Purpose: The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke data models, knowledgebases, and one- off customized resources for data analysis often lack adequate governance and quality control needed for these resources to be “clinical-grade”. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support data collection, deposition, curation, harmonization, integration, and analytics to support case review and treatment planning. Design: In this review, we evaluate and summarize the landscape of available tools, resources and evidence used in the evaluation of somatic and germline tumor variants within the context of molecular tumor boards. Results: Molecular Tumor Boards (MTBs) are collaborative efforts of multi-disciplinary cancer experts equipped with genomic interpretation resources to aid in the delivery of accurate and timely clinical interpretations of genomic results for each patient case, within an institution or hospital network. Virtual Molecular Tumor Boards (VMTBs) provide an online forum for collaborative governance, provenance and information sharing between experts outside a given hospital network with the potential to enhance MTB discussions. Knowledge sharing in VMTBs and communication with guideline-developing organizations can lead to progress evidenced by data harmonization across resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence. Conclusion: Advances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.