The AACR Project GENIE is an international data-sharing consortium focused on generating an evidence base for precision cancer medicine by integrating clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. In conjunction with the first public data release from approximately 19,000 samples, we describe the goals, structure, and data standards of the consortium and report conclusions from high-level analysis of the initial phase of genomic data. We also provide examples of the clinical utility of GENIE data, such as an estimate of clinical actionability across multiple cancer types (>30%) and prediction of accrual rates to the NCI-MATCH trial that accurately reflect recently reported actual match rates. The GENIE database is expected to grow to >100,000 samples within 5 years and should serve as a powerful tool for precision cancer medicine. Significance The AACR Project GENIE aims to catalyze sharing of integrated genomic and clinical datasets across multiple institutions worldwide, and thereby enable precision cancer medicine research, including the identification of novel therapeutic targets, design of biomarker-driven clinical trials, and identification of genomic determinants of response to therapy.

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

Precision oncology relies on the accurate discovery and interpretation of genomic variants to enable individualized therapy selection, diagnosis, or prognosis. However, knowledgebases containing clinical interpretations of somatic cancer variants are highly disparate in interpretation content, structure, and supporting primary literature, reducing consistency and impeding consensus when evaluating variants and their relevance in a clinical setting. With the cooperation of experts of the Global Alliance for Genomics and Health (GA4GH) and of six prominent cancer variant knowledgebases, we developed a framework for aggregating and harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations covering 3,437 unique variants in 415 genes, 357 diseases, and 791 drugs. We demonstrated large gains in overlapping terms between resources across variants, diseases, and drugs as a result of this 1 reuse, remix, or adapt this material for any purpose without crediting the original authors. preprint (which was not peer-reviewed) in the Public Domain. It is no longer restricted by copyright. Anyone can legally share,The copyright holder has placed this . http://dx.doi.org/10.1101/366856 doi: bioRxiv preprint first posted online Jul. 11, 2018; harmonization. We subsequently demonstrated improved matching between patients of the GENIE cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 34% to 57% in aggregate. We developed an open and freely available web interface for exploring the harmonized interpretations from these six knowledgebases at search.cancervariants.org . MAINThe promise of precision oncology-in which a cancer patient's treatment is informed by the mutational profile of their tumor-requires concise, standardized, and searchable clinical interpretations of the detected variants. These structured interpretations of biomarker-disease associations are therapeutic (predictive of favorable or adverse response to therapy), diagnostic (determinant for disease type or subtype), or prognostic (an indicator for overall patient outcome). Additionally, clinical interpretations include germline variants that may predispose a patient to develop cancer. Isolated institutional efforts have contributed to the curation of the biomedical literature to collect and formalize these interpretations into knowledgebases [1][2][3][4][5][6][7][8][9][10][11][12] , but the vast scale of this overall activity and the rapid generation of new knowledge makes development of a single comprehensive curated knowledgebase infeasible 13 . In addition to the extent and diversity of the curated literature, the content and structure of interpretations within each knowledgebase is shaped by the institution that created it, thus increasing the burden of translating interpretations from multiple knowledgebases into a consensus interpretation for one or more genomic variants. Consequently, stakeholders interested in the effects of genomic variant...

The NavDx® blood test analyzes tumor tissue modified viral (TTMV)-HPV DNA to provide a reliable means of detecting and monitoring HPV-driven cancers. The test has been clinically validated in a large number of independent studies and has been integrated into clinical practice by over 1000 healthcare providers at over 400 medical sites in the US. This Clinical Laboratory Improvement Amendments (CLIA), high complexity laboratory developed test, has also been accredited by the College of American Pathologists (CAP) and the New York State Department of Health. Here, we report a detailed analytical validation of the NavDx assay, including sample stability, specificity as measured by limits of blank (LOBs), and sensitivity illustrated via limits of detection and quantitation (LODs and LOQs). LOBs were 0–0.32 copies/μL, LODs were 0–1.10 copies/μL, and LOQs were <1.20–4.11 copies/μL, demonstrating the high sensitivity and specificity of data provided by NavDx. In-depth evaluations including accuracy and intra- and inter-assay precision studies were shown to be well within acceptable ranges. Regression analysis revealed a high degree of correlation between expected and effective concentrations, demonstrating excellent linearity (R2 = 1) across a broad range of analyte concentrations. These results demonstrate that NavDx accurately and reproducibly detects circulating TTMV-HPV DNA, which has been shown to aid in the diagnosis and surveillance of HPV-driven cancers.