Integrative single-cell analysis of longitudinal t(8;21) AML reveals heterogeneous immune cell infiltration and prognostic signatures.

Introduction: Immunotherapies targeting T cells in solid cancers are revolutionizing clinical treatment. Novel immunotherapies have had extremely limited benefit for acute myeloid leukemia (AML). Here, we characterized the immune microenvironment of t(8;21) AML patients to determine how immune cell infiltration status influenced prognosis. Methods: Through multi-omics studies of primary and longitudinal t(8;21) AML samples, we characterized the heterogeneous immune cell infiltration in the tumor microenvironment and their immune checkpoint gene expression. Further external cohorts were also included in this research. Results: CD8+ T cells were enriched and HAVCR2 and TIGIT were upregulated in the CD34+CD117dim%-High group; these features are known to be associated with immune exhaustion. Data integration analysis of single-cell dynamics revealed that a subset of T cells (cluster_2) (highly expressing GZMB, NKG7, PRF1 and GNLY) evolved and expanded markedly in the drug-resistant stage after relapse. External cohort analysis confirmed that the cluster_2 T-cell signature could be utilized to stratify patients by overall survival outcome. Discussion: In conclusion, we discovered a distinct T-cell signature by scRNA-seq that was correlated with disease progression and drug resistance. Our research provides a novel system for classifying patients based on their immune microenvironment.

Protocol for the application of single-cell damage in murine intestinal organoid models.

Spatially defined organoid damage enables the study of cellular repair processes. However, capturing dynamic events in living tissues is technically challenging. Here, we present a protocol for the application of single-cell damage in intestinal organoid models. We describe steps for isolating and cultivating murine colon organoids, lentivirus generation and transduction of organoids, single-cell ablation by a femtosecond laser, and follow-up imaging analysis. We provide examples for the image acquisition pipeline of dynamic processes in organoids using a confocal microscope. For complete details on the use and execution of this protocol, please refer to Donath et al.1,2.

Protocol for machine-learning-based 3D image analysis of nuclear envelope tubules in cultured cells.

The nuclear envelope can form complex structures in physiological and pathological contexts. Current approaches to quantify nuclear envelope structures can be time-consuming or inaccurate. Here, we present a protocol to measure nuclear envelope tubules induced by DNA double-strand breaks using a mid-throughput approach. We describe steps for the induction of these nuclear envelope structures and 3D image analysis using machine-learning-based image segmentation. This protocol can be applied to analyze various nuclear envelope structures in contexts beyond DNA repair. For complete details on the use and execution of this protocol, please refer to Shokrollahi et al.1.

Isolation, expansion, and adoptive transfer of human ILC2s for the treatment of mice bearing liquid and solid tumors.

Type 2 innate lymphoid cells (ILC2s) are crucial in regulating immune responses and various physiological processes, including tissue repair, metabolic homeostasis, inflammation, and cancer surveillance. Here, we present a protocol that outlines the isolation, expansion, and adoptive transfer of human ILC2s from peripheral blood mononuclear cells for an in vivo lineage tracking experiment in a mouse model. Additionally, we detail the steps involved in the adoptive transfer of human ILC2s to recipient mice bearing human liquid or solid tumors. For complete details on the use and execution of this protocol, please refer to Li et al.1.

Data acquisition approaches for single cell proteomics.

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

The octapeptide-repeat surface protein of Entamoeba nuttalli is a novel virulence factor that promotes adherence to host cells.

Entamoeba nuttalli is genetically the closest to Entamoeba histolytica, the causative agent of human amebiasis, and its natural host is Macaca species. A unique E. nuttalli specific surface protein (PTORS) containing 42 repeats of octapeptide was identified by comparative genomic analysis of Entamoeba species. We aimed to elucidate the function of this protein. When trophozoites from various E. nuttalli strains were examined by immunofluorescence microscopy and flow cytometry using a PTORS-specific monoclonal antibody, only a limited proportion of trophozoites were stained, indicating that the protein was not commonly expressed in all E. nuttalli trophozoite. The proportion of trophozoites expressing PTORS increased after passage in hamster livers, suggesting that the protein functions in the virulence of trophozoites in the liver tissue. Single-cell analysis revealed that in the cluster including trophozoites with PTORS gene expression, genes of virulence-related proteins were also upregulated. Trophozoites of E. histolytica transfected with PTORS showed enhanced adherence and subsequent phagocytic activity towards human Jurkat cells, independent of the lectin. E. histolytica trophozoites expressing PTORS formed larger liver abscesses in hamsters. These results demonstrate that PTORS is a novel virulence factor in Entamoeba species.

