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Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. This consortium has regularly held topic-focused biannual face-to-face symposiums. These meetings are a place to review cancer informatics and data science priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues that we faced at our respective institutions and cancer centers. Here, we provide meeting highlights from the latest CI4CC Symposium, which was delayed from its original April 2020 schedule because of the COVID-19 pandemic and held virtually over three days (September 24, October 1, and October 8) in the fall of 2020. In addition to the content presented, we found that holding this event virtually once a week for 6 hours was a great way to keep the kind of deep engagement that a face-to-face meeting engenders. This is the second such publication of CI4CC Symposium highlights, the first covering the meeting that took place in Napa, California, from October 14-16, 2019. We conclude with some thoughts about using data science to learn from every child with cancer, focusing on emerging activities of the National Cancer Institute's Childhood Cancer Data Initiative., Competing Interests: Anne C. KirchhoffStock and Other Ownership Interests: Medtronic Kara L. DavisHonoraria: NovartisResearch Funding: Jazz Pharmaceuticals Karlyne ReillyConsulting or Advisory Role: Saul Ewing LLCPatents, Royalties, Other Intellectual Property: I hold a patent to a potential therapeutic: Reilly KM, Beutler JA, Turbyville T, Wiemer DF: The Natural Product Schweinfurthin A and Synthetic Schweinfurthin Analogs Specifically Inhibit Nf1-Null Cells and may be Useful as a Therapy for Neurofibromatosis Type 1. US patent 61/174,338, April 19, 2014; International patent PCT/US10/33153. There are no royalties or licensing fees from this patent at the time Richard AplencExpert Testimony: Vorys Samuel L. VolchenboumStock and Other Ownership Interests: Litmus HealthConsulting or Advisory Role: AccordantTravel, Accommodations, Expenses: Sanford Health Bruce J. AronowPatents, Royalties, Other Intellectual Property: Patents issued for some data analysis algorithms related to data mining and discovery approaches to drug repositioning for new clinical indications Olena VaskeEmployment: NantWorksStock and Other Ownership Interests: NantHealth Casey GreeneOther Relationship: Alex's Lemonade Stand Foundation Jack DiGiovannaEmployment: Biogen (I)Stock and Other Ownership Interests: Biogen Sorena NadafThis author is a member of the JCO Clinical Cancer Informatics Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript. Warren A. KibbeThis author is a member of the JCO Clinical Cancer Informatics Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.No other potential conflicts of interest were reported.

Purpose: Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions., Patients and Methods: Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step., Results: We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance., Conclusion: Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

Next-generation sequencing (NGS) diagnostic assays increasingly are becoming the standard of care in oncology practice. As the scale of an NGS laboratory grows, management of these assays requires organizing large amounts of information, including patient data, laboratory processes, genomic data, as well as variant interpretation and reporting. Although several Laboratory Information Systems and/or Laboratory Information Management Systems are commercially available, they may not meet all of the needs of a given laboratory, in addition to being frequently cost-prohibitive. Herein, we present the System for Informatics in the Molecular Pathology Laboratory (SIMPL), a free and open-source Laboratory Information System/Laboratory Information Management System for academic and nonprofit molecular pathology NGS laboratories, developed at the Genomic and Molecular Pathology Division at the University of Chicago Medicine. SIMPL was designed as a modular end-to-end information system to handle all stages of the NGS laboratory workload from test order to reporting. We describe the features of SIMPL, its clinical validation at University of Chicago Medicine, and its installation and testing within a different academic center laboratory (University of Colorado), and we propose a platform for future community co-development and interlaboratory data sharing., (Copyright © 2018 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.)