Triple negative breast cancer in Bulgaria: epidemiological data and treatment patterns based on real world evidence and patient registries

Abstract Breast cancer is the most common oncologic disease among women worldwide. Survival rates vary significantly and depend on early diagnosis practice and access to treatment. Triple negative breast cancer (TNBC) accounts for 12–15% of all breast cancers. We performed a one-year real-life retrospective study on the patho-histological status and treatment of a representative cohort of patients with TNBC in Bulgaria. We collected anonymised data for TNBC patients from the electronic artificial intelligence platform Sqilline - Danny Platform. Demographic characteristics, data on biomarkers, TNM (Tumor, Nodule, Metastasis) stage, therapeutic regime, line and changes in treatment were chronologically analysed for all patients. The results were processed through descriptive statistics. For the observed period, Jan 2019–Dec 2019, 6880 breast cancer patients from eight major oncology hospitals were included in the database. The average age of the women was 60 y; 234 (3.4%) of them were diagnosed with TNBC; 10% had unknown TNM stage. The majority of the patients were assigned to chemotherapy (84%) of which 35% were on adjuvant. Most changes in the therapy were observed in the neo-adjuvant group).The results from this study provide evidence that the treatment patterns of TNBC, and changes in therapy are in compliance with international guidelines. We identified less patients with TNBC than the frequencies reported in international epidemiological studies. This might be attributed to lack of funding of necessary tests or insufficient data in patients record. The study confirms that dynamic patient registers are important for performing a real-world studies of treatment patterns.

[1]  G. Petrova,et al.  Comparative analysis of the access to health-care services and breast cancer therapy in 10 Eastern European countries , 2020, SAGE open medicine.

[2]  T. Vlaykova,et al.  The Role of the Molecular Subtypes in the Prognosis of Breast Cancer Patients , 2020, Open Access Macedonian Journal of Medical Sciences.

[3]  V. R. Dasari,et al.  Tumor microenvironment: Challenges and opportunities in targeting metastasis of triple negative breast cancer. , 2020 .

[4]  Boyang Zhao,et al.  Clinical Data Extraction and Normalization of Cyrillic Electronic Health Records Via Deep-Learning Natural Language Processing , 2019, JCO clinical cancer informatics.

[5]  Walter Ricciardi,et al.  Benefits and challenges of Big Data in healthcare: an overview of the European initiatives , 2019, European journal of public health.

[6]  Jenni A. M. Sidey-Gibbons,et al.  Machine learning in medicine: a practical introduction , 2019, BMC Medical Research Methodology.

[7]  E. Harris,et al.  Innovations in Personalized and Targeted Therapies for Breast Cancer , 2018, International journal of breast cancer.

[8]  N S El Saghir,et al.  4th ESO–ESMO International Consensus Guidelines for Advanced Breast Cancer (ABC 4)† , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[9]  Angappa Gunasekaran,et al.  Big Data in Healthcare Management: A Review of Literature , 2018 .

[10]  Issam Ibnouhsein,et al.  The Big Data Revolution for Breast Cancer Patients. , 2018, European journal of breast health.

[11]  J. Chun,et al.  Clinical Characteristics in Patients with Triple Negative Breast Cancer , 2017, International journal of breast cancer.

[12]  Eberechukwu Onukwugha,et al.  Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis , 2016, PharmacoEconomics.

[13]  R. Kaneva,et al.  Spectrum and frequencies of BRCA1/2 mutations in Bulgarian high risk breast cancer patients , 2015, BMC Cancer.

[14]  J. Dheeba,et al.  Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach , 2014, J. Biomed. Informatics.

[15]  S. Benhamou,et al.  Cancer registries can provide evidence-based data to improve quality of care and prevent cancer deaths , 2014, Ecancermedicalscience.

[16]  Patrick S Tarpey,et al.  What is next generation sequencing? , 2013, Archives of Disease in Childhood: Education & Practice Edition.

[17]  Katherine Payne,et al.  Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: a report of the ISPOR personalized medicine special interest group. , 2012, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[18]  P. Boyle,et al.  Triple-negative breast cancer: epidemiological considerations and recommendations. , 2012, Annals of oncology : official journal of the European Society for Medical Oncology.

[19]  Ruchi Patel,et al.  Application of Machine Learning Techniques in Clinical Information Extraction , 2019, Smart Techniques for a Smarter Planet.

[20]  L. Schwartzberg,et al.  Treatment patterns, clinical outcomes, health resource utilization, and cost in patients with BRCA-mutated metastatic breast cancer treated in community oncology settings. , 2019, Cancer treatment and research communications.

[21]  C. Isaacs,et al.  BRCA1/2 mutations and triple negative breast cancers. , 2010, Breast disease.

[22]  Steven R. Head,et al.  Next-generation sequencing , 2010, Nature Reviews Drug Discovery.

[23]  D. Weed,et al.  Cancer prevention and control. , 1990, Seminars in oncology.