Personalized medicine for laparoscopic gastrectomy in gastric cancer

Abstract

Predictive medicine is crucial for improving medical quality and health. Traditionally, epidemiological studies are used for patient’s outcome with a disease in the pretreatment setting to predict outcomes before therapeutic intervention with surgery, drugs, or both. Given the heterogeneity and complexity of chronic multifactorial disorders, such as cancer, emerging biomedical research is based on genomics, epigenomics, and microRNAs along with systems computational biology and mathematical models for the development of novel biomarkers and more accurate predictions [1, 2]. Based on the establishment and success of laparoscopic resection for colorectal cancer to improve quality of life by equal oncological outcomes compared with open surgery, laparoscopic gastrectomy has rapidly evolved during the past decade [3–10]. Pretreatment identification of gastric patients at risk for developing early postoperative complications or recurrence after laparoscopic gastrectomy has crucial clinical implications. In general for cancer, such markers can guide a tailored treatment for improving patient’s outcomes and this is a major goal of personalized medicine [11]. In the December issue of Surgical Endoscopy, Yoshikawa et al. [12] evaluated several markers that might be clinical useful for patients who are going to undergo laparoscopic gastrectomy for gastric cancer. The authors have classified 66 patients who underwent laparoscopy assisted gastrectomy (LAG) into two groups: body mass index (BMI) [\25 BMI-L group: n = 53; C25 BMI-H group: n = 13] and visceral fat areas (VFAs) [\100 cm AF-L group: n = 35; C100 cm AF-H group: n = 31]. Yoshikawa et al. [12] report that the BMI did not impact postoperative complications (p = 0.18) or blood loss (p = 0.21), whereas by contrast VFA significantly influenced complications, blood loss, and number of lymph nodes retrieved. The authors conclude that the area of visceral fat tissue assessed by CT and software was useful to predict risks of LAG and postoperative complications with higher precision compared with BMI. This study evaluated the area of visceral fat tissue and provides a new aspect for identifying predictive and prognostic markers for LAG for gastric cancer. However, it is limited not only by its retrospective nature but also by the very small number of patients in subgroups evaluated. Therefore, it could be considered an underpowered study for assessing valid significant differences. However, the data provided are promising and the authors may perform a study with large and accurate samples of patients to validate the potential impact of the area of visceral fat tissue in patients undergoing laparoscopic gastrectomy. Development of robust biomarkers for personalized cancer treatment has been proven to be much more difficult and complicated than we have supposed. Personalized medicine can improve dramatically the outcome in public health, but much more research work is needed. In the era of next-generation sequencing for whole-genome sequencing, functional genomics, transcriptomics, and epigenomics, new directions are being shaped. Assembling large-scale omics data together with clinical data and integrating all of this genome and clinical information into computational and mathematical models could result in the development of systems biology and systems medicinebased biomarkers [13–32]. C. Hottenrott (&) Chirurgische Klinik, St. Elisabethenkrankenhaus, Ginnheimer Strase 3, 60487 Frankfurt, Germany e-mail: info@gastricbreastcancer.com