Enrichment Of Cells Research Articles

Abstract 2304: Detection of early-disseminated cancer cells with deep learning-enabled holographic imaging

Abstract Recent research increasingly validates the early dissemination of tumor cells in peripheral blood during the nascent stages of cancer, often preceding clinical identification of the primary tumor. This emerging evidence emphasizes the potential of Circulating Tumor Cells (CTCs) as early indicators for cancer. Despite their significance, CTCs are exceptionally rare compared to normal blood cells, which presents a considerable challenge in detection, particularly during early stages of cancer. Traditional detection methods, such as those based on nucleated cell enrichment, antibody labeling and fluorescence imaging, encounter notable limitations when applied in the context of early detection. These limitations arise from the inherent heterogeneity of CTCs, and the multistep process involved in labeling and manual enumeration, compromising cell viability and resulting in cell loss. This loss becomes particularly problematic in the context of early cancer detection scenarios where the tumor burden is extremely low, and any cell loss can significantly impact assay sensitivity. To overcome these challenges, we propose an innovative CTC detection approach employing a deep learning framework combined with holographic imaging. This technique utilizes coherent light to generate holograms from cell samples, capturing detailed 3D morphological and optical properties of individual cells. We developed a custom convolutional neural network designed to facilitate high-throughput cell sorting in real-time. Trained on healthy blood samples and diverse cancer cell lines, the dataset consists of more than 50 million cellular holograms enhanced by proprietary Gaussian annotation and image augmentation strategies. Moreover, by combining the morphological signatures from holography with antigen signatures (e.g. HER2/ER/PR) in real-time, our model demonstrates the ability to detect less than 1 false positive per 1 million nucleated cells, highlighting its potential as a robust tool for targeted early-stage breast cancer detection. Our technology represents highly sensitive alternative to traditional screening methods, with the promise of enhancing patient outcomes. Citation Format: Nicholas Heller, Kevin Mallery, Nathaniel Bristow, Yash Travadi, Eng Hock Lee, Jiarong Hong. Detection of early-disseminated cancer cells with deep learning-enabled holographic imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2304.

Abstract 2533: In-depth multi-omic profiling of immune cells in cancer patients developing immune-related adverse events

Abstract Background: The clinical efficacy of immune-checkpoint inhibitors is complicated by the risk for immune-related adverse events (irAEs) that can involve any organ systems. To date, (1) we cannot predict irAE risk reliably at patient level and (2) irAEs remain a diagnosis of exclusion as there are no specific clinical or biological markers. The goal of this study was to elucidate intricate transcriptomic and proteomic signatures of immune cells in patients evolving irAEs. Methods: This pilot trial was conducted as part of a prospective multicenter cohort study (NCT04631731). The study cohort consisted of n=71 patients who were treated with ICIs across Blacktown and Westmead Hospitals, NSW, Australia. Peripheral blood mononuclear cells (PBMCs) were collected from both pre-treatment and after 6-8 weeks post-ICI commencement. Samples were further utilized for RNA sequencing, flow cytometry and Chromium 3’ gene expression analysis (10x Genomics). Results: The median age was 67 years old with the 78% (n=56) of Caucasian origin. The predominant number of patients were treated with dual ICIs. N=21 patients experienced irAEs. Firstly, a differential analysis established a set of genes which were significantly (FDR<0.01) dysregulated between patients with (group A) and without irAEs (group B) and were mostly related to cell cycle and regulation. Notably, phospholipase D signaling pathway was exclusively upregulated in group A and is known to be linked to B cell expansion. Secondly, a cell enrichment analysis within the group A revealed a significant increase of CD4+ and CD8+ T central memory (Tcm) cells upon the toxicity onset as compared to baseline stage. Thirdly, the flow cytometry data established that group A had (1) higher expression of CD27 on CD4+ T cells, a member of Traf-linked tumor necrosis factor receptor family essential for T cells’ maturation and (2) lower expression of inhibitory receptor LAG3 (CD223) which is crucial for maintaining homeostasis of immune system through suppression of T cell proliferation. Finally, we sequenced CD45+ cells from n=4 patients from group A and n=4 from group B at both pre- and post-treatment stages. The unsupervised clustering established 17 different clusters in our single cell dataset with cluster 11 was unique only to patients from group A. The differential analysis established that a set of n=62 genes was significantly upregulated (p<0.05) in cluster 11. Functional annotation established that B cell activation and proliferation pathways were enriched by those genes further stressing B cells’ role in irAEs development. Conclusions: Our real-world cohort study established significant molecular and cellular differences between patients with and without irAEs. Further research in larger cohorts is imperative to validate the established findings and to elucidate the precise mechanisms contributing to the development of irAEs. Citation Format: Dmitrii Shek, Bo Gao, Joey Lai, Brian Gloss, Adnan Nagrial, Ines Pires da Silva, Matteo S. Carlino, Scott A. Read, Golo Ahlenstiel. In-depth multi-omic profiling of immune cells in cancer patients developing immune-related adverse events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2533.

