Addressing cellular heterogeneity in tumor and circulation for refined prognostication

Area of Science:

• Oncology
• Genomics
• Molecular Diagnostics

Background:

• Non-small cell lung cancer (NSCLC) exhibits significant genomic and transcriptomic intratumor heterogeneity (ITH).
• Current prognostication methods, including multigene tests (MGTs), often rely on single-tissue samples, potentially overlooking ITH's impact.
• There is a critical need for advanced genomic strategies to improve risk prediction accuracy in NSCLC.

Purpose of the Study:

• To investigate the impact of ITH on the prediction of recurrence in NSCLC.
• To develop a refined prognostic model that incorporates ITH and circulating tumor cell (CTC) gene expression data.
• To identify novel gene signatures associated with metastatic potential using single-cell CTC analysis.

Main Methods:

• Utilized label-free, inertial-focusing microfluidics for single-cell isolation of circulating tumor cells (CTCs).
• Analyzed gene expression profiles of CTCs to identify signatures related to metastatic potential.
• Developed and validated a refined prognostic risk model integrating ITH levels and CTC gene expression data.

Main Results:

• Demonstrated an unanticipated impact of ITH on NSCLC recurrence risk prediction.
• Identified specific gene signatures in CTCs correlating with differing metastatic potential.
• The refined prognostic model, accounting for ITH and CTC data, significantly outperformed the initial classifier in predicting recurrence-free survival (RFS).

Conclusions:

• ITH poses a significant challenge to accurate NSCLC prognostication.
• Single-cell CTC analysis combined with ITH assessment offers a more reliable genomic strategy for risk stratification.
• Tailored approaches are proposed to provide reliable risk estimates for NSCLC patients by accounting for ITH-driven variance.