Single Cell Research Articles

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

Abstract LB242: Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome

Abstract Introduction: The tumor microenvironment (TME) is a complex and dynamic ecosystem that plays critical roles in tumor development and clinical outcome. While the multifarious cellular interactions within the TME have been extensively studied with a significant emphasis on immunotherapy, understanding its role in chemotherapy outcome remains less explored. To this end, we present a novel generic computational framework named DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning). DECODEM facilitates the exploration of the association of cell-type-specific gene expression profiles with the effectiveness of neoadjuvant chemotherapy (NAC) in treating breast cancer (BC). This approach holds promise for elucidating the nuanced ways in which the TME can modulate therapeutic efficacy, potentially paving the way for more personalized and effective cancer treatments. Methods: DECODEM employs cellular deconvolution of bulk transcriptomics to extract cell-type-specific gene expression profiles within the TME, and subsequently harnesses machine learning to construct cell-type-specific predictors of clinical outcomes. Utilizing DECODEM, we analyzed three publicly available cohorts of HER2-negative BC patients who received NAC. We assessed the predictive capabilities of the individual cell types as well as their collective influence, benchmarking against a state-of-the-art predictor that employs both clinical and transcriptomic features. We corroborated our findings in a single-cell (SC) transcriptomics cohort of patients with triple negative breast cancer (TNBC) undergoing chemotherapy. Further, we expanded DECODEM to DECODEMi, which accounts for cell-cell interactions (CCIs) among the predominant cell types, offering potential insights into the mechanistic processes governing clinical responses. Results: Our analysis indicated that the heterogeneous composition of the TME is crucial for chemotherapy response prediction in HER2-negative BC, beyond the influence of malignant cells alone. The key insights from our study are as follows:1. Gene expression of immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, cancer-associated fibroblasts) displayed comparable-to-superior predictive powers over both bulk expression and the state-of-the-art predictor.2. The predictive accuracy is further augmented by multi-cell-type ensembles, with a triad ensemble comprising endothelial, myeloid, and plasmablast cells achieving the highest predictive accuracy across multiple cohorts.3. DECODEMi can effectively capture the regulatory impact of CCIs (validated in a SC dataset sourced from the SC-TNBC cohort) and has identified novel CCIs likely to influence chemotherapy response in HER2-negative BC. Conclusion: We provide an unprecedented computational methodology to quantitatively assess the cell-type-specific influences of the TME on clinical outcome. Our analysis offers insights into the complex interactions among TME cells and their correlation with chemotherapy efficacy in HER2-negative BC, thereby enriching the knowledge base of BC treatment effectiveness. Citation Format: Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, Di Wu, Yael Schmidt, Eldad Shulman, Sanju Sinha, Stephen-John Sammut, Carlos Caldas, Kun Wang, Eytan Ruppin. Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB242.

Context-dependent modification of PFKFB3 in hematopoietic stem cells promotes anaerobic glycolysis and ensures stress hematopoiesis.

Metabolic pathways are plastic and rapidly change in response to stress or perturbation. Current metabolic profiling techniques require lysis of many cells, complicating the tracking of metabolic changes over time after stress in rare cells such as hematopoietic stem cells (HSCs). Here, we aimed to identify the key metabolic enzymes that define differences in glycolytic metabolism between steady-state and stress conditions in murine HSCs and elucidate their regulatory mechanisms. Through quantitative 13C metabolic flux analysis of glucose metabolism using high-sensitivity glucose tracing and mathematical modeling, we found that HSCs activate the glycolytic rate-limiting enzyme phosphofructokinase (PFK) during proliferation and oxidative phosphorylation (OXPHOS) inhibition. Real-time measurement of ATP levels in single HSCs demonstrated that proliferative stress or OXPHOS inhibition led to accelerated glycolysis via increased activity of PFKFB3, the enzyme regulating an allosteric PFK activator, within seconds to meet ATP requirements. Furthermore, varying stresses differentially activated PFKFB3 via PRMT1-dependent methylation during proliferative stress and via AMPK-dependent phosphorylation during OXPHOS inhibition. Overexpression of Pfkfb3 induced HSC proliferation and promoted differentiated cell production, whereas inhibition or loss of Pfkfb3 suppressed them. This study reveals the flexible and multilayered regulation of HSC glycolytic metabolism to sustain hematopoiesis under stress and provides techniques to better understand the physiological metabolism of rare hematopoietic cells.

