A review of prognostic and predictive biomarkers in breast cancer

2022 ◽  
Author(s):  
Elaheh Tarighati ◽  
Hadi Keivan ◽  
Hojjat Mahani
Keyword(s):  
Breast Cancer ◽  
Predictive Biomarkers

Transfer RNA-Derived RNAs As Novel Predictive Biomarkers of Chemotherapy Resistance in Breast Cancer

2019 ◽  
Author(s):  
Han Ge ◽  
Yangyang Cui ◽  
Yue Huang ◽  
Mingjie Zheng ◽  
Xiaowei Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Chemotherapy Resistance ◽  
Predictive Biomarkers ◽  
Transfer Rna

MiRNAs-181a/b as Predictive biomarkers for olaparib sensitivity in triple-negative breast cancer cells.

2017 ◽  
Vol 12 (1) ◽  
pp. 221-229
Author(s):  
Abeer M. Ashmawy ◽  
Mona A. Sheta ◽  
Faten Zahran ◽  
Abdel Hady A. Abdel Wahab
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Triple Negative Breast Cancer ◽  
Breast Cancer Cells ◽  
Triple Negative ◽  
Predictive Biomarkers

Identification of biomarkers and functional modules from genomic data in stage-wise breast cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Athira K ◽  
Vrinda C ◽  
Sunil Kumar P V ◽  
Gopakumar G
Keyword(s):  
Breast Cancer ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Predictive Biomarkers ◽  
Stage I ◽  
Stage Ii ◽  
Stage Iii ◽  
Functional Modules ◽  
Incidence And Mortality ◽  
Multiple Stages

Background: Breast cancer is the most common cancer in women across the world, with high incidence and mortality rates. Being a heterogeneous disease, gene expression profiling based analysis plays a significant role in understanding breast cancer. Since expression patterns of patients belonging to the same stage of breast cancer vary considerably, an integrated stage-wise analysis involving multiple samples is expected to give more comprehensive results and understanding of breast cancer. Objective: The objective of this study is to detect functionally significant modules from gene co-expression network of cancerous tissues and to extract prognostic genes related to multiple stages of breast cancer. Methods: To achieve this, a multiplex framework is modelled to map the multiple stages of breast cancer, which is followed by a modularity optimization method to identify functional modules from it. These functional modules are found to enrich many Gene Ontology terms significantly that are associated with cancer. Result and Discussion: predictive biomarkers are identified based on differential expression analysis of multiple stages of breast cancer. Conclusion: Our analysis identified 13 stage-I specific genes, 12 stage-II specific genes, and 42 stage-III specific genes that are significantly regulated and could be promising targets of breast cancer therapy. That apart, we could identify 29, 18 and 26 lncRNAs specific to stage I, stage II and stage III respectively.

BIOM-32. ENDOPLASMIC RETICULUM PROTEIN SSR3 DETERMINES AND PREDICTS RESPONSE TO PACLITAXEL IN BREAST CANCER AND GLIOBLASTOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi17-vi18
Author(s):  
Crismita Dmello ◽  
Aarón Sonabend ◽  
Víctor Arrieta ◽  
Daniel Zhang ◽  
Deepak Kanojia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Endoplasmic Reticulum ◽  
Glioma Cells ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Precision Oncology ◽  
Patient Response ◽  
Knock Out ◽  
Genome Wide ◽  
A Genome

Abstract Paclitaxel (PTX) is one the most potent and commonly used chemotherapies for breast and pancreatic cancer. Given the potency of this drug for glioblastomas (GBM) several ongoing clinical trials are investigating means of enhancing delivery of PTX across the blood-brain barrier for this disease. In spite of the efficacy of PTX, individual tumors exhibit variable susceptibility to this drug, with response rate in the range of 30%-60%. To identify predictive biomarkers for response to PTX, we performed a genome-wide CRISPR knock-out screen using human glioma cells. The most enriched genes in the CRISPR screen underwent further selection based on their correlation with survival in the breast cancer patient cohorts treated with PTX and not in patients treated with other chemotherapies, a finding that was validated on a second independent patient cohort. This led to the discovery of endoplasmic reticulum (ER) protein SSR3 as a putative predictive biomarker for PTX. SSR3 protein levels showed positive correlation with response to PTX in breast cancer cells, glioma cells, in multiple intracranial glioma xenografts and in GBM patient derived explant cultures. Knockout of SSR3 turned the cells resistant to PTX while its overexpression sensitized the cells to PTX. In gliomas, SSR3-mediated susceptibility to PTX relates to modulation of phosphorylation of ER stress sensor IRE1α. Thus, by using genome-wide screen combined with patient response data, we discovered a biomarker that demonstrates causal and correlative relationship with response to PTX in breast cancer and GBM. Prospective validation of this biomarker is warranted for its broad implementation for precision oncology.

scholarly journals Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics with Invasive Breast Cancer Risk

2021 ◽  
Author(s):  
Mustapha Abubakar ◽  
Shaoqi Fan ◽  
Erin Aiello Bowles ◽  
Lea Widemann ◽  
Máire A Duggan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Invasive Breast Cancer ◽  
Statistical Tests ◽  
Predictive Biomarkers ◽  
Breast Cancer Diagnosis ◽  
Breast Cancer Risk Factor ◽  
Computer Assisted ◽  
Tissue Composition

Abstract Background Benign breast disease (BBD) is a strong breast cancer risk factor but identifying patients that might develop invasive breast cancer remains a challenge. Methods By applying machine-learning to digitized H&E-stained biopsies and computer-assisted thresholding to mammograms obtained circa BBD diagnosis, we generated quantitative tissue composition metrics and determined their association with future invasive breast cancer diagnosis. Archival breast biopsies and mammograms were obtained for women (18-86 years of age) in a case-control study, nested within a cohort of 15,395 BBD patients from Kaiser Permanente Northwest (1970-2012), followed through mid-2015. Cases (n = 514) who developed incident invasive breast cancer and controls (n = 514) were matched on BBD diagnosis age and plan membership duration. All statistical tests were 2-sided. Results Increasing epithelial area on the BBD biopsy was associated with increasing breast cancer risk (Odds ratio [OR]Q4 vs Q1=1.85, 95% confidence interval [CI] = 1.13-3.04; Ptrend=0.02). Conversely, increasing stroma was associated with decreased risk in non-proliferative, but not proliferative, BBD (Pheterogeneity=0.002). Increasing epithelium-to-stroma proportion [ORQ4 vs Q1=2.06, 95% CI = 1.28-3.33; Ptrend=0.002) and percent mammographic density (MBD) (ORQ4 vs Q1=2.20, 95% CI = 1.20-4.03; Ptrend=0.01) were independently and strongly predictive of increased breast cancer risk. In combination, women with high epithelium-to-stroma proportion/high MBD had substantially higher risk than those with low epithelium-to-stroma proportion/low MBD [OR = 2.27, 95% CI = 1.27-4.06; Ptrend=0.005), particularly among women with non-proliferative (Ptrend=0.01) versus proliferative (Ptrend=0.33) BBD. Conclusion Among BBD patients, increasing epithelium-to-stroma proportion on BBD biopsies and percent MBD at BBD diagnosis were independently and jointly associated with increasing breast cancer risk. These findings were particularly striking for women with non-proliferative disease (comprising approximately 70% of all BBD patients), for whom relevant predictive biomarkers are lacking.

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