Impact of p53-associated acute myeloid leukemia hallmarks on metabolism and the immune environment.

Acute myeloid leukemia (AML), an aggressive malignancy of hematopoietic stem cells, is characterized by the blockade of cell differentiation, uncontrolled proliferation, and cell expansion that impairs healthy hematopoiesis and results in pancytopenia and susceptibility to infections. Several genetic and chromosomal aberrations play a role in AML and influence patient outcomes. TP53 is a key tumor suppressor gene involved in a variety of cell features, such as cell-cycle regulation, genome stability, proliferation, differentiation, stem-cell homeostasis, apoptosis, metabolism, senescence, and the repair of DNA damage in response to cellular stress. In AML, TP53 alterations occur in 5%-12% of de novo AML cases. These mutations form an important molecular subgroup, and patients with these mutations have the worst prognosis and shortest overall survival among patients with AML, even when treated with aggressive chemotherapy and allogeneic stem cell transplant. The frequency of TP53-mutations increases in relapsed and recurrent AML and is associated with chemoresistance. Progress in AML genetics and biology has brought the novel therapies, however, the clinical benefit of these agents for patients whose disease is driven by TP53 mutations remains largely unexplored. This review focuses on the molecular characteristics of TP53-mutated disease; the impact of TP53 on selected hallmarks of leukemia, particularly metabolic rewiring and immune evasion, the clinical importance of TP53 mutations; and the current progress in the development of preclinical and clinical therapeutic strategies to treat TP53-mutated disease.

Interleukin-21 engineering enhances NK cell activity against glioblastoma via CEBPD.

Glioblastoma (GBM) is an aggressive brain cancer with limited therapeutic options. Natural killer (NK) cells are innate immune cells with strong anti-tumor activity and may offer a promising treatment strategy for GBM. We compared the anti-GBM activity of NK cells engineered to express interleukin (IL)-15 or IL-21. Using multiple in vivo models, IL-21 NK cells were superior to IL-15 NK cells both in terms of safety and long-term anti-tumor activity, with locoregionally administered IL-15 NK cells proving toxic and ineffective at tumor control. IL-21 NK cells displayed a unique chromatin accessibility signature, with CCAAT/enhancer-binding proteins (C/EBP), especially CEBPD, serving as key transcription factors regulating their enhanced function. Deletion of CEBPD resulted in loss of IL-21 NK cell potency while its overexpression increased NK cell long-term cytotoxicity and metabolic fitness. These results suggest that IL-21, through C/EBP transcription factors, drives epigenetic reprogramming of NK cells, enhancing their anti-tumor efficacy against GBM.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

CD28 costimulation augments CAR signaling in NK cells via the LCK/CD3Z/ZAP70 signaling axis.

Multiple factors in the design of a chimeric antigen receptor (CAR) influence CAR T-cell activity, with costimulatory signals being a key component. Yet, the impact of costimulatory domains on the downstream signaling and subsequent functionality of CAR-engineered natural killer (NK) cells remains largely unexplored. Here, we evaluated the impact of various costimulatory domains on CAR-NK cell activity, using a CD70-targeting CAR. We found that CD28, a costimulatory molecule not inherently present in mature NK cells, significantly enhanced the antitumor efficacy and long-term cytotoxicity of CAR-NK cells both in vitro and in multiple xenograft models of hematologic and solid tumors. Mechanistically, we showed that CD28 linked to CD3Z creates a platform that recruits critical kinases, such as LCK and ZAP70, initiating a signaling cascade that enhances CAR-NK cell function. Our study provides insights into how CD28 costimulation enhances CAR-NK cell function and supports its incorporation in NK-based CARs for cancer immunotherapy.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.

BACKGROUND: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. OBJECTIVE: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. METHODS: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. RESULTS: Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. CONCLUSION: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.

Background: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. Objective: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. Methods: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. Results: Our multimodal model achieved a lead time of at least 12 hours ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. Conclusion: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Transcriptional and phenotypic heterogeneity underpinning venetoclax resistance in AML.