A High-Throughput Microdroplet-Based Single Cell Transfection Method for Gene Knockout Based on the CRISPR/Cas9 System.

The efficient application of the newly developed gene-editing method CRISPR/Cas9 requires more accurate intracellular gene delivery. Traditional delivery approaches, such as lipotransfection and non-viral delivery methods, must contend with major problems to overcome the drawbacks of low efficiency, high toxicity, and cell-type dependency. The high-throughput microdroplet-based single-cell transfection method presented herein provides an alternative method for delivering genome-editing reagents into single living cells. By accurately controlling the number of exogenous plasmids in microdroplets, this method can achieve high-efficiency delivery of nucleic acids to different types of single cells. This paper presents a high-throughput quantitative DNA transfection method for single cells and explores the optimal DNA transfection conditions for specific cell lines. The transfection efficiency of cells at different concentrations of DNA in microdroplets is measured. Under the optimized transfection conditions, the method is used to construct gene-knockout cancer cell lines to determine specific gene functions through the CRISPR/Cas9 knockout system. In a case study, the migration ability of TRIM72 knockout cancer cells is inhibited, and the tumorigenicity of cells in a zebrafish tumor model is reduced. A single-cell microfluidic chip is designed to achieve CRISPR/Cas9 DNA transfection, dramatically improving the transfection efficiency of difficult-to-transfect cells. This research demonstrates that the microdroplet method developed herein has a unique advantage in CRISPR/Cas9 gene-editing applications.

Feasibility Study of Three-Phase Modular Converter for Dual-Purpose Application in DC and AC Microgrids

The modern concept of a universal converter is intended as a power converter suitable for application in both dc or ac grids using the same external connectors. This novel family was recently proposed to allow an easier integration of renewable energy sources and energy storage systems, interfacing with dc/ac grids and/or loads with a minimum redundancy of power switches and passive elements. This kind of solution and applications are expected to be a reality in the nearest decade, as ac and dc low voltage distribution networks will coexist. Nevertheless, the initial solutions proposed as universal converter were focused on ac single-phase power conversion systems. In this sense, this article proposes and describes a new member of the universal converter family suitable for dc to dc and dc to ac power conversion (both in three-phase three-wire and in three-phase four-wire). The proposed power topology is derived as a modular extension from the single buck-boost bidirectional cell. Its main operation modes (buck and boost) are discussed, and a pulse-width-modulation technique is developed to generate the corresponding switching patterns. The proposed solution is successfully validated in open-loop mode both in simulation and experimentally with a laboratory prototype. The measured efficiency of the power converter was above 97% in the dc to ac mode and around 99% in the dc to dc mode.

Cadherin-dependent adhesion is required for muscle stem cell niche anchorage and maintenance.

Adhesion between stem cells and their niche provides stable anchorage and signaling cues to sustain properties such as quiescence. Skeletal muscle stem cells (MuSCs) adhere to an adjacent myofiber via cadherin-catenin complexes. Previous studies on N- and M-cadherin in MuSCs revealed that although N-cadherin is required for quiescence, they are collectively dispensable for MuSC niche localization and regenerative activity. Although additional cadherins are expressed at low levels, these findings raise the possibility that cadherins are unnecessary for MuSC anchorage to the niche. To address this question, we conditionally removed from MuSCs β- and γ-catenin, and, separately, αE- and αT-catenin, factors that are essential for cadherin-dependent adhesion. Catenin-deficient MuSCs break quiescence similarly to N-/M-cadherin-deficient MuSCs, but exit the niche and are depleted. Combined in vivo, ex vivo and single cell RNA-sequencing approaches reveal that MuSC attrition occurs via precocious differentiation, re-entry to the niche and fusion to myofibers. These findings indicate that cadherin-catenin-dependent adhesion is required for anchorage of MuSCs to their niche and for preservation of the stem cell compartment. Furthermore, separable cadherin-regulated functions govern niche localization, quiescence and MuSC maintenance.

