Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.

BACKGROUND Glioblastoma (GBM) is the most prevalent and aggressive primary malignant brain tumor in adults, with a median survival of only 15 months. Despite rigorous multimodal therapy including surgery, radiation, and temozolomide (TMZ), 96% of patients experience relapse within seven to nine months post diagnosis. Currently there is no standardized treatment for recurrent GBM (rGBM) and treatment failure is driven by extensive intertumoral and intratumoral heterogeneity. While the biology of treatment-naïve primary GBM (pGBM) is well studied, the evolution of GBM under therapy-induced selective pressure is not fully understood. This study utilizes a validated preclinical model to predict the molecular trajectory of a patient’s recurrence and develop a personalized therapeutic regimen before relapse. METHODS We performed an integrated multi-omic analysis (whole-genome sequencing, single-cell RNA sequencing, and proteomics) on a patient’s matched primary and recurrent GBM samples. In parallel, we generated a therapy-adapted patient-derived xenograft (PDX) model of the patient’s treatment plan to predict tumor evolution. Upon establishing the pGBM PDX, we implemented a three-arm study: (1) control, (2) TMZ chemoradiotherapy and (3) TMZ chemoradiotherapy with ABT414 (anti-EGFR ADC as primary GBM had EGFR overexpression). RESULTS In vivo studies demonstrated significant survival benefits in treated mice compared to controls, however, mice receiving ABT-414 relapsed earlier. Omic profiling of rGBM revealed increased immunosuppressive macrophages and proteins that suppress the anti-GBM immune response compared to pGBM.Single-cell RNA sequencing identified Indoleamine 2,3-dioxygenase 1 (IDO1) as a key regulator of the immunosuppressive tumor microenvironment in rGBM. As IDO1 is implicated in mediating resistance to PD-1 immune checkpoint blockade, its inhibition in combination with PD-1 therapy may overcome immune resistance, presenting a personalized therapeutic target for this patient. CONCLUSION In summary, we established a predictive disease model, gaining insights into GBM’s evolution and identifying actionable targets in the patient’s rGBM, offering potential strategies to overcome treatment resistance.

Tumors comprise functionally diverse subpopulations of cells with distinct proliferative potential. Here, we show that dynamic epigenetic states defined by the linker histone H1.0 determine which cells within a tumor can sustain the long-term cancer growth. Numerous cancer types exhibit high inter- and intratumor heterogeneity of H1.0, with H1.0 levels correlating with tumor differentiation status, patient survival, and, at the single-cell level, cancer stem cell markers. Silencing of H1.0 promotes maintenance of self-renewing cells by inducing derepression of megabase-sized gene domains harboring downstream effectors of oncogenic pathways. Self-renewing epigenetic states are not stable, and reexpression of H1.0 in subsets of tumor cells establishes transcriptional programs that restrict cancer cells' long-term proliferative potential and drive their differentiation. Our results uncover epigenetic determinants of tumor-maintaining cells.

