VIST - a Variant-Information Search Tool for precision oncology

Abstract

Background: Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patient's genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information; however, the vast majority of scientific publications focus on basic science and have no direct clinical impact. We develop the Variant-Information Search Tool (VIST), a search engine designed for the targeted search of clinically relevant publications given an oncological mutation profile.

Results: VIST indexes all PubMed abstracts and content from ClinicalTrials.gov. It applies advanced text mining to identify mentions of genes, variants and drugs and uses machine learning based scoring to judge the clinical relevance of indexed abstracts. Its functionality is available through a fast and intuitive web interface. We perform several evaluations, showing that VIST's ranking is superior to that of PubMed or a pure vector space model with regard to the clinical relevance of a document's content.

Conclusion: Different user groups search repositories of scientific publications with different intentions. This diversity is not adequately reflected in the standard search engines, often leading to poor performance in specialized settings. We develop a search engine for the specific case of finding documents that are clinically relevant in the course of cancer treatment. We believe that the architecture of our engine, heavily relying on machine learning algorithms, can also act as a blueprint for search engines in other, equally specific domains. VIST is freely available at https://vist.informatik.hu-berlin.de/.

Keywords: Biomedical information retrieval; Clinical relevance; Document classification; Document retrieval; Document triage; Personalized oncology.

Fig. 1

VIST System Architecture. Left: VIST backend with indexed and preprocessed documents. Right: VIST web interface for query processing and result presentation

Fig. 2

VIST web interface: Top: Search bar for entering queries. Left: Filter options (by keywords, genes, journals, cancer type, and year of publication. Main pane: List of matching documents, ranked by score according to clinical relevance. Matching clinical trials are available as a second tab

Fig. 3

Detailed view on matching document in VIST. Entities (genes, drugs, variations) as recognized by VIST’s NER modules are highlighted. Sentences are colored according to the propbability of carrying the main message of the abstract (key phrases)

Fig. 4

Precision (P),Recall (R) and F1 scores of three evaluated classification tasks, i.e., classification by relatedness to cancer, by clinical relevance, and by cancer type. MTL: Multi-Task Learning; HATT: Hierarchical Attention Network; SVM: Support Vector Machine; RF: Random Forest

Fig. 5

Evaluation results based on the UserStudy data set: Precision at k (P@k) and recall at k (R@k) of three different ranking schemes, i.e, PubMed, KeywordScore, and VIST SVM. Here, k refers to the k’th document in a ranked list that is also contained in the reference list