Prediction of prognosis of patients with radical resection of intrahepatic cholangiocarcinoma based on single cell omics

Objectives: To analyze the survival benefit of intrahepatic cholangiocarcinoma (ICC) radical resection based on single cell omics. Methods: This is a retrospective case-series study. ICC single-cell sequencing was integrated from four data sets in the Gene Expression Omnibus Database, with a total of 46 patients undergoing radical resection, to explore the characteristics of the microenvironment. Microarray data of 100 ICC cases was analyzed in the EMBI database with survival data. The infiltration abundance of each epithelial cell cluster was calculated in each microarray data sample using the ssGSEA algorithm. The key epithelial cell cluster associated with poor patient outcomes was explored. The clinical value of key marker genes in this subgroup was studied. Prognostic marker genes were selected using the univariate and multivariate Cox proportional hazards(CoxPH) model. The The CoxPH model was constructed by the target genes and a nomogram was drawn. Kaplan-Meier survival analysis was used to verify the relationship between score and prognosis of patients. The predictive power of the model was evaluated by receiver operating characteristic(ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Epithelial cell clusters infiltrated almost exclusively in tumor tissue. The MT2A+ epithelial cell subset was associated with a poorer prognosis for patients with a high invasion abundance and patients characterized by infiltration of this group were defined as antioxidant. After screening marker genes in this cluster using a univariate and multivariate CoxPH model, the following genes were found to be independent prognostic factors: FILPIL, NFKBIA, PEG10, SERPINB5. The CoxPH model was constructed using the four gene expression levels, and the survival rate of patients in the high-risk group was significantly lower than those in the low-risk group (all P<0.05). The constructed nomogram had good discrimination and validity. The ROC curve showed that the predicted area under the curve was 0.779, 0.844 and 0.845 at 1, 3 and 5 years, respectively. Compared to clinical indicators, the model had better predictive power using the calibration curve and the DCA test. Conclusions: The MT2A+ epithelial cell group may be associated with the prognosis of patients with ICC, and the concept of ICC tissue typing of antioxidant and non-antioxidant types is proposed. The type of antioxidant may predict the poor prognosis of the patients, and postoperative adjuvant therapy and other means could be considered to improve the survival of the patients.

Sodium fluorescein uptake by the tumor microenvironment in human gliomas and brain metastases.

Intravenous sodium fluorescein (SF) is increasingly used during surgery of gliomas and brain metastases to improve tumor resection. Currently, SF is believed to permeate the brain regions where the blood-brain barrier (BBB) is damaged and to accumulate in the extracellular space but not in tumor or healthy cells, making it possible to demarcate tumor margins to guide resection. By evaluating the immune contexture of a number of freshly resected gliomas and brain metastases from patients undergoing SF-guided surgery, the authors recurrently observed fluorescence-positive cells. Therefore, the aim of this study was to determine if SF accumulates inside the cells of the tumor microenvironment (TME), and if so, in which type of cells, and whether incorporation can also be observed in the leukocytes of peripheral blood. Freshly resected tumor specimens were dissociated to single cells and analyzed by multiparametric flow cytometry. Peripheral blood leukocytes, macrophages, and a glioma cell line were treated with SF in vitro, and their cell uptake was assessed by multiparametric and imaging flow cytometry and by confocal microscopy. The ex vivo and in vitro analyses revealed that SF accumulates intracellularly in leukocytes as well as in tumor cells, but with a high variability of incorporation in the different cell subsets analyzed. Myeloid cells showed the highest level of fluorescence. In vitro uptake experiments showed that SF accumulation increases over time. The imaging analyses confirmed the internalization of the compound inside the cells. SF is not just a marker of BBB damage, but its intracellular detection suggests that it selectively accumulates intracellularly. Future efforts should target the mechanisms of its differential uptake by the different TME cell types in depth.

