Chemotherapy Knowledge Base Management in the Era of Precision Oncology.

Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Representing this increasingly granular knowledge is a significant challenge. As users and managers of a freely available chemotherapy drug and regimen database, we describe the changes we have made to accommodate these challenges. These include the development of a domain ontology and increased granularity in the classification of cancer types on the website.

Seven decades of chemotherapy clinical trials: a pan-cancer social network analysis.

Clinical trials establish the standard of cancer care, yet the evolution and characteristics of the social dynamics between the people conducting this work remain understudied. We performed a social network analysis of authors publishing chemotherapy-based prospective trials from 1946 to 2018 to understand how social influences, including the role of gender, have influenced the growth and development of this network, which has expanded exponentially from fewer than 50 authors in 1946 to 29,197 in 2018. While 99.4% of authors were directly or indirectly connected by 2018, our results indicate a tendency to predominantly connect with others in the same or similar fields, as well as an increasing disparity in author impact and number of connections. Scale-free effects were evident, with small numbers of individuals having disproportionate impact. Women were under-represented and likelier to have lower impact, shorter productive periods (P < 0.001 for both comparisons), less centrality, and a greater proportion of co-authors in their same subspecialty. The past 30 years were characterized by a trend towards increased authorship by women, with new author parity anticipated in 2032. The network of cancer clinical trialists is best characterized as strategic or mixed-motive, with cooperative and competitive elements influencing its appearance. Network effects such as low centrality, which may limit access to high-profile individuals, likely contribute to the observed disparities.

Standardizing Chemotherapy Regimen Nomenclature: A Proposal and Evaluation of the HemOnc and National Cancer Institute Thesaurus Regimen Content.

PURPOSE: Due to decades of nonstandardized approaches to the naming of chemotherapy regimens, representation in electronic health records and secondary systems is highly variable. This hampers efforts to understand patterns of chemotherapy usage at the population level. In this article, we describe a proposal for rules to standardize the nomenclature of chemotherapy regimens and illustrate applications of these rules. METHODS: Through our experience with building HemOnc.org, which has been under construction since 2011, we formulated a set of guidelines and recommendations for the standard representation of chemotherapy regimen names. We then performed a mapping between the HemOnc and National Cancer Institute Thesaurus vocabulary's regimens and evaluated conformance with the naming conventions. Finally, we assembled a database of acronyms and names for multiple myeloma regimens to illustrate the scope of the problem. RESULTS: For the first use case, 242 of 527 (45.1%) of the regimen names differed. The schema was able to allocate a preferred source for 217 (89.4%) of these regimens. For the second use case, we expanded 130 multiple myeloma regimens to 1,138 unique regimen names and demonstrate ways in which the schema can collapse these into disambiguated, but abbreviated, regimen names. CONCLUSION: To our knowledge, this is the first proposal to normalize chemotherapy regimen nomenclature. If our recommendations are adopted, we expect that the uniformity of treatment exposure representation in hematology/oncology will increase, which will enable large-scale efforts such as ASCO's CancerLinQ to achieve better standardization.

HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model.

Systematic application of observational data to the understanding of impacts of cancer treatments requires detailed information models allowing meaningful comparisons between treatment regimens. Unfortunately, details of systemic therapies are scarce in registries and data warehouses, primarily due to the complex nature of the protocols and a lack of standardization. Since 2011, we have been creating a curated and semi-structured website of chemotherapy regimens, HemOnc.org. In coordination with the Observational Health Data Sciences and Informatics (OHDSI) Oncology Subgroup, we have transformed a substantial subset of this content into the OMOP common data model, with bindings to multiple external vocabularies, e.g., RxNorm and the National Cancer Institute Thesaurus. Currently, there are >73,000 concepts and >177,000 relationships in the full vocabulary. Content related to the definition and composition of chemotherapy regimens has been released within the ATHENA tool (athena.ohdsi.org) for widespread utilization by the OHDSI membership. Here, we describe the rationale, data model, and initial contents of the HemOnc vocabulary along with several use cases for which it may be valuable.

Computerized Approach to Creating a Systematic Ontology of Hematology/Oncology Regimens.

Purpose: The systemic treatment of cancer is primarily through the administration of complex chemotherapy protocols. To date, this knowledge has not been systematized, because of the lack of a consistent nomenclature and the variation in which regimens are documented. For example, recording of treatment events in electronic health record notes is often through shorthand and acronyms, limiting secondary use. A standardized hierarchic ontology of cancer treatments, mapped to standard nomenclatures, would be valuable to a variety of end users. Methods: We leveraged the knowledge contained in a large wiki of hematology/oncology drugs and treatment regimens, HemOnc.org. Through algorithmic parsing, we created a hierarchic ontology of treatment concepts in the World Wide Web Consortium Web Ontology Language. We also mapped drug names to RxNorm codes and created optional filters to restrict the ontology by disease and/or drug class. Results: As of December 2017, the main ontology includes 30,526 axioms (eg, doxorubicin is an anthracycline), 1,196 classes (eg, regimens used in the neoadjuvant treatment of human epidermal growth factor receptor 2-positive breast cancer, nitrogen mustards), and 1,728 individual entities. More than 13,000 of the axioms are annotations including RxNorm codes, drug synonyms, literature references, and direct links to published articles. Conclusion: This approach represents, to our knowledge, the largest effort to date to systematically categorize and relate hematology/oncology drugs and regimens. The ontology can be used to reason individual components from regimens mentioned in electronic health records (eg, R-CHOP maps to rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) and also to probabilistically reconstruct regimens from individual drug components. These capabilities may be particularly valuable in the implementation of rapid-learning health systems on the basis of real-world evidence. The derived Web Ontology Language ontology is freely available for noncommercial use through the Creative Commons 4.0 Attribution-NonCommercial-ShareAlike license.

