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According to the , one in five people will be diagnosed with cancer in their lifetimes. Maintaining a healthy lifestyle can help reduce this risk and early detection coupled with appropriate treatment can be effective in many types of cancer.������

����԰AV (����԰AV) molecular biology and biochemistry professor Ryan Morin leads the Morin Lab which uses cutting-edge molecular and computational methods to study the genetic architecture of cancer. His research aims to identify what genetic changes lead to the onset and progression of cancers of the immune system, particularly those that may lead to new therapeutic strategies. The lab also looks at biomarkers that may be used to predict and follow the clinical course of cancers.

Morin’s research group has been working to better understand how changes to the genetic material in B cells—mutations—leads to various forms of non-Hodgkin lymphomas. Follicular lymphoma (FL) is one of the most common forms of non-Hodgkin lymphoma and, although effective treatments can usually manage disease symptoms, patients diagnosed with FL are rarely cured. Although many patients can live with the disease for years, in about 15% of patients, the disease transforms into aggressive diffuse large B-cell lymphoma (DLBCL), which grows and spreads more quickly.

For their recent study, led by ����԰AV postdoctoral fellow , now a Staff Scientist at the Michael Smith Genome Sciences Centre, researchers used machine learning to identify two genetically distinct subgroups of patients. This important finding can help better predict the risk and progression of malignancy using genetic testing.

The study, was published in the American Society of Hematology’s journal Blood, the most cited peer-reviewed publication in the field of hematology.

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We spoke to Morin and Dreval about their research.

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Please tell us what you discovered about the progression of follicular lymphoma in this study.

Dreval: Most genomic studies of cancer focus on the effect of mutations that affect what is known as the exome or the protein-coding part of the genome, which is only about 1.5% of the entire genome. In this study, we performed a more comprehensive analysis of lymphoma genomes by identifying and studying the entire genome, including the many non-coding mutations. Certain genetic alterations were found to be associated with transformation to more aggressive disease and worse patient outcomes.

Morin: The remarkable feature of this study was that at the outset, we were working towards a better understanding of the genetic heterogeneity across patients rather than specifically seeking genetic features associated with patient outcomes. Nonetheless, after identifying robust subgroupings defined by genetic features and assigning patients to our new genetic subdivisions, we found that patients in one subgroup were significantly more likely to have their disease transform to a more aggressive form. On average, patients in that subgroup experienced this transformation ten years earlier than the remaining patients.

What makes this discovery significant in the field of cancer and hematology research?��

Dreval: The study unravels the diverse genetic landscape of follicular lymphoma, aiding in deeper understanding of its heterogeneity and clinical behaviors. Since we identified the distinct genetic subdivisions, we could potentially develop a personalized treatment approach, tailored to the genetic profile of individual patients.

Morin: FL is something of an enigma among the hematologic cancers, because although it is generally considered incurable, patients can live with this cancer for many years, even decades, when they receive adequate treatments. Many of the genetic changes that are common in FL are also seen in DLBCL. In this study, we highlight even more genetic commonalities between DLBCL and FL, in particular one of the two new subgroups we defined, which we named “DLBCL-like FL.”

Tell us about your interdisciplinary research methods. How do you use machine learning and bioinformatics together to understand the genetic architecture of cancer?

Dreval: This study was a team effort and benefited from the diverse, interdisciplinary contributions from a research team with expertise in molecular biology, genomics, statistics, computational biology and bioinformatics. We worked together generating reproducible and open-source pipelines for data analysis and interpretation. Importantly, each step in the pipeline generated data for each next step of the process, allowing us to take advantage of the outputs of bioinformatics pipelines (somatic mutations) to use as features for machine learning algorithms.

Are you pursuing further research in this area and where might that lead? Could it result in novel therapies or earlier detection?

Dreval: Our group continues to advance the understanding of follicular lymphoma and is leading another large study of a separate group of patients to find underlying biologically informed subgroupings. We expect it to uncover novel genetic markers, pathways, and potentially therapeutic targets, ultimately advancing our understanding of cancer biology.

Morin: This work is part of a larger effort to delineate the genetic underpinnings of all the common B-cell lymphomas known as (Genomic Analysis of Mature B-cell Lymphomas). We are using a multi-faceted approach of assembling published data sets and sequencing additional samples collected by colleagues at BC Cancer, other Canadian institutions and from international collaborators. This current study brings us closer to understanding how and why some FL are more prone to transforming into DLBCL and might help us predict the timing of this event and treat patients accordingly. More generally, we are seeking new vulnerabilities in each of these groups that could allow the development of better treatment options.

For more on the work of the Morin Lab, .

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