Abstract 3523: Self-supervised foundation model captures high-dimensional morphology data from single cell brightfield images

Abstract Our knowledge of cancer biology has advanced with the characterization of distinct cell types and cell states within heterogeneous tumor environments. Many methods for imaging and sorting tumor cells require biomarker labels that alter cell characteristics and create a selection bias. The use of label-free single-cell methods should further improve cancer studies involving viable, unperturbed cells for downstream assays such as RNA-Seq, cell culture expansion, and functional assays. To eliminate biomarkers and enable broader assessment of cells, we developed REM-I, a platform that characterizes and sorts unlabeled single cells based on high-dimensional morphology. Cells are captured with brightfield imaging and processed in real-time by self-supervised deep learning models to generate quantitative AI embeddings representative of cell morphology. A significant technical challenge in building REM-I was developing an AI model based on extracted features from cell images without prior knowledge of cell types, cell preparation, or other application-specific knowledge. Accordingly, we developed the Human Foundation Model (HFM), a hybrid architecture that combines self-supervised learning (SSL) and morphometrics (computer vision) to extract 115 dimensional embeddings representing cell morphology from high-resolution REM-I cell images. SSL produces a foundation model with high generalization capacity that enables hypothesis-free sample exploration and efficient generation of application-specific models. Meanwhile, computer vision extracts features that represent measurable and interpretable concepts (e.g., cell size, shape, texture, intensity). The training process for the HFM self-supervised backbone model utilizes the discriminatory power of supervised tasks. Using synthetic cells and three cancer cell lines, we trained and then validated the reproducibility and generalization capabilities of the resulting model. Results show combining deep learning and morphometrics improve interpretability of data and enable rapid characterization and classification of tumor cells with high accuracy. We also report on the Deepcell® Axon data suite, a tool to analyze data and customize reusable workflows. This includes the ability to store and manage data, visualize high dimensional data as low-dimensional projections, and train classifiers to identify and sort cell populations. To enable compatibility with user-preferred downstream analysis pipelines, Axon provides data export options for images, plots, and embeddings. Our approach allows users of all skill levels to access and interpret AI-enabled morphological profiling. Applications of REM-I include hypothesis-free evaluation of heterogeneous tumor samples, label-free cancer cell enrichment, characterization of distinct cell states, and multi-omic integration. Citation Format: Kiran Saini, Senzeyu Zhang, Ryan Carelli, Kevin B. Jacobs, Amy Wong-Thai, Cris Luengo, Vivian Lu, Anastasia Mavropoulos, Andreja Jovic, Jeanette Mei, Thomas Vollbrecht, Stephane C. Boutet, Mahyar Salek, Maddison Masaeli. Self-supervised foundation model captures high-dimensional morphology data from single cell brightfield images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3523.

Abstract 2883: HDAC6 mediates the radioresistance of NSCLC through LSD1, independent of its own deacetylation activity

Abstract Resistance to radiation therapy significantly affects the prognosis of solid tumors. Histone deacetylase 6 (HDAC6) is a stress-responsive lysine deacetylase that has emerged as a promising target for cancer therapy, with numerous clinical trials investigating interventions to modulate its activity. While HDAC6 appears to be linked to responses to DNA-damaging therapeutics, the reported responses and underlying mechanisms remain intriguing and varied. In this study, we elucidate a novel mechanism of radioresistance mediated by HDAC6 in non-small cell lung cancer (NSCLC). We classified nine NSCLC cell lines into three groups based on their radioresistance using survival assays and correlated this resistance with the induced expression of six deacetylases. We conducted gain-of-function experiments (GOF) using plasmid transfection and loss-of-function experiments (LOF) employing inhibitors or RNAi. Furthermore, we quantified the repair of damaged dsDNA through FACS-based GFP assays and assessed the mechanism of homologous recombination repair (HRR) end-resection via single-strand quantitative PCR. Co-immunoprecipitation assays, cancer stem cell enrichment, DNA damage-induced senescence assays, reverse-phase protein assays (RPPA), and RNA sequencing were also performed to investigate gene expression changes resulting from the loss of HDAC6. Our findings revealed that HDAC6 induction by radiation is strongly correlated with NSCLC resistance to radiation leading to CSC survival and escape from radiation led senescence LOF of HDAC6 through RNAi or inhibitors suppressed resistance, while GOF achieved through wild-type or deacetylase activity-deficient mutant transfection, induced resistance. Importantly, this resistance mechanism was independent of deacetylation activity. RNAi-mediated loss of HDAC6 reduced both HRR and non-homologous end-joining, whereas pharmacological inhibition of HDAC6 activity did not. Notably, DNA end-resection, a critical step in HRR, was decreased by RNA interference but not affected by the inhibitors. RPPA assays demonstrated that Histone demethylase 1A (LSD1/KDM1A) levels decreased significantly upon HDAC6 RNA interference but remained unaffected by pharmacological intervention. Loss of LSD1 alone resulted in decreased HRR and end-resection, likely mediated through HDAC6-induced ubiquitination. Moreover, overexpression of LSD1 rescued the HDAC6 loss-induced sensitization of NSCLCs. In summary, our study unveils a novel mechanism by which HDAC6 mediates HRR after radiation through the stabilization of LSD1. This mechanism operates independently of deacetylase activity. Therefore, our findings suggest that interventions targeting both deacetylation and non-deacetylation roles of HDAC6 should be considered to overcome HDAC6-derived radioresistance in NSCLCs and enhances the radiation therapy efficacy. Citation Format: Sojung Ha, Hyejin Kim, Hani Lee, Seokgyeong Choi, Ho-young Lee, Woo-Young Kim. HDAC6 mediates the radioresistance of NSCLC through LSD1, independent of its own deacetylation activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2883.

