scholarly journals Grading of invasive breast carcinoma: the way forward

2021 ◽  
Author(s):  
C. van Dooijeweert ◽  
P. J. van Diest ◽  
I. O. Ellis
Keyword(s):  
Breast Cancer ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Adverse Outcomes ◽  
Breast Cancer Patients ◽  
Histologic Grading ◽  
Patients At Risk ◽  
History Of ◽  
Cancer Grading

AbstractHistologic grading has been a simple and inexpensive method to assess tumor behavior and prognosis of invasive breast cancer grading, thereby identifying patients at risk for adverse outcomes, who may be eligible for (neo)adjuvant therapies. Histologic grading needs to be performed accurately, on properly fixed specimens, and by adequately trained dedicated pathologists that take the time to diligently follow the protocol methodology. In this paper, we review the history of histologic grading, describe the basics of grading, review prognostic value and reproducibility issues, compare performance of grading to gene expression profiles, and discuss how to move forward to improve reproducibility of grading by training, feedback and artificial intelligence algorithms, and special stains to better recognize mitoses. We conclude that histologic grading, when adequately carried out, remains to be of important prognostic value in breast cancer patients.

Gene expression profiles of circulating tumor cells in metastatic breast cancer patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10504-10504
Author(s):  
Bianca Mostert ◽  
Anieta M Sieuwerts ◽  
Jaco Kraan ◽  
Joan Bolt-de Vries ◽  
Dieter Peeters ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Metastatic Breast Cancer ◽  
Cancer Patients ◽  
Prognostic Value ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Metastatic Breast ◽  
Breast Cancer Patients ◽  
Gene Profile

10504 Background: A circulating tumor cell (CTC) count is an established prognostic factor in metastatic breast cancer. Besides enumeration, CTC characterization promises to further improve outcome prediction and treatment guidance. We have previously shown the feasibility of measuring the expression of a panel of 96 clinically relevant genes in CTCs in a leukocyte background, and in the current study, we determined the prognostic value of CTC gene expression profiling in metastatic breast cancer. Methods: CTCs were isolated and enumerated from blood of 130 metastatic breast cancer patients prior to start of first-line systemic, endocrine or chemotherapeutic, therapy. Of these, 103 were evaluable for mRNA gene expression levels measured by quantitative RT-PCR in relation to time to treatment switch (TTS). Separate prognostic CTC gene profiles were generated by leave-one-out cross validation for all patients and for patients with ≥5 CTCs per 7.5 mL blood, and cut-offs were chosen to ensure optimal prediction of patients who might benefit from an early therapy switch. Results: In the total cohort, of whom 56% received chemotherapeutic and 44% endocrine therapy, baseline CTC count (≥5 versus <5 CTCs/7.5 mL blood) predicted for TTS (Hazard Ratio (HR) 2.92 [95% Confidence Interval (CI) 1.71 – 4.95] P <0.0001). A 16-gene CTC profile for all patients and a separate 9-gene CTC profile applicable for patients with ≥5 CTCs were identified, which distinguished those patients with TTS or death within 9 months from those with a more favorable outcome. Test performance for both profiles was favorable; the 16-gene profile had 90% sensitivity, 38% specificity, 50% positive predictive value (PPV) and 85% negative predictive value (NPV), and the 9-gene profile performed slightly better at 92% sensitivity, 52% specificity, 66% PPV and 87% NPV. In multivariate Cox regression analysis, the 16-gene profile was the only factor independently associated with TTS (HR 3.15 [95%CI 1.35 – 7.33] P 0.008). Conclusions: Two CTC gene expression profiles were discovered, which provide prognostic value in metastatic breast cancer patients. This study further underscores the potential of molecular characterization of CTCs.

Genomic Prediction of Locoregional Recurrence After Mastectomy in Breast Cancer

2006 ◽  
Vol 24 (28) ◽  
pp. 4594-4602 ◽  
Author(s):  
Skye H. Cheng ◽  
Cheng-Fang Horng ◽  
Mike West ◽  
Erich Huang ◽  
Jennifer Pittman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Eligible Patient ◽  
Gene Expression Profiles ◽  
Breast Cancer Patients ◽  
Predictive Index ◽  
Validation Data ◽  
Data Set

Purpose This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. Patients and Methods A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. Results Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. Conclusion Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression–based predictive index can be used to select patients for PMRT.

