This May, we’re recognizing #GrayMay, a month dedicated to raising awareness about brain tumors. In this blog post, I want to share something practical regarding precision medicine of gliomas: for gliomas, the diagnosis is no longer based on histology alone. Molecular profiling now helps define the tumor entity, clarify prognosis, and increasingly guide treatment options. If your lab supports CNS cases, molecular profiling can increase diagnostic yield.
Glioma diagnosis is increasingly defined by molecular markers
Two results often set the direction immediately:
- IDH mutation status is a major branching point in diffuse gliomas.
- 1p/19q codeletion is classification-defining when paired with an IDH mutation.
After that, a small set of additional markers does an outsized amount of clinical work. Depending on context, they can support classification, refine prognosis, or indicate glioblastoma-like biology even when histology is ambiguous. Common examples include ATRX, TP53, TERT promoter, EGFR amplification, +7/−10, and CDKN2A/B alterations. Many labs also capture MGMT promoter methylation as part of the clinical picture.
It’s not just small variants anymore
Glioma profiling is multi-variant-type by nature. A practical workflow usually needs to handle:
- Small variants like IDH1/2, TERT promoter, TP53
- Copy-number changes like 1p/19q codeletion, EGFR amplification, CDKN2A/B loss
- Structural variants and fusions, including actionable events like NTRK fusions and glioma-relevant rearrangements such as FGFR3–TACC3
- Pediatric and young adult markers such as H3 K27 / H3 G34 alterations
- Select actionable alterations like BRAF V600E in the right clinical setting
This is where many teams get stuck operationally. The data exists, but the evidence is scattered across spreadsheets, PDFs, and one-off notes, which makes consistency hard across reviewers and across time.
Enterprise genomics is an operations problem
Even strong labs hit the same bottleneck: running the assay is not the hard part. The hard part is doing it the same way every time, across teams, across time, with clean traceability and a reliable handoff into interpretation and reporting.
That is the core value of VSWarehouse 3 for neuro-oncology programs. It is not just a data store. It is the enterprise platform and deployment engine that lets teams run the VarSeq Suite at scale, on-premises or in a private cloud, with the controls institutions expect.
In a CNS workflow, that translates to a few concrete advantages:
- Deploy and execute: run pipelines under a governed system so you can answer what ran, when, with what inputs, and where the logs live.
- Annotate from history: reuse your own prior observations and internal frequencies during new analyses, so interpretation gets faster and more consistent over time.
- Assessment catalogs: codify variant classifications and interpretive notes into a shared knowledge base so new cases benefit from prior decisions.
- Enterprise security: support single sign-on, role-based access control, and isolated workspaces so the platform fits institutional requirements.
- Integrate and exchange: connect to upstream and downstream systems through a REST API, including laboratory and clinical systems, and standardize outputs for reporting and follow-up.
For gliomas, the message is simple: if molecular markers are required for classification, then the workflow needs enterprise-grade repeatability, governance, and reuse of institutional knowledge, not just a way to open a variant file.
Brain tumors by the numbers
The following quick stats come from the National Brain Tumor Society and are useful for #GrayMay awareness posts:
- 93,000+ Americans will be diagnosed with a primary brain tumor in 2025
- 35.7% is the five-year relative survival rate for patients with malignant brain tumors
- 18,330 Americans will die from a malignant brain tumor in 2025
Genomics is how we get to the right glioma label and the right pathway. The enterprise challenge is making that repeatable: consistent evidence capture, consistent interpretation, and consistent reporting, at the scale your program is expected to run.