Abstract 7644: Prognostication of genomic characteristics of residual breast cancer after neoadjuvant chemotherapy

Abstract Residual cancer burden (RCB) based on pathologic characteristics is the most powerful prognostic factor in breast cancer (BC) which received neoadjuvant chemotherapy (NAC) followed by surgery. According to RCB classification, class III predicted short survival duration regardless of BC subtypes according to hormone receptor (HR) and human epidermal growth factor receptor-2 (HER-2) state. However, up to 20-40% of BCs with RCB class III did not experience BC recurrence and had long term survival without disease recurrence. In our study, we evaluated genomic characteristics of BC which had high RCB class. We collected 96 fresh frozen BC specimens which had undergone surgery after NAC between 2010 and 2018 from Samsung Medical Center Biobank. We performed whole genome or exome sequencing, RNA sequencing and high-plex single-cell spatial transcriptomics. Of 96 BCs, 65 BCs had experienced BC recurrence and 31 BCs have not been BC recurrence. Of 65 BCs with dismal prognosis, 12 were HR+HER2-, four in HR+HER2+, 39 in triple negative breast cancer (TNBC) and 10 in HR-HER2+ BC. Of 31 BCs without recurrence, 11 were HR+HER2-, two in HR+HER2+, 14 in TNBC and four in HR-HER2+ BC. In HR+HER2- BC, ten of 12 BCs with dismal prognosis were categorized into basal like intrinsic subtype and two were into luminal B subtype compared that six of luminal A and five of luminal B subtype categorization in good prognosis BC group. Age associated mutation signature were enriched in HR+HER2- BC with good prognosis (p=0.027). Tumor microenvironment (TME) analysis using Kassandra suggested that high monocytes were associated with BC with dismal prognosis (p=0.00031). In single-cell spatial transcriptome analysis, T-cell abundance in the tumoral niche is associated with dismal prognosis in HR+HER2- BC (p=0.039). In TNBC, almost BCs (88.7%) were categorized into basal-like intrinsic subtype regardless of their prognosis. TME analysis presented that immune cells including macrophages and lymphocytes with CD4 and CD8 T cells were significantly enriched in TNBC with good prognosis (ps <0.05, respectively). In HER2+ BC, intrinsic subtypes were heterogeneous in both groups. Focal amplification in HER2+BC patient harbored DNA segments from different chromosomes more frequently than in those with good prognosis (p=0.00091), which was prominent in oncogenic extrachromosomal DNA amplifications compared to other oncogenic amplifications. In TME analysis, there were no specific cell enrichments according to BC prognosis but sing-cell spatial transcriptome analysis revealed that higher T-cell abundance in the tumoral niche was associated with HR+HER2+ BC with good prognosis (p=0.047). In conclusion, each BC subtype had their own genomic characteristics to determine their prognosis. Our study also suggested that TME characteristics was common prognostic factor for survival outcome of BC with residual tumor after NAC. Citation Format: Ji-Yeon Kim, Sabin Park, Sepil Ahn, Eun Seop Seo, Soyeon Kim, Mark Gregory, Emily Killingbeck, Eun Yoon Cho, Jeong Eon Lee, Jin Seok Ahn, Yeon Hee Park, Woong-Yang Park, Hoon Kim, Semin Lee, Young-Hyuck Im. Prognostication of genomic characteristics of residual breast cancer after neoadjuvant chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7644.

Abstract 2486: Dissecting tumor-immune interaction in response and resistance to immune checkpoint blockade in metastatic melanoma