The detection and classification of acute myeloid leukaemia blood cell images based on different YOLO approaches

Medical image examination with a deep learning approach is greatly beneficial in the healthcare industry for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as you only look once (YOLO) developed for object detection is a successful state-ofthe-art algorithm in real-time object detection systems. Although YOLO is continuously improving in the object detection area, there are still questions about how different YOLO versions compare in terms of performance. We utilize eight YOLO versions to classify acute myeloid leukaemia (AML) blood cells in image examinations. We also acquired the publicly available AML dataset from the cancer imaging archive (TCIA) which consists of expert-labeled single cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that eight types of YOLO approaches have outstanding performances of more than 90% in precision and sensitivity. In comparison, YOLOv4-tiny has a more reliable performance than the other seven approaches. Consistently, the YOLOv4-tiny also achieved the highest AUC score. Therefore, this work can potentially provide a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers.

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

High-Throughput Single-Cell, Single-Mitochondrial DNA Assay Using Hydrogel Droplet Microfluidics.

There is growing interest in understanding the biological implications of single cell heterogeneity and heteroplasmy of mitochondrial DNA (mtDNA), but current methodologies for single-cell mtDNA analysis limit the scale of analysis to small cell populations. Although droplet microfluidics have increased the throughput of single-cell genomic, RNA, and protein analysis, their application to sub-cellular organelle analysis has remained a largely unsolved challenge. Here, we introduce an agarose-based droplet microfluidic approach for single-cell, single-mtDNA analysis, which allows simultaneous processing of hundreds of individual mtDNA molecules within >10,000 individual cells. Our microfluidic chip encapsulates individual cells in agarose beads, designed to have a sufficiently dense hydrogel network to retain mtDNA after lysis and provide a robust scaffold for subsequent multi-step processing and analysis. To mitigate the impact of the high viscosity of agarose required for mtDNA retention on the throughput of microfluidics, we developed a parallelized device, successfully achieving ~95 % mtDNA retention from single cells within our microbeads at >700,000 drops/minute. To demonstrate utility, we analyzed specific regions of the single-mtDNA using a multiplexed rolling circle amplification (RCA) assay. We demonstrated compatibility with both microscopy, for digital counting of individual RCA products, and flow cytometry for higher throughput analysis.

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

PurposeThe aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci. MethodsTo do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA. ResultsOur research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose. ConclusionWe searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.

Detecting abnormal cell behaviors from dry mass time series

The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.

Abstract 7418: LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data

Abstract Gene fusion resulted from genomic translocation plays important oncogenic roles including but not limited to gene activation and changing protein coding sequences. Historically, fusion gene analysis has been limited to bulk levels resulting in mixed oncogene activation models. Single cell Nanopore RNA sequencing (scNanoRNAseq) has the unique strength in detecting full-length chimeric transcripts in thousands of single cells in parallel. However, robust computational tools for detecting fusion genes in single cells from scNanoRNAseq data are missing. Here, we developed LongFuse, a fast and accurate tool, to detect fusion genes and their splicing isoforms in single cells from scNanoRNAseq data. LongFuse implements an XOR logic gate algorithm to speed up fusion candidate searching and stringent criteria to eliminate false discoveries due to trans-splicing events and randomly ligated chimeras. We applied LongFUSE onto scNanoRNAseq data of both human prostate cancer cell line, VCaP, and tumor tissues with known TMPRSS2-ERG fusion. Our results showed that both cancer cell line and prostate tumors harbored a mixture of fusion positive and fusion negative tumor cells. Gene expression analysis revealed transcriptional differences between fusion positive and negative cells including genes involved in cancer cell proliferation and invasion programs such as ELL2, ZBTB16, IGF1R, ANK3, MYPOP and PLPP1 genes. LongFUSE also calculates the splicing isoforms of fusion gene transcripts, allowing prediction of protein sequences of fusion gene transcripts. Taken together, our data demonstrated that LongFUSE is a robust computational tool for detecting gene fusions and their splicing isoforms at single cell level from scNanoRNAseq data. Citation Format: Cheng-Kai Shiau, Yueying He, Hsiao-Yun Lin, Lina Lu, Xiao-Dong Lu, Jindan Yu, Ruli Gao. LongFuse: Detecting gene fusion transcripts from high throughput long-read single cell RNA sequencing data [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 7418.

