TIMP-1 is an activator of MHC-I expression in myeloid dendritic cells with implications for tumor immunogenicity.

Immune checkpoint therapies (ICT) for advanced solid tumors mark a new milestone in cancer therapy. Yet their efficacy is often limited by poor immunogenicity, attributed to inadequate priming and generation of antitumor T cells by dendritic cells (DCs). Identifying biomarkers to enhance DC functions in such tumors is thus crucial. Tissue Inhibitor of Metalloproteinases-1 (TIMP-1), recognized for its influence on immune cells, has an underexplored relationship with DCs. Our research reveals a correlation between high TIMP1 levels in metastatic melanoma and increased CD8 + T cell infiltration and survival. Network studies indicate a functional connection with HLA genes. Spatial transcriptomic analysis of a national melanoma cohort revealed that TIMP1 expression in immune compartments associates with an HLA-A/MHC-I peptide loading signature in lymph nodes. Primary human and bone-marrow-derived DCs secrete TIMP-1, which notably increases MHC-I expression in classical type 1 dendritic cells (cDC1), especially under melanoma antigen exposure. TIMP-1 affects the immunoproteasome/TAP complex, as seen by upregulated PSMB8 and TAP-1 levels of myeloid DCs. This study uncovers the role of TIMP-1 in DC-mediated immunogenicity with insights into CD8 + T cell activation, providing a foundation for mechanistic exploration and highlighting its potential as a new target for combinatorial immunotherapy to enhance ICT effectiveness.

Target-specific compound selectivity for multi-target drug discovery and repurposing.

Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound's potency against the target of interest (absolute potency), and 2) the compound's potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.

Multiobjective optimization identifies cancer-selective combination therapies.

Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.