Overcoming the Straw Man Effect in Oncology: Visualization and Ranking of Chemotherapy Regimens Using an Information Theoretic Approach.

PURPOSE: Despite the plethora of randomized controlled trial (RCT) data, most cancer treatment recommendations are formulated by experts. Alternatively, network meta-analysis (NMA) is one method of analyzing multiple indirect treatment comparisons. However, NMA does not account for mixed end points or temporality. Previously, we described a prototype information theoretical approach for the construction of ranked chemotherapy treatment regimen networks. Here, we propose modifications to overcome an apparent straw man effect, where the most studied regimens were the most negatively valued. METHODS: RCTs from two scenarios-upfront treatment of chronic myelogenous leukemia and relapsed/refractory multiple myeloma-were assembled into ranked networks using an automated algorithm based on effect sizes, statistical significance, surrogacy of end points, and time since RCT publication. Vertex and edge color, transparency, and size were used to visually analyze the network. This analysis led to the additional incorporation of value propagation. RESULTS: A total of 18 regimens with 42 connections (chronic myelogenous leukemia) and 28 regimens with 25 connections (relapsed/refractory multiple myeloma) were analyzed. An initial negative correlation between vertex value and size was ameliorated after value propagation, although not eliminated. Updated rankings were in close agreement with published guidelines and NMAs. CONCLUSION: Straw man effects can distort the comparative efficacy of newer regimens at the expense of older regimens, which are often cheaper or less toxic. Using an automated method, we ameliorated this effect and produced rankings consistent with common practice and published guidelines in two distinct cancer settings. These findings are likely to be generalizable and suggest a new means of ranking efficacy in cancer trials.

HemOnc.org: A Collaborative Online Knowledge Platform for Oncology Professionals.

PURPOSE: Cancer care involves extensive knowledge about numerous chemotherapy drugs and chemotherapy regimens. This information is constantly evolving, and there has been no freely available, comprehensive, centralized repository of chemotherapy information to date. METHODS: We created an online, freely accessible, ad-free, collaborative wiki of chemotherapy information entitled HemOnc.org to address the unmet need for a central repository of this information. This Web site was developed with wiki development software and is hosted on a cloud platform. Chemotherapy drug and regimen information (including regimen variants), as well as other information of interest to hematology/oncology professionals, is housed on the site in a fully referenced and standardized format. Accredited users are allowed to freely contribute information to the site. RESULTS: From its inception in November 2011, HemOnc.org has grown rapidly and most recently has detailed information on 383 drugs and 1,298 distinct chemotherapy regimens (not counting variants) in 92 disease subtypes. There are regularly more than 2,000 visitors per week from the United States and international locations. A user evaluation demonstrated that users find the site useful, usable, and recommendable. CONCLUSION: HemOnc.org is now the largest free source of chemotherapy drug and regimen information and is widely used. Future enhancements, including more metadata about drugs and increasingly detailed efficacy and toxicity information, will continue to improve the value of the resource.

Binding and susceptibility to postentry restriction factors in monkey cells are specified by distinct regions of the human immunodeficiency virus type 1 capsid.

In cells of Old World and some New World monkeys, dominant factors restrict human immunodeficiency virus type 1 (HIV-1) infections after virus entry. The simian immunodeficiency virus SIV(mac) is less susceptible to these restrictions, a property that is determined largely by the viral capsid protein. For this study, we altered exposed amino acid residues on the surface of the HIV-1 capsid, changing them to the corresponding residues found on the SIV(mac) capsid. We identified two distinct pathways of escape from early, postentry restriction in monkey cells. One set of mutants that were altered near the base of the cyclophilin A-binding loop of the N-terminal capsid domain or in the interdomain linker exhibited a decreased ability to bind the restricting factor(s). Consistent with the location of this putative factor-binding site, cyclophilin A and the restricting factor(s) cooperated to achieve the postentry block. A second set of mutants that were altered in the ridge formed by helices 3 and 6 of the N-terminal capsid domain efficiently bound the restricting factor(s) but were resistant to the consequences of factor binding. These results imply that binding of the simian restricting factor(s) is not sufficient to mediate the postentry block to HIV-1 and that SIV(mac) capsids escape the block by decreases in both factor binding and susceptibility to the effects of the factor(s).

Human and simian immunodeficiency virus capsid proteins are major viral determinants of early, postentry replication blocks in simian cells.

The cells of most Old World monkey species exhibit early, postentry restrictions on infection by human immunodeficiency virus type 1 (HIV-1) but not by simian immunodeficiency virus of macaques (SIV(mac)). Conversely, SIV(mac), but not HIV-1, infection is blocked in most New World monkey cells. By using chimeric HIV-1/SIV(mac) viruses capable of a single round of infection, we demonstrated that a major viral determinant of this restriction is the capsid (CA) protein. The efficiency of early events following HIV-1 and SIV(mac) entry is apparently determined by the interaction of the incoming viral CA and species-specific host factors.