The venetoclax BCL2 inhibitor in combination with hypomethylating agents represents a cornerstone of induction therapy for older AML patients, unfit for intensive chemotherapy. Like other targeted therapies, venetoclax-based therapies suffer from innate and acquired resistance. While several mechanisms of resistance have been identified, the heterogeneity of resistance mechanism across patient populations is poorly understood. Here we utilized integrative analysis of transcriptomic and ex-vivo drug response data in AML patients to identify four transcriptionally distinct VEN resistant clusters (VR_C1-4), with distinct phenotypic, genetic and drug response patterns. VR_C1 was characterized by enrichment for differentiated monocytic- and cDC-like blasts, transcriptional activation of PI3K-AKT-mTOR signaling axis, and energy metabolism pathways. They showed sensitivity to mTOR and CDK inhibition. VR_C2 was enriched for NRAS mutations and associated with distinctive transcriptional suppression of HOX expression. VR_C3 was characterized by enrichment for TP53 mutations and higher infiltration by cytotoxic T cells. This cluster showed transcriptional expression of erythroid markers, suggesting tumor cells mimicking erythroid differentiation, activation of JAK-STAT signaling, and sensitivity to JAK inhibition, which in a subset of cases synergized with venetoclax. VR_C4 shared transcriptional similarities with venetoclax-sensitive patients, with modest over-expression of interferon signaling. They were also characterized by high rates of DNMT3A mutations. Finally, we projected venetoclax-resistance states onto single cells profiled from a patient who relapsed under venetoclax therapy capturing multiple resistance states in the tumor and shifts in their abundance under venetoclax selection, suggesting that single tumors may consist of cells mimicking multiple VR_Cs contributing to intra-tumor heterogeneity. Taken together, our results provide a strategy to evaluate inter- and intra-tumor heterogeneity of venetoclax resistance mechanisms and provide insights into approaches to navigate further management of patients who failed therapy with BCL2 inhibitors.

Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19+ B cell tumors: a phase 1/2 trial.

There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19+ B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR). Secondary objectives included day 100 response, progression-free survival, overall survival and CAR19/IL-15 NK cell persistence. No notable toxicities such as cytokine release syndrome, neurotoxicity or graft-versus-host disease were observed. The day 30 and day 100 OR rates were 48.6% for both. The 1-year overall survival and progression-free survival were 68% and 32%, respectively. Patients who achieved OR had higher levels and longer persistence of CAR-NK cells. Receiving CAR-NK cells from a cord blood unit (CBU) with nucleated red blood cells ≤ 8 × 107 and a collection-to-cryopreservation time ≤ 24 h was the most significant predictor for superior outcome. NK cells from these optimal CBUs were highly functional and enriched in effector-related genes. In contrast, NK cells from suboptimal CBUs had upregulation of inflammation, hypoxia and cellular stress programs. Finally, using multiple mouse models, we confirmed the superior antitumor activity of CAR/IL-15 NK cells from optimal CBUs in vivo. These findings uncover new features of CAR-NK cell biology and underscore the importance of donor selection for allogeneic cell therapies. ClinicalTrials.gov identifier: NCT03056339 .

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux.

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Loss of metabolic fitness drives tumor resistance after CAR-NK cell therapy and can be overcome by cytokine engineering.

Chimeric antigen receptor (CAR) engineering of natural killer (NK) cells is promising, with early-phase clinical studies showing encouraging responses. However, the transcriptional signatures that control the fate of CAR-NK cells after infusion and factors that influence tumor control remain poorly understood. We performed single-cell RNA sequencing and mass cytometry to study the heterogeneity of CAR-NK cells and their in vivo evolution after adoptive transfer, from the phase of tumor control to relapse. Using a preclinical model of noncurative lymphoma and samples from a responder and a nonresponder patient treated with CAR19/IL-15 NK cells, we observed the emergence of NK cell clusters with distinct patterns of activation, function, and metabolic signature associated with different phases of in vivo evolution and tumor control. Interaction with the highly metabolically active tumor resulted in loss of metabolic fitness in NK cells that could be partly overcome by incorporation of IL-15 in the CAR construct.

