Analysis of screening technology performance and patient demographics to improve blood-based early lung cancer detection

Principal Investigator

Name
Toumy Guettouche

Degrees
Ph.D.

Institution
Mercy BioAnalytics

Position Title
CSO

Email
toumy@mercybio.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-851

Initial CDAS Request Approval
Nov 15, 2021

Title
Analysis of screening technology performance and patient demographics to improve blood-based early lung cancer detection

Summary
Mercy BioAnalytics is developing a blood test for early-stage lung cancer based on the colocalization of biomarkers on single extracellular vesicles. Our goal is to develop this assay for clinical use as a low-cost, robust and non-invasive complement to available screening methods such as low-dose computed tomography.
The NLST data and images contain a wealth of harmonized information about patient characteristics, disease states, progression, and mortality. We aim to use this data resource to source relevant clinical samples and ensure comprehensive patient representation as we build our Mercy Halo Lung Cancer assay. The NLST data would provide utility in the design of both retrospective and prospective clinical studies that reflect the demographics and cancer subtypes found in the general population.
NLST will also enable us to compare our assay performance to existing diagnostic tests used routinely in clinical practice. As early detection technologies continue to be developed and made available, it is crucial to understand the advantages and drawbacks compared to current screening recommendations. We aim to identify features of cases that are missed with traditional screening, as well as groups that suffer from high rates of false positive results. While many of these numbers are available in the NLST manuscripts, having the raw data enables analyses of sensitivity and predictive value at each stage of cancer development and in patient subgroups of high interest.
Additionally, as one of the most common and deadly cancers, new lung cancer datasets and findings are published regularly. The NLST dataset is an ideal gold standard to use for evaluation of new data resources and lung related findings as they are identified.

Aims

1. Design accurate clinical studies for Mercy Halo Lung that reflect the patient demographics and disease characteristics observed in the NLST dataset.
2. Specifically investigate patient subgroups that are commonly detected and commonly missed using existing screening technologies (low-dose CT and chest radiography).
3. Compare the demographics, test performance, and patient outcomes of Mercy Halo Lung and other early detection tests currently in clinical trials and development to current clinical practice.

Collaborators

Kelly Biette, Mercy BioAnalytics
Karen Copeland, Mercy BioAnalytics