Correlation between Oncotype DX recurrence score and classical risk factors in early breast cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12011-e12011
Author(s):  
Fernando Namuche ◽  
Rossana Esther Ruiz Mendoza ◽  
Claudio J. Flores ◽  
Alfredo Aguilar ◽  
Henry Leonidas Gomez
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Recurrence Score ◽  
Risk Category ◽  
Tumor Diameter ◽  
Case Scenario ◽  
Chi Square ◽  
Er Positive ◽  
Chi Square Test ◽  
Clinicopathological Variables

e12011 Background: OncotypeDx(ODX) predicts the likelihood of estrogen receptor (ER) positive breast cancer (BC) recurrence and assesses the likely benefit from both hormonal therapy and chemotherapy. Many clinical scores that estimate the risk category of ODX are being tested. Ki67 is frequently incorporated into these assessments, although there is no standard cut-off for its use. Methods: We retrospectively reviewed the electronic medical files of 190 patients with early stage ER+ BC for whom ODX recurrence score (RS) was available from 2014 to 2016. Our objective was to find out the degree to which classical clinicopathological variables -as defined by St. Gallen(SG) 2015- could predict ODX risk category, also to determine an optimal Ki67 cut-off in order to establish an accurate classification. Chi square test was used. Results: Mean age at diagnosis was 59 years (28-89). Mean tumor diameter was 15mm, 84.2% were intermediate grade. Mean expression of ER, PR and Ki67 were 87%, 53% and 22%, respectively. According to ODX 62.1% patients were low risk, 30.5% intermediate risk and 7.4% high risk. An overall concordance of 46.8% (73/190) was found between SG 2015 and the risk category of ODX (75.7% for low, 33.3% for intermediate and 23.9% for high RS). When changing SG Ki67 cutoffs to ≤20% (for low Ki67) and ≥30% (for high Ki67), an overall concordance of 56.3% (107/190) was found (69.6% for low, 47.3% for intermediate and 23.9% for high RS, with p=0.00) (Table 1). Conclusions: In best-case scenario, SG classical clinicopathological variables correctly classified 56.3% of patients of our series. Despite being a specialized center, the utility of classical clinicopathological variables for predicting ODX risk category is limited. [Table: see text]

scholarly journals Optimizing the Use of Gene Expression Profiling in Early-Stage Breast Cancer

2016 ◽  
Vol 34 (36) ◽  
pp. 4390-4397 ◽  
Author(s):  
Hyun-seok Kim ◽  
Christopher B. Umbricht ◽  
Peter B. Illei ◽  
Ashley Cimino-Mathews ◽  
Soonweng Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Decision Making ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Early Stage ◽  
Recurrence Score ◽  
Clinical Samples ◽  
Risk Category ◽  
Clinical Risk

Purpose Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor–positive, lymph node–negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clinicopathologic data. Hence, we developed a model that uses routine pathologic parameters to estimate Oncotype DX recurrence score (ODX RS) and independently tested its ability to predict ODX RS in clinical samples. Patients and Methods We retrospectively reviewed ordered ODX RS and pathology reports from five institutions (n = 1,113) between 2006 and 2013. We used locally performed histopathologic markers (estrogen receptor, progesterone receptor, Ki-67, human epidermal growth factor receptor 2, and Elston grade) to develop models that predict RS-based risk categories. Ordering patterns at one site were evaluated under an integrated decision-making model incorporating clinical treatment guidelines, immunohistochemistry markers, and ODX. Final locked models were independently tested (n = 472). Results Distribution of RS was similar across sites and to reported clinical practice experience and stable over time. Histopathologic markers alone determined risk category with > 95% confidence in > 55% (616 of 1,113) of cases. Application of the integrated decision model to one site indicated that the frequency of testing would not have changed overall, although ordering patterns would have changed substantially with less testing of estimated clinical risk–high or clinical risk–low cases and more testing of clinical risk–intermediate cases. In the validation set, the model correctly predicted risk category in 52.5% (248 of 472). Conclusion The proposed model accurately predicts high- and low-risk RS categories (> 25 or ≤ 25) in a majority of cases. Integrating histopathologic and molecular information into the decision-making process allows refocusing the use of new molecular tools to cases with uncertain risk.

The effect of non-oncology drugs on clinical and genomic risk in early luminal breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12505-e12505
Author(s):  
Barliz Waissengrin ◽  
Ido Wolf ◽  
Tamar Zahavi ◽  
Mali Salmon‐Divon ◽  
Amir Sonnenblick
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Early Stage ◽  
Recurrence Score ◽  
Luminal Breast Cancer ◽  
Breast Cancer Patients ◽  
Clinical Risk ◽  
Er Positive ◽  
The Impact ◽  
Genomic Risk

e12505 Background: In women with ER-positive, HER2-negative early stage breast cancer (BC), treatment decisions for adjuvant chemotherapy are based on genomic risk and clinical risk. Recently, an effect of non-oncology medications on cancer cell lines and cancer outcome have been suggested. In this study we aimed to systematically examine the impact of non- oncology drugs on clinical risk and genomic risk (based on OncotypeDx recurrence score [RS]) in early BC. Methods: We collected data from 1385 files of ER positive HER2 negative breast cancer patients regarding their clinical risk (stage and grade), genomic risk (OncotypeDx RS) as well as data regarding medications the patients received during the month before their surgery. Statistical analysis was applied to identify the influence of various medications on OncotypeDx RS. Results: Out of the various medications we examined, we found that Levothyroxine was significantly associated with high median OncotypeDx RS (RS median = 25 ;p < 0.0001) and Metformin was associated with low median OncotypeDx RS (RS median = 12 p < 0.0011) in comparison to patients not receiving these medications (RS median = 17). By contrast there were no differences in the clinical risk between patients who received Metformin or Levothyroxine. Notably, Levothyroxine and metformin did not impact proliferation marker (Ki67) levels but did impact progesterone-related features, suggesting they influence genomic risk through estrogen dependent modules. Indeed, scores of other genomic tests (PAM50, Mammaprint), which are determined largely by proliferative features, were not influenced by Levothyroxine or Metformin. Finally, by using contemporary guidelines to recommend adjuvant chemotherapy based on clinical risk and genomic risk (OncotypeDx ) we show that patients (Age > 50) who received Metformin treatment had 14.5% chance to be recommended adjuvant chemotherapy while patients who received Levothyroxine had 49% (p = 0.0001). Conclusions: The results of this study indicate significant impact of Metformin and Levothyroxine on clinical decisions with potential impact on early BC patients.

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