The second CIViC-hosted hackathon and curation workshop was held as an open-format one and a half day pre-conference to the 2018 ASHG meeting in San Diego. Over 50 Attendees were present representing over 20 organizations and institutions from multiple countries. Session topics were suggested by attendees and CIViC team members and covered coding (hackathon) and issues in cancer variant representation and curation. On the morning of the second day, groups presented the outcome of each session in short presentations covering multiple topics. Topics in the cancer variant sessions included the expansion of cancer variant databases to cover structural variants and copy number variants. The current capabilities of CIViC and other cancer variant knowledgebases to represent such variants was assessed, and strategies for future instances of such knowledgebases to implement these representations were covered. In addition, a system for quantifying somatic cancer variant oncogenicity/pathogenicity was proposed, and discussed extensively. This system was derived from the current standard for germline variant pathogenicity assessment based on ACMG codes. These discussions informed subsequent proposals for potential future guidelines. Other topics in the cancer variant sessions included machine learning in cancer variant annotation, and the standardization of generalized categories for cancer variant classification. SEPIO modeling of cancer variants was also covered. Parallel curation sessions covered a broad set of topics including methods to incentivize community curation of free and public knowledgebases such as CIViC, and hands-on curation of diagnostic evidence in pediatric cancer was performed. In multiple hackathon sessions, work was performed integrating CIViC and CRAVAT, integrating CIViC with igv.js, JBrowse and IGV genome browsers and CIViC-Wikidata integration. A session was held to work on a system for data transfer between the ClinGen VCI and CIViC named Linked Data Hub. NDEx made available CIViC drug-variant, gene-disease, gene-variant and variant-disease association networks.