Abstract 1151: Reconstruction of the spatial ecosystem of glioblastoma reveals relationships between tumor cell states and microenvironment

Abstract Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor, with no curative treatment options. Multiple studies have characterized at single cell resolution the GBM as being composed of transcriptional cell states interconnected with components in the tumor immune microenvironment (TME). Our group proposed and validated the first single cell guided functional classification of GBM in four tumor-intrinsic cell states which informed clinical outcome and delivered therapeutic options. However, single cell technologies are unable to unravel the spatial relationships among the cell states of GBM and between GBM cell states and TME. Spatially resolved transcriptomic technologies are emerging as powerful tools to reconstruct the spatial architecture of a tissue. We performed spatial transcriptomics of multicellular regions of interest (ROI) in 6 primary IDH wild-type GBM and 2 recurrent GBM with both CosMx Spatial Molecular Imager, which analyzes 1,000 RNA probes and 64 proteins at single cell resolution, and GeoMx Digital Spatial Profiler which profiles the whole transcriptome (~18,000 genes) at ROI resolution. The development of computational tools aimed to integrate spatial proximity and CosMx derived single-cell transcriptomics revealed spatial segregation of the tumor cell clones and cellular states and highlighted recurrent patterns of cell states, distinct TME cell types associated with coherent histopathological features across multiple samples. The development of a spatial informed intercellular communication algorithm and the reconstruction of ligand-receptor-target networks will allow the discovery of tumor cell states-TME cross-talks and the biological signaling regulated by these interactions that are driving the heterogeneity of GBM and therefore potentially therapeutically targetable. Analysis of matched regions of interest profiled by GeoMx and spatial proteomics with CosMx further cross-validated the spatial ecosystem of glioblastoma as reconstructed at single-cell resolution. Our studies established a scalable approach to resolve the transcriptional heterogeneity of GBM and reconstruct the architecture of GBM cell states and tumor microenvironment. Citation Format: Bruno Adabbo, Simona Migliozzi, Luciano Garofano, Fulvio D'Angelo, Pedro Davila, Sakir H. Gultekin, Daniel Bilbao Cortes, Benjamin Currall, Sion L. Williams, Marc Sanson, Franck Bielle, Anna Luisa Di Stefano, Michele Ceccarelli, Anna Lasorella, Antonio Iavarone. Reconstruction of the spatial ecosystem of glioblastoma reveals relationships between tumor cell states and 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 1151.

Abstract 4399: HMGA1 is an epigenetic regulator and plays a critical role in melanoma recurrence

Abstract Among various genetic mutation in melanoma, BRAFV600E is one of the key drivers of melanoma initiation and progression and has become the frontline target in clinical trials. Although BRAF/MEK inhibitors show very promising therapeutic outcomes, recurrence or relapse is still a major challenge and its underlying mechanisms are still poorly understood. We hypothesized that survival of recurrent tumors is BRAF independent and relies on epigenetic reprogramming. To test our hypothesis, we used either pharmacological inhibition of BRAF/MEK or CRISPR/Cas9 mediated knockout of BRAF to create cell/tumor models of BRAF-independent survival. Cut&amp;Run sequencing was then used to identify epigenetic alterations regulated by H3K4me3 or H3K27Ac in a genome scale and capture the landscape of epigenetic alterations of BRAF-independent cell survival. Combining regular bulk RNAseq, we narrowed down our top candidates and focused on one of the functionally validated candidates, HMGA1. Interestingly, CRISPR/Cas9 mediated HMGA1 knockout cells show significantly slower growth in both in vitro colony formation assays and in vivo xenografts assay in BRAF/MEK resistant Patient-Derived-Xenograft (PDX) tumor models resembling melanoma recurrence. Furthermore, we selected CRISPR/Cas9 single knockout cells of HMGA1, which is a pure genetic depletion model, and confirmed the phenotype of impaired tumor growth of HMGA1 knockout cells. We then performed RNAseq and ChIPseq, and identified downstream targets of HMGA1 and elucidated mechanisms by which HMGA1 reprograms epigenetics of recurrence. Utilizing the single knockout cell, we re-introduced HMGA1 in the pure knockout cells, and anticipated that restoration of HMGA1 in pure HMGA1 knockout cells rescues impaired tumor growth and confer cells resistance to BRAF/MEK inhibition. Finally, we subcutaneously injected the HMGA1 single knockout cells into humanized mice and tested whether epigenetic alterations perturb the immune microenvironment and sensitize cells to immunotherapy. In conclusion, this study sheds new lights on how melanoma cells reprogram the epigenome to develop mechanisms of recurrence. Identification of HMGA1 and its upstream or downstream targets provides potential druggable targets of overcoming relapse after BRAF/MEK inhibitor therapy. Citation Format: Haiwei Mou, Kristen DeRosa, Veronika Yakovishina, Yeqing Chen, Abdiel Mandella Reynolds, Min Xiao, Monzy Thomas, Meenhard Herlyn. HMGA1 is an epigenetic regulator and plays a critical role in melanoma recurrence [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 4399.

