Journal articles on the topic 'Single cell omic'
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Yang, Xiaoxi, Yuqi Wen, Xinyu Song, Song He, and Xiaochen Bo. "Exploring the classification of cancer cell lines from multiple omic views." PeerJ 8 (August 18, 2020): e9440. http://dx.doi.org/10.7717/peerj.9440.
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Background Cancer classification is of great importance to understanding its pathogenesis, making diagnosis and developing treatment. The accumulation of extensive omics data of abundant cancer cell line provide basis for large scale classification of cancer with low cost. However, the reliability of cell lines as in vitro models of cancer has been controversial. Methods In this study, we explore the classification on pan-cancer cell line with single and integrated multiple omics data from the Cancer Cell Line Encyclopedia (CCLE) database. The representative omics data of cancer, mRNA data, miRNA data, copy number variation data, DNA methylation data and reverse-phase protein array data were taken into the analysis. TumorMap web tool was used to illustrate the landscape of molecular classification.The molecular classification of patient samples was compared with cancer cell lines. Results Eighteen molecular clusters were identified using integrated multiple omics clustering. Three pan-cancer clusters were found in integrated multiple omics clustering. By comparing with single omics clustering, we found that integrated clustering could capture both shared and complementary information from each omics data. Omics contribution analysis for clustering indicated that, although all the five omics data were of value, mRNA and proteomics data were particular important. While the classifications were generally consistent, samples from cancer patients were more diverse than cancer cell lines. Conclusions The clustering analysis based on integrated omics data provides a novel multi-dimensional map of cancer cell lines that can reflect the extent to pan-cancer cell lines represent primary tumors, and an approach to evaluate the importance of omic features in cancer classification.
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Gao, Chao, Jialin Liu, April R. Kriebel, Sebastian Preissl, Chongyuan Luo, Rosa Castanon, Justin Sandoval, et al. "Iterative single-cell multi-omic integration using online learning." Nature Biotechnology 39, no. 8 (April 19, 2021): 1000–1007. http://dx.doi.org/10.1038/s41587-021-00867-x.
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Chappell, Lia, Andrew J. C. Russell, and Thierry Voet. "Single-Cell (Multi)omics Technologies." Annual Review of Genomics and Human Genetics 19, no. 1 (August 31, 2018): 15–41. http://dx.doi.org/10.1146/annurev-genom-091416-035324.
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Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.
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Glass, David R., Albert G. Tsai, John Paul Oliveria, Felix J. Hartmann, Samuel C. Kimmey, Ariel A. Calderon, Luciene Borges, et al. "An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity." Immunity 53, no. 1 (July 2020): 217–32. http://dx.doi.org/10.1016/j.immuni.2020.06.013.
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Regner, Matthew J., Kamila Wisniewska, Susana Garcia-Recio, Aatish Thennavan, Raul Mendez-Giraldez, Venkat S. Malladi, Gabrielle Hawkins, et al. "A multi-omic single-cell landscape of human gynecologic malignancies." Molecular Cell 81, no. 23 (December 2021): 4924–41. http://dx.doi.org/10.1016/j.molcel.2021.10.013.
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Mannello, Ferdinando, Daniela Ligi, and Mauro Magnani. "Deciphering the single-cell omic: innovative application for translational medicine." Expert Review of Proteomics 9, no. 6 (December 2012): 635–48. http://dx.doi.org/10.1586/epr.12.61.
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Yang, Ming-Chao, Zi-Chen Wu, Liang-Liang Huang, Farhat Abbas, and Hui-Cong Wang. "Systematic Methods for Isolating High Purity Nuclei from Ten Important Plants for Omics Interrogation." Cells 11, no. 23 (December 3, 2022): 3919. http://dx.doi.org/10.3390/cells11233919.