Abstract Background: Immune checkpoint blockade (ICB) has revolutionized the treatment and prognosis of various cancers, including melanoma. Nevertheless, intrinsic or acquired resistance is common, and the interactions between tumor-intrinsic features and the microenvironment underlying these resistances remain unclear for most patients. Methods: In this study, we developed a framework to analyze response and resistance, both intrinsic and acquired, via tumor-intrinsic programs, immune features, and tumor-stromal-immune interactions in a standardized, uniformly processed, and deeply clinically annotated cohort of metastatic melanoma patients (n=61) treated with ICB as part of the NCI funded human tumor atlas network (HTAN) initiative. From the same tumor samples (pre-treatment (n=33) and post-progression (n=28)), we conducted bulk whole-exome sequencing (WES), single-nucleus RNA sequencing, and for a subset of the samples high-resolution spatial imaging (including protein mIHC, CODEX, and transcriptomics MERFISH). Standardized processing and data pipelines allowed for integration of genomic, transcriptomic, and spatial features to elucidate shared tumor and microenvironmental states and their relationships with resistance. Results: Our preliminary analysis on a subset of 28 samples identified an enrichment of B cells, plasma cells, and T follicular helper-like cells in the tumor microenvironment (TME) of patients sensitive to treatment. The TME of non-responders was predominantly characterized by macrophage/monocyte cell populations. Post-treatment responder samples showed an enrichment of CD4+ T cells. Genomic heterogeneity, assessed through WES, was confirmed as a feature of intrinsic resistance across different ICB treatment settings, while ploidy was associated with treatment response in pre-treatment, ipilimumab-naïve patients treated with PD-1 inhibitors. Comparing the immune compartments of samples with high and low heterogeneity revealed NK cell enrichment in samples with a high proportion of subclonal mutations. Conclusions and Future Directions: Overall, our work provides a high-resolution understanding of the tumor-stromal-immune interaction in metastatic melanoma, shedding light on the factors contributing to therapy response and resistance, and contributing a repository of data from deeply clinically annotated, multimodally characterized and uniformly processed patient samples. Ongoing spatial analysis will further enhance our understanding of these compartments at a spatial level. The results of our study have the potential to guide more personalized and effective treatment strategies for metastatic melanoma in the future. Citation Format: Giuseppe Tarantino, Yiwen He, Priyanka Solanky, Aaron Thorner, Tyler Aprati, Ryan Sullivan, Emily Robitschek, Timothy Blosser, Xiaowei Zhuang, Xiaowei Zhuang, Elliot Boblitt, Allison Frangieh, Marta Holovatska, Aleigha Lawless, Michael Manos, Kathleen Pfaff, Karla Helvie, Tatyana Sharova, Dennie Frederick, James Liam Fahey, Diego Villamarin, Sami Farhi, Scott Rodig, Bruce Johnson, Alex K. Shalek, Eliezer Van Allen, Stephen Hodi, Genevieve M. Boland, David Liu. Dissecting tumor-immune interaction in response and resistance to immune checkpoint blockade in metastatic melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2486.

Abstract 5490: Spatial profiling of inflamed tumor microenvironment of clear cell renal cell carcinoma

Abstract Clear cell renal cell carcinoma (ccRCC) presents a distinctive tumor microenvironment (TME) characterized by extensive vascularization and immune infiltration. This unique TME makes ccRCC an attractive target for anti-angiogenesis and immune checkpoint blockade (ICB). To better understand the complexity of ccRCC and the associated cell types and states involved in disease progression, we conducted high-throughput single-cell immunofluorescence imaging on tissue sections from 437 localized tumors that were surgically resected. These samples included regions from the tumor center, tumor edge, and adjacent benign areas (TMA cores, n=1859). Our primary focus was on tumors with elevated immune cell enrichment (CD45high), as these tumors are known to be aggressive and may respond well to immunotherapy. Our study reveals the intricate interactions between tumor, vascular, stromal, and immune cells within ccRCC. We find that CD45high tumors, upon losing epithelial marker expression, acquire tumor cell PD-L1 and mesenchymal markers such as Fibroblast activation protein (FAP). This shift is indicative of the epithelial-mesenchymal transition (EMT) process. These CD45high and EMThigh tumors are enriched with changes in TME, including elevated FAP+ cancer-associated fibroblasts (CAFs) and subsets of immune suppressive T cells and macrophages. We report that ccRCC patients with high tumor PD-L1 expression or abundant FAP+ CAFs are associated with earlier metastasis and, in sunitinib-treated patients, shorter progression-free survival. This study illuminates the intricate connection between immune cell infiltration, stromal biology, and EMT in ccRCC, offering a deeper understanding of the disease's complex biology and potentially guiding future therapeutic approaches and strategies. Citation Format: Teijo Pellinen, Lassi Luomala, Kalle Mattila, Jenni Säilä, Annabrita Hemmes, Katja Valimaki, Oscar Bruck, Lassi Paavolainen, Harry Nisen, Petrus Järvinen, Olli Kallioniemi, Paula Vainio, Panu Jaakkola, Tuomas Mirtti. Spatial profiling of inflamed tumor microenvironment of clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5490.

