Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease.

Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require user-specified heuristics which add time and complexity to bioinformatic workflows; second, they rely on post-selective differential expression analyses to identify marker genes driving cluster differences, which has been shown to be subject to inflated false discovery rates. We address these challenges by introducing nonparametric clustering of single-cell populations (NCLUSION): an infinite mixture model that leverages Bayesian sparse priors to identify marker genes while simultaneously performing clustering on single-cell expression data. NCLUSION uses a scalable variational inference algorithm to perform these analyses on datasets with up to millions of cells. By analyzing publicly available scRNA-seq studies, we demonstrate that NCLUSION (i) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (ii) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Chronic metabolic stress drives developmental programs and loss of tissue functions in non-transformed liver that mirror tumor states and stratify survival.

Under chronic stress, cells must balance competing demands between cellular survival and tissue function. In metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD/NASH), hepatocytes cooperate with structural and immune cells to perform crucial metabolic, synthetic, and detoxification functions despite nutrient imbalances. While prior work has emphasized stress-induced drivers of cell death, the dynamic adaptations of surviving cells and their functional repercussions remain unclear. Namely, we do not know which pathways and programs define cellular responses, what regulatory factors mediate (mal)adaptations, and how this aberrant activity connects to tissue-scale dysfunction and long-term disease outcomes. Here, by applying longitudinal single-cell multi -omics to a mouse model of chronic metabolic stress and extending to human cohorts, we show that stress drives survival-linked tradeoffs and metabolic rewiring, manifesting as shifts towards development-associated states in non-transformed hepatocytes with accompanying decreases in their professional functionality. Diet-induced adaptations occur significantly prior to tumorigenesis but parallel tumorigenesis-induced phenotypes and predict worsened human cancer survival. Through the development of a multi -omic computational gene regulatory inference framework and human in vitro and mouse in vivo genetic perturbations, we validate transcriptional (RELB, SOX4) and metabolic (HMGCS2) mediators that co-regulate and couple the balance between developmental state and hepatocyte functional identity programming. Our work defines cellular features of liver adaptation to chronic stress as well as their links to long-term disease outcomes and cancer hallmarks, unifying diverse axes of cellular dysfunction around core causal mechanisms.

Engineering kinetics of TLR7/8 agonist release from bottlebrush prodrugs enables tumor-focused immune stimulation.

Imidazoquinolines (IMDs), such as resiquimod (R848), are of great interest as potential cancer immunotherapies because of their ability to activate Toll-like receptor 7 (TLR7) and/or TLR8 on innate immune cells. Nevertheless, intravenous administration of IMDs causes severe immune-related toxicities, and attempts to improve their tissue-selective exposure while minimizing acute systemic inflammation have proven difficult. Here, using a library of R848 "bottlebrush prodrugs" (BPDs) that differ only by their R848 release kinetics, we explore how the timing of R848 exposure affects immune stimulation in vitro and in vivo. These studies led to the discovery of R848-BPDs that exhibit optimal activation kinetics to achieve potent stimulation of myeloid cells in tumors and substantial reductions in tumor growth following systemic administration in mouse syngeneic tumor models without any observable systemic toxicity. These results suggest that release kinetics can be tuned at the molecular level to provide safe yet effective systemically administered immunostimulant prodrugs for next-generation cancer immunotherapies.

Synthesis and characterization of polycaprolactone urethane hollow fiber membranes as small diameter vascular grafts.

The design of bioresorbable synthetic small diameter (<6mm) vascular grafts (SDVGs) capable of sustaining long-term patency and endothelialization is a daunting challenge in vascular tissue engineering. Here, we synthesized a family of biocompatible and biodegradable polycaprolactone (PCL) urethane macromers to fabricate hollow fiber membranes (HFMs) as SDVG candidates, and characterized their mechanical properties, degradability, hemocompatibility, and endothelial development. The HFMs had smooth surfaces and porous internal structures. Their tensile stiffness ranged from 0.09 to 0.11N/mm and their maximum tensile force from 0.86 to 1.03N, with minimum failure strains of approximately 130%. Permeability varied from 1 to 14×10(-6)cm/s, burst pressures from 1158 to 1468mmHg, and compliance from 0.52 to 1.48%/100mmHg. The suture retention forces ranged from 0.55 to 0.81N. HFMs had slow degradation profiles, with 15 to 30% degradation after 8weeks. Human endothelial cells proliferated well on the HFMs, creating stable cell layer coverage. Hemocompatibility studies demonstrated low hemolysis (<2%), platelet activation, and protein adsorption. There were no significant differences in the hemocompatibility of HFMs in the absence and presence of endothelial layers. These encouraging results suggest great promise of our newly developed materials and biodegradable elastomeric HFMs as SDVG candidates.