Current clinical practice and outcome of neoadjuvant chemotherapy for early breast cancer: analysis of individual data from 94,638 patients treated in 55 breast cancer centers

Neoadjuvant chemotherapy (NACT) is frequently used in patients with early breast cancer. Randomized controlled trials have demonstrated similar survival after NACT or adjuvant chemotherapy (ACT). However, certain subtypes may benefit more when NACT contains regimes leading to high rates of pathologic complete response (pCR) rates. In this study we analyzed data using the OncoBox research from 94,638 patients treated in 55 breast cancer centers to describe the current clinical practice of and outcomes after NACT under routine conditions. These data were compared to patients treated with ACT. 40% of all patients received chemotherapy. The use of NACT increased over time from 5% in 2007 up to 17.3% in 2016. The proportion of patients receiving NACT varied by subtype. It was low in patients with HR-positive/HER2-negative breast cancer (5.8%). However, 31.8% of patients with triple-negative, 31.9% with HR-negative/HER2-positive, and 26.5% with HR-positive/HER2-positive breast cancer received NACT. The rates of pCR were higher in patients with HR-positive/HER2-positive, HR negative/HER2-positive and triple-negative tumors (36, 53 and 38%) compared to HR-positive/HER2-negative tumors (12%). PCR was achieved more often in HER2-positive and triple-negative tumors over time. This is the largest study on use and effects of NACT in German breast cancer centers. It demonstrates the increased use of NACT based on recommendations in current clinical guidelines. An improvement of pCR was shown in particular in HER2-positive and triple-negative breast cancer, which is consistent with data from randomized controlled trails

DZDconnect: Using connected data to fight diabetes.

Data in translational healthcare research is complex and highly connected. Information on wide-spread diseases like diabetes and cancer is extensive, heterogeneous and rapidly growing. Data are available at various locations and are neither interconnectable with other data sources nor searchable. Consequently, it is difficult for researchers to access data and to cope with the amount of literature. Collecting data and knowledge is still done manually by comparing data tables. However, a flexible and efficient approach to processing biomedical data is offered by graph databases. Based on the open source Neo4j graph database, the German Center for Diabetes Research (DZD) developed DZDconnect—a knowledge graph that links data from basic research and clinical studies across sites, disciplines and species with external knowledge. DZDconnect collects, structures, interconnects and makes available various data and information on wide-spread diseases and its long-term complications. Information from well-established databases is connected on the metadata level, raw data level as well as on the insight level. In addition, in-house data from translational research can be integrated. The enabling technology is a flexible and scalable graph database. DZDconnect thus bridges the gap between healthcare research and state-of-the-art information technology and helps to make disease research faster and more efficient. With DZDconnect scientists can quickly and efficiently generate hypotheses regarding the underlying mechanisms of these diseases and how to intervene medically. DZDconnect is developed as an open-source project