs hold promise as noninvasive cancer biomarkers due to their inherent stability and resilience in tissues and bodily fluids. There is increasing evidence that specific microRNAs play a functional role at different steps of the metastatic cascade, behaving as signaling mediators to enable the colonization of a specific organ. Herein, we aimed to evaluate the biomarker performance of microRNAs previously reported as associated with prognosis for predicting BrC progression in liquid biopsies. Methods Selected microRNAs were assessed using a quantitative reverse transcription-polymerase chain reaction in a testing cohort of formalin-fixed paraffin-embedded primary (n = 16) and metastatic BrC tissues (n = 22). Then, miR-30b-5p and miR-200b-3p were assessed in a validation cohort #1 of formalin-fixed paraffin-embedded primary (n = 82) and metastatic BrC tissues (n = 93), whereas only miR-30b-5p was validated on a validation cohort #2 of liquid biopsies from BrC patients with localized (n = 20) and advanced (n = 25) disease. ROC curve was constructed to evaluate prognostic performance. Results MiR-30b-5p was differentially expressed in primary tumors and paired metastatic lesions, with bone metastases displaying significantly higher miR-30b-5p expression levels, paralleling the corresponding primary tumors. Interestingly, patients with advanced disease disclosed increased circulating miR-30b-5p expression compared to patients with localized BrC. Conclusions MiR-30b-5p might identify BrC patients at higher risk of disease progression, thus, providing a useful clinical tool for patients’ monitoring, entailing earlier and more effective treatment. Nonetheless, validation in larger multicentric cohorts is mandatory to confirm these findings.

scholarly journals Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sijia Cui ◽  
Tianyu Tang ◽  
Qiuming Su ◽  
Yajie Wang ◽  
Zhenyu Shu ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Main Pancreatic Duct ◽  
Validation Cohort ◽  
External Validation ◽  
High Grade ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Training Cohort ◽  
Independent Validation ◽  
Medical Centers ◽  
Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

scholarly journals Deconvolution of sarcoma methylomes reveals varying degrees of immune cell infiltrates with association to genomic aberrations

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Malte Simon ◽  
Sadaf S. Mughal ◽  
Peter Horak ◽  
Sebastian Uhrig ◽  
Jonas Buchloh ◽  
...  
Keyword(s):  
Immune Cell ◽  
Validation Cohort ◽  
Soft Tissue Sarcomas ◽  
Immune Cell Infiltration ◽  
Methylation Data ◽  
Tumor Infiltration ◽  
Mesenchymal Tumors ◽  
Independent Validation ◽  
Genomic Aberrations ◽  
Immune Cell Infiltrates

Abstract Background Soft-tissue sarcomas (STS) are a heterogeneous group of mesenchymal tumors for which response to immunotherapies is not well established. Therefore, it is important to risk-stratify and identify STS patients who will most likely benefit from these treatments. Results To reveal shared and distinct methylation signatures present in STS, we performed unsupervised deconvolution of DNA methylation data from the TCGA sarcoma and an independent validation cohort. We showed that leiomyosarcoma can be subclassified into three distinct methylation groups. More importantly, we identified a component associated with tumor-infiltrating leukocytes, which suggests varying degrees of immune cell infiltration in STS subtypes and an association with prognosis. We further investigated the genomic alterations that may influence tumor infiltration by leukocytes including RB1 loss in undifferentiated pleomorphic sarcomas and ELK3 amplification in dedifferentiated liposarcomas. Conclusions In summary, we have leveraged unsupervised methylation-based deconvolution to characterize the immune compartment and molecularly stratify subtypes in STS, which may benefit precision medicine in the future.

scholarly journals Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Richard Buus ◽  
Zsolt Szijgyarto ◽  
Eugene F. Schuster ◽  
Hui Xiao ◽  
Ben P. Haynes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Early Stage ◽  
Recurrence Score ◽  
Distant Recurrence ◽  
Research Assessment ◽  
Expression Data ◽  
Her2 Breast Cancer ◽  
Validation Set ◽  
Development And Validation

AbstractMulti-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (Risk Of Recurrence, ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor-positive (ER+), HER2-negative breast cancer. Here, we describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data. RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2− breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology. Gene expression was measured using NanoString nCounter. For RS and EP, conversion factors to adjust for cross-platform variation were estimated using linear regression. For ROR, the steps to perform subgroup-specific normalisation of the gene expression data and calibration factors to calculate the 46-gene ROR score were assessed and verified. Training with bootstrapping (n = 59) was followed by validation (n = 48) using adjusted, research use only (RUO) NanoString-based algorithms. In the validation set, there was excellent concordance between the RUO scores and their commercial counterparts (rc(RS) = 0.96, 95% CI 0.93–0.97 with level of agreement (LoA) of −7.69 to 8.12; rc(EP) = 0.97, 95% CI 0.96–0.98 with LoA of −0.64 to 1.26 and rc(ROR) = 0.97 (95% CI 0.94–0.98) with LoA of −8.65 to 10.54). There was also a strong agreement in risk stratification: (RS: κ = 0.86, p < 0.0001; EP: κ = 0.87, p < 0.0001; ROR: κ = 0.92, p < 0.001). In conclusion, the calibrated algorithms recapitulate the commercial RS and EP scores on individual biopsies and ROR scores on samples based on subgroup-centreing method using NanoString expression data.

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

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