A team led by researchers from Weill Cornell Medicine, the New York Genome Center, Harvard Medical School, Massachusetts General Hospital and the Broad Institute of MIT and Harvard profiled in unprecedented detail thousands of individual cells sampled from of brain tumors of patients. The results, as well as the methods developed to obtain these results, represent a significant advance in cancer research and could ultimately lead to better methods of detecting, monitoring and treating cancers.
As the researchers reported on September 30 in Genetics of nature, they used advanced techniques to record gene mutations, gene activity, and programming marks of gene activity on DNA called methylations, in individual tumor cells sampled from patients with gliomas, the type of most common brain cancer. In this way, they mapped distinct behaviors or “states” of tumor cells in gliomas and identified key programming marks that appear to move glioma cells from one state to another. These programming brands, in principle, could be targeted with future drugs.
By combining their single-cell methods with a molecular clock technique, the researchers created “ancestral trees” for the sampled tumor cells, describing their histories of state changes.
“It’s like having a time machine – we can take a sample of a patient’s tumor and deduce a lot of details about how that tumor grew,” said co-lead author Dr Dan Landau. , Associate Professor of Medicine in the Division of Medical Hematology and Oncology and Fellow of the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine, and Senior Fellow of the New York Genome Center.
“We were able to make observations here that have fundamental implications for how we should think about treating gliomas,” said co-lead author Dr Mario Suva, associate professor of pathology at Harvard Medical School, pathologist at Massachusetts General Hospital. and Fellow of the Broad Institute at MIT and Harvard.
Tumor cells have traditionally been characterized as a whole, rather than individually, and relatively simply, for example by their cell type of origin and the receptors they carry on their surface. Drs. Landau and Suva, however, helped pioneer the use of “single-cell multi-omics” methods to profile tumor cells individually and in much more detail.
In the new study, they used a three-layered method: recording not only genetic sequence and genetic transcription information, but also methylation marks controlling âepigeneticâ transcription on DNA; for the first time on individual tumor cells directly from patients. Scientists sampled more than 100 tumor cells on average from each of the seven patients with HDI-mutant glioma and from seven patients with more treatment-resistant glioma called HDI wild-type glioblastoma.
They found that cells from both cancers tended to be in one of four distinct states, ranging from stem cell-like states to states like those in more mature brain cells. They also identified distinct patterns of DNA methylation that seem to explain the changes between these states; such patterns could in principle be disrupted by future therapies to suppress such state changes and slow tumor development.
While the researchers captured what was essentially a snapshot of cellular states in the sampled tumors, they also devised a molecular clock method, based on the random changes in DNA methylations that occur naturally over time. , to calculate a lineage tree for each cell-; illustrating its history of different states, dating back to the origin of the tumor.
The lineage trees revealed among other things that glioblastoma cells, compared to lower grade glioma cells, had a high degree of “plasticity” allowing them to switch relatively easily between stem-like states and more mature states.
The highly plastic cellular architecture of HDI wild-type glioblastoma may allow it to survive stem cell destruction treatments by regenerating these cells from its pool of more mature cells. “
Dr Federico Gaiti, co-first author, postdoctoral fellow at the Landau laboratory
The results in general offer a wealth of information on the dynamics of gliomas, information that should be useful in developing better methods of detection, staging, monitoring and treatment.
The researchers now plan to use their single-cell multi-omics approach to study how gliomas respond to different treatments. In principle, they said, the approach can be used to study the development of any type of tumor, or even genetic mutations that accumulate with age in healthy tissue.
Chaligne, R., et al. (2021) Epigenetic coding, heritability and plasticity of states of glioma transcriptional cells. Genetics of nature. doi.org/10.1038/s41588-021-00927-7.