New insights into the genetic dynamics of chronic lymphocytic leukemia
CLL exists as a slow- or fast-growing cancer and is associated with certain genetic mutations, but it has not been fully characterized. Previous analyzes provided only segments of the CLL “map”, each segment focusing on specific patient types or limited data. To provide a more thorough understanding of the biological underpinnings of CLL and its molecular subtypes, the scientists set out to build a map from the largest CLL dataset to date.
To build the CLL map, the team analyzed variations in genetic sequences, gene expression patterns, and chemical modifications to DNA — or genomic, transcriptional, and epigenetic data — from 1,148 patients. “Such a CLL map could eventually be leveraged in the clinic, where new patients’ genomic features can be compared with the treatments and outcomes of patients with similar gene profiles.” self,” said Catherine Wu, MD, co-senior and co-author respectively. Head of the Division of Stem Cell Transplantation and Cell Therapy at Dana-Farber Cancer Institute and professor of Medicine at Harvard Medical School. “This profiling can help more precisely tailor the prognosis and treat a new patient based on their specific molecular features, moving closer to precision medicine.”
The scientists identified 202 genes (109 of which were novel) when mutated potentially leading to CLL, and they refined the subtypes of CLL with characteristics and prognosis. separate genes. In addition to genetic sequences, the expression patterns of certain genes are further classified as CLL and provide valuable prognostic information. “Our study has revealed that the genetic and biological contexts of CLL are more complex than previously appreciated,” said co-senior author and co-senior, Gad Getz, PhD, Director Bioinformatics at the Joint Cancer Center and Director of Cancer Genomes said. Computational Analysis Group at the Broad Institute.
Patient clinical outcomes are linked to a combination of genomic, transcriptional, and epigenetic features — so that integration of these data can predict a patient’s likelihood of going into remission versus developing advanced cancer. more developed.
“We are releasing a CLL map ‘portal’ that is based on the CLL map and will be an interactive website for translation researchers to use as a resource for further investigation — such as finding learn more about the different CLL drivers and subtypes,” Getz.