Targeting PSG1 to enhance chemotherapeutic efficacy: new application for anti-coagulant the dicumarol

2016 ◽  
Vol 130 (24) ◽  
pp. 2267-2276 ◽  
Author(s):  
Dong-xu He ◽  
Feng Gu ◽  
Jian Wu ◽  
Xiao-Ting Gu ◽  
Chun-Xiao Lu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Transforming Growth Factor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Transforming Growth Factor Β ◽  
Clinical Samples ◽  
Breast Cancer Patients

Chemotherapeutic response is critical for the successful treatment and good prognosis in cancer patients. In this study, we analysed the gene expression profiles of preoperative samples from oestrogen receptor (ER)-negative breast cancer patients with different responses to taxane-anthracycline-based (TA-based) chemotherapy, and identified a group of genes that was predictive. Pregnancy specific beta-1-glycoprotein 1 (PSG1) played a central role within signalling pathways of these genes. Inhibiting PSG1 can effectively reduce chemoresistance via a transforming growth factor-β (TGF-β)-related pathway in ER-negative breast cancer cells. Drug screening then identified dicumarol (DCM) to target the PSG1 and inhibit chemoresistance to TA-based chemotherapy in vitro, in vivo, and in clinical samples. Taken together, this study highlights PSG1 as an important mediator of chemoresistance, whose effect could be diminished by DCM.

scholarly journals Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen

2008 ◽  
Vol 113 (2) ◽  
pp. 275-283 ◽  
Author(s):  
Marleen Kok ◽  
Sabine C. Linn ◽  
Ryan K. Van Laar ◽  
Maurice P. H. M. Jansen ◽  
Teun M. van den Berg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Breast Cancer Patients

scholarly journals Integrative analysis of DNA methylation and gene expression profiles identified potential breast cancer-specific diagnostic markers

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xinhua Liu ◽  
Yonglin Peng ◽  
Ju Wang
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Diagnostic Model ◽  
Breast Cancer Patients ◽  
Gene Expressions ◽  
Normal Tissues

Abstract Breast cancer is a common malignant tumor among women whose prognosis is largely determined by the period and accuracy of diagnosis. We here propose to identify a robust DNA methylation-based breast cancer-specific diagnostic signature. Genome-wide DNA methylation and gene expression profiles of breast cancer patients along with their adjacent normal tissues from the Cancer Genome Atlas (TCGA) were obtained as the training set. CpGs that with significantly elevated methylation level in breast cancer than not only their adjacent normal tissues and the other ten common cancers from TCGA but also the healthy breast tissues from the Gene Expression Omnibus (GEO) were finally remained for logistic regression analysis. Another independent breast cancer DNA methylation dataset from GEO was used as the testing set. Lots of CpGs were hyper-methylated in breast cancer samples compared with adjacent normal tissues, which tend to be negatively correlated with gene expressions. Eight CpGs located at RIIAD1, ENPP2, ESPN, and ETS1, were finally retained. The diagnostic model was reliable in separating BRCA from normal samples. Besides, chromatin accessibility status of RIIAD1, ENPP2, ESPN and ETS1 showed great differences between MCF-7 and MDA-MB-231 cell lines. In conclusion, the present study should be helpful for breast cancer early and accurate diagnosis.

scholarly journals Hormone Receptor-status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

2020 ◽  
Author(s):  
Seokhyun Yoon ◽  
Hye Sung Won ◽  
Keunsoo Kang ◽  
Kexin Qiu ◽  
Woong June Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Hormone Receptor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Intrinsic Subtypes ◽  
Receptor Status

AbstractThe cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer (BRCA) patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from the cancer genome atlas TCGA BRCA and METABRIC datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal BRCA. We also confirmed that non-matching subgroup classification affected the survival of BRCA patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

scholarly journals Gene Expression Profiling of Evening Fatigue in Women Undergoing Chemotherapy for Breast Cancer

2016 ◽  
Vol 18 (4) ◽  
pp. 370-385 ◽  
Author(s):  
Kord M. Kober ◽  
Laura Dunn ◽  
Judy Mastick ◽  
Bruce Cooper ◽  
Dale Langford ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sickness Behavior ◽  
Cell Types ◽  
Breast Cancer Patients ◽  
Oncology Patients ◽  
Public Data ◽  
Comorbidity Scores

Moderate-to-severe fatigue occurs in up to 94% of oncology patients undergoing active treatment. Current interventions for fatigue are not efficacious. A major impediment to the development of effective treatments is a lack of understanding of the fundamental mechanisms underlying fatigue. In the current study, differences in phenotypic characteristics and gene expression profiles were evaluated in a sample of breast cancer patients undergoing chemotherapy (CTX) who reported low ( n = 19) and high ( n = 25) levels of evening fatigue. Compared to the low group, patients in the high evening fatigue group reported lower functional status scores, higher comorbidity scores, and fewer prior cancer treatments. One gene was identified as upregulated and 11 as downregulated in the high evening fatigue group. Gene set analysis found 24 downregulated and 94 simultaneously up- and downregulated pathways between the two fatigue groups. Transcript origin analysis found that differential expression (DE) originated primarily from monocytes and dendritic cell types. Query of public data sources found 18 gene expression experiments with similar DE profiles. Our analyses revealed that inflammation, neurotransmitter regulation, and energy metabolism are likely mechanisms associated with evening fatigue severity; that CTX may contribute to fatigue seen in oncology patients; and that the patterns of gene expression may be shared with other models of fatigue (e.g., physical exercise and pathogen-induced sickness behavior). These results suggest that the mechanisms that underlie fatigue in oncology patients are multifactorial.

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