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.

Computational counterselection identifies nonspecific therapeutic biologic candidates.

Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.

Ultra-high-diversity factorizable libraries for efficient therapeutic discovery.

The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call stochastically annealed product spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions.

Existing computational methods that use single-cell RNA-sequencing (scRNA-seq) for cell fate prediction do not model how cells evolve stochastically and in physical time, nor can they predict how differentiation trajectories are altered by proposed interventions. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from time-series scRNA-seq data. We validate PRESCIENT on an experimental lineage tracing dataset, where we show that PRESCIENT is able to predict the fate biases of progenitor cells in hematopoiesis when accounting for cell proliferation, improving upon the best-performing existing method. We demonstrate how PRESCIENT can simulate trajectories for perturbed cells, recovering the expected effects of known modulators of cell fate in hematopoiesis and pancreatic ß cell differentiation. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations, and is available at https://github.com/gifford-lab/prescient .

MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin.

BACKGROUND: A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell lineages. MAE is highly cell-type specific and mapping it in a large number of cell and tissue types can provide insight into its biological function. Its detection, however, remains challenging. RESULTS: We previously reported that a sequence-independent chromatin signature identifies, with high sensitivity and specificity, genes subject to MAE in multiple tissue types using readily available ChIP-seq data. Here we present an implementation of this method as a user-friendly, open-source software pipeline for monoallelic gene inference from chromatin (MaGIC). The source code for the MaGIC pipeline and the Shiny app is available at https://github.com/gimelbrantlab/magic . CONCLUSION: The pipeline can be used by researchers to map monoallelic expression in a variety of cell types using existing models and to train new models with additional sets of chromatin marks.

The effects of IKK-beta inhibition on early NF-kappa-B activation and transcription of downstream genes.

Small molecule approaches targeting the nuclear factor kappa B (NF-kB) pathway, a regulator of inflammation, have thus far proven unsuccessful in the clinic in part due to the complex pleiotropic nature of this network. Downstream effects depend on multiple factors including stimulus-specific temporal patterns of NF-kB activity. Despite considerable advances, genome-level impact of changes in temporal NF-kB activity caused by inhibitors and their stimulus dependency remains unexplored. This study evaluates the effects of pathway inhibitors on early NF-κB activity and downstream gene transcription. 3T3 fibroblasts were treated with SC-514, an inhibitor targeted to the NF-kB pathway, prior to stimulation with interleukin 1 beta (IL-1ß) or tumor necrosis factor alpha (TNF-α). Stimulus induced NF-κB activation was quantified using immunofluorescence imaging over 90-minutes and gene expression tracked over 6-hours using mRNA TagSeq. When stimulated with IL-1ß or TNF-α, significant differences (P < 0.05, two-way ANOVA), were observed in the temporal profiles of NF-κB activation between treated and untreated cells. Increasing numbers of differentially expressed genes (P < 0.01) were observed at higher inhibitor concentrations. Individual gene expression profiles varied in an inhibitor concentration and stimulus-dependent manner. The results in this study demonstrate small molecule inhibitors acting on pleiotropic pathway components can alter signal dynamics in a stimulus-dependent manner and affect gene response in complex ways.