Aptamer-Based Multiparameter Analysis for Molecular Profiling of Hematological Malignancies.

Abstract

The subtypes of hematological malignancies (HM) with minimal molecular profile differences display an extremely heterogeneous clinical course and a discrepant response to certain treatment regimens. Profiling the surface protein markers offers a potent solution for precision diagnosis of HM by differentiating among the subtypes of cancer cells. Herein, we report the use of Cell-SELEX technology to generate a panel of high-affinity aptamer probes that are able to discriminate subtle differences among surface protein profiles between different HM cells. Experimental results show that these aptamers with apparent dissociation constants (Kd) below 10 nM display a unique recognition pattern on different HM subtypes. By combining a machine learning model on the basis of partial least-squares discriminant analysis, 100% accuracy was achieved for the classification of different HM cells. Furthermore, we preliminarily validated the effectiveness of the aptamer-based multiparameter analysis strategy from a clinical perspective by accurately classifying complex clinical samples, thus providing a promising molecular tool for precise HM phenotyping.