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

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.

2020 ◽  
Vol 31 ◽  
pp. S365-S366
Author(s):  
N. Lindegger ◽  
C. Ike ◽  
N.R.M. Schwartz ◽  
A. Surinach ◽  
Y. Liu ◽  
...  

Author(s):  
Juan Luis Gomez Marti ◽  
Adam Brufsky ◽  
Alan Wells ◽  
Xia Jiang

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.


2015 ◽  
Vol 21 (3) ◽  
pp. 318-321 ◽  
Author(s):  
Barbara Pistilli ◽  
Andrea Marcellusi ◽  
Luciano Latini ◽  
Roberto Accardi ◽  
Benedetta Ferretti ◽  
...  

2021 ◽  
Author(s):  
Nadia Harbeck ◽  
Meaghan Bartlett ◽  
Dean Spurden ◽  
Becky Hooper ◽  
Lin Zhan ◽  
...  

Background: This review aims to qualitatively summarize the published real-world evidence (RWE) for CDK4/6 inhibitors (CDK4/6i) approved for treating HR+, HER2-negative advanced/metastatic breast cancer (HR+/HER2- a/mBC). Materials & methods: A systematic literature review was conducted to identify RWE studies of CDK4/6i in HR+/HER2- a/mBC published from 2015 to 2019. Results: This review identified 114 studies, of which 85 were only presented at scientific conferences. Most RWE studies investigated palbociclib and demonstrated improved outcomes. There are limited long-term and comparative data between CDK4/6i and endocrine monotherapy, and within the CDK4/6i class. Conclusion: Available RWE suggests that CDK4/6i are associated with improved outcomes in HR+/HER2- a/mBC, although additional studies with longer follow-up periods are needed.


Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 253
Author(s):  
Juan Luis Gomez Marti ◽  
Adam Brufsky ◽  
Alan Wells ◽  
Xia Jiang

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.


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