Abstract 6945: Dissecting the interplay of cell identity and extrachromosomal DNA in glioblastoma

Abstract The five-year survival rate of glioblastoma multiforme (GBM) is 5.8%, and its standard of care has not changed since 2005. This stagnation in therapeutic development is due, in part, to its characteristic heterogeneity–multiforme–highlighting a critical need to understand the biological processes underlying this GBM hallmark. Despite the well-documented individual relationships between extrachromosomal DNA (ecDNA), GBM, and heterogeneity, the interplay between the three is not well understood. The Furnari lab has generated a library of cancer avatars, or human induced pluripotent stem cells (hiPSCs) engineered to harbor cancer driving mutations. Single cell RNA sequencing (scRNA-seq) was used to characterize the longitudinal evolution of the first avatars engineered to have TP53−/−;PDGFRAΔ8-9 and PTEN−/−;NF1−/− genotypes. The TP53−/−;PDGFRAΔ8-9 avatar acquired ecDNA, while the PTEN−/−; NF1−/− avatar did not. Re-analysis of the scRNA-seq data identified a subpopulation of cells in the TP53−/−;PDGFRAΔ8-9 avatar that had high expression of CDK4 and PDGFRA, two genes that are commonly co-amplified, found on ecDNA, and drive cellular states. This subpopulation of cells also had highly expressed genes near the CDK4 locus indicating a structural variant and possibly ecDNA. This population diverged transcriptionally from other cells and was associated with the OPC-like cellular state, which has been shown to be associated with high levels of PDGFRA and, at times, co-expression with CDK4. While TP53−/−;PDGFRAΔ8-9 tumors contained cells across all four GBM cellular states, PTEN−/−;NF1−/− tumors had a significantly less diverse composition of cellular states suggesting that ecDNA may facilitate cellular state diversity. With the eventual goal to directly test whether ecDNA enables transcriptional state heterogeneity compared to chromosomally inherited amplifications, CRISPR-Cas9 and CRISPR-C were implemented to engineer EGFRvIII ecDNA in U87. While inverse PCR confirmed the successful generation of ecDNA in U87, ecDNA was lost following long term in vitro cell passaging. Current follow up work aims to identify different intrinsic (oncogene amplified, genetic background, cell type, cell line) and extrinsic (in vitro conditions, orthotopic engraftment) contexts that promote the selection and amplification of engineered ecDNA, as it would be would be valuable to have genotypically identical cancer models with different classes of amplifications (i.e., engineered ecDNA vs. lentiviral overexpression, which is chromosomally inherited). Together, these results highlight the potential to use hiPSC-derived models and single cell methods to investigate gliomagenesis and cell identity. Citation Format: Brett Taylor, Brandon Jones, Daisuke Kawauchi, Yohei Miyake, Raghav Vadla, Nathan Jameson, Shunichiro Miki, Tomoyuki Koga, Bing Ren, Frank Furnari. Dissecting the interplay of cell identity and extrachromosomal DNA in glioblastoma [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 6945.

