Single-cell RNA-seq could play a key role in personalized medicine

Researchers at MIT and Linköping University discuss the potential of single-cell RNA sequencing (scRNA-seq) to empower clinical implementation of personalized medicine. On the basis of work to date, they emphasize applications in cancer. Nevertheless, the underlying problems and solutions should be generally applicable to other complex diseases.

One of health care’s largest outstanding issues is that many patients do not respond to treatment. By recent estimates, about 90% of drugs are effective for less than 50% of patients. This causes enormous physical, social, and economic suffering. The annual cost of ineffective treatment is estimated at $350 billion/year in the United States alone. Moreover, variable treatment response contributes to the rising cost of drug development, currently around $2.6 billion per drug. A fundamental driver of these inefficiencies is the cellular heterogeneity that exists within and between patients in cancer and other complex diseases, which can involve altered behaviors across multiple cell types and hundreds of genes…

Ideally, personalized medicine would be guided by complete information about all disease-associated factors that might affect a patient’s treatment, from dysregulated genes and cells to lifestyle, diet, and environment…

An intuitive way to think of scRNA-seq is to liken it to demographics. Whereas average population statistics (representing bulk genome-wide RNA-seq mRNA expression values) may tell us that an American (average cell) is around 37.8 years old, has about 1.14 children, and makes about $30,000/year, scRNA-seq comprehensively collects these same data points for each individual. By applying computational methods, we can identify distinct structures in our individual-resolved demographics (gene modules coexpressed across cells) that may reveal valuable groupings, for example, professions (cell types)…

scRNA-seq applications in cancer medicine


(A) Bulk analysis of a tumor identifies the predominant malignant clone and suggests a drug to target it but not other clones. (B) scRNA-seq resolves each clone within a tumor, as well as the corresponding biomarkers and cognate drugs, enabling successful therapy. (C) Longitudinal profiling of patient samples with scRNA-seq (or biomarkers discovered with it) can be used to monitor disease state and select the appropriate time to treat, given the benefits and costs of intervention. (D) Analysis of samples before and after treatment may reveal subsets refractory to a given therapy, as well as their biomarkers and mechanisms of resistance. (E) Because of its sensitivity, scRNA-seq might also be used in a clinical setting for detection of rare disease-associated cells (such as minimal residual disease), which would have been missed by bulk analyses.

(read more at Science Translational Medicine)

Shalek AK, Benson M. (2017) Single-cell analyses to tailor treatments. Sci Transl Med 9(408). [article]

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