Introduction: Triple Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis that accounts for 10-20% of breast cancer cases worldwide. Intra-tumoral heterogeneity and tumor cell plasticity are thought to contribute to drug resistance in TNBCs. Our work aims to: 1) precisely identify the intra-tumoral heterogeneity in cellular states present within TNBC, and 2) test whether drugs can block or initiate plasticity between subpopulations to improve drug sensitivity. We hypothesize that treatment(s) induce cellular plasticity, thus causing cells to shift into resistant states that persist until treatment is removed. We further hypothesize that these resistant subpopulations give rise to new tumor outgrowths once the treatment stops. Methods: To test this, we are treating TP53-/- Genetically Engineered Mouse Model (GEMM) syngeneic transplant tumors of the basal-like TNBC phenotype with the chemotherapeutic doublet of carboplatin/paclitaxel, and targeted agents implicated in plasticity including the MEK inhibitor trametinib, a chromatin remodeling inhibitor I-BET151, and the dihydroorotate dehydrogenase inhibitor brequinar. To identify cellular subpopulations and examine their response to treatment, we performed both in vivo and in vitro drug sensitivity testing, as well as gene expression profiling using single cell RNA-sequencing (scRNAseq). Results: We have identified clear intra-tumoral heterogeneity with at least 6 distinct cell states present, including basal, mesenchymal/claudin-low, and proliferative subpopulations in vivo in most TNBC GEMM models. We performed 18 individual scRNAseq experiments on the TP53-/- 2225L GEMM transplant line with the aforementioned treatments and untreated controls in triplicate and compared subpopulation frequencies in treated versus untreated tumors. Notably, treatment with trametinib and brequinar caused the rise of two rare subpopulations (i.e. 1% to 3-4% of total tumor cells) that express genes consistent with previously described drug-tolerant persisters (DTP), which we have called “Epithelial-DTP” (Tacstd2, Krt6a, and Cryab enriched) and “Mesenchymal-DTP” (Snai2 and Sca-1 enriched). A gene signature generated from the Epithelial-DTP subpopulation predicted poor patient outcomes in neoadjuvant chemotherapy treated TNBC patients. Further, in TNBC patient-derived xenografts (PDX), these two DTP subpopulations are also present and induced by treatment to an even greater frequency. Ongoing experiments include the use of fluorescence-activated cell sorting to isolate and functionally test the tumor-initiating capabilities of these two rare cell subpopulations. In addition, many experiments are underway to identify means to therapeutically target these DTP cells, with these results to be presented. Ultimately, identifying these rare drug resistant subpopulations, and identifying means to eradicate them, could vastly improve therapeutic regimens and outcomes for patients with TNBCs. Citation Format: Cherise R. Glodowski, Kevin R. Mott, Denis Okumu, Michael P. East, Timothy C. Elston, Gary L. Johnson, Charles M. Perou. PD4-09 Single cell RNA-sequencing identifies intra-tumoral cellular heterogeneity and drug-induced subpopulation shifts in Triple Negative Breast Cancer mouse models [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD4-09.

Highlights from Recent Cancer Literature

Endometrial cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states. Current models do not adequately reflect oncogenic origin and pathological progression in patients. Here we use single-cell RNA sequencing to profile cells from normal endometrium, atypical endometrial hyperplasia, and endometrioid endometrial cancer (EEC), which altogether represent the step-by-step development of endometrial cancer. We find that EEC originates from endometrial epithelial cells but not stromal cells, and unciliated glandular epithelium is the source of EEC. We also identify LCN2 + /SAA1/2 + cells as a featured subpopulation of endometrial tumorigenesis. Finally, the stromal niche and immune environment changes during EEC progression are described. This study elucidates the evolution of cell populations in EEC development at single-cell resolution, which would provide a direction to facilitate EEC research and diagnosis.

Although critical for understanding the underlying cancer biology, the study of tumor cell phenotype and epigenetic state from bulk gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) data is often challenging because tumor samples are typically mixtures of different cell populations due to normal tissue contamination, immune cell infiltration, and tumor subclonality. These cell populations may have vastly divergent transcriptomic and epigenetic characteristics, resulting in mixtures of signals that are often impossible to deconvolute. Here we present scBayes, a computational method that allows one to characterize the transcriptional behavior and/or epigenetic state corresponding to each tumor subclone present in a tumor sample or multiple serial or multisite samples from a given cancer patient. This tool starts with genomically defined subclones reconstructed from bulk DNA sequencing data, e.g., using our own published SubcloneSeeker algorithm. scBayes then layers single-cell gene expression (scRNA-seq) and/or chromatin accessibility (scATAC-seq) data on this genomic subclone “backbone” to assign individual cells to one of the tumor subclones or normal cell compartment, facilitating subclone-specific expression or epigenetic analysis. This allows (1) the identification of tumor expression or epigenetic signals without normal tissue interference, (2) the comparison between tumor and normal cells from the same sample, (3) the comparison of expression and epigenetic state across distinct tumor subclones, and (4) the study of transcriptional/epigenetic plasticity through the comparison of the cell states of the same genomically defined subclone across different time points or metastatic sites. scBayes achieves cell-to-subclone assignment by assessing the presence of somatic variants (CNVs or SNVs) that define cancer subclones in each individual cell from the single-cell sequencing reads. Because per-cell sequencing coverage is sparse, and therefore variant dropout frequent, scBayes utilizes a Bayesian probabilistic framework to calculate the likelihood of a particular cell originating from each subclone (including the normal clone, defined as having no somatic mutations) and makes a probabilistic assignment, maximizing the utilization of the sparse single-cell sequencing data for subclone assignment. We successfully applied this method to multiple longitudinal and metastatic cancer patient datasets, representing both bulk WES and WGS DNA sequencing, as well as single-cell datasets collected using the 10X Chromium and Fluidigm C1 platforms. Our algorithmic approach enables comparative gene expression and pathway analysis, and open chromatin accessibility assessment, across subclones. scBayes is open source and freely available at https://github.com/yiq/scBayes. Citation Format: Yi Qiao, Xiaomeng Huang, Gabor Marth. scBayes: A computational method to study tumor subclone-specific gene expression and chromatin accessibility using single-cell RNA sequencing and single-cell ATAC sequencing in combination of bulk DNA sequencing [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 40.

