The largest and most detailed molecular analysis yet of an aggressive brain cancer, called glioblastoma, reveals fundamental details that drive its growth.
The work, led by a team of scientists from the U.S. Department of Energy’s (DOE) Pacific Northwest National Laboratory (PNNL), Washington University School of Medicine in St. Louis, Case Western Reserve University, and the National Cancer Institute of the National Institutes of Health (NIH), was published online February 11 in the journal Cancer Cell. The results might one day be useful to tailor a patient’s treatment.
Glioblastoma is the most common brain tumor, with almost 12,000 patients diagnosed every year in the U.S. The genetic information in this cancer is well characterized, but genetic differences do not seem to affect how a patient responds to treatment.
The scientists wanted a deeper understanding of additional molecular details of glioblastoma. A more detailed classification based on patterns of gene expression, protein activation, and metabolite production might also help doctors identify treatments that are most effective for particular tumor subtypes.
At PNNL, a team of scientists led by biomedical scientist Tao Liu performed mass spectrometry-based analysis to identify proteins, lipids, and metabolites in 99 different tumor samples. They did this using capabilities at EMSL, the Environmental Molecular Sciences Laboratory, a DOE Office of Science user facility located at PNNL.
The protein analysis, called proteomics, measured all the proteins in the tumor samples as well as two specific modifications, called phosphorylation and acetylation, that affect biological functions such as cell signaling.
Integrating the multiple layers of protein details with available genomics information led to new biological and clinical findings in glioblastoma.
“Acetylation changes a protein’s shape and often results in opening up DNA-protein complexes to facilitate gene expression. By adding protein acetylation to our study, we were able to complete the loop from proteins to genes and gene expression, shedding light on important regulatory changes in glioblastoma,” said co-senior author Karin Rodland, chief scientist for biomedical research at PNNL.
Detailed genetic analysis of single tumor cells at Washington University revealed that the immune landscape of individual tumors fit into four separate categories based on the amount and type of immune cells present.
“The most immediate implications for these findings are better design of clinical trials,” said co-author Milan G. Chheda, MD, an assistant professor of medicine who treats patients at Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine. “For most clinical trials, we take all comers and give them the same treatment. We are not designing trials in the most precise way because we have not fully understood the molecular differences between each patient’s tumor. This leads us to call a treatment a failure when in fact it may be helping specific people.”
Connecting the dots with multi-omics
Integrating analysis of biomolecules such as lipids and metabolites with the genetic and protein information about these tumors provided additional insight into disease outcome.
This integrated multi-omic analysis revealed a small subset of tumors that did not fit neatly into any of the subtypes of this cancer, as organized by genomic information. ‘Mixed subtype’ tumors identified by the multi-omic analysis were associated with a poor clinical outcome, providing biomedical researchers with clues to the factors affecting prognosis that were not evident from genetic information alone.
“This multi-omics analysis provides an unprecedented level of molecular detail, which is starting to connect the missing dots in glioblastoma,” Liu said.
PNNL’s participation in this work was supported by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the NIH; the National Human Genome Research Institute and the NIH provided additional support. PNNL researchers have previously studied similar proteomic details of ovarian cancer, colon cancer, and endometrial cancer for CPTAC.