Abstract 6834: Periostin involvement in cellular senescence in ovarian cancer

Abstract Purpose: Despite improvements in care and advancements in the understanding of ovarian cancer (OC) pathobiology and genetics, survival rates remain disappointingly low compared to other types of cancer. This is due to a lack of diagnosis strategies, high recurrence rates, and the development of chemo-resistant diseases, which require synergy between cancer cells and the tumor microenvironment (TME). We have demonstrated the functional importance of a TME gene, periostin (POSTN), in OC and recurrent OC, using in vitro and in vivo models. Our objectives were to identify differentially expressed genes by POSTN expression in the TME that contribute to the progression of ovarian cancers. Procedures: Gene expression data was generated from an ovarian cancer cell line, HEYA8, cultured in a high POSTN conditioned 3D cell culture environment, using the NovaSeq 6000 system to generate RNA-seq data. The data was analyzed to determine the genes expressed at different levels between the HEYA8 cells grown with or without POSTN-high conditioned medium (CM). The gene functional annotation and KEGG pathway enrichment were further analyzed from 10,115 significant differentially expressed genes between the HEYA8 cells grown in high levels of POSTN (CMPOSTNhigh) or low levels of POSTN (CMCTL) culture conditions, using DAVID bioinformatics. Results: Using a cell culture and co-culture model, we found that OC cells cultured under conditioned media containing CMPOSTNhigh exhibited faster cell migration, more invasiveness (p=0.006), more chemo-resistance (p<0.05), and increased resistance to paclitaxel-induced apoptosis, compared to OC cells cultured with control medium (CMCTL). OC cell lines cultured with CMPOSTNhigh showed increased side population cells relative to CMCTL -cultured cells, suggesting POSTN plays important roles in cancer stem cell enrichment. POSTN-transfected 3T3-L1 cells exhibited more intracellular and extracellular lipids and were linked to increased cancer cell expression of the oncogene fatty acid synthetase. In correspondence to the functions of POSTN in OC cells, the pathways identified to be significantly impacted by POSTN expression included, but were not limited to, the cell cycle, apoptosis, p53 signaling, MAPK signaling, and PI3K-Akt signaling pathways. Interestingly, we also found cellular senescence pathways was one of the most significantly impacted by POSTN suggesting that POSTN may play roles in ovarian cancer via the regulation of cellular senescence, which has not yet been explored. Conclusions: These data emphasize the importance of TME factors in tumor progression and prognosis via various ways in ovarian cancer, establishing promising potential therapeutic targets of OC. Citation Format: Jihua Feng, Ethan Nguyen, Jocelyn Reyes, Zhiqing Huang. Periostin involvement in cellular senescence in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6834.

Abstract 5853: Regulatory network inference of lung adenocarcinoma drug-tolerant persister cells

Abstract Despite the effectiveness of the latest generation of cancer drugs, partial tumor response and subsequent disease relapse is commonly observed in patients. Recent evidence suggests that treatment resistance emerges from drug-tolerant persister cells (DTPC) that survive through non-genetic tumor cell adaptation. Although DTPC are mostly slow-cycling, a rare subset can re-enter the cell cycle under constitutive presence of treatment. Understanding how DTPC dynamically remodel their molecular portraits at the single-cell level may provide high-resolution insights into the molecular circuits associated with adaptive survival to therapy.Here, we use single-cell RNA sequencing (scRNA-seq) to identify potential mechanisms that promote survival of DTPC and decipher their underlying gene regulatory networks (GRNs), in a model of EGFR-mutant lung adenocarcinoma treated with osimertinib (EGFR inhibitor). Gene expression analysis revealed fewer cell states in DTP compared to treatment-naïve cells (TN). We confirmed the acquisition of DTPC hallmarks (e.g., high epithelial-mesenchymal transition, slow-cycling phenotype, deficient DNA repair). We also identified subpopulations of cells that display intermediate transcriptional states: a pre-existing DTP-like state in a bulk of TN cells, and DTPC with enriched cell cycle signatures, evoking a phenotype of cycling DTPC. To characterize the GRN that govern TN cells, DTPC and the intermediate cell states, we applied the network inference tool PYSCENIC, which associates transcription factors (TFs) to their candidate target genes (TGs) on the basis of the scRNA-seq data and enrichment of TF binding motifs in the genomic regions around TGs. The resulting (GRN) consisted of 5 TF modules clustered on the basis of correlated TF activity.This analysis revealed that, compared to TN cells, DTPC and the intermediate cell states display a marked decreased expression of TFs and TGs involved in mRNA splicing, and more specifically in the initiation of spliceosome assembly through E-complex and A-complex formation. Interestingly, the splicing-related TFs all appear to have affinity for binding to a GCCxC consensus motif or close variant in genes (i.e., SNRPB and SNRPE) that code for key effectors of these initiation complexes. This pilot study highlights that scRNA-seq analyses coupled with inference of regulatory networks might enable the identification of molecular mechanisms that potentially trigger cell state transitions during drug-tolerance. The role of altered mRNA splicing in persistence to EGFR-targeted therapies will be explored experimentally in patient-derived cell lines and 3D organoid cultures. Citation Format: Lisa Chabrier, Camille Leonce, Raphael Schneider, Cyril Degletagne, Pierre Saintigny, Anton Crombach, Sandra Ortiz-Cuaran. Regulatory network inference of lung adenocarcinoma drug-tolerant persister cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5853.

