The promise of precision medicine is to identify the ideal treatment for each patient, eliminating failed treatment cycles and reducing treatment burdens on patients and payors. While gene sequencing can predict effective treatment options in some cases, studies have estimated that only 10-15% of cancer patients are treated with genotype matched drugs. Gene expression is theoretically an attractive alternative to predicting therapeutic response because, it provides a direct readout of dysregulated cellular activities including druggable pathways. However, the use of bulk gene expression to predict therapeutic response is hampered by tumor heterogeneity; that is, a biopsied tumor contains multiple cells types and different tumor clones and a bulk analysis only generates an average gene expression signal across all of the cells, leading to a decreased sensitivity for identifying biomarkers found only in specific cell subgroups.

More recently, single-cell gene expression analysis has shown promise in improving precision medicine through a focused look into the biology of the tumor cells themselves. For example, we previously showed (Scolnick, et al ASCO 2022) that a model trained on single-cell gene expression data collected from 17 Multiple Myeloma (MM) patient's plasma cells (PCs) could be used to predict the therapeutic response of seven independent MM patient samples at 100% accuracy.

Here we combine single-cell gene expression analysis with Singleron Biotechnology's MapResponse™ machine learning algorithms to extend our predictions to data from peripheral blood mononuclear cells (PBMCs) of plasma cell leukemia patients who were treated with daratumumab. Our model accurately predicted the response to daratumumab treatment for both patients based on the PCs collected from blood samples.

These early findings point to the potential value of combining single-cell RNA sequencing with machine learning methods such as MapResponse™ to bring precision medicine to more patients based on minimal invasive assessment of PBMCs.

Merz:Janssen: Honoraria; BMS Celgene: Honoraria.

Author notes


Asterisk with author names denotes non-ASH members.

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