scholarly journals Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

2018 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Mariana Díaz-Almirón ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Flux Balance Analysis ◽  
Risk Groups ◽  
Prognostic Models ◽  
Breast Cancer Patients ◽  
Proteomics Data ◽  
Cancer Subtypes ◽  
Flux Balance ◽  
Balance Analysis

AbstractAims:Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux Balance Analysis is used to explore these differences as well as drug response.Materials & Methods:Proteomics data from breast tumors were obtained by mass-spectrometry. Flux Balance Analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models.Results:Flux activities of vitamin A, tetrahydrobiopterin and beta-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups.Conclusions:Flux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.

scholarly journals Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

2019 ◽  
Vol 15 (30) ◽  
pp. 3483-3490 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Mariana Díaz-Almirón ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Flux Balance Analysis ◽  
Risk Groups ◽  
Prognostic Models ◽  
Breast Cancer Patients ◽  
Proteomics Data ◽  
Cancer Subtypes ◽  
Flux Balance ◽  
Balance Analysis

Aim: Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux balance analysis is used to explore these differences as well as drug response. Materials & methods: Proteomics data from breast tumors were obtained by mass-spectrometry. Flux balance analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models. Results: Flux activities of vitamin A, tetrahydrobiopterin and β-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups. Conclusion: Flux activities summarize flux balance analysis data and can be associated with prognosis in cancer.

Impact of the EndoPredict-clin score on risk stratification in ER-positive, HER2-negative breast cancer after considering clinical guidelines.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 542-542
Author(s):  
Martin Filipits ◽  
Peter Christian Dubsky ◽  
Margaretha Rudas ◽  
Jan C. Brase ◽  
Ralf Kronenwett ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Endocrine Therapy ◽  
Clinical Guidelines ◽  
Risk Groups ◽  
Low Risk ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Er Positive

542 Background: Many ER-positive, HER2-negative breast cancer patients are treated by adjuvant chemotherapy according to current clinical guidelines. We retrospectively assessed whether the combined gene expression/ clinicopathological EndoPredict-clin (EPclin) score improved the accuracy of risk classification in addition to considering clinical guidelines. Methods: Three clinical breast cancer guidelines (National Comprehensive Cancer Center Network (NCCN), German S3 and St. Gallen 2011), and the EPclin score - assessed by quantitative RT-PCR in formalin-fixed paraffin-embedded tissue - were used to assign risk groups in 1,702 ER-positive, HER2-negative breast cancer patients from two randomized phase III trials (Austrian Breast and Colorectal Cancer Study Group 6 and 8) treated with endocrine therapy only. Results: Although all analyzed clinical guidelines identified a low-risk group with improved metastasis-free survival, the overwhelming majority of all patients (81-94%) were classified as intermediate / high risk. In contrast to that, the EPclin classified only 37% of all patients as high risk and that stratification resulted in the best separation between low and high risk groups (p < 0.001, HR = 5.11 (3.48-7.51). Consequently, the majority of all patients deemed intermediate / high risk by the clinical guidelines was re-classified as low risk by the EPclin score. Kaplan Meier analyses demonstrated that the re-classified subgroups (47 to 57% of all patients) had an excellent 10-year metastasis-free survival of 95% comparable to the clinical assigned low-risk groups although encompassing a higher proportion of the trial patients. Conclusions: The EPclin score predicted distant recurrence more accurately than all three clinical guidelines and is especially useful to reclassify patients considered as intermediate / high risk by the guidelines. The data suggests that the EPclin score provides clinically useful prognostic information beyond common clinical guidelines and can be used to accurately identify the clinically relevant group of patients who are adequately and sufficiently treated with adjuvant endocrine therapy alone.

Comparative performance of Breast Cancer Index (BCIN+) and CTS5 in hormone receptor-positive (HR+) lymph node-positive (N+) breast cancer patients with one to three positive nodes (N1).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 555-555
Author(s):  
Dennis Sgroi ◽  
Yi Zhang ◽  
Catherine A. Schnabel
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Endocrine Therapy ◽  
Tumor Size ◽  
Risk Groups ◽  
Cox Proportional Hazards ◽  
Low Risk ◽  
Intermediate Risk ◽  
Breast Cancer Patients