Glioblastoma (GBM) remains the most common adult brain tumor, with poor survival expectations, and no new therapeutic modalities approved in the last decade. Our laboratories have recently demonstrated that the integration of a transcriptional disease signature obtained from The Cancer Genome Atlas’ GBM dataset with transcriptional cell drug-response signatures in the LINCS L1000 dataset yields possible combinatorial therapeutics. Considering the extreme intra-tumor heterogeneity associated with the disease, we hypothesize that the utilization of single-cell RNA-sequencing (scRNA-seq) of patient tumors will further strengthen our predictive model by providing insight on the unique transcriptomes of the cellular niches present within these tumors, and into the transcriptional dynamics of these same cellular niches. By sequencing single-cell transcriptomes from recurrent GBM tumors resected from patients at the University of Miami, and integrating our datasets with previously published scRNA-seq data from primary GBM tumors, we are able to gain additional insight into the differences between these clinical distinctions. We have analyzed the differential expression of kinases both across and within distinct cell populations of primary and recurrent GBM tumors. This transcriptional map of kinase expression represents the heterogeneity of potential targets within individual tumors and between recurrent and primary GBM. Additionally, by generating disease signatures unique to each cellular population, and integrating these with transcriptional drug-response signatures from LINCS, we are able to predict compounds to target specific cell populations within GMB tumors. Additional computational techniques such as RNA velocity analysis and cell cycle scoring elucidate temporal insights to further prioritize these cell-type specific therapeutics, and reveal the intra-cellular dynamics present within these tumors. Collectively, our studies suggest that we have developed a novel omics pipeline based on the single cell RNA-sequencing of individual GBM cells that addresses intra-tumor heterogeneity, and may lead to novel therapeutic combinations for the treatment of this incurable disease.

BackgroundAlthough the involvement of intra-tumor genetic heterogeneity in tumor progression, treatment resistance, and metastasis is established, genetic heterogeneity is seldom examined in clinical trials or practice. Many studies of heterogeneity have had prespecified markers for tumor subpopulations, limiting their generalizability, or have involved massive efforts such as separate analysis of hundreds of individual cells, limiting their clinical use. We recently developed a general measure of intra-tumor genetic heterogeneity based on whole-exome sequencing (WES) of bulk tumor DNA, called mutant-allele tumor heterogeneity (MATH). Here, we examine data collected as part of a large, multi-institutional study to validate this measure and determine whether intra-tumor heterogeneity is itself related to mortality.Methods and FindingsClinical and WES data were obtained from The Cancer Genome Atlas in October 2013 for 305 patients with head and neck squamous cell carcinoma (HNSCC), from 14 institutions. Initial pathologic diagnoses were between 1992 and 2011 (median, 2008). Median time to death for 131 deceased patients was 14 mo; median follow-up of living patients was 22 mo. Tumor MATH values were calculated from WES results. Despite the multiple head and neck tumor subsites and the variety of treatments, we found in this retrospective analysis a substantial relation of high MATH values to decreased overall survival (Cox proportional hazards analysis: hazard ratio for high/low heterogeneity, 2.2; 95% CI 1.4 to 3.3). This relation of intra-tumor heterogeneity to survival was not due to intra-tumor heterogeneity’s associations with other clinical or molecular characteristics, including age, human papillomavirus status, tumor grade and TP53 mutation, and N classification. MATH improved prognostication over that provided by traditional clinical and molecular characteristics, maintained a significant relation to survival in multivariate analyses, and distinguished outcomes among patients having oral-cavity or laryngeal cancers even when standard disease staging was taken into account. Prospective studies, however, will be required before MATH can be used prognostically in clinical trials or practice. Such studies will need to examine homogeneously treated HNSCC at specific head and neck subsites, and determine the influence of cancer therapy on MATH values. Analysis of MATH and outcome in human-papillomavirus-positive oropharyngeal squamous cell carcinoma is particularly needed.ConclusionsTo our knowledge this study is the first to combine data from hundreds of patients, treated at multiple institutions, to document a relation between intra-tumor heterogeneity and overall survival in any type of cancer. We suggest applying the simply calculated MATH metric of heterogeneity to prospective studies of HNSCC and other tumor types.