Identifying signatures of EV secretion in metastatic breast cancer through functional single-cell profiling.

Extracellular vesicles (EVs) regulate the tumor microenvironment by facilitating transport of biomolecules. Despite extensive investigation, heterogeneity in EV secretion among cancer cells and the mechanisms that support EV secretion are not well characterized. We developed an integrated method to identify individual cells with differences in EV secretion and performed linked single-cell RNA-sequencing on cloned single cells from the metastatic breast cancer cells. Differential gene expression analyses identified a four-gene signature of breast cancer EV secretion: HSP90AA1, HSPH1, EIF5, and DIAPH3. We functionally validated this gene signature by testing it across cell lines with different metastatic potential in vitro. Analysis of the TCGA and METABRIC datasets showed that this signature is associated with poor survival, invasive breast cancer types, and poor CD8+ T cell infiltration in human tumors. We anticipate that our method for directly identifying the molecular determinants of EV secretion will have broad applications across cell types and diseases.

KLF5 governs sphingolipid metabolism and barrier function of the skin.

Stem cells are fundamental units of tissue remodeling whose functions are dictated by lineage-specific transcription factors. Home to epidermal stem cells and their upward-stratifying progenies, skin relies on its secretory functions to form the outermost protective barrier, of which a transcriptional orchestrator has been elusive. KLF5 is a Krüppel-like transcription factor broadly involved in development and regeneration whose lineage specificity, if any, remains unclear. Here we report KLF5 specifically marks the epidermis, and its deletion leads to skin barrier dysfunction in vivo. Lipid envelopes and secretory lamellar bodies are defective in KLF5-deficient skin, accompanied by preferential loss of complex sphingolipids. KLF5 binds to and transcriptionally regulates genes encoding rate-limiting sphingolipid metabolism enzymes. Remarkably, skin barrier defects elicited by KLF5 ablation can be rescued by dietary interventions. Finally, we found that KLF5 is widely suppressed in human diseases with disrupted epidermal secretion, and its regulation of sphingolipid metabolism is conserved in human skin. Altogether, we established KLF5 as a disease-relevant transcription factor governing sphingolipid metabolism and barrier function in the skin, likely representing a long-sought secretory lineage-defining factor across tissue types.

Bi-order multimodal integration of single-cell data.

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Sensei: how many samples to tell a change in cell type abundance?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html .

Uncoupling of gene expression from copy number presents therapeutic opportunities in aneuploid cancers.

Uncoupling of mRNA expression from copy number (UECN) might be a strategy for cancer cells to a tolerate high degree of aneuploidy. To test the extent and role of UECN across cancers, we perform integrative multiomic analysis of The Cancer Genome Atlas (TCGA) dataset, encompassing ∼5,000 individual tumors. We find UECN is common in cancers and is associated with increased oncogenic signaling, proliferation, and immune suppression. UECN appears to be orchestrated by complex regulatory changes, with transcription factors (TFs) playing a prominent role. To further dissect the regulatory mechanisms, we develop a systems-biology approach to identify candidate TFs, which could serve as targets to disrupt UECN and reduce tumor fitness. Applying our approach to TCGA data, we identify 21 putative targets, 42.8% of which are validated by independent sources. Together, our study indicates that UECN is likely an important mechanism in development of aneuploid tumors and might be therapeutically targetable.

Decoupling Lineage-Associated Genes in Acute Myeloid Leukemia Reveals Inflammatory and Metabolic Signatures Associated With Outcomes.

