AACR Project GENIE: Powering Precision Medicine through an International Consortium

Cancer Discovery, 2017

1,418 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d41751a94ff33b97a2f099fdab7fe9f43535b4c3

Citations in the corpus

• Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition (2021, neutral): “…obtained processed copy-number segment and overall survival data from n = 1,600 cases from CPI-treated patients profiled using the MSK-IMPACT panel (Consortium, 2017; Samstein et al., 2019) (referred to hereafter as the MSK1600 cohort; RECIST response outcome data were not available for this…”
• How Machine Learning will Transform Biomedicine (2020, methodology): “The AACR Project GENIE (AACR Project GENIE Consortium, 2017) has collected genomic profiles and clinical data for more than 19,000 patients, and the ASCO CancerLinQ is building a similar database of hundreds of thousands of patients.”
• A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT) (2018, neutral): “research findings to clinical benefit [42].”