High-Throughput Absorbance-Activated Droplet Sorting for Engineering Aldehyde Dehydrogenases.

Recent decades have seen a dramatic increase in the commercial use of biocatalysts, transitioning from energy-intensive traditional chemistries to more sustainable methods. Current enzyme engineering techniques, such as directed evolution, require the generation and testing of large mutant libraries to identify optimized variants. Unfortunately, conventional screening methods are unable to screen such large libraries in a robust and timely manner. Droplet-based microfluidic systems have emerged as a powerful high-throughput tool for library screening at kilohertz rates. Unfortunately, almost all reported systems are based on fluorescence detection, restricting their use to a limited number of enzyme types that naturally convert fluorogenic substrates or require the use of surrogate substrates. To expand the range of enzymes amenable to evolution using droplet-based microfluidic systems, we present an absorbance-activated droplet sorter that allows of droplet sorting at kilohertz rates without the need for optical monitoring of the microfluidic system. To demonstrate the utility of the sorter, we rapidly screen a 105-member aldehyde dehydrogenase library towards D-glyceraldehyde using a NADH mediated coupled assay that generates WST-1 formazan as the colorimetric product. We successfully identify a variant with a 51% improvement in catalytic efficiency and a significant increase in overall activity across a broad substrate spectrum.

Establishment and transcriptome analysis of single blastomere-derived cell lines from zebrafish.

Maintaining chromosome euploidy in zebrafish embryonic cells is challenging because of the degradation of genomic integrity during cell passaging. In this study, we report the derivation of zebrafish cell lines from single blastomeres. These cell lines have a stable chromosome status attributed to BMP4 and exhibit continuous proliferation in vitro. Twenty zebrafish cell lines are successfully established from single blastomeres. Single-cell transcriptome sequencing analysis confirms the fidelity of gene expression profiles throughout long-term culturing of at least 45 passages. The long-term cultured cells are specialized into epithelial cells, exhibiting similar expression patterns validated by integrative transcriptomic analysis. Overall, this work provides a protocol for establishing zebrafish cell lines from single blastomeres, which can serve as valuable tools for in vitro investigations of epithelial cell dynamics in terms of life-death balance and cell fate determination during normal homeostasis.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.

BACKGROUND: Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. RESULTS: We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl . CONCLUSIONS: The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Stellate cells are in utero markers of pancreatic disease in cystic fibrosis.

BACKGROUND: Pancreatic fibrosis is an early diagnostic feature of the common inherited disorder cystic fibrosis (CF). Many people with CF (pwCF) are pancreatic insufficient from birth and the replacement of acinar tissue with cystic lesions and fibrosis is a progressive phenotype that may later lead to diabetes. Little is known about the initiating events in the fibrotic process though it may be a sequela of inflammation in the pancreatic ducts resulting from loss of CFTR impairing normal fluid secretion. Here we use a sheep model of CF (CFTR-/-) to examine the evolution of pancreatic disease through gestation. METHODS: Fetal pancreas was collected at six time points from 50-days of gestation through to term, which is equivalent to ~ 13 weeks to term in human. RNA was extracted from tissue for bulk RNA-seq and single cells were prepared from 80-day, 120-day and term samples for scRNA-seq. Data were validated by immunochemistry. RESULTS: Transcriptomic evidence from bulk RNA-seq showed alterations in the CFTR-/- pancreas by 65-days of gestation, which are accompanied by marked pathological changes by 80-days of gestation. These include a fibrotic response, confirmed by immunostaining for COL1A1, αSMA and SPARC, together with acinar loss. Moreover, using scRNA-seq we identify a unique cell population that is significantly overrepresented in the CFTR-/- animals at 80- and 120-days gestation, as are stellate cells at term. CONCLUSION: The transcriptomic changes and cellular imbalance that we observe likely have pivotal roles in the evolution of CF pancreatic disease and may provide therapeutic opportunities to delay or prevent pancreatic destruction in CF.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection.