Abstract 4886: An in-depth single-cell map of inflammatory breast cancer reveals wide-scaled restructuring associated to an immunosuppressive and inflammation-like microenvironment

Abstract Inflammatory Breast Cancer (IBC) has increasingly been recognised as a distinct form of breast cancer, that is characterised by its distinctive clinical presentation, as well as its fast onset and poor prognosis. Despite previous efforts, the underlying molecular heterogeneity of IBC remains poorly understood. We used single-cell transcriptomics to dissect the transcriptional landscape of IBC at single-cell resolution, and elucidate differences to non-inflammatory breast cancer (non-IBC), of which we created and curated a subtype-matched comparison dataset. Our analysis revealed a previously underappreciated role of the microenvironment in driving the immune suppressive and angiogenic phenotype of IBC. Furthermore, we identified key transcriptional signatures associated with IBC aggressiveness, including dysregulated pathways across all major cell compartments. Within each compartment, we used a combination of approaches to assign functional roles to subsets of cells, including expression of marker genes, gene programmes and projection onto thoroughly annotated reference-spaces. We thus identify cell populations present in significantly different proportions between IBC and non-IBC. Interestingly, among the populations enriched in IBC we identified iCAFs, pro-inflammatory M1-like myeloid cells as well as regulatory T cells. In non-IBC on the other hand, enriched cell types include myCAFs, M2-like myeloid cells and cytotoxic T cells.In addition to differences in the cell composition, we also find differences in the expression of certain markers and gene programmes. A pseudo-time analysis of T cells reveals a shift towards an exhausted T cell phenotype in IBC, in line with a lower expression of HLA genes in its epithelial compartment. In agreement with the known angiogenic phenotype, we also see an enrichment in mitogenic signatures in IBC-associated endothelial cells. In summary, our data describe an immunosuppressive environment, highlighted by the presence of cell types with an inflammation-like phenotype, signs of immune evasion and exhaustion and angiogenesis. Our comprehensive and unique single-cell transcriptomic dataset of this rare form of breast cancer provides valuable insights into the complex cellular composition of IBC, offering a foundation for further functional exploration of involved pathways, and finally development of new targeted therapy approaches. Our study contributes to a deeper understanding of IBC biology and pathology, enabling future identification of direly needed biomarkers and therapeutic approaches for this hard-to-treat breast cancer type. Citation Format: Paul Schwerd-Kleine, Tasneem Cheytan, Roberto Würth, Ewgenija Gutjahr, Laura Michel, Verena Thewes, Peter Lichter, Andreas Schneeweiss, Andreas Trumpp, Martin Sprick. An in-depth single-cell map of inflammatory breast cancer reveals wide-scaled restructuring associated to an immunosuppressive and inflammation-like microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4886.

Enrichment of liver MAIT cells in a mouse model of Alzheimer's disease

Emerging evidence has supported a role for the immune system and liver in Alzheimer's disease (AD). However, our understanding of how hepatic immune cells are altered in AD is limited. We previously found that brain mucosal-associated invariant T (MAIT) cell numbers are increased in AD. Furthermore, loss of MAIT cells and their antigen-presenting molecule, MR1, reduced amyloid-β accumulation in the brain. MAIT cells are also significantly present in the liver. Therefore, we sought to analyze MAIT and other immune cells in the AD liver. Increased frequency of activated MAIT cells (but not conventional T cells) were found in 8-month-old 5XFAD mouse livers. Therefore, these data raise the possibility that there is a role for peripheral MAIT cells in AD pathology.

EIF2Ss, a Novel c-Myc-Correlated Gene Family, is Associated with Poor Prognosis and Immune Infiltration in Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor in urgent need of novel diagnostics, prognostic markers, and treatments. Eukaryotic translation initiation factor 2 subunits (EIF2Ss), comprising Eukaryotic translation initiation factor 2 subunit alpha (EIF2S1), Eukaryotic translation initiation factor 2 subunit beta (EIF2S2), and Eukaryotic translation initiation factor 2 subunit gamma (EIF2S3), is a family of eukaryotic initiation factors that participate in early protein synthesis and are crucial for tumor initiation and progression. However, the role of EIF2Ss in PAAD has yet to be reported. The aim of this study was therefore to analyze EIF2Ss in relation to the diagnosis, prognosis, and treatment of PAAD. The cancer genome atlas (TCGA) database was used to investigate gene expression and patient survival. Gene alterations, immune cell infiltration, and immune checkpoints in PAAD were also evaluated. Univariate and multivariate analysis, nomograms, calibration curves, and Decision Curve Analysis (DCA) diagrams were used to develop and evaluate a prediction model for patient outcome. Single-cell RNA-seq (scRNA) analysis, functional enrichment, co-IP assay, mass spectrometry, and western blot were used to study the relationship between EIF2Ss and c-myc in PAAD. EIF2Ss are over-expressed in PAAD tissue and are associated with poor prognosis. The frequency of EIF2S1, EIF2S2, and EIF2S3 gene alteration in PAAD was 0.2%, 0.4%, and 0.2%, respectively. High EIF2Ss expression was associated with Th2 cell infiltration, whereas low expression was associated with pDC infiltration. Moreover, EIF2Ss expression was positively correlated with the expression of the NT5E, ULBP1, PVR, CD44, IL10RB, and CD276 checkpoints. A prediction model developed using EIF2Ss and important clinicopathologic features showed good predictive value for the overall survival of PAAD patients. ScRNA-Seq data showed that EIF2Ss was associated with enrichment for endothelial cells, fibroblasts, malignant cells, and ductal cells. EIF2Ss expression was also correlated with adipogenesis, interferon-alpha response, epithelial-mesenchymal transition, myc targets, G2M checkpoint, oxidative phosphorylation, and hypoxia. Functional enrichment analysis of EIF2Ss showed a close correlation with the myc pathway, and interactions between EIF2Ss and c-myc were confirmed by co-IP assay and mass spectrometry. Importantly, knockdown of c-myc decreased the expression of EIF2S1, EIF2S2, and EIF2S3 in PAAD cells. EIF2Ss were found to have significant clinical implications for the prognosis and treatment of PAAD. Inhibition of c-myc caused the downregulation of EIF2S1, EIF2S2, and EIF2S3 expression.

