Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma, and while treatable, its outcomes can vary widely depending on the molecular characteristics of the tumor. To better predict outcomes and guide treatment, researchers classify DLBCL into subtypes based on its “cell of origin” (COO). These subtypes offer insights into the tumor’s biology and prognosis.

Traditional methods, like immunohistochemistry (IHC), have been used for COO classification. However, these methods often produce inconsistent results, with limited value in predicting patient outcomes. In recent years, RNA-based tests like the NanoString Lymphoma Subtyping Test (LST) have emerged as more reliable tools. They use gene expression profiles to classify DLBCL into molecular subtypes with greater precision and proven prognostic relevance.

A recent study led by researchers at Genentech, Inc introduces gneSeqCOO, a groundbreaking algorithm that leverages bulk RNA sequencing (RNASeq) data to classify the COO of individual tumor biopsies. This method uses a fixed reference to analyze gene expression patterns, offering several key advantages:

1. Consistency and Accuracy: gneSeqCOO produced stable results for individual samples, even when RNA quality varied or sequencing biases were present. This makes it a dependable tool for analyzing real-world samples.
2. Validation in Large Cohorts: The algorithm was tested on over 1,000 DLBCL samples and showed high agreement with the NanoString LST assay, even in samples dominated by a single COO subtype.
3. Efficiency and Versatility: Because it uses data from RNASeq—a common technique already used in many research and clinical settings—gneSeqCOO reduces the need for additional testing or extra sample collection.

This advancement is a big step forward for lymphoma research and patient care. By providing a robust and versatile alternative to existing assays, gneSeqCOO makes it easier to classify DLBCL subtypes accurately. This is particularly valuable in cases where RNASeq data is already available, saving time and resources while delivering reliable results.

Accurate COO classification not only helps researchers better understand the biology of DLBCL but also allows clinicians to tailor treatments to specific subtypes, improving patient outcomes. As the study shows, RNASeq-based tools like gneSeqCOO have the potential to replace older, less reliable methods, making precision medicine more accessible for lymphoma patients.

This innovation highlights the power of RNA sequencing in advancing cancer diagnostics and underscores its growing role in modern oncology. With tools like gneSeqCOO, we are one step closer to ensuring that every patient receives the most effective and personalized care possible.

Availability – The package is available at https://github.com/Genentech/gneSeqCOO.