Glioblastoma cell lines derived from different patients are widely used in tumor biology research and drug screening. A key feature of glioblastoma is the high level of inter- and intratumor heterogeneity that accounts for treatment resistance. Our aim was to investigate whether intratumor heterogeneity is maintained in cell models. Single-cell RNA sequencing was used to investigate the cellular composition of a tumor sample and six patient-derived glioblastoma cell lines. Three cell lines preserved the mutational profile of the original tumor, whereas three others differed from their precursors. Copy-number variation analysis showed significantly rearranged genomes in all the cell lines and in the tumor sample. The tumor had the most complex cell composition, including cancer cells and microenvironmental cells. Cell lines with a conserved genome had less diverse cellularity, and during cultivation, a relative increase in the stem-cell-derived progenitors was noticed. Cell lines with genomes different from those of the primary tumors mainly contained neural progenitor cells and microenvironmental cells. The establishment of cell lines without the driver mutations that are intrinsic to the original tumors may be related to the selection of clones or cell populations during cultivation. Thus, patient-derived glioblastoma cell lines differ substantially in their cellular profile, which should be taken into account in translational studies.

Background and Aim: The standard-of-care for patients with glioblastoma (GBM) is surgery followed by concurrent chemotherapy with temozolomide and radiotherapy. O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is commonly assessed in GBM as a predictive marker of response to temozolomide. Although MGMT methylation status has been shown to change between primary and recurrent GBM, no indication exists on retesting MGMT in recurrent GBM. In addition, what causes the change in MGMT methylation has yet to be identified. In this study, we aimed to investigate whether MGMT promoter methylation in recurrent GBM was influenced by intratumor heterogeneity in the initial GBM tumor. Materials and Methods: We investigated the status of MGMT promoter methylation in different samples taken from concentric layers of 24 GBMs and in 11-paired surgically resected recurrences. The neoplastic nature of samples submitted for methylation analysis was preliminary verified through histological examination; the fragments were accurately chosen to have adequate cellularity and minimal amount of nontumor contaminants. Results: About 27% (3 out of 11) of the recurrences had changed MGMT methylation status compared to the initial tumor. Initial tumor heterogeneity might play a role in this change, as all three cases had intratumor heterogeneity (with the central part of the tumor methylated and the peripheral part unmethylated) in the primary GBM. Conclusion: This study suggests that MGMT methylation variation in recurrent GBM may depend on intratumor heterogeneity in the initial tumor. Intratumor heterogeneity and possible changes in the recurrence should be taken into account when testing MGMT promoter methylation status as a predictive factor orienting therapeutic decisions in patients with GBM.

Single-Cell and Spatial Analyses Characterize Distinct Subsets of Malignant T Cells in Angioimmunoblastic T Cell Lymphoma

