Inside Precision Medicine: AI Model Digs Up Rare Somatic Variants for Precision Oncology Pipelines

By Jonathan D. Grinstein

In a clinical genetics lab, a technician is staring at a screen, scanning fresh sequencing data—tens of millions of reads, a line of colored peaks representing one of the four DNA nucleotides—searching for a faint somatic variant hidden in the heterogeneous mess that is a cancer genome. Occasionally, there’s a small bump overlapping the peak, indicating the detection of a different nucleotide in that same position. The lab tech squints, trying to assess if that little bump is indeed a real mutation or just a sequencing artifact. If incorrect, the answer could influence a child’s diagnosis and treatment, which could mean the difference between life and death.

That single uncertainty, distinguishing a true mutation from a technical artifact amid the chaos of cancer genomes, defines much of somatic variant calling. It’s a deceptively simple goal—find the mutations that occur only in tumor cells—but one that’s proven to be remarkably elusive, particularly for long-read technologies that produce different types of errors than traditional short-read methods.

Until recently, even the best tools struggled with that distinction, especially when it comes to long-read sequencing data. But a collaboration between researchers at the UC Santa Cruz (UCSC) Genomics Institute, Google Research, and Children’s Mercy Hospital may have finally given those clinical genomics labs a way to confidently see through the noise. Their new AI-powered algorithm, DeepSomatic, described in a Nature Biotechnology research paper, pushes somatic variant detection to new levels of precision and robustness.

Read the full article via Inside Precision Medicine

The Children's Mercy Research Institute

10 Years of Clinical Whole Genome Sequencing at Children's Mercy