scholarly journals Cardiotoxicity Related to Long-Term Trastuzumab Therapy in Metastatic Breast Cancer: The Potential Role of Treatment Duration and Cardiac Risk Factors

2015 ◽  
Vol 21 (3) ◽  
pp. 318-321 ◽  
Author(s):  
Barbara Pistilli ◽  
Andrea Marcellusi ◽  
Luciano Latini ◽  
Roberto Accardi ◽  
Benedetta Ferretti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Treatment Duration ◽  
Potential Role ◽  
Metastatic Breast ◽  
Cardiac Risk ◽  
Trastuzumab Therapy

scholarly journals The potential role of HER 2 upregulation in metastatic breast cancer to the uterus: a case report

2016 ◽  
Vol 4 (10) ◽  
pp. 928-934 ◽  
Author(s):  
Melissa Y. Y. Moey ◽  
Omer A. Hassan ◽  
Christos N. Papageorgiou ◽  
Stephanie L. Schnepp ◽  
John T. Hoff
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Case Report ◽  
Potential Role ◽  
Metastatic Breast ◽  
Her 2

scholarly journals Long term continuous Trastuzumab use for HER2-positive advanced breast cancer

2020 ◽  
Vol 6 (5) ◽  
Author(s):  
Haider Y. Shukur
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Metastatic Breast ◽  
Loading Dose ◽  
Breast Cancer Patients ◽  
Her2 Positive ◽  
Trastuzumab Therapy ◽  
Locally Recurrent ◽  
Positive Breast Cancer

Objectives: Trastuzumab is the standard of care for locally advanced / metastatic her2-positive breast cancer. However, most of these patients will progress within 12 months of trastuzumab therapy. In contrast, there is a paucity of data available on the long-term treatment of patients with Trastuzumab. Our study was conducted to report efficacy and safety data for patients with locally recurrent / metastatic her2-positive breast cancer who received long-term therapy with Trastuzumab (≥5 years). Methods: This study was a prospective single-arm study of continuous Trastuzumab in patients who were histologically her2-positive and radiologically confirmed inoperable locally recurrent or metastatic breast cancer after complete one year of Trastuzumab plus chemotherapy (in hormone negative/hormone resistance) treatment then continuous Trastuzumab alone , or with hormone therapy (in sensitive hormone positive) without progression [complete or partial response or stable disease].  A total of 50 inoperable local recurrent and metastatic breast cancer  patients were treated with continuous intravenous Trastuzumab therapy administered according to the standard Trastuzumab every 3-weeks (8 mg/kg loading dose followed by 3-weekly 6  mg/kg maintenance doses starting 3 weeks after the loading dose) schedule, from January 2014 to January 2019 at the Najaf Cancer Clinic (NCC). Results: All 50 patients were evaluated with CR occur only in 20% (10/50) with an OAR of 50% (25/50). The cardiac status of these patients remained stable over time for the majority of patients with no marked changes in LVEF%. No treatment-related death was observed. The median OS and median PFS is 61 months and 20 months, respectively. Conclusion: In her2-positive recurrent and metastatic breast cancer patients, who initially respond to palliative treatment with trastuzumab, continuous trastuzumab can achieve a long-term tumor remission of several years and had significantly improved survival with tolerated and acceptable adverse events.

scholarly journals Machine-Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer

2021 ◽  
Author(s):  
Juan Luis Gomez Marti ◽  
Adam Brufsky ◽  
Alan Wells ◽  
Xia Jiang
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Metastatic Breast ◽  
Large Data ◽  
Late Recurrence ◽  
Individual Risk ◽  
Metastatic Recurrence

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being conducted to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms may predict not only risk factors but also interactions among these risks, which consequently lead to metastatic breast cancer. We proposed to apply a previously developed machine-learning method to predict risk factors of 5-, 10- and 15-year metastasis. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive risk factor Learner (MBIL) on the electronic health record (EHR)-based Lynn Sage database (LSDB) from the Lynn Sage Comprehensive Breast Cancer at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastasis from LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and the reliance on interactivity between risk factors. Results: We found that with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term.

scholarly journals Venous and Arterial Thrombosis Following Abemaciclib Therapy for Metastatic Breast Cancer

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4063-4063
Author(s):  
Nathan Watson ◽  
Seth A Wander ◽  
Hanny Al-Samkari
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Brain Metastases ◽  
Risk Score ◽  
Real World ◽  
Arterial Thrombosis ◽  
Research Funding ◽  
Metastatic Breast