Background: In neuroblastoma (NB), intratumor heterogeneity (ITH) is frequently observed, but the role of cell-to-cell allele-specific copy number alterations in phenotypic variation, clonal evolution and treatment response remains to be determined. Here we investigate ITH, timing of specific genomic aberrations, single-cell replication timing and the co-evolution of the genome and transcriptome in NB tumors at single-cell resolution, with an aim to analyse subclonal dynamics and clone-specific response or resistance under targeted therapeutic pressure. Methods: In addition to germline/tumor bulk whole exome sequencing (WES), ultra-low depth (0.25x) single-cell whole-genome DNA sequencing (scDNAseq) was performed using 10x genomics Chromium single-cell CNV (scCNV) kit and 9410 tumor cells were characterized from 14 patient-derived xenografts (PDX) NB-models and 4 tumor biopsies from NB-patients, either at diagnosis (n=7), progression (n=3) or relapse (n=8). Single-cell RNA sequencing (scRNAseq) data was obtained from the same PDX and patient tumor samples (Thirant et al, 2023). 6/14 PDX models were subjected to different treatment combinations (targeted treatment with/without chemotherapy) and bulk WES was performed at two time-points, pre- and post-treatment. Results: Both monoclonal (n=7) and polyclonal (n=11) genomes were determined by allele and haplotype specific copy number (CN) alteration using both scDNAseq and scRNAseq data analysis, with 2 to 11 clones observed per polyclonal NB tumor. Whole genome duplication events (n=7) were observed in both polyclonal and monoclonal genomes. Known driver CN (segmental loss in chr1p and chr11q and gain at chr17q, or MYCN/ALK amplification) or somatic mutations (ALK/ATRX/TP53/NF1) were early clonal events.Study of replication timing (RT) based on scDNAseq revealed significant differences in RT between the MYCN amplified (n=7) and non-amplified groups (n=5), with NB tumors without MYCN amplification (no MNA) characterized by a predominance of late replicating domains, in contrast to MYCN amplified (MNA) tumors, which are enriched in early replicating domains In a PDX model of interest, scDNAseq analysis showed parallel copy number evolution of two distinct clones, subclone s1/s2. Data integration of clonal mutational profiles with pre- and post-targeted therapy (Lorlatinib) revealed clone-specific treatment response. Subclone s2 was partially responding with extinction of a sub-set of somatic alterations, whereas no change was observed in subclone s1. The replication timing (RT) profile of these two clones, subclone s1 (early-RT) and s2 (late-RT) were mutually exclusive. Genotype to phenotype analysis revealed subclone s1 genotype was preferentially expressed at transcriptomic level. Conclusion: Together, these results determine the evolutionary trajectories of NB tumors, linked to distinct replication timing and highlight opportunities for targetable early clonal alteration detection. Citation Format: Jaydutt Bhalshankar, Angela Bellini, Irene Jimenez, Cécile Thirant, Elnaz Saberi-Ansari, Yasmine Iddir, Alexandra Saint Charles, Charlotte Butterworth, Amira Kramdi, Virginie Raynal, Sylvain Baulande, Didier Surdez, Sakina Zaidi, Gaelle Pierron, Angel Montero Carcaboso, Birgit Geoerger, Andrei Zinovyev, Olivier Delattre, Isabelle Janoueix-lerosey, Gudrun Schleiermacher. Clonal decomposition and DNA replication states defined by scaled single-cell DNA and RNA sequencing suggest clone-specific therapeutic vulnerabilities in neuroblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B074.

Pancreatic ductal adenocarcinoma (PDAC) is the most frequent and aggressive pancreatic tumor characterized by high metastatic risk and special tumor microenvironment. To comprehensively delineate the complex intra-tumoral heterogeneity and the underlying mechanism during metastatic lesions malignant progression, single-cell RNA sequencing (scRNA-seq) was employed. PCA and TSNE were used for dimensionality reduction analysis and cell clustering. Find All Markers function was used to calculate differential genes in each cluster, and Do Heatmap function was used to plot the distribution of differential genes in each cluster. GSVA was employed to assign pathway activity estimates to individual cells. Lineage trajectory progression was inferred by monocle. CNV status was inferred to compare the heterogeneity among patients and subtypes by infercnv. Ligand-receptor interactions were identified by CellPhoneDB, and regulons network of cells was analyzed by SCENIC. Through RNA-sequencing of 6236 individual cells from 5 liver metastatic PDAC lesions, 10 major cell clusters are identified by using unbiased clustering analysis of expression profiling and well-known cell markers. Cells with high CNV level were considered as malignant cells and pathway analyses were carried out to highlight intratumor heterogeneity in PDAC. Pseudotime trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. The complex cellular communication suggested potential immunotherapeutic targets in PDAC. Regulon network identified multiple candidates for promising cell-specific transcriptional factors. Finally, metastatic-related genes expression levels and signaling pathways were validated in bulk RNA Sequencing data. This study contributed a comprehensive single-cell transcriptome atlas and contributed into novel insight of intratumor heterogeneity and molecular mechanism in metastatic PDAC.