Abstract 13: Discovery and development of T cell receptors targeting MAGE family antigens for adoptive T cell therapy against solid tumors

Abstract Background: T cell receptors (TCRs) recognize epitopes from intracellular and cell surface antigens, enabling targeting of diverse tumor antigen classes including overexpressed, differentiation, cancer-testis antigens, as well as those from driver mutations, frameshift mutations, splice variants, and human endogenous retroviral elements (HERV). Melanoma-associated antigens (MAGE) are highly expressed in various solid tumor types, including melanoma, non-small cell lung cancers (NSCLC), bladder, and head and neck cancers, representing promising targets for TCR-T therapy. However, discovering robust and specific MAGE-reactive TCRs remains challenging due to limited throughput of TCR screening methodologies, and the inherently low frequency of tumor-reactive T cells in tumor samples and peripheral blood. Methods: We employ a proprietary mass-cytometry-based multiplexed tetramer staining approach, TargetScape®, to screen and characterize CD8+ T cells in healthy donors and patients’ samples. Our screening panels contain multiple peptides for MAGE family antigens including A1, C2, and A10. Subsequent single cell sequencing or single cell PCR (scPCR) of identified tumor antigen-specific T cells with high sensitivity enables comprehensive analysis of T cell phenotype, transcriptome, TCR specificity, and paired TCR sequence. TCRs identified through this method are transduced for expression in Jurkat reporter cell lines and their reactivity to corresponding peptides is assessed. Selected TCR candidates are then transduced into primary T cells and evaluated for activation and effector function using a variety of cancer cell lines expressing the corresponding endogenous antigens. Specificity assessment is performed through alanine scans and identification of potential off-target peptides and screening for reactivity. Results: We utilized this approach to discover natural TCRs that target several MAGE family antigens. Through Jurkat reporter screening, we observed specific activation of multiple TCRs in the presence of antigen presenting cells and the peptide initially employed to identify the TCRs. Primary T-cells expressing promising TCR candidates exhibited killing activities and cytokine release when confronted with multiple solid tumor cell lines expressing endogenous levels of the MAGE antigen. Specificity assessment is currently in progress, with preliminary data indicating overall favorable specificity profiles for selected TCRs. Conclusions: Our Deep Immunomics platform enables us to detect, characterize, and validate TCR candidates against highly clinically relevant shared cancer antigens. Leveraging this platform, we identified various natural tumor specific TCRs exhibiting activities against a range of solid tumor cell lines expressing MAGE antigens. Promising candidates are currently undergoing in vivo efficacy studies. Citation Format: Juliana Velez Lujan, Hannah Fields, Kan Xing Wu, Yovita Purwanti, Emily Xin Zi Tang, Michael Cordeau, Kathleen Mendez, Melissa Wirawan, Michael Fehlings, Katja Fink, Zilei Liu, Daniel MacLeod. Discovery and development of T cell receptors targeting MAGE family antigens for adoptive T cell therapy against solid tumors [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 13.

Abstract 582: Oncogenic PIK3CA fortifies immune determinants in vascular cancers

Abstract Angiosarcomas are a group of vascular cancers that form malignant blood vessels. These malignancies are seemingly inflamed primarily due to their pathognomonic nature, which consists of irregular endothelium and tortuous blood channels. The malignant vascular spaces host a variety of blood and immune components, where a controlled scavenging system is necessary for tumor maintenance. Oncogenes may convey molecular signals to foster the tumor immune niche, and PIK3CA mutations that induce continuous activation of the PI3K pathway occur in a subset of angiosarcomas. Despite the undoubtedly oncogenic properties of PIK3CA mutations, the mechanistic role of these mutations - particularly the potential for controlling the hemato-endothelial system in angiosarcomas - remains undetermined. In this study, we demonstrate the functional consequences of oncogenic PIK3CA and its molecular properties in angiosarcoma cells. We created isogenic cell clones from two angiosarcoma cell lines engineered to harbor hotspot mutations in PIK3CA using CRISPR/Cas9: two homozygous PIK3CA H1047R cell clones (C8 and C35) and one wild-type (WT) clone were generated from DHSA-1426 angiosarcoma cells; and one homozygous mutant clone was created from COSB cells. We found that PIK3CA mutant cells constitutively activated the PI3K/AKT/mTOR pathway at a greater level than that of WT cells. Bulk- and single cell(sc)-RNA-seq data revealed that mutant cells enriched gene sets associated with PI3K signaling and other oncogenic pathways including KRAS, MAPK, and TGF-beta. Also, mutant cells significantly increased cytokines regulating immune cell migration and chemotaxis such as IL-8 and MCP-1. In addition to the common features, we identified functional and molecular divergence between mutant clones. Noticeable changes in cellular morphology, growth kinetics, and tube-forming capacity were observed between two mutant clones. C35 mutant cells were more sensitive to both pan-PI3K and PI3K-alpha specific inhibitor, and DNA damage was greater than in the C8 clone. C35 cells enhanced activation of IL-6/STAT3 signaling compared to C8, despite the increment of the aforementioned immune cytokines in both mutants. Moreover, our comprehensive analysis of bulk- and sc-RNA-seq, ATAC-seq, and metabolomics data identified molecular signatures associated with metabolic vulnerabilities of C35 cells. In summary, we demonstrate that oncogenic PIK3CA perpetuates PI3K signaling activation and reinforces a supply for immune signaling. Furthermore, our data suggest that PIK3CA mutations contribute to establishing cellular heterogeneity that reveals a spectrum of vascular, immunogenic, and metabolic activities. Our ongoing work involves developing an experimental model of angiosarcoma using induced pluripotent stem cells to examine if oncogenic PIK3CA controls immune determinants during malignant endothelial transformation. Citation Format: Donghee Lee, Emma Kozurek, Md Abdullah, Rong Li, Erin B. Dickerson, Jong Hyuk Kim. Oncogenic PIK3CA fortifies immune determinants in vascular cancers [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 582.