LINC00909 up-regulates pluripotency factors and promotes cancer stemness and metastasis in pancreatic ductal adenocarcinoma by targeting SMAD4

BackgroundPancreatic cancer stem cells are crucial for tumorigenesis and cancer metastasis. Presently, long non-coding RNAs were found to be associated with Pancreatic Ductal Adenocarcinoma stemness characteristics but the underlying mechanism is largely known. Here, we aim to explore the function of LINC00909 in regulating pancreatic cancer stemness and cancer metastasis.MethodsThe expression level and clinical characteristics of LINC00909 were verified in 80-paired normal pancreas and Pancreatic Ductal Adenocarcinoma tissues from Guangdong Provincial People’s Hospital cohort by in situ hybridization. RNA sequencing of PANC-1 cells with empty vector or vector encoding LINC00909 was experimented for subsequent bioinformatics analysis. The effect of LINC00909 in cancer stemness and metastasis was examined by in vitro and in vivo experiments. The interaction between LINC00909 with SMAD4 and the pluripotency factors were studied.ResultsLINC00909 was generally upregulated in pancreatic cancer tissues and was associated with inferior clinicopathologic features and outcome. Over-expression of LINC00909 enhanced the expression of pluripotency factors and cancer stem cells phenotype, while knock-down of LINC00909 decreased the expression of pluripotency factors and cancer stem cells phenotype. Moreover, LINC00909 inversely regulated SMAD4 expression, knock-down of SMAD4 rescued the effect of LINC00909-deletion inhibition on pluripotency factors and cancer stem cells phenotype. These indicated the effect of LINC00909 on pluripotency factors and CSC phenotype was dependent on SMAD4 and MAPK/JNK signaling pathway, another downstream pathway of SMAD4 was also activated by LINC00909. Specifically, LINC00909 was localized in the cytoplasm in pancreatic cancer cells and decreased the stability the SMAD4 mRNA. Finally, we found over-expression of LINC00909 not only accelerated tumor growth in subcutaneous mice models, but also facilitated tumorigenicity and spleen metastasis in orthotopic mice models.ConclusionWe demonstrate LINC00909 inhibits SMAD4 expression at the post-transcriptional level, which up-regulates the expression of pluripotency factors and activates the MAPK/JNK signaling pathway, leading to enrichment of cancer stem cells and cancer metastasis in pancreatic cancer.

Identification of key genes for atherosclerosis in different arterial beds

Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence.

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Anti-ENO1 antibody combined with metformin against tumor resistance: a novel antibody-based platform.

Antibody-based platforms (i.e., ADC) have emerged as one of the most encouraging tools for the cancer resistance caused by cancer stem cells (CSCs) enrichment. Our study might provide a promising therapeutic direction against drug resistance and serve as a potential precursor platform for screening ADC. The cell migration, invasion, drug resistance, and self-renewal were assessed by the cell invasion and migration assay, wound healing assay, CCK-8 assay, colony formation assay, and sphere formation assay, respectively. The expression profiles of CSCs (ALDH+ and CD44+) subpopulations were screened by flow cytometry. The western blot and cell immunofluorescence assay were used to evaluate pathway-related protein expression in both anti-ENO1 antibody, MET combined with DPP/CTX-treated CSCs. In the present study, western blot and flow cytometry verified that anti-ENO1 antibody target the CD44+ subpopulation by inhibiting the PI3K/AKT pathway, while metformin might target the ALDH+ subpopulation through activation of the AMPK pathway and thus reverse drug resistance to varying degrees. Subsequently, in vitro investigation indicated that anti-ENO1 antibody, metformin combined with cisplatin/cetuximab could simultaneously target ALDH+ and CD44+ subpopulations. The combination also inhibited the CSCs proliferation, migration, invasion, and sphere formation; which may result in overcoming the drug resistance. Then, molecular mechanism exploration verified that the anti-ENO1 antibody, metformin combined with cisplatin/cetuximab inhibited the Wnt/β-catenin signaling. The study preliminarily revealed anti-ENO1 antibody combined with metformin could overcome drug resistance against CSCs by inhibiting the Wnt//β-catenin pathway and might serve as a potential precursor platform for screening ADC. More importantly, it is reasonably believed that antibody-based drug combination therapy might function as an encouraging tool for oncotherapy.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Salinity Tolerance and Osmoadaptation Strategies in Four Genera of Anammox Bacteria: Brocadia, Jettenia, Kuenenia, and Scalindua.

