Queries on Chronic Cancer

Although chronic lymphocytic leukemia (CLL) and its cousin, small lymphocytic leukemia, are known for their slow long-term progression, the process of Richter’s transformation (RT) can effectively turn them into a much more aggressive – and much more deadly – large B cell lymphoma. This sudden switch from a relatively manageable form of cancer to aggressive lymphoma is, understandably, of great interest to oncologists and patients – and yet we still don’t fully understand how RT works. A recent investigation, however, has brought us one step closer to answers.

The revelations come from a study focused on characterizing the genomics of CLL cases in which patients eventually developed RT (1). The team analyzed 54 samples, identifying a specific set of subclones already present when patients were diagnosed with CLL. Surprisingly, these subclones – sharing the same genomic, immunogenetic, and transcriptomic features of RT cells – lay dormant for up to 19 years before finally emerging. Alongside this knowledge, the team also identified new genomic drivers and epigenomic reconfigurations of RT and discovered that inhibition of the oxidative phosphorylation pathway curtails RT cell proliferation. They hope that being able to detect these signatures could help us to understand how CLL evolves and perhaps even support the development of therapies that could prevent the development of more aggressive cancers.

Crafting a CLL map
 

Making detailed molecular and clinical “maps” of each cancer type has not been a simple task. As our knowledge of cancer characteristics grows, we become increasingly aware of the vast diversity of types. But researchers may have just found a solution to this cartographical cancer quandary. Their answer? To bring together integrated genomic, transcriptomic, and epigenomic data in CLL to create an “oncological atlas” (2). Most previous investigations have focused on individual pieces of the CLL map puzzle, which are useful on a granular level, but much less so when the goal is to create a high-resolution picture of disease characteristics. For that, we need information integration.

Equipped with information from 1,148 patients, researchers sought to assemble the largest CLL dataset ever – and, from this, identify previously undiscovered areas of the CLL molecular map. For example, the number of unrecognized genes believed to be drivers of cancer doubled – jumping from 38.8 percent in 500 cases to 74.5 percent in roughly 1,000 cases. Most of these new driver mutations were present in less than 2 percent of patients. The researchers also noted that seven of the new drivers had clustered mutations in functional domains – such as those found in INO80, which is often associated with hepatosplenic T cell lymphoma. Another seven drivers – including RFX7 – had a role observed in other mature B cell malignancies.

This integration of data into detailed cancer maps may continue to help us locate important cellular pathways and fill in the gaps in our developing genetic picture of hematologic malignancies – and, eventually, other cancers as well.