e20521 Background: Lung adenocarcinoma (LUAD), at the cellular level, has a high degree of intratumor heterogeneity. Advanced single-cell sequencing technologies have offered tools to analyze intratumor heterogeneity and identify the biomarkers, thereby aiding cancer diagnosis and prediction of the patient prognosis. Methods: From the Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/geo) database, the single-cell RNA sequencing (scRNA-seq) data from two LUAD and two para-cancerous tissue samples were obtained. We performed a dimensionality reduction and unsupervised clustering to identify the different cell clusters within the tumor tissues. To identify the most relevant modules and important cell subpopulations (clusters) in LUAD tissues, a weighted gene co-expression network analysis (WGCNA) was performed. Subsequently, we classified the LUAD molecular subtypes according to the marker genes of these clusters. The Limma package ( www.bioconductor.org/packages/release/bioc/html/limma ) was used to screen for the differentially expressed genes (DEGs) between the subtypes. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses, the gene signature most significantly associated with the prognosis of LUAD patients was determined. Results: A total of 14 cell clusters belonging to 10 cell types in LUAD was identified. The turquoise module was found to be the most relevant to LUAD among all the modules; cluster 10 (C10) was found to be the most strongly associated with the turquoise module. In The Cancer Genome Atlas (TCGA), patients with LUAD were divided into two groups of distinct molecular subtypes. Based on the 165 shared genes between the turquoise module and C10, 511 DEGs between the two molecular subtypes were obtained, and five of them were selected to construct the gene signature, which was validated to be an independent prognostic marker of LUAD. Conclusions: 14 cell clusters co-existed in LUAD, which contributed to its intratumor heterogeneity. In addition, two molecular subtypes of LUAD were identified and a five-gene signature was developed and validated to be significantly associated with prognostic and clinical characteristics of LUAD patients. Keywords: single-cell RNA sequencing, lung adenocarcinoma, intratumor heterogeneity, molecular subtypes, prognosis, five-gene signature.

BackgroundLung cancer, one of the most common malignant tumors, exhibits high inter- and intra-tumor heterogeneity which contributes significantly to treatment resistance and failure. Single-cell RNA sequencing (scRNA-seq) has been widely used to dissect the cellular composition and characterize the molecular properties of cancer cells and their tumor microenvironment in lung cancer. However, the transcriptomic heterogeneity among various cancer cells in non-small cell lung cancer (NSCLC) warrants further illustration.MethodsTo comprehensively analyze the molecular heterogeneity of NSCLC, we performed high-precision single-cell RNA-seq analyses on 7364 individual cells from tumor tissues and matched normal tissues from 19 primary lung cancer patients and 1 pulmonary chondroid hamartoma patient.ResultsIn 6 of 16 patients sequenced, we identified a significant proportion of cancer cells simultaneously expressing classical marker genes for two or even three histologic subtypes of NSCLC—adenocarcinoma (ADC), squamous cell carcinoma (SCC), and neuroendocrine tumor (NET) in the same individual cell, which we defined as mixed-lineage tumor cells; this was verified by both co-immunostaining and RNA in situ hybridization. These data suggest that mixed-lineage tumor cells are highly plastic with mixed features of different types of NSCLC. Both copy number variation (CNV) patterns and mitochondrial mutations clearly showed that the mixed-lineage and single-lineage tumor cells from the same patient had common tumor ancestors rather than different origins. Moreover, we revealed that patients with high mixed-lineage features of different cancer subtypes had worse survival than patients with low mixed-lineage features, indicating that mixed-lineage tumor features were associated with poorer prognosis. In addition, gene signatures specific to mixed-lineage tumor cells were identified, including AKR1B1. Gene knockdown and small molecule inhibition of AKR1B1 can significantly decrease cell proliferation and promote cell apoptosis, suggesting that AKR1B1 plays an important role in tumorigenesis and can serve as a candidate target for tumor therapy of NSCLC patients with mixed-lineage tumor features.ConclusionsIn summary, our work provides novel insights into the tumor heterogeneity of NSCLC in terms of the identification of prevalent mixed-lineage subpopulations of cancer cells with combined signatures of SCC, ADC, and NET and offers clues for potential treatment strategies in these patients.