555 Background: Identification of N+ breast cancer patients with a limited risk of recurrence improves selection of those for which chemotherapy and/or extended endocrine therapy (EET) may be most appropriate to reduce overtreatment. BCIN+ integrates gene expression with tumor size and grade, and is highly prognostic for overall (0-10yr) and late (5-10yr) distant recurrence (DR) in N1 patients. Clinical Treatment Score post-5-years (CTS5) is a prognostic model based on clinicopathological factors (nodes, age, tumor size and grade) and significantly prognostic for late DR. The current analysis compares BCIN+ and CTS5 for risk of late DR in N1 patients. Methods: 349 women with HR+, N1 disease and recurrence-free for ≥5 years were included. BCIN+ results were determined blinded to clinical outcome. CTS5 was calculated as previously described (Dowsett et al, JCO 2018; 36:1941). Kaplan-Meier analysis and Cox proportional hazards regression for late DR (5-15y) were evaluated. Results: 64% of patients were > 50 years old, 34% with tumors > 2cm, 79% received adjuvant chemotherapy and 64% received up to 5 years of ET. BCIN+ stratified 23% of patients as low-risk with 1.3% risk for late DR vs those classified as high-risk with 16.1% [HR 12.4 (1.7-90.4), p = 0.0014]. CTS5 classified patients into 3 risk groups: 29% of patients as low-risk (4.2% DR), 37% as intermediate-risk (10.6% DR), and 34% as high-risk (22.1% DR) [HR intermediate vs. low: 2.3 (0.7-7.0), p = 0.16; high vs. low: 5.3 (1.8-15.5), p = 0.002]. In a subset of patients who completed 5 years of ET (N = 223), BCIN+ identified 22% of patients as low-risk with a late DR rate of 2.1%, while CTS5 identified 29% and 37% of patients as low- and intermediate-risk with late DR rates of 5.2% and 10.3%, respectively. Conclusions: BCIN+ classified N1 patients into binary risk groups and identified 20% patients with limited risk of late DR ( < 2%) that may be advised to forego EET and its attendant toxicities/side effects. In comparison, CTS5 classified patients into 3 risk groups, with low- and intermediate-risk of late DR of 4-5% and 10%, wherein the risk-benefit profile for extension of endocrine therapy is less clear.

scholarly journals Identification of the prognostic value of ferroptosis-related gene signature in breast cancer patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ding Wang ◽  
Guodong Wei ◽  
Ju Ma ◽  
Shuai Cheng ◽  
Longyuan Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Cancer Patients ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Kaplan Meier

Abstract Background Breast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women’s health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients’ survival. Methods Gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score. Results We constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001). Conclusion Our study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients’ prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Recurrence risk stratification by Dutch clinical risk criteria for early-stage luminal breast cancer patients in Taiwan: A population-based analysis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e12509-e12509
Author(s):  
Lei Lei ◽  
Han-Ching Chan ◽  
Wang Xiao Jia ◽  
Tzu-Pin Lu ◽  
Skye Hung-Chun Cheng
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Early Stage ◽  
Risk Groups ◽  
Low Risk ◽  
Luminal Breast Cancer ◽  
Breast Cancer Patients ◽  
Risk Patients

e12509 Background: Dutch clinical risk criteria (low-risk definition: age > 35 years and (grade 1 with tumor ≤3cm, grade 2 with tumor ≤2cm, or grade 3 with tumor ≤1cm) have been used to stratify the benefit of MammaPrint and Oncotype DX for the decision-making regarding adjuvant chemotherapy for early-stage luminal breast cancer. We propose that the criteria could help to identify low-risk patients who could barely benefit from multi-gene testing. Methods: Breast cancer patients from Taiwan Cancer Database initially treated with primary surgeries between 2008 and 2012 who met the following criteria: (1) pathologic node-negative, (2) hormone receptor-positive, (3) HER2-negative, (4) undergone hormonal therapy, and (5) a minimum follow-up time of 5-year if free from any event, were enrolled in this study. Out of the total 2679 eligible patients, 1074 (40.1%) patients received adjuvant chemotherapy in addition to endocrine therapy. The study endpoints included breast cancer-specific survival (BCSS) and overall survival (OS). Kaplan-Meier statistics estimated the difference between clinical outcomes in low- and high-risk groups. Results: The median follow-up time of BSCC and OS was 5.9 years (range, 0-7 years) and 5.8 years (range, 0-7 years), respectively. There were statistical significances of 5-year BCSS (n = 2679) and 5-year OS (n = 2636) between low-risk and high-risk groups (in both endpoints, P < 0.0001). According to the Dutch criteria, low-risk patients with and without adjuvant chemotherapy had a 5-year BCSS of 99.0% vs. 99.2% and a 5-year OS of 98.4% vs. 97.4%, respectively. High-risk patients with and without adjuvant chemotherapy had a 5-year BCSS of 97.7% vs. 98.1% and a 5-year OS of 96.4% vs. 95.3%, respectively. Conclusions: The benefit of chemotherapy in low-risk patients classified by Dutch criteria might be very small since the breast cancer mortality was less than 1% with a minimum of 5-year follow-up. Dutch criteria cannot identify high-risk patients who would benefit from chemotherapy. We assumed that multi-gene testing in low-risk patients would not be cost-effective.