Abstract Introduction: Over the past several years, inhibitors of cyclin-dependent kinases 4 and 6 (CDK 4/6) have revolutionized the treatment of hormone receptor (HR)-positive breast cancer. However, evidence suggests an increased risk of venous thromboembolism (VTE) with use of these agents. Recent studies additionally suggest higher VTE rates in real-world populations receiving palbociclib as compared with the highly selected population of published clinical trials. Such study in real-world patients has not been performed for abemaciclib, a newer CDK 4/6 inhibitor with unique pharmacokinetic and pharmacodynamic properties. This study evaluated rates and predictors of thrombosis in patients receiving abemaciclib for metastatic breast cancer. Methods: We conducted a multicenter observational cohort study of patients with metastatic breast cancer receiving abemaciclib at 5 affiliated hospitals. A research patient data repository was queried to identify all patients receiving abemaciclib and manual chart review was used to extract all data. Patient demographics, concurrent medications, labs, Khorana risk score, tumor characteristics, and relevant venous and arterial thrombotic risk factors (including age, BMI, prior thrombosis, recent surgery, hereditary thrombophilia, systemic inflammatory diseases, presence of brain metastases, hypertension, hyperlipidemia, diabetes mellitus, atrial fibrillation, heart failure, and atherosclerosis) were collected for all patients. The primary endpoint was thrombosis during abemaciclib treatment or within 30 days of discontinuation. Multivariable logistic models assessed predictors of VTE and a multivariable Cox proportional hazards model was used to compare mortality in patients developing VTE with those who did not. Data are presented as median (IQR) or number (%). Results: Patient Cohort and Thrombosis Risk Factors. 364 patients were included in the analysis. 360 (98.9%) patients were female, with median (interquartile range) age of 61 (53-71) years. 320 (88.7%) were post-menopausal and 291 (79.9%) were concurrently on endocrine therapy (of which 19 (5.2%) were on tamoxifen). At the time of abemaciclib initiation, 51 (14.0%) were receiving long-term anticoagulation and 47 (12.9%) were receiving aspirin. Khorana scores were between 0-3 with 339 (93.1%) patients having a score of 0 or 1. 267 (73.4%) and 46 (12.6%) were diagnosed with invasive ductal and lobular carcinoma, respectfully. Brain metastases were present in 71 (19.5%) patients. Venous and arterial thrombosis risk factors for this cohort are highlighted in TABLE 1. The median duration of abemaciclib therapy was 5.5 (2.8-13.0) months and median duration of follow-up was 12.7 (6.2-22.1) months. Thrombotic Events. 27 patients (7.4%) developed one or more thrombotic event (17 VTE, 9 arterial thrombosis, 1 both). Events are described in TABLE 2. Risk Factors for VTE. In a multivariable logistic model including age, race, BMI, receipt of long-term anticoagulation, receipt of aspirin, brain metastases, Khorana risk score, receipt of tamoxifen, prior VTE, systemic autoimmune disease, and known thrombophilia, HER2 positivity was predictive of VTE during or after abemaciclib treatment (odds ratio 5.20, 95% CI 1.29-20.93, P=0.020). Association of VTE with Mortality. In a multivariable Cox model controlling for age, race, HER2 status, receipt of long-term anticoagulation, receipt of aspirin, brain metastases, Khorana risk score, receipt of tamoxifen, prior VTE, systemic autoimmune disease, and known thrombophilia, patients developing VTE during abemaciclib therapy had a significantly higher risk of death (hazard ratio, 2.04, 95% CI, 1.03-4.01, P=0.040), FIGURE 1. Median survival in patients developing a VTE vs. those who did not was 9.6 months vs. 25.8 months, respectively. Conclusions: In this study, we provide the first real-world data describing risk of venous and arterial thrombosis in a large cohort of patients with metastatic breast cancer treated with the CDK 4/6 inhibitor abemaciclib. As the role of abemaciclib continues to expand both within and beyond the metastatic disease setting, understanding the VTE risk of this agent has become critical. Thrombosis occurred in 7.4%, and in multivariable models controlling for relevant covariates, HER2 positivity predicted for development of VTE, and patients developing VTE had an approximate 2-fold risk of mortality. Figure 1 Figure 1. Disclosures Al-Samkari: Moderna: Consultancy; Amgen: Research Funding; Novartis: Consultancy; Rigel: Consultancy; Argenx: Consultancy; Dova/Sobi: Consultancy, Research Funding; Agios: Consultancy, Research Funding.

Longitudinal analysis of trastuzumab use in metastatic breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 11513-11513
Author(s):  
A. Hammerman ◽  
S. Klang ◽  
N. Liebermann ◽  
N. Ben-Baruch
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Longitudinal Analysis ◽  
Real Life ◽  
Metastatic Breast ◽  
Molecular Characteristics ◽  
Duration Of Treatment ◽  
Trastuzumab Therapy ◽  
Sick Fund

11513 Background: Six years after including Trastuzumab in the Israeli ‘National Health Basket‘ (NHB) for metastatic breast cancer (MBC), we evaluated its ‘real-life‘ use in terms of duration of treatment (DOT) and survival after initiation of treatment. Methods: The computerized database of Clalit Health Services’ (CHS), the largest Israeli sick fund, was used to determine DOT and survival (at Dec. 31, 2005), in all CHS patients with MBC that started Trastuzumab therapy during years 2000–2003. Results: * At Dec. 31, 2005. ** However, most received AC as adjuvant. Conclusions: A longitudinal analysis of Trastuzumab use shows a difference of 40–100% between the median and mean DOT. This difference is attributed to a sizeable proportion (∼15%) of patients that are “long-term responders” and are treated for more than 24 months. In these patients, we can assume that the HER-2 amplification is an essential transforming event. We recommend investigating the molecular characteristics of long-term responders. [Table: see text] No significant financial relationships to disclose.

scholarly journals Machine Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 253
Author(s):  
Juan Luis Gomez Marti ◽  
Adam Brufsky ◽  
Alan Wells ◽  
Xia Jiang
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Factors ◽  
Metastatic Breast Cancer ◽  
Metastatic Breast ◽  
Large Data ◽  
Late Recurrence ◽  
Individual Risk ◽  
Metastatic Recurrence

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being used to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms can identify not only risk factors but also interactions among these risks, which consequently may increase the risk of developing metastatic breast cancer. We proposed to apply a previously developed machine-learning method to discern risk factors of 5-, 10- and 15-year metastases. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive Risk Factor Learner (MBIL) to the electronic health record (EHR)-based Lynn Sage Database (LSDB) from the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastases from the LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and reliance on interactivity between risk factors. Results: We found that, with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long-term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term.

Sign in / Sign up

Export Citation Format

Share Document