Multiomic analysis in Waldenstrom’s Macroglobulinemia
Dr Zachary Hunter, Dana-Farber Cancer Institute, Boston (USA)
Funding period: 2020-2022.
This project is co-funded under the IWMF-LLS Strategic Research Roadmap initiative in collaboration with the Waldenstrom’s Macroglobulinemia Foundation of Canada.
This project is kindly supported by Peter Carr and WMozzies.
Multiomic analysis of DNA, RNA and epigenomic networks for prognostication and novel target identification in Waldenstrom’s Macroglobulinemia (WM)
Dr Hunter has assembled a team of WM experts, bioinformaticians, epigenomics experts, machine learning experts, and pathologists specializing in blood cancers representing three separate institutions (Dana-Farber Cancer Institute, Harvard School of Public Health and Memorial Sloan Kettering) to conduct an in depth integrative analysis of 300 bone marrow samples from untreated patients with WM.
Preliminary experiments used these samples to perform large scale genomic sequencing studies in order to investigate how WM cells have changed from their healthy donor counter parts. Specifically, Dr Hunter and the team looked for changes in the genetic code using technique known as whole exome sequencing (WES) while also measuring the levels of gene usage also known as gene expression using RNA sequencing (RNASeq).
Chemical modifications or physical winding of chromosomes means that not all sections of the DNA in chromosomes are available. The study of these modifications and availability of parts of chromosomes is known as epigenomics. From the same biopsies Dr Hunter has sequenced 41 patients to determine which regions are open and available to the cells, measured DNA modifications in 140 samples. This represents the largest sample set where DNA mutations, gene expression, and epigenomic changes are measured together ever conducted in WM.
While the researchers have begun to characterize each of these data sets individually, in reality, they form a signaling network giving them a snapshot of what was happening in WM cells. These networks are often complex and difficult to decern from one of these datasets alone and this project will combine multiple types of analysis (multiomic analysis) of DNA, RNA and epigenomic networks for prognostication and novel target identification in WM.
The specific aims of this project are:
- to characterize the genetic subtypes of untreated WM
- to conduct the first multiomic network analysis of WM
- to assess genomic subtype and network activation status impact on the WM microenvironment.