New Multi-Omics Tool Illuminates Cancer Progression

Tracking how cancers evolve into more aggressive and therapy-resistant forms has long been a challenge for researchers. Many current tools can only capture limited genetic information from tumor samples, especially when the samples are preserved rather than fresh. A new technology now allows scientists to record multiple gene mutations while simultaneously tracking gene activity in single cancer cells, enabling unprecedented insights into how cancers progress and resist treatment.

Developed by Weill Cornell Medicine (New York, NY, USA) in collaboration with the University of Adelaide (Adelaide, Australia), the new tool, called GoT-Multi, is an advanced version of the earlier GoT (Genotyping of Transcriptomes) platform. While GoT was innovative, it was limited in the number and types of mutations it could detect and could only work on fresh or frozen samples. GoT-Multi overcomes these barriers, handling preserved samples embedded in wax, a widely available resource in hospital pathology labs worldwide.

GoT-Multi is a “single-cell multi-omics” tool that integrates multiple layers of data from thousands of individual cancer cells. It can process large sample sets rapidly and link cellular gene activity with genetic mutations, revealing how specific mutations drive different cancer cell behaviors such as growth and inflammation. This expanded functionality allows researchers to map disease progression with unprecedented clarity.

In a key demonstration, the team applied GoT-Multi to tissue samples from patients whose chronic lymphocytic leukemia had progressed into aggressive large B-cell lymphoma, a process known as Richter Transformation. According to findings published in Cell Genomics, the tool analyzed more than 45,000 tumor cells, identifying over two dozen mutations and correlating them with distinct cellular activities, providing new insight into how malignancy develops.

Researchers are now using GoT-Multi to study therapy-resistant lymphomas and to map other cancers and precancerous stages. By linking genetic and functional data at single-cell resolution, the tool can uncover key pathways of resistance and evolution, potentially pointing to new therapeutic targets. The team anticipates that this platform will guide the development of precision treatments and deepen understanding of tumor biology.

“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 study co-senior author Dr. Anna S. Nam.

Related Links:
Weill Cornell Medicine
University of Adelaide