Analytical validation and initial clinical testing of quantitative microscopic evaluation for PD-L1 and HLA I expression on circulating tumor cells from patients with non-small cell lung cancer

Biomark Res. 2022 Apr 25;10(1):26. doi: 10.1186/s40364-022-00370-8.

ABSTRACT

INTRODUCTION: PD-L1 expression in non-small cell lung cancer (NSCLC) predicts response to immune checkpoint blockade, however is an imperfect biomarker given tumor heterogeneity, and the antigen presentation pathway requiring other components including HLA I expression. HLA I downregulation may contribute to resistance, warranting its evaluation in attempts to guide patient selection. In addition, earlier detection of acquired resistance could prompt earlier change in treatment and prolong patient survival. Analysis of circulating tumor cells (CTCs) captures heterogeneity across multiple sites of metastases, enables detection of changes in tumor burden that precede radiographic response, and can be obtained in serial fashion.

METHODS: To quantify the expression of both PD-L1 and HLA I on CTCs, we developed exclusion-based sample preparation technology, achieving high-yield with gentle magnetic movement of antibody-labeled cells through virtual barriers of surface tension. To achieve clinical-grade quantification of rare cells, we employ high quality fluorescence microscopy image acquisition and automated image analysis together termed quantitative microscopy.

RESULTS: In preparation for clinical laboratory implementation, we demonstrate high precision and accuracy of these methodologies using a diverse set of control materials. Preliminary testing of CTCs isolated from patients with NSCLC demonstrate heterogeneity in PD-L1 and HLA I expression and promising clinical value in predicting PFS in response to PD-L1 targeted therapies.

CONCLUSIONS: By confirming high performance, we ensure compatibility for clinical laboratory implementation and future application to better predict and detect resistance to PD-L1 targeted therapy in patients with NSCLC.

PMID:35468853 | DOI:10.1186/s40364-022-00370-8

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