P09.55 A Platform to Prospectively Link Real-World Clinico-Genomic, Imaging, and Outcomes Data for Patients With Lung Cancer


      Developing personalized diagnostics and treatments for patients with cancer requires a comprehensive understanding of the patient journey. Real-world data can help to advance personalized medicine, but retrospective analyses are limited by data quality, bias, and other confounding factors. We present a multi-stakeholder platform to prospectively collect and link real-world clinico-genomic, imaging, and outcomes data to longitudinal blood genomic profiling for lung cancer patients treated in both the community and academic settings.


      This study is enrolling approximately 1,000 patients with metastatic non-small cell lung cancer (NSCLC) or extensive-stage small cell lung cancer (SCLC) who will initiate standard-of-care systemic anti-neoplastic treatment, regardless of line of therapy, at ≥20 practices within the Flatiron Health network, predominantly in the community oncology setting. Designated pre-specified clinical data are being collected from electronic health records (EHR) via technology-enabled abstraction, without the need for case report forms. Clinical images will be collected at standard-of-care visits. Circulating tumor DNA (ctDNA) profiling using FoundationOne®Liquid is being evaluated at enrollment, first tumor assessment, and progression or end of treatment. Tumor tissue may also be submitted at baseline for genomic profiling using FoundationOne®CDx and capture of digital pathology images. Patients are followed until death, withdrawal of consent, loss to follow-up, or end of study. With focused efforts to integrate into routine clinic workflows and minimize site burden, this study is leveraging existing infrastructure for ongoing centralized data abstraction and additionally creating a new, prospective data model for linking clinico-genomic data. The objectives of the study are to evaluate 1) the feasibility of building a linked, multi-modal, longitudinal, scalable, prospective research platform and 2) the associations between ctDNA and real-world clinical outcomes.


      TableBaseline demographics and clinical characteristics
      CharacteristicAll Patients (N = 233)
      Age, years, median [IQR]68 [62, 75]
      Female, n (%)118 (51%)
      Smoking status, n (%)
      History of smoking216 (93%)
      No history of smoking17 (7.3%)
      ECOG PS, n (%)
      073 (31%)
      191 (39%)
      243 (18%)
      3+6 (2.6%)
      Not assessed20 (8.6%)
      Race, n (%)
      Asian1 (0.43%)
      Black or African American20 (8.6%)
      White175 (75%)
      Other27 (12%)
      Unknown10 (4.3%)
      Therapy type/class, n (%)
      Anti-VEGF + chemotherapy combinations11 (4.7%)
      Chemoimmunotherapy92 (39%)
      Clinical study drug-based therapies2 (0.86%)
      Immunotherapy46 (20%)
      Non-platinum-based chemotherapy combinations2 (0.86%)
      Platinum-based chemotherapy combinations24 (10%)
      Single agent chemotherapy24 (10%)
      Targeted therapy11 (4.7%)
      Study therapy not yet started14 (6.0%)
      Not treated7 (3.0%)
      Non-small cell lung cancer, n (%)
      Total194 (83%)
      Non-squamous cell carcinoma141 (73%)
      Squamous cell carcinoma48 (25%)
      NSCLC NOS5 (2.6%)
      Small cell lung cancer, n (%)
      Total39 (17%)
      AJCC Stage at diagnosis, n (%)
      I15 (6.4%)
      II4 (1.7%)
      III29 (12%)
      IV182 (78%)
      Unknown3 (1.3%)
      AJCC, American Joint Committee on Cancer; ECOG PS, Eastern Cooperative Oncology Group performance status; IQR, Interquartile Range; NOS, not otherwise specified; VEGF, vascular endothelial growth factor.


      This novel prospective research platform, anchored to EHR-based centralized data collection infrastructure and an integrated data model, has the potential to scale and incorporate maturing personalized medicine capabilities. This study will deepen our understanding of the lung cancer patient journey across multiple data modalities in the real-world setting. Enrollment is ongoing.