BACKGROUND: Single-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions. RESULT: In this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources. CONCLUSIONS: COMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

Single-cell transcriptome analysis deciphers the CD74-mediated immune evasion and tumour growth in lung squamous cell carcinoma with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) contributes to the incidence and prognosis of lung cancer. The presence of COPD significantly increases the risk of lung squamous cell carcinoma (LSCC). COPD may promote an immunosuppressive microenvironment in LSCC by regulating the expression of immune-inhibitory factors in T cells, although the mechanisms remain unclear. In this study, we aimed to decipher the tumour microenvironment signature for LSCC with COPD at a single-cell level. METHODS: We performed single-cell RNA sequencing on tumour tissues from LSCC with or without COPD, then investigated the features of the immune and tumour cells. We employed multiple techniques, including multispectral imaging, flow cytometry, tissue microarray analysis, survival analysis, co-culture systems and in vitro and in vivo treatment experiments, to validate the findings obtained from single-cell analyses. RESULTS: LSCC with COPD showed increased proportions of tumour-associated macrophages (TAMs) and higher levels of CD8+ T cell exhaustion molecules, which contributed to an immunosuppressive microenvironment. Further analysis revealed a critical cluster of CD74+ tumour cells that expressed both epithelial and immune cell signatures, exhibited a stronger capacity for tumorigenesis and predicted worse overall survival. Notably, migration inhibitory factor (MIF) secreted by TAMs from LSCC with COPD may promote the activation of CD74. MIF-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-programmed cell death-1 ligand 1 signalling pathway, facilitating tumour proliferation and immune evasion. CONCLUSIONS: Our comprehensive picture of the tumour ecosystem in LSCC with COPD provides deeper insights into relevant immune evasion mechanisms and potential targets for immunotherapy. HIGHLIGHT: Our results demonstrated higher proportions of tumour-associated macrophages (TAMs) and higher levels of exhaustion molecules in CD8+ T cells in the microenvironment of LSCC with COPD. CD74+tumour cells were associated with poor disease prognosis. Migration inhibitory factor (MIF)-CD74 may interact with CD8+ T cells and impair their anti-tumour activity by regulating the PI3K-STAT3-PD-L1 signalling pathway, facilitating immune evasion.

Transcriptional reprogramming post-peripheral nerve injury: A systematic review.

Neuropathic pain is characterised by periodic or continuous hyperalgesia, numbness, or allodynia, and results from insults to the somatosensory nervous system. Peripheral nerve injury induces transcriptional reprogramming in peripheral sensory neurons, contributing to increased spinal nociceptive input and the development of neuropathic pain. Effective treatment for neuropathic pain remains an unmet medical need as current therapeutics offer limited effectiveness and have undesirable effects. Understanding transcriptional changes in peripheral nerve injury-induced neuropathy might offer a path for novel analgesics. Our literature search identified 65 papers exploring transcriptomic changes post-peripheral nerve injury, many of which were conducted in animal models. We scrutinize their transcriptional changes data and conduct gene ontology enrichment analysis to reveal their common functional profile. Focusing on genes involved in 'sensory perception of pain' (GO:0019233), we identified transcriptional changes for different ion channels, receptors, and neurotransmitters, shedding light on its role in nociception. Examining peripheral sensory neurons subtype-specific transcriptional reprograming and regeneration-associated genes, we delved into downstream regulation of hypersensitivity. Identifying the temporal program of transcription regulatory mechanisms might help develop better therapeutics to target them effectively and selectively, thus preventing the development of neuropathic pain without affecting other physiological functions.

Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.

Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.

scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data.

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis.

Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.

Deciphering the immune-metabolic nexus in sepsis: a single-cell sequencing analysis of neutrophil heterogeneity and risk stratification.