The salinity tolerance and osmoadaptation strategies in four phylogenetically distant anammox species, Brocadia, Jettenia, Kuenenia, and Scalindua, were investigated by using highly enriched cell cultures. The first-emerged "Ca. Scalindua sp." showed optimum growth at 1.5-3% salinity and was tolerant to ∼10% salinity (a slight halophile). The second-emerged "Ca. Kuenenia stuttgartiensis" was tolerant to ∼6% salinity with optimum growth at 0.25-1.5% (a halotolerant). These early-emerged "Ca. Scalindua sp." and ″Ca. K. stuttgartiensis" rapidly accumulated K+ ions and simultaneously synthesized glutamate as a counterion. Subsequently, part of the glutamate was replaced by trehalose. In contrast, the late-emerged "Ca. B. sinica" and "Ca. J. caeni" were unable to accumulate sufficient amounts of K+─glutamate and trehalose, resulting in a significant decrease in activity even at 1-2% salinity (nonhalophiles). In addition, the external addition of glutamate may increase anammox activity at high salinity. The species-dependent salinity tolerance and osmoadaptation strategies were consistent with the genetic potential required for the biosynthesis and transport of these osmolytes and the evolutionary history of anammox bacteria: Scalindua first emerged in marine environments and then Kuenenia and other two species gradually expanded their habitat to estuaries, freshwater, and terrestrial environments, while Brocadia and Jettenia likely lost their ability to accumulate K+─glutamate.

Identification of Hedyotisdiffusa Willd-specific mRNA–miRNA–lncRNA network in rheumatoid arthritis based on network pharmacology, bioinformatics analysis, and experimental verification

Hedyotisdiffusa Willd (HDW) possesses heat-clearing, detoxification, anti-cancer, and anti-inflammatory properties. However, its effects on rheumatoid arthritis (RA) remain under-researched. In this study, we identified potential targets of HDW and collected differentially expressed genes of RA from the GEO dataset GSE77298, leading to the construction of a drug-component-target-disease regulatory network. The intersecting genes underwent GO and KEGG analysis. A PPI protein interaction network was established in the STRING database. Through LASSO, RF, and SVM-RFE algorithms, we identified the core gene MMP9. Subsequent analyses, including ROC, GSEA enrichment, and immune cell infiltration, correlated core genes with RA. mRNA–miRNA–lncRNA regulatory networks were predicted using databases like TargetScan, miRTarBase, miRWalk, starBase, lncBase, and the GEO dataset GSE122616. Experimental verification in RA-FLS cells confirmed HDW’s regulatory impact on core genes and their ceRNA expression. We obtained 11 main active ingredients of HDW and 180 corresponding targets, 2150 RA-related genes, and 36 drug-disease intersection targets. The PPI network diagram and three machine learning methods screened to obtain MMP9, and further analysis showed that MMP9 had high diagnostic significance and was significantly correlated with the main infiltrated immune cells, and the molecular docking verification also showed that MMP9 and the main active components of HDW were well combined. Next, we predicted 6 miRNAs and 314 lncRNAs acting on MMP9, and two ceRNA regulatory axes were obtained according to the screening. Cellular assays indicated HDW inhibits RA-FLS cell proliferation and MMP9 protein expression dose-dependently, suggesting HDW might influence RA’s progression by regulating the MMP9/miR-204-5p/MIAT axis. This innovative analytical thinking provides guidance and reference for the future research on the ceRNA mechanism of traditional Chinese medicine in the treatment of RA.

Bacteria can compensate the fitness costs of amplified resistance genes via a bypass mechanism

Antibiotic heteroresistance is a phenotype in which a susceptible bacterial population includes a small subpopulation of cells that are more resistant than the main population. Such resistance can arise by tandem amplification of DNA regions containing resistance genes that in single copy are not sufficient to confer resistance. However, tandem amplifications often carry fitness costs, manifested as reduced growth rates. Here, we investigated if and how these fitness costs can be genetically ameliorated. We evolved four clinical isolates of three bacterial species that show heteroresistance to tobramycin, gentamicin and tetracyclines at increasing antibiotic concentrations above the minimal inhibitory concentration (MIC) of the main susceptible population. This led to a rapid enrichment of resistant cells with up to an 80-fold increase in the resistance gene copy number, an increased MIC, and severely reduced growth rates. When further evolved in the presence of antibiotic, these strains acquired compensatory resistance mutations and showed a reduction in copy number while maintaining high-level resistance. A deterministic model indicated that the loss of amplified units was driven mainly by their fitness costs and that the compensatory mutations did not affect the loss rate of the gene amplifications. Our findings suggest that heteroresistance mediated by copy number changes can facilitate and precede the evolution towards stable resistance.