Critical clinical activities involve decision making. For both individual patients and for society at large, making good healthcare decisions is a paramount task. The objective of this research is to develop a novel decision support system that utilizes both the clinical features and the genomic profile of a breast cancer patient to assit the physician in integrating information about a specific patient (diagnostic subtype, tumor stage and grade, age, comorbidities) to make therapeutic plans for the patient. Traditional clinical data are becoming increasingly available in electronic form. Unprecedentedly abundant genomic data are available to researchers as the results of advanced sequencing technologies such as next generation sequencing. Patient-specific genomic data are likely to become available for most patients in the foreseeable future. These sources of data provide significant opportunities for developing new generation clinical decision support systems that can achieve substantial progress over what is currently possible. However, the sheer magnitude of the number of variables in these data (often in the millions) presents formidable computational and modeling challenges. Also, integrating the heterogeneous information in multiple clinical datasets and genomic datasets presents an arduous challenge. Breast cancer is the commonest cancer among women. Various breast cancer subtypes have been defined which, along with tumor stage, predict response to therapy and survival, albeit imperfectly. For example, HER2-amplified breast cancer is a subtype with poor prognosis, and therapy with an antibody to HER2 (Herceptin) has vastly improved the survival of such patients. Although Herceptin is used in the therapy of all patients with HER2-amplified tumors, only some respond. Also, it is expensive and can cause cardiac toxicity. So, it is important to give it only o patients benefiting from it. Studies show thousands of genes are associated with subtype and prognosis of breast cancer, and particular allele combinations may usefully guide the selection of effective treatment. The proposed system will amass all this genomic information and combine it with clinical information and therefore holds promise to provide accurate classification and treatment choices. We will build the knowledge base of the proposed system using the following sources: 1) The Medical Archival Systems at the University of Pittsburgh Medical Center; 2) The Lynn Sage Database used by the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital; 3) The breast cancer data sets from The Cancer Genome Atlas project; and 4) Dream 7 Breast Cancer Challenge Data. The proposed system will build on previous results of the investigators in using Bayesian Network to learn from high-dimensional data sets. Our multidisciplinary team has a track record, including NIH funding, publications in biomedical informatics and artificial intelligence, and experience developing cutting-edge decision support systems.

Public Health Relevance

Even a modest improvement in the efficacy of clinical decision making has the potential to significantly improve patient outcomes and reduce healthcare costs. This project will develop a novel decision support system that utilizes both the clinical features and the genomic profile of a breast cancer patient to assist the physician in integrating information about a specific patient (diagnostic subtype, tumor stage and grade, age, comorbidities) to make therapeutic plans for the patient. We call this system A Clinical Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC).

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Sim, Hua-Chuan
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University of Pittsburgh
Schools of Medicine
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Zeng, Zexian; Espino, Sasa; Roy, Ankita et al. (2018) Using natural language processing and machine learning to identify breast cancer local recurrence. BMC Bioinformatics 19:498
Rathnam, Chandramouli; Lee, Sanghoon; Jiang, Xia (2017) An algorithm for direct causal learning of influences on patient outcomes. Artif Intell Med 75:1-15
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Cai, Binghuang; Jiang, Xia (2016) Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences. BMC Bioinformatics 17:116
Zeng, Zexian; Jiang, Xia; Neapolitan, Richard (2016) Discovering causal interactions using Bayesian network scoring and information gain. BMC Bioinformatics 17:221
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Neapolitan, Richard; Jiang, Xia; Ladner, Daniela P et al. (2016) A Primer on Bayesian Decision Analysis With an Application to a Kidney Transplant Decision. Transplantation 100:489-96
Jiang, Xia; Neapolitan, Richard E (2015) Evaluation of a two-stage framework for prediction using big genomic data. Brief Bioinform 16:912-21

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