Search

Target occupancy is often insufficient to elicit biological activity, particularly for RNA, compounded by the longstanding challenges surrounding the molecular recognition of RNA structures by small molecules. Here we studied molecular recognition patterns between a natural-product-inspired small-molecule collection and three-dimensionally folded RNA structures. Mapping these interaction landscapes across the human transcriptome defined structure–activity relationships. Although RNA-binding compounds that bind to functional sites were expected to elicit a biological response, most identified interactions were predicted to be biologically inert as they bind elsewhere. We reasoned that, for such cases, an alternative strategy to modulate RNA biology is to cleave the target through a ribonuclease-targeting chimera, where an RNA-binding molecule is appended to a heterocycle that binds to and locally activates RNase L1. Overlay of the substrate specificity for RNase L with the binding landscape of small molecules revealed many favourable candidate binders that might be bioactive when converted into degraders. We provide a proof of concept, designing selective degraders for the precursor to the disease-associated microRNA-155 (pre-miR-155), JUN mRNA and MYC mRNA. Thus, small-molecule RNA-targeted degradation can be leveraged to convert strong, yet inactive, binding interactions into potent and specific modulators of RNA function.

Purpose: The aim of this study is to determine immune-related biomarkers to predict effective antitumor immunity in myelodysplastic syndrome (MDS) during immunotherapy (IMT, αCTLA-4, and/or αPD-1 antibodies) and/or hypomethylating agent (HMA). Experimental Design: Peripheral blood samples from 55 patients with MDS were assessed for immune subsets, T-cell receptor (TCR) repertoire, mutations in 295 acute myeloid leukemia (AML)/MDS-related genes, and immune-related gene expression profiling before and after the first treatment. Results: Clinical responders treated with IMT ± HMA but not HMA alone showed a significant expansion of central memory (CM) CD8+ T cells, diverse TCRβ repertoire pretreatment with increased clonality and emergence of novel clones after the initial treatment, and a higher mutation burden pretreatment with subsequent reduction posttreatment. Autophagy, TGFβ, and Th1 differentiation pathways were the most downregulated in nonresponders after treatment, while upregulated in responders. Finally, CTLA-4 but not PD-1 blockade attributed to favorable changes in immune landscape. Conclusions: Analysis of tumor–immune landscape in MDS during immunotherapy provides clinical response biomarkers.

Purpose:The aim of this study is to determine immune-related biomarkers to predict effective antitumor immunity in myelodysplastic syndrome (MDS) during immunotherapy (IMT, αCTLA-4, and/or αPD-1 antibodies) and/or hypomethylating agent (HMA).Experimental Design:Peripheral blood samples from 55 patients with MDS were assessed for immune subsets, T-cell receptor (TCR) repertoire, mutations in 295 acute myeloid leukemia (AML)/MDS-related genes, and immune-related gene expression profiling before and after the first treatment.Results:Clinical responders treated with IMT ± HMA but not HMA alone showed a significant expansion of central memory (CM) CD8+ T cells, diverse TCRβ repertoire pretreatment with increased clonality and emergence of novel clones after the initial treatment, and a higher mutation burden pretreatment with subsequent reduction posttreatment. Autophagy, TGFβ, and Th1 differentiation pathways were the most downregulated in nonresponders after treatment, while upregulated in responders. Finally, CTLA-4 but not PD-1 blockade attributed to favorable changes in immune landscape.Conclusions:Analysis of tumor–immune landscape in MDS during immunotherapy provides clinical response biomarkers.

Figure S2. Schema for study design

Table S8. Genes used for gene set analysis

Figure S3. Flow-cytometry gating strategy. CD, cluster of differentiation, Treg, regulatory T cell

Figure S4. Changes in T cell subsets at the time of the best responses

Figure S1. Information on treatment and available testing results for analysis

Table S1. Baseline characteristics of patients Table S2-S7. The 40 most statistically significant differential expression results