Abstract 5485: Spatial characterization of the immunosuppressive tumor microenvironment in estrogen receptor positive breast cancer at single cell level

Abstract We characterized immune cell exclusion in estrogen receptor positive (ER+) breast cancer through the study of spatial relationships among various subsets of cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and other immune cells using a novel in-situ spatial transcriptomic technology. ER+ breast cancer is the most common breast cancer subtype. While endocrine therapy and immune checkpoint inhibitors have considerably reduced relapse and mortality for breast cancer patients, tumor resistance to treatment remains a major challenge. The tumor microenvironment (TME) plays an important role in this mechanism of resistance. Our previous spatial proteomic analysis of ER+ breast FFPE samples demonstrated the intricate relationship between tumor and immunosuppressive cells such as CAFs and TAMs via the CXCR4-CXCL12 axis associated with the exclusion of CD8 T cells from the TME. However, the limited number of antibodies in the panel made it challenging to investigate the diversity of those CAF and TAM populations and better understand their contribution to T cell exclusion. For this purpose, we further characterized the TME of 4 human ER+ breast cancer FFPE cores using the Xenium in-situ transcriptomic platform with a breast cancer-focused panel of 280 genes from 10x Genomics plus 100 additional genes selected from literature and publicly available single cell datasets to delineate CAF and TAM sub-populations. Image analysis enabled the quantification and spatial mapping of the target genes within each cell boundary. Cells were manually annotated through unsupervised clustering of over 1 million cells. The cell phenotyping analysis allowed us to annotate major cell types including tumor cells, macrophages, T cells and fibroblasts. With our curated panel, we further characterized CAF and TAM sub-populations by identifying myofibroblastic CAFs (myCAFs), inflammatory CAFs (iCAFs), and antigen-presenting CAFs (apCAFs) in addition to M1 and M2-like macrophages as reported in the literature. We then performed spatial neighborhood analysis based on the similarity of the cell type composition in the vicinity of each cell. The results showed that myCAFs and M2-like macrophages are found in proximity to tumor cells and may play a role in T-cell exclusion. In addition, iCAFs mainly express CXCL12, a chemokine known to attract immunosuppressive cells such as regulatory T cells and M2-like macrophages as well as repel cytotoxic T cells from infiltrating the tumor via the CXCR4-CXCL12 axis. Overall, our work shows the potential of spatial transcriptomics to elucidate the spatial distribution of cells and their contribution to the immunosuppressive TME, which may enable researchers to improve understanding of tumor biology and drug target discovery efforts. Citation Format: Julien Tessier, Sandra Curras-Alonso, Joon Sang Lee, Fabien Delahaye, Hong Ma, Katie Malley, Dinesh Bangari, Donald Jackson, Angela Hadjipanayis. Spatial characterization of the immunosuppressive tumor microenvironment in estrogen receptor positive breast cancer at single cell level [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 5485.