Knowledge bases and software support for variant interpretation in precision oncology.

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

Analysis of Not Structurable Oncological Study Eligibility Criteria for Improved Patient-Trial Matching.

BACKGROUND: Higher enrolment rates of cancer patients into clinical trials are necessary to increase cancer survival. As a prerequisite, an improved semiautomated matching of patient characteristics with clinical trial eligibility criteria is needed. This is based on the computer interpretability, i.e., structurability of eligibility criteria texts. To increase structurability, the common content, phrasing, and structuring problems of oncological eligibility criteria need to be better understood. OBJECTIVES: We aimed to identify oncological eligibility criteria that were not possible to be structured by our manual approach and categorize them by the underlying structuring problem. Our results shall contribute to improved criteria phrasing in the future as a prerequisite for increased structurability. METHODS: The inclusion and exclusion criteria of 159 oncological studies from the Clinical Trial Information System of the National Center for Tumor Diseases Heidelberg were manually structured and grouped into content-related subcategories. Criteria identified as not structurable were analyzed further and manually categorized by the underlying structuring problem. RESULTS: The structuring of criteria resulted in 4,742 smallest meaningful components (SMCs) distributed across seven main categories (Diagnosis, Therapy, Laboratory, Study, Findings, Demographics, and Lifestyle, Others). A proportion of 645 SMCs (13.60%) was not possible to be structured due to content- and structure-related issues. Of these, a subset of 415 SMCs (64.34%) was considered not remediable, as supplementary medical knowledge would have been needed or the linkage among the sentence components was too complex. The main category "Diagnosis and Study" contained these two subcategories to the largest parts and thus were the least structurable. In the inclusion criteria, reasons for lacking structurability varied, while missing supplementary medical knowledge was the largest factor within the exclusion criteria. CONCLUSION: Our results suggest that further improvement of eligibility criterion phrasing only marginally contributes to increased structurability. Instead, physician-based confirmation of the matching results and the exclusion of factors harming the patient or biasing the study is needed.

Finding Options Beyond Standard of Care in Oncology: A Proposal for Workflows Utilizing Knowledge Databases.

With the novel approach of molecularly stratified therapies based on genetic characteristics of individual tumors, the need for databases providing information on molecular alterations and targeted treatment options is increasing rapidly. In Molecular Tumor Boards (MTB) professionals discuss molecular alterations and provide biological context for therapeutic options using external knowledge databases. The identification of informative databases and the information on their specific contents can greatly facilitate and standardize the functioning of a MTB. In this work we present a list of databases which have been deemed useful and relevant for MTB in a clinical setting. We describe workflows to recommend the use of specific databases at different steps in the clinical curation process. Information obtained from these databases is a necessary prerequisite to evaluate molecular alterations and devise rational targeted therapies in MTB.

An Interactive Timeline Visualization for Patient Cohorts in the Oncological Routine: A Use Case on Multiple Myeloma.

With the growing interdisciplinarity of cancer treatment and increasing amounts of data and patients, it is getting increasingly difficult for physicians to capture a patient's medical history as a basis for adequate treatment and to compare different medical histories of similar patients to each other. Furthermore, in order to tackle the etiological mechanisms of cancer, it is crucial to identify patients exhibiting a different disease course than their corresponding cohort. Several timeline visualizations have already been proposed. However, the functions and design of such visualizations are always use case dependent. We constructed a cohort timeline prototype mock-up for a specific oncological use case involving multiple myeloma, where the chronological monitoring of various parameters is crucial for patient diagnosis and treatment. Our proposed cohort timeline is a synthesis between elements described in the literature and our own approaches regarding function and design.

Optimizing a Query by Transformation and Expansion.

In the biomedical sector not only the amount of information produced and uploaded into the web is enormous, but also the number of sources where these data can be found. Clinicians and researchers spend huge amounts of time on trying to access this information and to filter the most important answers to a given question. As the formulation of these queries is crucial, automated query expansion is an effective tool to optimize a query and receive the best possible results. In this paper we introduce the concept of a workflow for an optimization of queries in the medical and biological sector by using a series of tools for expansion and transformation of the query. After the definition of attributes by the user, the query string is compared to previous queries in order to add semantic co-occurring terms to the query. Additionally, the query is enlarged by an inclusion of synonyms. The translation into database specific ontologies ensures the optimal query formulation for the chosen database(s). As this process can be performed in various databases at once, the results are ranked and normalized in order to achieve a comparable list of answers for a question.