Platinum resistance is a major therapeutic obstacle in the treatment of patients with high-grade serous ovarian cancer (HGSOC), and predicting poor response to first-line therapies would allow for more rapid implementation of alternative treatment approaches. At diagnosis, clinicians often have limited access to heavily processed genomics data to predict treatment responses. The use of rich clinical data accumulated into electronic health records (EHRs) could have features able to discriminate nonresponsive patients, but their use is currently hampered by multiple manual steps and lack of automated analytic systems. We have developed a cloud-based machine learning system (CLOBNET), with streamlined collection and real-time analysis of rich clinical data from EHRs, and use the CLOBNET to predict platinum resistance in patients with HGSOC.
Data protection and patient privacy are strictly protected while patient data are accessed over the internet by using several steps; CLOBNET can be accessed only through Turku University Hospital’s proxy server using double SSH tunnel and public key authentication and every authentication step and database access also require strong passwords. Encryption used in our server traffic is on par with online banking encryption.
Clinical data of 235 patients with informed consent were collected during a prospective study from Turku University Hospital during 2009 to 2017. We next used CLOBNET to predict the response to platinum-based therapies using a linear normal-based classifier algorithm created with Matlab. Feature selection for the model was done using branch and bound algorithm and features with over 20% missing values were omitted. With a subpopulation of 56 patients who had had one operation and thus had a defined tumor dissemination, a model was trained to differentiate between complete response and progressive disease. With AUC of 0,80 our model could predict complete response with 94.1% sensitivity and 45.5% specificity. Using a 40% missing value cut point, 28 patients remained for testing a model trained to identify patients with platinum resistance (platinum-free interval of less than six months). The model achieved sensitivity of 84.2% and specificity of 77.8% with AUC of 0.86.
We have herein shown the viability of accessing clinical data from live EHRs and using machine learning to analyze it over traditionally restricting barriers caused by manual steps and interfaces between different organizations and systems. Even with a limited dataset we can train a linear model to predict primary progressive disease and platinum resistance in patients with HGSOC. Thus, automated machine learning approaches, such as CLOBNET, show promise in becoming clinical tools in predicting challenging outcomes, such as platinum resistance in HGSOC.
Citation Format: Veli-Matti Isoviita, Liina Salminen, Jimmy Azar, Johanna Hynninen, Rainer Lehtonen, Pia Röring, Seija Grénman, Anniina Färkkilä, Sampsa Hautaniemi. Development of a cloud-based machine learning system (CLOBNET) to predict platinum resistance in high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr A56.