scholarly journals Flux Balance Analysis with Objective Function Defined by Proteomics Data—Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0134014 ◽  
Author(s):  
Daniel Montezano ◽  
Laura Meek ◽  
Rashmi Gupta ◽  
Luiz E. Bermudez ◽  
José C. M. Bermudez
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Objective Function ◽  
Flux Balance Analysis ◽  
Proteomics Data ◽  
Flux Balance ◽  
Balance Analysis

Breast Cancer Onset in Twins and Women With Bilateral Disease

2008 ◽  
Vol 26 (25) ◽  
pp. 4086-4091 ◽  
Author(s):  
Mikael Hartman ◽  
Per Hall ◽  
Gustaf Edgren ◽  
Marie Reilly ◽  
Linda Lindstrom ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Relative Risk ◽  
High Risk ◽  
Bilateral Breast Cancer ◽  
Risk Groups ◽  
Population Based ◽  
Dizygotic Twin ◽  
Breast Cancer Patients ◽  
Degree Relative ◽  
Risk Of Cancer

Purpose Little is known of the onset of breast cancer in high-risk populations. We investigated the risk of breast cancer in twin sisters and in the contralateral breast taking family history into consideration. Patients and Methods We analyzed a Scandinavian population-based cohort of 2,499 female twin pairs, in which at least one had a diagnosis of breast cancer and estimated the risk of breast cancer in the sister. Using a total of 11 million individuals in Sweden with complete family links, we identified 93,448 women with breast cancer and estimated the risk of a bilateral breast cancer. Results The incidence of breast cancer in twin sisters of breast cancer patients was 0.64% per year and 0.42% per year in mono- and dizygotic twin sisters, respectively. In comparison, the risk of familial (affected first-degree relative) and nonfamilial bilateral breast cancer was 1.03% per year and 0.68% per year, respectively. Contrary to the risk of unilateral disease, the risk of cancer in the nonaffected twin and the opposite breast was not affected by age or time since first event. The relative risk of familial bilateral cancer was 52% higher (incidence rate ratio [IRR] = 1.52; 95% CI, 1.42 to 1.64) and the relative risk in the dizygotic twin sister was 25% lower (IRR = 0.75; 95% CI, 0.61 to 0.91) compared with the risk of nonfamilial bilateral cancer. Conclusion The elevated risk of breast cancer in high-risk groups is little affected by age and time since diagnosis. Our findings suggest that susceptible groups of women might have already aggregated genetic prerequisites for breast cancer.

scholarly journals Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2772
Author(s):  
Michael A. Jacobs ◽  
Christopher B. Umbricht ◽  
Vishwa S. Parekh ◽  
Riham H. El Khouli ◽  
Leslie Cope ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Group ◽  
Area Under The Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Intermediate Risk ◽  
Breast Cancer Patients ◽  
Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.

scholarly journals Comprehensive multi-omics analysis identifies an immune related predicting model in early stage breast cancer

2020 ◽  
Author(s):  
Xuan Li ◽  
Wei Jin
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Early Stage ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Early Stage Breast Cancer ◽  
Breast Cancer Patients

Abstract Background Breast cancer is known the highest incidence cancer in women. Tumor-infiltrating immune cells were reported closely related to cancers’ fate but it’s function still not very clear in breast cancer. The aim of our study is to build a infiltrating immune cells based nomogram model to better predict patients survival and explore its relationship with immune features. Methods We first use CIBERSORT to analyze 22 immune cells’ status in two unrelated breast cancer cohorts (TCGA and METRABRIC). The univariate and multivariate Cox analyses were used to analyze the prognostic ability of immune cells and other clinicopathological factors. Nomogram model were built by using independent prognostic factors. Different immune signatures and gene enrichment analysis were performed in different nomogram risk groups. Results Multivariate cox analysis showed that Macrophages M2 with HR of 1.733 (95% CI: 1.013-2.966) in TCGA cohort and 1.334 (95% CI: 1.125-1.581) in METABRIC cohort is the only independent prognostic factor among the 22 immune cells in early stage breast cancer. Macrophages M2, age, TNM stage and molecular types were used to build the nomogram model. The AUC of the ROC of nomogram reached 0.732 in TCGA cohort and 0.702 in METABRIC cohort. Using the nomogram score can better classified patients to low and high risk group. High risk group showed higher malignant signatures and predicted immunotherapy response rates than low risk group which consistent with the gene entrenchment analysis. Conclusion In this study, we revealed that M2 macrophages could predict the OS of breast cancer patients. Based on M2 and other clinical features we established a nomogram model which were significantly different in immune features that can better assess the OS risk or be a predictor for the immunotherapy response in breast cancer for further research.

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