Fine-Tuning Single-Cell Tumor Profiling to Predict Cancer Outcomes
Newly developed computational tool accurately identifies, characterizes, and differentiates cell types in tumor samples
Swathi Kodaikal, MSc, holds a master’s degree in biotechnology and has worked in places where actual science and research happen. Blending her love for writing with science, Swathi enjoys demystifying complex research findings for readers from all walks of life. On the days she doesn’t write, she learns and performs Kathak, sings, makes plans to travel, and obsesses over cleanliness.
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Single-cell RNA and single-nuclei RNA sequencing (scRNA and snRNA-seq, together referred to as scnRNA-seq) help characterize the heterogeneity in tumor microenvironments and inform therapeutic decisions when treating cancers. But these techniques entail high costs and stringent sample-collection requirements to produce consistent and clinically viable solutions.
In a recent study published in Genome Biology, the researchers at Baylor College of Medicine, Ghent University, and collaborating institutions showed the efficacy of computational deconvolution, a strategy that can potentially overcome the limitations of using scnRNA-seq data in the clinic.
SQUID makes scnRNA-seq accessible and versatile
Bulk-analyzing transcriptomes from tumor samples may not accurately identify and infer insights about the small percentage of chemoresistant cells. Ultrasensitive tumor profiling techniques such as scnRNA-seq tackle this challenge and help differentiate between chemosensitive and chemoresistant cells before therapy begins.
To make scnRNA-seq accessible and reliable, the researchers developed a computational deconvolution method called Single-cell RNA Quantity Informed Deconvolution (SQUID). SQUID combines bulk RNA-seq transformation and gene expression deconvolution approaches to profile cell types in cell mixtures and tissue samples.
The researchers compared SQUID’s performance with that of other computational methods designed to analyze scRNA-seq transcriptomes from bulk data. “We found that SQUID significantly outperformed the other methods,” said co-corresponding author Pavel Sumazin, PhD, associate professor of pediatrics and member of the Dan L Duncan Comprehensive Cancer Center at Baylor, in a recent press release. “Even the best methods were not able to predict cancer treatment outcomes, but SQUID anticipated treatment outcomes for both types of pediatric cancer we work with, neuroblastoma and acute myeloid leukemia. This represents a significant advance in the field because methods to predict bulk composition had not worked well before.”
Though using SQUID may incur marginal extra effort and cost, per Sumazin and team, the benefits may dramatically outweigh any initial setup cost. “The long-term vision is to simplify this process further and prove its clinical utility. Moreover, we expect to be able to extend its use for other conditions beyond cancer, such as those of the heart, brain, or lungs,” said Sumazin.