Abstract P4-15-10: Initial therapy among patients newly diagnosed with operable early stage human epidermal growth factor receptor 2-overexpressed (HER2+) breast cancer in the US: A real-world retrospective study

2015 ◽  
Author(s):  
Stacey DaCosta Byfield ◽  
Philip O Buck ◽  
Cori Blauer-Peterson ◽  
Sara A Poston
Keyword(s):  
Breast Cancer ◽  
Epidermal Growth Factor Receptor ◽  
Retrospective Study ◽  
Early Stage ◽  
Growth Factor Receptor ◽  
Initial Therapy ◽  
Newly Diagnosed ◽  
Her2 Breast Cancer ◽  
The Us ◽  
Epidermal Growth

scholarly journals Preclinical Characteristics of the Irreversible Pan-HER Kinase Inhibitor Neratinib Compared with Lapatinib: Implications for the Treatment of HER2-Positive and HER2-Mutated Breast Cancer

Cancers ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 737 ◽  
Author(s):  
Denis M. Collins ◽  
Neil T. Conlon ◽  
Srinivasaraghavan Kannan ◽  
Chandra S. Verma ◽  
Lisa D. Eli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Epidermal Growth Factor Receptor ◽  
Growth Factor ◽  
Epidermal Growth Factor ◽  
Adjuvant Treatment ◽  
Early Stage ◽  
Growth Factor Receptor ◽  
Her2 Positive ◽  
Her2 Breast Cancer ◽  
Epidermal Growth

An estimated 15–20% of breast cancers overexpress human epidermal growth factor receptor 2 (HER2/ERBB2/neu). Two small-molecule tyrosine kinase inhibitors (TKIs), lapatinib and neratinib, have been approved for the treatment of HER2-positive (HER2+) breast cancer. Lapatinib, a reversible epidermal growth factor receptor (EGFR/ERBB1/HER1) and HER2 TKI, is used for the treatment of advanced HER2+ breast cancer in combination with capecitabine, in combination with trastuzumab in patients with hormone receptor-negative metastatic breast cancer, and in combination with an aromatase inhibitor for the first-line treatment of HER2+ breast cancer. Neratinib, a next-generation, irreversible pan-HER TKI, is used in the US for extended adjuvant treatment of adult patients with early-stage HER2+ breast cancer following 1 year of trastuzumab. In Europe, neratinib is used in the extended adjuvant treatment of adult patients with early-stage hormone receptor-positive HER2+ breast cancer who are less than 1 year from the completion of prior adjuvant trastuzumab-based therapy. Preclinical studies have shown that these agents have distinct properties that may impact their clinical activity. This review describes the preclinical characterization of lapatinib and neratinib, with a focus on the differences between these two agents that may have implications for patient management.

scholarly journals Role of Fcγ receptors in HER2-targeted breast cancer therapy

2022 ◽  
Vol 10 (1) ◽  
pp. e003171
Author(s):  
Antonino Musolino ◽  
William J Gradishar ◽  
Hope S Rugo ◽  
Jeffrey L Nordstrom ◽  
Edwin P Rock ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Epidermal Growth Factor Receptor ◽  
Growth Factor ◽  
Epidermal Growth Factor ◽  
Ex Vivo ◽  
Early Stage ◽  
Growth Factor Receptor ◽  
Her2 Breast Cancer ◽  
Immunological Mechanisms ◽  
Epidermal Growth

Several therapeutic monoclonal antibodies (mAbs), including those targeting epidermal growth factor receptor, human epidermal growth factor receptor 2 (HER2), and CD20, mediate fragment crystallizable gamma receptor (FcγR)–dependent activities as part of their mechanism of action. These activities include induction of antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP), which are innate immune mechanisms of cancer cell elimination. FcγRs are distinguished by their affinity for the Fc fragment, cell distribution, and type of immune response they induce. Activating FcγRIIIa (CD16A) on natural killer cells plays a crucial role in mediating ADCC, and activating FcγRIIa (CD32A) and FcγRIIIa on macrophages are important for mediating ADCP. Polymorphisms in FcγRIIIa and FcγRIIa generate variants that bind to the Fc portion of antibodies with different affinities. This results in differential FcγR-mediated activities associated with differential therapeutic outcomes across multiple clinical settings, from early stage to metastatic disease, in patients with HER2+ breast cancer treated with the anti-HER2 mAb trastuzumab. Trastuzumab has, nonetheless, revolutionized HER2+ breast cancer treatment, and several HER2-directed mAbs have been developed using Fc glyco-engineering or Fc protein-engineering to enhance FcγR-mediated functions. An example of an approved anti-HER2 Fc-engineered chimeric mAb is margetuximab, which targets the same epitope as trastuzumab, but features five amino acid substitutions in the IgG 1 Fc domain that were deliberately introduced to increase binding to activating FcγRIIIa and decrease binding to inhibitory FcγRIIb (CD32B). Margetuximab enhances Fc-dependent ADCC in vitro more potently than the combination of pertuzumab (another approved mAb directed against an alternate HER2 epitope) and trastuzumab. Margetuximab administration also enhances HER2-specific B cell and T cell–mediated responses ex vivo in samples from patients treated with prior lines of HER2 antibody-based therapies. Stemming from these observations, a worthwhile future goal in the treatment of HER2+ breast cancer is to promote combinatorial approaches that better eradicate HER2+ cancer cells via enhanced immunological mechanisms.

