New Multi-Omics Tool Sheds Light on Cancer Evolution

A research team from Weill Cornell Medicine and the University of Adelaide has introduced a new single-cell multi-omics tool, GoT-Multi, that advances the ability to monitor multiple gene mutations while recording gene activity in individual cancer cells. Detailed in Cell Genomics, the method enables the study of cancer progression from initial mutations through therapy resistance, using a broader range of tissue types than before.

GoT-Multi builds on an earlier innovation called Genotyping of Transcriptomes (GoT), which could analyze mutations in fresh or frozen samples but was limited in scope and sensitivity. The enhanced version substantially overcomes these constraints, allowing analysis of formalin-fixed, paraffin-embedded tissues—samples that make up a large portion of pathology archives. This capability opens the door to studying vast collections of preserved human specimens, linking genetic mutations to cell function at single-cell resolution.

In the study, the team demonstrated GoT-Multi’s capabilities by examining samples from patients with chronic lymphocytic leukemia progressing toward large B-cell lymphoma, a process known as Richter Transformation. By profiling tens of thousands of tumor cells, the researchers identified more than two dozen distinct gene mutations and correlated them with the cells’ activity levels and inflammatory responses as the disease advanced.

“This technology gives us substantial new power to answer important questions about how cancers evolve, from the beginnings of pre-cancerous neoplastic outgrowths to transformation into malignancy and finally to therapy resistance,” said co-senior author Anna Nam.

GoT-Multi is classified as a “single-cell multi-omics” approach because it integrates multiple layers of genomic and transcriptomic data from individual cells, offering a more complete view of tumor heterogeneity. The tool’s ability to process large numbers of cells efficiently provides researchers with a practical means to trace how genetic changes manifest in cell behavior and contribute to cancer progression.

The investigators are now using GoT-Multi to study larger cohorts of lymphomas that have developed therapy resistance and to map the molecular progression of other cancers and precancerous conditions. By applying this expanded capability, they anticipate uncovering mechanisms that could inform future therapeutic strategies.