Inhibition of MALT1 and BCL2 induces synergistic anti-tumor 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.

Prolonged cytopenia following CD19 CAR T cell therapy is linked with bone marrow infiltration of clonally expanded IFNγ-expressing CD8 T cells.

Autologous anti-CD19 chimeric antigen receptor T cell (CAR T) therapy is highly effective in relapsed/refractory large B cell lymphoma (rrLBCL) but is associated with toxicities that delay recovery. While the biological mechanisms of cytokine release syndrome and neurotoxicity have been investigated, the pathophysiology is poorly understood for prolonged cytopenia, defined as grade ≥3 cytopenia lasting beyond 30 days after CAR T infusion. We performed single-cell RNA sequencing of bone marrow samples from healthy donors and rrLBCL patients with or without prolonged cytopenia and identified significantly increased frequencies of clonally expanded CX3CR1hi cytotoxic T cells, expressing high interferon (IFN)-γ and cytokine signaling gene sets, associated with prolonged cytopenia. In line with this, we found that hematopoietic stem cells from these patients expressed IFN-γ response signatures. IFN-γ deregulates hematopoietic stem cell self-renewal and differentiation and can be targeted with thrombopoietin agonists or IFN-γ-neutralizing antibodies, highlighting a potential mechanism-based approach for the treatment of CAR T-associated prolonged cytopenia.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer.

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.