Background: Metabolic dysregulation following sepsis can significantly compromise patient prognosis by altering immune-inflammatory responses. Despite its clinical relevance, the exact mechanisms of this perturbation are not yet fully understood. Methods: Single-cell RNA sequencing (scRNA-seq) was utilized to map the immune cell landscape and its association with metabolic pathways during sepsis. This study employed cell-cell interaction and phenotype profiling from scRNA-seq data, along with pseudotime trajectory analysis, to investigate neutrophil differentiation and heterogeneity. By integrating scRNA-seq with Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning techniques, key genes were identified. These genes were used to develop and validate a risk score model and nomogram, with their efficacy confirmed through Receiver Operating Characteristic (ROC) curve analysis. The model's practicality was further reinforced through enrichment and immune characteristic studies based on the risk score and in vivo validation of a critical gene associated with sepsis. Results: The complex immune landscape and neutrophil roles in metabolic disturbances during sepsis were elucidated by our in-depth scRNA-seq analysis. Pronounced neutrophil interactions with diverse cell types were revealed in the analysis of intercellular communication, highlighting pathways that differentiate between proximal and core regions within atherosclerotic plaques. Insight into the evolution of neutrophil subpopulations and their differentiation within the plaque milieu was provided by pseudotime trajectory mappings. Diagnostic markers were identified with the assistance of machine learning, resulting in the discovery of PIM1, HIST1H1C, and IGSF6. The identification of these markers culminated in the development of the risk score model, which demonstrated remarkable precision in sepsis prognosis. The model's capability to categorize patient profiles based on immune characteristics was confirmed, particularly in identifying individuals at high risk with suppressed immune cell activity and inflammatory responses. The role of PIM1 in modulating the immune-inflammatory response during sepsis was further confirmed through experimental validation, suggesting its potential as a therapeutic target. Conclusion: The understanding of sepsis immunopathology is improved by this research, and new avenues are opened for novel prognostic and therapeutic approaches.

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data.

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses to estimate the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

T cell receptor clonotype in tumor microenvironment contributes to intratumoral signaling network in patients with colorectal cancer.

Single-cell RNA sequencing (scRNA-seq) has contributed to understanding cellular heterogeneity and immune profiling in cancer. The aim of the study was to investigate gene expression and immune profiling in colorectal cancer (CRC) using scRNA-seq. We analyzed single-cell gene expression and T cell receptor (TCR) sequences in 30 pairs of CRC and matched normal tissue. Intratumoral lymphocytes were measured with digital image analysis. CRC had more T cells, epithelial cells, and myeloid cells than normal colorectal tissue. CRCs with microsatellite instability had more abundant T cells than those without microsatellite instability. Immune cell compositions of CRC and normal colorectal tissue were inversely correlated. CD4 + or CD8 + proliferating T cells, CD4 + effector memory T cells, CD8 + naïve T cells, and regulatory T cells of CRC showed higher TCR clonal expansion. Tumor epithelial cells interacted with immune cells more strongly than normal. T cells, myeloid cells, and fibroblasts from CRCs of expanded T cell clonotypes showed increased expression of genes related to TNF and NFKB signaling and T cell activation. CRCs of expanded T cell clonotypes also showed stronger cellular interactions among immune cells, fibroblasts, and endothelial cells. Pro-inflammatory CXCL and TNF signaling were activated in CRCs of expanded T cell clonotype. In conclusion, scRNA-seq analysis revealed different immune cell compositions, differential gene expression, and diverse TCR clonotype dynamics in CRC. TCR clonality expansion is associated with immune activation through T cell signaling and chemokine signaling. Patients with CRCs of expanded clonotype can be promising candidates for immunotherapy.

Neutrophils Recruited by NKX2-1 Suppression via Activation of CXCLs/CXCR2 Axis Promote Lung Adenocarcinoma Progression.

NK2 Homeobox 1 (NKX2-1) is a well-characterized pathological marker that delineates lung adenocarcinoma (LUAD) progression. The advancement of LUAD is influenced by the immune tumor microenvironment through paracrine signaling. However, the involvement of NKX2-1 in modeling the tumor immune microenvironment is still unclear. Here, the downregulation of NKX2-1 is observed in high-grade LUAD. Meanwhile, single-cell RNA sequencing and Visium in situ capturing profiling revealed the recruitment and infiltration of neutrophils in orthotopic syngeneic tumors exhibiting strong cell-cell communication through the activation of CXCLs/CXCR2 signaling. The depletion of NKX2-1 triggered the expression and secretion of CXCL1, CXCL2, CXCL3, and CXCL5 in LUAD cells. Chemokine secretion is analyzed by chemokine array and validated by qRT-PCR. ATAC-seq revealed the restrictive regulation of NKX2-1 on the promoters of CXCL1, CXCL2, and CXCL5 genes. This phenomenon led to increased tumor growth, and conversely, tumor growth decreased when inhibited by the CXCR2 antagonist SB225002. This study unveils how NKX2-1 modulates the infiltration of tumor-promoting neutrophils by inhibiting CXCLs/CXCR2-dependent mechanisms. Hence, targeting CXCR2 in NKX2-1-low tumors is a potential antitumor therapy that may improve LUAD patient outcomes.