Acute myeloid leukemia (AML) is a heterogeneous disease with variable responses to therapy. Cytogenetic and genomic features are used to classify AML patients into prognostic and treatment groups. However, these molecular characteristics harbor significant patient-to-patient variability and do not fully account for AML heterogeneity. RNA-based classifications have also been applied in AML as an alternative approach, but transcriptomic grouping is strongly associated with AML morphologic lineages. We used a training cohort of newly diagnosed AML patients and conducted unsupervised RNA-based classification after excluding lineage-associated genes. We identified three AML patient groups that have distinct biological pathways associated with outcomes. Enrichment of inflammatory pathways and downregulation of HOX pathways were associated with improved outcomes, and this was validated in 2 independent cohorts. We also identified a group of AML patients who harbored high metabolic and mTOR pathway activity, and this was associated with worse clinical outcomes. Using a comprehensive reverse phase protein array, we identified higher mTOR protein expression in the highly metabolic group. We also identified a positive correlation between degree of resistance to venetoclax and mTOR activation in myeloid and lymphoid cell lines. Our approach of integrating RNA, protein, and genomic data uncovered lineage-independent AML patient groups that share biologic mechanisms and can inform outcomes independent of commonly used clinical and demographic variables; these groups could be used to guide therapeutic strategies.

Targeting the αv integrin/TGF-ß axis improves natural killer cell function against glioblastoma stem cells.

Glioblastoma multiforme (GBM), the most aggressive brain cancer, recurs because glioblastoma stem cells (GSCs) are resistant to all standard therapies. We showed that GSCs, but not normal astrocytes, are sensitive to lysis by healthy allogeneic natural killer (NK) cells in vitro. Mass cytometry and single-cell RNA sequencing of primary tumor samples revealed that GBM tumor-infiltrating NK cells acquired an altered phenotype associated with impaired lytic function relative to matched peripheral blood NK cells from patients with GBM or healthy donors. We attributed this immune evasion tactic to direct cell-to-cell contact between GSCs and NK cells via αv integrin-mediated TGF-ß activation. Treatment of GSC-engrafted mice with allogeneic NK cells in combination with inhibitors of integrin or TGF-ß signaling or with TGFBR2 gene-edited allogeneic NK cells prevented GSC-induced NK cell dysfunction and tumor growth. These findings reveal an important mechanism of NK cell immune evasion by GSCs and suggest the αv integrin/TGF-ß axis as a potentially useful therapeutic target in GBM.

Metabolic Reprogramming of GMP Grade Cord Tissue Derived Mesenchymal Stem Cells Enhances Their Suppressive Potential in GVHD.

Acute graft-vs.-host (GVHD) disease remains a common complication of allogeneic stem cell transplantation with very poor outcomes once the disease becomes steroid refractory. Mesenchymal stem cells (MSCs) represent a promising therapeutic approach for the treatment of GVHD, but so far this strategy has had equivocal clinical efficacy. Therapies using MSCs require optimization taking advantage of the plasticity of these cells in response to different microenvironments. In this study, we aimed to optimize cord blood tissue derived MSCs (CBti MSCs) by priming them using a regimen of inflammatory cytokines. This approach led to their metabolic reprogramming with enhancement of their glycolytic capacity. Metabolically reprogrammed CBti MSCs displayed a boosted immunosuppressive potential, with superior immunomodulatory and homing properties, even after cryopreservation and thawing. Mechanistically, primed CBti MSCs significantly interfered with glycolytic switching and mTOR signaling in T cells, suppressing T cell proliferation and ensuing polarizing toward T regulatory cells. Based on these data, we generated a Good Manufacturing Process (GMP) Laboratory protocol for the production and cryopreservation of primed CBti MSCs for clinical use. Following thawing, these cryopreserved GMP-compliant primed CBti MSCs significantly improved outcomes in a xenogenic mouse model of GVHD. Our data support the concept that metabolic profiling of MSCs can be used as a surrogate for their suppressive potential in conjunction with conventional functional methods to support their therapeutic use in GVHD or other autoimmune disorders.