Clinical High and Genomic Low Hormone Receptor-positive Early Breast Cancer Patients With or Without Adjuvant Chemotherapy

2020 ◽  
Author(s):  
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Early Breast Cancer ◽  
Hormone Receptor ◽  
Breast Cancer Patients ◽  
Hormone Receptor Positive

Independent validation of stromal uPA in archived tumor samples from the prospectively randomized ABCSG8 trial to provide level 1b evidence for a prognostic value of uPA immunohistochemistry in endocrine-treated postmenopausal breast cancer patients.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 536-536
Author(s):  
Christian F. Singer ◽  
Stephan W Jahn ◽  
Margaretha Rudas ◽  
Zsuzsanna Bago-Horvath ◽  
Florian Fitzal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Early Breast Cancer ◽  
Hormone Receptor ◽  
Prognostic Value ◽  
Breast Cancer Patients ◽  
Prognostic Role ◽  
Hormone Receptor Positive ◽  
Pai 1

536 Background: We have recently demonstrated that urokinase Plasminogen Activator (uPA), together with its inhibitor PAI-1, have prognostic value in hormone-receptor positive early breast cancer, and can be measured in FFPE archived tumor samples. We have now aimed to validate the prognostic role of uPA protein expression in FFPE archived tumor samples in an independent cohort of endocrine-treated breast cancer patients. Methods: 303 postmenopausal women with hormone receptor–positive, early breast cancer who had received 5 years of endocrine therapy in the prospectively designed ABCSG-08 trial, and in whom FFPE tumor tissue was available, were included in this analysis. Stromal uPA and PAI-1 protein expression was evaluated by immunohistochemistry and correlated with distant recurrence-free survival (DRFS) and overall survival (OS). Results: Stromal uPA was detected in 132 of 297 tumors (44.4%), and 74 out of 269 samples (27.5%) exhibited stromal PAI-1, while co-expression of both proteins was found in 48 of 294 (16.3%) samples. Neither uPA nor PAI-1 expression were associated with tumor size, age, nodal status, grading, or receptor status. Patients whose tumor stroma expressed uPA protein were more likely to have a shorter DRFS (adjusted HR for relapse: 2.78; 95% CI 1.31-5.93; p=0.008 Cox regression analysis) and OS (adjusted HR for death: 1.29; 95% CI 0.86-12.50; p=0.161) than women without uPA expression. No such association was observed for PAI-1 and for the uPA/PAI1 ratio. After a median follow-up of 5.6 years women with uPA-positive tumors experienced a significantly shorter DRFS (93.3% vs 84.8%; p<0.013 log rank test) and tended to have a worse OS (83.0.4% vs 77.3%; p=0.106) compared to women with uPA negative tumors. Conclusions: By confirming the clinical utility of stromal uPA IHC in archived breast cancer samples from an independent prospective randomized trial, we now provide level 1b evidence for a prognostic role of stromal uPA in women with endocrine-responsive early breast cancer.

Survival advantage associated with neoadjuvant chemotherapy compared to neoadjuvant hormonal therapy in postmenopausal women with hormone receptor positive breast cancer: A National Cancer Data Base study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12119-e12119
Author(s):  
Alina Basnet ◽  
Dongliang Wang ◽  
Abirami Sivapiragasam
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Hormone Receptor ◽  
National Cancer Data Base ◽  
Breast Cancer Patients ◽  
Cancer Data ◽  
Hormone Receptor Positive ◽  
Positive Breast Cancer

e12119 Background: Neoadjuvant endocrine therapy (NET) and neoadjuvant chemotherapy (NCT) are both considered effective strategies in postmenopausal, hormone receptor positive breast cancer patients. Small prospective studies show comparable response rates and breast conservation rates. Using National Cancer Data Base (NCDB) we report overall survival (OS) differences between these two strategies with subgroup analysis by Estrogen Receptor (ER), Progesterone Receptor (PgR) status. Methods: We extracted data on hormone receptor positive breast cancer patients without metastasis in women aged ≥ 50 from the NCDB registry (2004-2014). We excluded patients who did not receive adjuvant endocrine therapy after NCT and patients who received adjuvant chemotherapy after NET as this could affect OS. We calculated OS using Kaplan Meier analysis with hazard ratio (HR) from cox regression model. Subgroup analysis was performed by ER, PgR status. Results: Out of 2,246,279 patients, 30,348 patients met our inclusion criteria. 7836 received NET and 22512 received NCT. OS rate was 70.8% vs 81.7% at 5 yrs and 42.5% vs 62.1% at 9 yrs for NET and NCT respectively with adjusted hazard ratio (HR) of 1.818; 95% CI (1.657-1.996). OS outcome for ER+/PgR+ group was 72.3% vs 83.5% at 5 yrs and 43.5% vs 64% at 9 yrs for NET and NCT respectively with adjusted HR of 1.807; 95% CI (1.624-2.010). OS for ER+/pgR- group was 62.9% vs 76.8% at 5 yrs and 33.1% vs 54.2% at 9 yrs for NET and NCT respectively with adjusted HR of 1.890; 95% CI (1.549-2.306). Our analysis also revealed that 5591 T1 patients received neoadjuvant therapy among which 2541 received NET and 3050 received NCT. Conclusions: We find a significant survival advantage in patients treated with NCT as opposed to NET. All subgroups showed imporved OS with NCT compared with NET. Limitations that should be considered in this registry based study are: not accounting for Her-2 status, differences in surgical technique, duration and choices of adjuvant chemotherapy and radiotherapy options.

scholarly journals Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soo Youn Cho ◽  
Jeong Hoon Lee ◽  
Jai Min Ryu ◽  
Jeong Eon Lee ◽  
Eun Yoon Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Hormone Receptor ◽  
Oncotype Dx ◽  
Breast Cancer Patients ◽  
Hormone Receptor Positive ◽  
Predicted Risk ◽  
Positive Breast Cancer

AbstractWe hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. HE slides were retrospectively collected from 1343 de-identified breast cancer patients at the Samsung Medical Center and used to develop the Lunit SCOPE algorithm. Lunit SCOPE was trained to predict the recurrence using the 21-gene assay (Oncotype DX) and histological parameters. The risk prediction model predicted the Oncotype DX score > 25 and the recurrence survival of the prognosis validation cohort and TCGA cohorts. The most important predictive variable was the mitotic cells in the cancer epithelium. Of the 363 patients who did not receive adjuvant therapy, 104 predicted high risk had a significantly lower survival rate. The top-300 genes highly correlated with the predicted risk were enriched for cell cycle, nuclear division, and cell division. From the Oncotype DX genes, the predicted risk was positively correlated with proliferation-associated genes and negatively correlated with prognostic genes from the estrogen category. An integrative analysis using Lunit SCOPE predicted the risk of cancer recurrence and the early-stage hormone receptor-positive breast cancer patients who would benefit from adjuvant chemotherapy.

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