Author

Kunal Rai

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Jul 2026

The novel hypomethylating agent NTX-301 reprograms epigenetic and Hippo signaling pathways and exhibits pre-clinical activity in venetoclax-resistant and TP53-mutant AML.

BACKGROUND Hypomethylating agent (HMA) and the BCL-2 inhibitor venetoclax (VEN) combinations have evolved into frontline therapies for patients with acute myeloid leukemia (AML), yielding high response rates. However, most patients ultimately relapse, particularly those with TP53 mutations. We investigated mechanisms of action and therapeutic efficacy of NTX-301, a next-generation HMA. Methods used include flow cytometry-based cell viability assays, Western blot, reverse-phase protein arrays, RNA-sequencing, CyTOF single-cell mass cytometry, and methylation profiling in various therapy-resistant AML models. RESULTS We demonstrate that NTX-301 exhibits superior efficacy compared to 5-azacytidine (5-AZA) in 5-AZA or VEN-resistant AML. It synergizes with VEN in VEN- or VEN/HMA-resistant and TP53-mutant AML blasts and stem/progenitor cells (combination index<1). NTX-301 inhibits DNMT1 and increases p73, caspase-8/activated caspase-8 levels in TP53-WT and TP53-mutant AML and activates p53 signaling. It extends survival (≥45%) in both, xenograft and PDX models. Methylation profiling revealed that NTX-301 is a more targeted HMA compared to 5-AZA, enabling suppression of functionally enriched genes/pathways. Pathway analysis of 954 commonly hypomethylated genes showed profoundly greater enrichment of Hippo signaling in NTX-301-treated compared to 5-AZA-treated cells, and enrichment of insulin signaling, VEGF pathway, and cell cycle selectively in NTX-301- but not in 5-AZA-treated cells. NTX-301-mediated Hippo signaling was validated at protein levels. CONCLUSION Data suggest that NTX-301 exerts potent anti-leukemia activities superior to 5-AZA and synergizes with VEN in VEN-resistant and TP53-mutant AML, in part by suppressing DNMT1 and inducing DNA damage responses and apoptosis, by inducing p53 signaling and demethylating LATS1/2, thus activating Hippo signaling.

B. Carter, P. Mak, Suresh Satpati et al. · Clinical Cancer Research · 0 citations

Preprint Jul 2026

LATTICE: Graph Self-Supervised Learning for Multimodal Spatial Omics Integration

Spatially resolved omics studies increasingly combine transcriptomic and epigenomic assays, yet downstream analysis is often still performed using single-modality pipelines. We present LATTICE (Latent Alignment of Tissue-level and Transcriptomic Information for Cross-modal Embedding), a graph-based self-supervised framework that learns spot-level representations from harmonized multimodal features. LATTICE integrates five aligned modality blocks per Visium spot: Visium RNA, scMultiome RNA, scMultiome ATAC, spatial ATAC, and spatial CUT\&Tag. These modalities capture spatial transcriptomic measurements, single-cell inferred regulatory activity, and in situ chromatin and histone states within a unified lattice representation. LATTICE constructs a spatial neighborhood graph and trains a TransformerConv encoder using masked reconstruction, cross-modal alignment, and spatial smoothness objectives. On a private 11-sample melanoma cohort from an anonymized clinical collaborator comprising 54{,}912 total spots, LATTICE demonstrated stable optimization behavior, reproducible embeddings across analysis seeds, and complete multimodal integration across all samples. Adding scMultiome RNA to Visium RNA alone substantially improved concordance with Space Ranger clusters across 11 runs (adjusted Rand index [ARI] +0.157, normalized mutual information [NMI] +0.143, and spatial contiguity +0.174). Additional modalities further improved spatial contiguity and multimodal utility score (MUS), although they sometimes reduced agreement with RNA-derived reference labels, likely because the learned embeddings captured chromatin and regulatory structure beyond transcriptomic similarity alone. These results position LATTICE as a practical and empirically grounded framework for multimodal spatial omics integration, while also highlighting the need for stronger supervision and broader external benchmarking.

J. Dwarampudi, V. Kochat, Suresh Satpati et al. · 0 citations