Diagnosing CNS Tumors More Precisely with Methylation Marks
, by Kendall Morgan, NCI-CONNECT Contributor
Molecular biologist Dr. Zied Abdullaev explains the value of methylation marks for improved accuracy and precision in diagnosing rare brain and spinal cord tumors.
An accurate diagnosis is the first critical step to ensure the best treatment for rare brain and spinal cord tumors. It enables treatment that is based on the unique biology of each cancer.
Pathologists typically classify cancers by examining tumor tissue under a microscope. Doctors also rely on molecular marker tests. These can show changes in specific proteins or genes that are known to drive tumor growth and help guide treatment decisions. A novel way to classify central nervous system (CNS) tumors more precisely is by looking at patterns of chemical methyl marks on the DNA of cancer cells. This is known as DNA methylation
The presence or absence of methyl marks across the genome, which includes all your DNA, affects how active the underlying genes will be. In cancer, turning genes involved in cell growth “up” could make cancer cells grow faster and spread. Turning them “down” would do the opposite. These marks are like “volume knobs.” Instead of sound, they turn gene activity up or down. All of this change can happen without any “typos” in the underlying sequence that changes how genes are “spelled.” The study of such reversible DNA alterations is known as epigenomics.
The NCI’s Center for Cancer Research Neuro-Oncology Branch is one of only a few places in the country where patients can get epigenomic or methylation testing on rare CNS cancers in the clinic. The approach is catching on at more cancer centers as its value for diagnosing tricky cases precisely and accurately is proven, says Zied Abdullaev, Ph.D., a staff scientist at NCI’s Comprehensive Oncologic Molecular Pathology and Sequencing Service (COMPASS)'s Clinical Methylation Unit.
“Using methylation molecular testing is becoming a very important modality for classification of tumors,” Dr. Abdullaev says. “Running this test can better diagnose patients and identify potential targets for treatment.”
Understanding the Epigenome
To understand the importance of these methylation patterns, consider that most all the cells in your body carry the same DNA. (Cancer cells often are an exception as new mutations that arise within these cells help to drive the growth and spread of the cancer. Still, most of the DNA in cancer cells is identical to the DNA in other parts of your body.) And yet, organs and tissues—such as the heart, brain, and muscle—show vast differences.
Those differences among tissues are, to a large extent, explained by epigenomic methylation patterns. These patterns program tissues in various parts of the healthy human body, as well as in cancers, to look and act in very different ways. These methylation patterns, or signatures, therefore, serve as a fingerprint for different cell types. As a result, they can help to identify important features of cancer cells, including which tissues they originated from. They also may help to identify the specific changes in gene activity that drive a particular cancer’s growth.
The methylation-based CNS tumor classification available at NCI-COMPASS is partly based on a study reported in the journal Nature in 2018. The study noted that there are about 100 recognized CNS tumor types that are clinically and biologically diverse. Some are benign and easily cured with surgery. Others are cancerous and hard-to-treat.Previous studies also showed a lot of variability in how CNS tumors were being diagnosed.
Against this backdrop was interest in devising more precise and consistent ways to diagnose CNS tumors. Evidence showed the potential of methylation profiles for improving diagnosis. Such molecular profiles also had led to the discovery of new CNS tumor subtypes.
In the Nature study, the international team of researchers first generated DNA methylation profiles representing nearly all recognized CNS tumor types. They then applied a machine learning approach to the data to devise an algorithm for classifying other CNS tumors. In machine learning, computers sift through vast quantities of data in search of patterns that aren’t possible to detect otherwise. Once the computer has trained itself to distinguish known entities, the resulting algorithm can be applied to other unknown cases.
The studies reported in Nature suggested that methylation-based classification could have a substantial impact on the precision of CNS cancer diagnoses. The initial evidence suggested that such analysis of CNS tumor biology could change the diagnosis in up to 12 percent of cases.
Putting Methylation Classifiers to Work
At NIH, all patients who visit the Neuro-Oncology Clinic have their diagnosis reviewed by a neuropathologist. The NCI-CONNECT team often receives tumor tissue from the hospital where a patient had their biopsy or surgery. If a patient has not already had surgery or a biopsy, they can consult with NIH's neurosurgical oncology team. They’ll examine the tissue closely under a microscope looking at alterations in the tissue.
But that’s just the start. Next, they’ll use the latest technologies, including state-of-the-art DNA sequencing and DNA methylation analysis, to understand a tumor’s underlying molecular alterations. By combining all the diagnostic information, the NCI-CONNECT team makes the most precise possible
A recent study in Neuro-Oncology led by Dr. Abdullaev, Kenneth Aldape, M.D., Ph.D., senior investigator and chief of the Laboratory of Pathology in the Center for Cancer Research at NCI, Mark Gilbert, M.D., co-lead of NCI-CONNECT and chief of the Neuro-Oncology Branch, and others, suggests that methylation-based classification is even more valuable than the original 2018 study suggested for diagnosing rare CNS cancers. The study included more than 1,200 neuropathology samples analyzed with the methylation classifier over two years. Among all the cases, the classifier offered a score with high confidence in more than 60 percent of them.
In the cases with a high-confidence score, the classifier affected the diagnosis about half the time. Nearly a third of the cases analyzed in this way also ended up with a substantially new diagnosis, they report. Their early experience using the classifier now offers a practical guide toward the use of methylation analysis in routine diagnosis of CNS tumors at NCI and other cancer centers.
We’ve established this technique in the laboratory. We’ve shown its utility and accuracy in the classification of tumors and helping patients ultimately to get a proper diagnosis.
Pursuing Methylation-Based Testing
Dr. Abdullaev says that every patient with a CNS tumor who comes to NIH or has their sample sent by their healthcare team anywhere in the world can get access to this kind of analysis free of charge. Every patient who comes to NIH is considered for clinical trials, he explains. Their samples are sent for evaluation, which includes methylation analysis and, oftentimes, next-generation sequencing. Much of the time, he says, the NCI-CONNECT team receives samples from doctors and patients who need assistance with hard-to-classify CNS tumors.
“Patients might be seen in an outside hospital and they cannot determine what type of cancer it is,” Dr. Abdullaev says. “They are looking for help and we provide the service. We can run the testing and then provide them with a report on what we found and how we classify the tumor.”
Each case they examine also generates important data that they use to further improve the methylation classifier and future diagnoses. The evidence also can lead to the identification of new CNS tumor subtypes.
“We may find one case that doesn’t fit the current picture,” he says. “Down the road, we may find another similar case. This is a very dynamic field. Its rapidly evolving. We’re one of multiple centers around the world now accepting methylation as a tool to help better diagnose patients and gather more knowledge about these cancers.”
Ultimately, the goal is to advance understanding, diagnosis and treatment for rare brain and spinal cord cancers.