scholarly journals Use of comorbidity indices in patients with any cancer, breast cancer, and human epidermal growth factor receptor-2-positive breast cancer: A systematic review

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252925
Author(s):  
Maribel Salas ◽  
Mackenzie Henderson ◽  
Meera Sundararajan ◽  
Nora Tu ◽  
Zahidul Islam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Epidermal Growth Factor Receptor ◽  
Growth Factor ◽  
Growth Factor Receptor ◽  
Survival Outcomes ◽  
Cancer Breast ◽  
Her2 Breast Cancer ◽  
Comorbidity Index ◽  
Epidermal Growth

Objective To identify comorbidity indices that have been validated in cancer populations, with a focus on breast cancer and human epidermal growth factor receptor-2-positive (HER2+) breast cancer. Study design and setting A systematic review of the literature on the use of comorbidity indices in any cancer, breast cancer, and HER2+ breast cancer using Ovid and PubMed. Results The final data set comprised 252 articles (252 any cancer, 39 breast cancer, 7 HER2+ breast cancer). The most common cancers assessed were hematologic and breast, and the most common comorbidity index used was the Charlson Comorbidity Index (CCI) or a CCI derivative. Most validity testing of comorbidity indices used predictive validity based on survival outcomes. Hazard ratios for survival outcomes generally found that a higher comorbidity burden (measured by CCI) increased mortality risk in patients with breast cancer. All breast-cancer studies that validated comorbidity indices used CCI-based indices. Only one article validated a comorbidity index in HER2+ breast cancer. Conclusion CCI-based indices are the most appropriate indices to use in the general breast-cancer population. There is insufficient validation of any comorbidity index in HER2+ breast cancer to provide a recommendation, indicating a future need to validate these instruments in this population.

Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer

2021 ◽  
pp. 550-560
Author(s):  
Matthew S. Alkaitis ◽  
Monica N. Agrawal ◽  
Gregory J. Riely ◽  
Pedram Razavi ◽  
David Sontag
Keyword(s):  
Breast Cancer ◽  
Epidermal Growth Factor Receptor ◽  
Logistic Regression ◽  
Standard Deviation ◽  
Early Stage ◽  
Area Under The Curve ◽  
Growth Factor Receptor ◽  
Treatment Discontinuation ◽  
High Dimensional ◽  
Epidermal Growth

PURPOSE Key oncology end points are not routinely encoded into electronic medical records (EMRs). We assessed whether natural language processing (NLP) can abstract treatment discontinuation rationale from unstructured EMR notes to estimate toxicity incidence and progression-free survival (PFS). METHODS We constructed a retrospective cohort of 6,115 patients with early-stage and 701 patients with metastatic breast cancer initiating care at Memorial Sloan Kettering Cancer Center from 2008 to 2019. Each cohort was divided into training (70%), validation (15%), and test (15%) subsets. Human abstractors identified the clinical rationale associated with treatment discontinuation events. Concatenated EMR notes were used to train high-dimensional logistic regression and convolutional neural network models. Kaplan-Meier analyses were used to compare toxicity incidence and PFS estimated by our NLP models to estimates generated by manual labeling and time-to-treatment discontinuation (TTD). RESULTS Our best high-dimensional logistic regression models identified toxicity events in early-stage patients with an area under the curve of the receiver-operator characteristic of 0.857 ± 0.014 (standard deviation) and progression events in metastatic patients with an area under the curve of 0.752 ± 0.027 (standard deviation). NLP-extracted toxicity incidence and PFS curves were not significantly different from manually extracted curves ( P = .95 and P = .67, respectively). By contrast, TTD overestimated toxicity in early-stage patients ( P < .001) and underestimated PFS in metastatic patients ( P < .001). Additionally, we tested an extrapolation approach in which 20% of the metastatic cohort were labeled manually, and NLP algorithms were used to abstract the remaining 80%. This extrapolated outcomes approach resolved PFS differences between receptor subtypes ( P < .001 for hormone receptor+/human epidermal growth factor receptor 2− v human epidermal growth factor receptor 2+ v triple-negative) that could not be resolved with TTD. CONCLUSION NLP models are capable of abstracting treatment discontinuation rationale with minimal manual labeling.

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