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A platform to prospectively link real-world clinico-genomic, imaging, and outcomes data for patients with lung cancer

Published

March 2021

Citation

Walia, G, Lu, MW, Bourla, AB, Santos, EC, Schulze, K, Cabili, MN, Williams, EH, Mirkovich, N, DiCecca, RH, Lee, SE, Fang, B, Schwartzberg, L, Herbst, RS, Chiang, AC. . World Conference on Lung Cancer. .

https://www.jto.org/article/S1556-0864(21)00525-6/fulltext

 

Authors:
Walia, G, Lu, MW, Bourla, AB, Santos, EC, Schulze, K, Cabili, MN, Williams, EH, Mirkovich, N, DiCecca, RH, Lee, SE, Fang, B, Schwartzberg, L, Herbst, RS, Chiang, AC

Introduction

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.

Methods

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.

Results

Between December 5, 2019 and June 30, 2020, 14 sites have been activated and 235 patients have been enrolled (233 patients with a confirmed diagnosis [Table]). At baseline, 83% had NSCLC, the median age was 68 years, and 51% were female.
TableBaseline demographics and clinical characteristics
Characteristic All Patients (N = 233)
Age, years, median [IQR] 68 [62, 75]
Female, n (%) 118 (51%)
Smoking status, n (%)
History of smoking 216 (93%)
No history of smoking 17 (7.3%)
ECOG PS, n (%)
0 73 (31%)
1 91 (39%)
2 43 (18%)
3+ 6 (2.6%)
Not assessed 20 (8.6%)
Race, n (%)
Asian 1 (0.43%)
Black or African American 20 (8.6%)
White 175 (75%)
Other 27 (12%)
Unknown 10 (4.3%)
Therapy type/class, n (%)
Anti-VEGF + chemotherapy combinations 11 (4.7%)
Chemoimmunotherapy 92 (39%)
Clinical study drug-based therapies 2 (0.86%)
Immunotherapy 46 (20%)
Non-platinum-based chemotherapy combinations 2 (0.86%)
Platinum-based chemotherapy combinations 24 (10%)
Single agent chemotherapy 24 (10%)
Targeted therapy 11 (4.7%)
Study therapy not yet started 14 (6.0%)
Not treated 7 (3.0%)
Non-small cell lung cancer, n (%)
Total 194 (83%)
Non-squamous cell carcinoma 141 (73%)
Squamous cell carcinoma 48 (25%)
NSCLC NOS 5 (2.6%)
Small cell lung cancer, n (%)
Total 39 (17%)
AJCC Stage at diagnosis, n (%)
I 15 (6.4%)
II 4 (1.7%)
III 29 (12%)
IV 182 (78%)
Unknown 3 (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.

Conclusion

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.
 

Sources:
World Conference on Lung Cancer

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