Researchers have developed a powerful new tool that provides an unprecedentedly clear view of how cancers evolve at the single-cell level. The technology simultaneously tracks numerous gene mutations while also recording the gene activity within individual cancer cells, offering new insights into how tumors become more aggressive and develop resistance to treatment. Developed by a collaborative team from Weill Cornell Medicine and the University of Adelaide, the tool overcomes significant technical hurdles that previously limited such detailed analyses.
This multi-omics technology, named GoT-Multi, was used to map the progression of a slow-growing leukemia as it transformed into a deadly, aggressive lymphoma. By profiling tens of thousands of cells, the investigators could link specific genetic mutations to the cellular behaviors that drive malignancy and therapy resistance. Published in the journal Cell Genomics, the advancement is a major step forward in cancer genomics, promising to accelerate the development of new therapeutic strategies and enhance the precision of personalized medicine by untangling the complex processes that govern a tumor’s evolution.
A Successor to Pioneering Technology
The new GoT-Multi platform is a next-generation advancement of an earlier, innovative tool known as GoT, which stands for Genotyping of Transcriptomes. While the original GoT was groundbreaking, it had several limitations. It was restricted in its ability to detect certain types of gene mutations and could only track a limited number at one time. This constrained the scope of analysis, making it difficult to capture the full genetic diversity within a complex tumor environment where multiple mutations can coexist and interact.
Overcoming Past Limitations
GoT-Multi substantially improves upon its predecessor by expanding the number and type of mutations it can detect in a single assay. Perhaps its most significant upgrade is its compatibility with a wide range of pathology samples. The new tool can successfully analyze tissues that have been preserved with formalin and embedded in wax—known as FFPE samples. This is a critical breakthrough because FFPE is the standard preservation method in hospitals worldwide, meaning researchers can now tap into vast archives of patient tissue samples for study. The previous technology was largely restricted to fresh or specially frozen tissues, which are far less common. This new capability bridges the gap between cutting-edge research and real-world clinical materials.
Decoding Cancer’s Evolutionary Pathways
To demonstrate the power of GoT-Multi, the research team applied it to one of oncology’s persistent challenges: understanding how a relatively mild, slow-moving cancer can suddenly transform into a highly aggressive and often fatal disease. They focused on chronic lymphocytic leukemia (CLL), a blood cancer that can sometimes evolve into a fast-growing lymphoma in a process called Richter Transformation. This transition is difficult to predict and treat, making it a critical area of study. The tool allowed the scientists to map this malignant progression with unparalleled detail.
From Indolent Leukemia to Aggressive Lymphoma
In the study, the investigators profiled 45,963 individual cancer cells from patients with CLL that had transformed. For each cell, they tracked more than two dozen different gene mutations while simultaneously recording the cell’s complete gene expression profile, or transcriptome. This dual data stream allowed them to see not just which mutations a cell carried, but also how those mutations were affecting its behavior. For example, they could link specific mutations to cells that were rapidly proliferating or to those that were creating an inflammatory environment to fuel tumor growth. This provided a dynamic picture of the cancer’s evolution, revealing common pathways and genetic drivers that lead to treatment resistance.
The Power of Single-Cell Multi-Omics
The term “multi-omics” refers to analytical approaches that integrate data from different biological layers, such as the genome (mutations), transcriptome (gene activity), and proteome (proteins). Analyzing just one of these layers provides an incomplete snapshot of a cell’s function. Cancer is an incredibly complex disease driven by changes at all these levels. By combining genomics and transcriptomics at the resolution of a single cell, GoT-Multi offers a more holistic and causational understanding of the disease. It moves beyond simply cataloging mutations to explaining their functional consequences.
Integrating Genotype and Transcriptome
The core strength of GoT-Multi is its ability to directly link a cell’s genotype (its specific set of mutations) with its phenotype (its observable characteristics and behaviors, reflected by its transcriptome). Within a single tumor, different cells can have different mutations, leading to a mosaic of cell populations with varied functions. Some populations may be responsible for growth, while others may drive metastasis or resist a particular drug. By profiling thousands of individual cells, the tool can identify these distinct subpopulations and determine which mutations are responsible for the most dangerous cellular activities. This helps distinguish “driver” mutations that actively push the cancer forward from “passenger” mutations that are simply along for the ride.
Implications for Clinical Oncology
The insights generated by GoT-Multi have significant potential to influence future cancer diagnosis and treatment. By providing a highly detailed map of a tumor’s cellular and genetic landscape, the technology can help pinpoint the specific mechanisms that allow a cancer to thrive and evade therapy. According to the researchers, this deeper understanding is expected to illuminate new therapeutic vulnerabilities that can be targeted with novel drugs. The ultimate goal is to move toward a new paradigm in cancer care where treatment is tailored to the unique molecular profile of a patient’s tumor.
Paving the Way for Precision Medicine
GoT-Multi is a key enabling technology for precision oncology. In the future, detailed analyses of patient tumors using this approach could provide clinicians with granular insights into how a cancer is evolving in real time. This could allow them to select the most effective therapies and potentially adapt treatment strategies as the cancer changes to develop resistance. The research team is already moving in this direction by applying GoT-Multi to a large collection of therapy-resistant lymphomas, as well as using it to map the development of other cancers and even pre-cancerous states. This work promises to uncover the hidden strategies that cancer cells use to survive, giving scientists a better chance to outsmart the disease.