Ravanmehr, Vida | Engineering Papers

DOE OSTI2024

Inhibition of MALT1 and BCL2 Induces Synergistic Antitumor Activity in Models of B-Cell Lymphoma

The activated B cell (ABC) subset of diffuse large B-cell lymphoma (DLBCL) is characterized by chronic B-cell receptor signaling and associated with poor outcomes when treated with standard therapy. In ABC-DLBCL, MALT1 is a core enzyme that is constitutively activated by stimulation of the B-cell receptor or gain-of-function mutations in upstream components of the signaling pathway, making it an attractive therapeutic target. We discovered a novel small-molecule inhibitor, ABBV-MALT1, that potently shuts down B-cell signaling selectively in ABC-DLBCL preclinical models leading to potent cell growth and xenograft inhibition. We also identified a rational combination partner for ABBV-MALT1 in the BCL2 inhibitor, venetoclax, which when combined significantly synergizes to elicit deep and durable responses in preclinical models. This work highlights the potential of ABBV-MALT1 monotherapy and combination with venetoclax as effective treatment options for patients with ABC-DLBCL.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning

Motivation: Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results: We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation: Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.

59 BASIC BIOLOGICAL SCIENCES↗