NUDT21 limits CD19 levels through alternative mRNA polyadenylation in B cell acute lymphoblastic leukemia.

B cell progenitor acute lymphoblastic leukemia (B-ALL) treatment has been revolutionized by T cell-based immunotherapies-including chimeric antigen receptor T cell therapy (CAR-T) and the bispecific T cell engager therapeutic, blinatumomab-targeting surface glycoprotein CD19. Unfortunately, many patients with B-ALL will fail immunotherapy due to 'antigen escape'-the loss or absence of leukemic CD19 targeted by anti-leukemic T cells. In the present study, we utilized a genome-wide CRISPR-Cas9 screening approach to identify modulators of CD19 abundance on human B-ALL blasts. These studies identified a critical role for the transcriptional activator ZNF143 in CD19 promoter activation. Conversely, the RNA-binding protein, NUDT21, limited expression of CD19 by regulating CD19 messenger RNA polyadenylation and stability. NUDT21 deletion in B-ALL cells increased the expression of CD19 and the sensitivity to CD19-specific CAR-T and blinatumomab. In human B-ALL patients treated with CAR-T and blinatumomab, upregulation of NUDT21 mRNA coincided with CD19 loss at disease relapse. Together, these studies identify new CD19 modulators in human B-ALL.

Connecting Chromatin Structures to Gene Regulation Using Dynamic Polymer Simulations.

The transfer of regulatory information between distal loci on chromatin is thought to involve physical proximity, but key biophysical features of these contacts remain unclear. For instance, it is unknown how close and for how long two loci need to be in order to productively interact. The main challenge is that it is currently impossible to measure chromatin dynamics with high spatiotemporal resolution at scale. Polymer simulations provide an accessible and rigorous way to test biophysical models of chromatin regulation, yet there is a lack of simple and general methods for extracting the values of model parameters. Here we adapt the Nelder-Mead simplex optimization algorithm to select the best polymer model matching a given Hi-C dataset, using the MYC locus as an example. The model's biophysical parameters predict a compartmental rearrangement of the MYC locus in leukemia, which we validate with single-cell measurements. Leveraging trajectories predicted by the model, we find that loci with similar Hi-C contact frequencies can exhibit widely different contact dynamics. Interestingly, the frequency of productive interactions between loci exhibits a non-linear relationship with their Hi-C contact frequency when we enforce a specific capture radius and contact duration. These observations are consistent with recent experimental observations and suggest that the dynamic ensemble of chromatin configurations, rather than average contact matrices, is required to fully predict long-range chromatin interactions.

Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. In this study, we utilize two distinct and complementary machine-learning methods of analyzing whole-slide images for predicting mutated BRAF. In the first method, whole-slide images of melanomas from 256 patients were used to train a deep convolutional neural network to develop a fully automated model that first selects for tumor-rich areas (area under the curve = 0.96) and then predicts for mutated BRAF (area under the curve = 0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, whole-slide images were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, showing that mutated BRAF nuclei were significantly larger and rounder than BRAF‒wild-type nuclei. Finally, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to an area under the curve of 0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, but machine learning‒based analysis of whole-slide images also has the potential to be integrated into higher-order models for understanding tumor biology.

Surface antigen-guided CRISPR screens identify regulators of myeloid leukemia differentiation.

Lack of cellular differentiation is a hallmark of many human cancers, including acute myeloid leukemia (AML). Strategies to overcome such a differentiation blockade are an approach for treating AML. To identify targets for differentiation-based therapies, we applied an integrated cell surface-based CRISPR platform to assess genes involved in maintaining the undifferentiated state of leukemia cells. Here we identify the RNA-binding protein ZFP36L2 as a critical regulator of AML maintenance and differentiation. Mechanistically, ZFP36L2 interacts with the 3' untranslated region of key myeloid maturation genes, including the ZFP36 paralogs, to promote their mRNA degradation and suppress terminal myeloid cell differentiation. Genetic inhibition of ZFP36L2 restores the mRNA stability of these targeted transcripts and ultimately triggers myeloid differentiation in leukemia cells. Epigenome profiling of several individuals with primary AML revealed enhancer modules near ZFP36L2 that associated with distinct AML cell states, establishing a coordinated epigenetic and post-transcriptional mechanism that shapes leukemic differentiation.

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

PURPOSE: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma. EXPERIMENTAL DESIGN: We used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan-Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400). RESULTS: The multivariable classifier predicted response with AUC 0.800 on images from the Aperio AT2 and AUC 0.805 on images from the Leica SCN400. The classifier accurately stratified patients into high versus low risk for disease progression. Vanderbilt patients classified as high risk for progression had significantly worse PFS than those classified as low risk (P = 0.02 for the Aperio AT2; P = 0.03 for the Leica SCN400). CONCLUSIONS: Histology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.

Three-dimensional chromatin landscapes in T cell acute lymphoblastic leukemia.

Differences in three-dimensional (3D) chromatin architecture can influence the integrity of topologically associating domains (TADs) and rewire specific enhancer-promoter interactions, impacting gene expression and leading to human disease. Here we investigate the 3D chromatin architecture in T cell acute lymphoblastic leukemia (T-ALL) by using primary human leukemia specimens and examine the dynamic responses of this architecture to pharmacological agents. Systematic integration of matched in situ Hi-C, RNA-seq and CTCF ChIP-seq datasets revealed widespread differences in intra-TAD chromatin interactions and TAD boundary insulation in T-ALL. Our studies identify and focus on a TAD 'fusion' event associated with absence of CTCF-mediated insulation, enabling direct interactions between the MYC promoter and a distal super-enhancer. Moreover, our data also demonstrate that small-molecule inhibitors targeting either oncogenic signal transduction or epigenetic regulation can alter specific 3D interactions found in leukemia. Overall, our study highlights the impact, complexity and dynamic nature of 3D chromatin architecture in human acute leukemia.