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Recent advances in developmental biology have been made possible by using multi-omic studies at single cell resolution. However, progress in plants has been slowed, owing to the tremendous difficulty in protoplast isolation from most plant tissues and/or oversize protoplasts during flow cytometry purification. Surprisingly, rapid innovations in nucleus research have shed light on plant studies in single cell resolution, which necessitates high quality and efficient nucleus isolation. Herein, we present efficient nuclei isolation protocols from the leaves of ten important plants including Arabidopsis, rice, maize, tomato, soybean, banana, grape, citrus, apple, and litchi. We provide a detailed procedure for nucleus isolation, flow cytometry purification, and absolute nucleus number quantification. The nucleus isolation buffer formula of the ten plants tested was optimized, and the results indicated a high nuclei yield. Microscope observations revealed high purity after flow cytometry sorting, and the DNA and RNA quality extract from isolated nuclei were monitored by using the nuclei in cell division cycle and single nucleus RNA sequencing (snRNA-seq) studies, with detailed procedures provided. The findings indicated that nucleus yield and quality meet the requirements of snRNA-seq, cell division cycle, and likely other omic studies. The protocol outlined here makes it feasible to perform plant omic studies at single cell resolution.
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Welch, Joshua D., Velina Kozareva, Ashley Ferreira, Charles Vanderburg, Carly Martin, and Evan Z. Macosko. "Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity." Cell 177, no. 7 (June 2019): 1873–87. http://dx.doi.org/10.1016/j.cell.2019.05.006.
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Gayoso, Adam, Zoë Steier, Romain Lopez, Jeffrey Regier, Kristopher L. Nazor, Aaron Streets, and Nir Yosef. "Joint probabilistic modeling of single-cell multi-omic data with totalVI." Nature Methods 18, no. 3 (February 15, 2021): 272–82. http://dx.doi.org/10.1038/s41592-020-01050-x.
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Sukovich, David J., Sarah E. B. Taylor, Katherine A. Pfeiffer, Michael J. T. Stubbington, Josephine Y. Lee, Jerald Sapida, Daniel P. Roidan, et al. "An advancement in single cell genomics allows for T cell population analysis at high resolution." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 131.13. http://dx.doi.org/10.4049/jimmunol.202.supp.131.13.
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Abstract Progress in our understanding of immunology and cancer immunotherapies requires a comprehensive view of immune cell behavior and the interactions of these cells with their environment. Recent technological innovations have facilitated the combination of cell-surface protein, transcriptome, immune repertoire, and antigen specificity measurements from the same single cells, providing thorough and high-throughput lymphocyte characterization. Using the 10x Genomics Single Cell Immune Profiling Solution with Feature Barcoding technology along with oligo-conjugated antibodies and peptide-MHC (pMHC) Dextramers®, we performed multi-omic characterization of PBMCs from cytomegalovirus (CMV) seronegative and seropositive patients. Next generation sequencing libraries were made following the 10x Genomics workflows, where transcriptome and immune repertoire libraries are generated alongside libraries from DNA barcodes conjugated to antibodies or pMHC. Full length, paired TCRα/β sequences with specificity to known CMV antigens were identified in the seropositive donor, but not in the seronegative donor. Interestingly, a large Epstein Barr Virus (EBV) pMHC specific T cell expansion was identified in the CMV seronegative donor, suggesting an active EBV response. Moreover, the combination of transcriptomic and cell surface protein information resulted in an increase in resolution of cell type identification. This multi-omic workflow allowed the identification of enriched amino acid motifs within the TCR sequences that contained novel and known CDR3 amino acid sequences specific to CMV. These technological advancements provide new biological insights that are critical for progress in the field.
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Haas, Simon. "Hematopoietic Stem Cells in Health and Disease—Insights from Single-Cell Multi-omic Approaches." Current Stem Cell Reports 6, no. 3 (July 6, 2020): 67–76. http://dx.doi.org/10.1007/s40778-020-00174-2.
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Hsieh, James J., Natalia Miheecheva, Akshaya Ramachandran, Yang Lyu, Ilia Galkin, Viktor Svekolkin, Ekaterina Postovalova, et al. "Integrated single-cell spatial multi-omics of intratumor heterogeneity in renal cell carcinoma." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e17106-e17106. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e17106.
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e17106 Background: Clear cell renal cell carcinoma (ccRCC) exhibits conspicuous intratumor heterogeneity (ITH) - a driver of tumor evolution and metastasis. ITH in RCC has been studied extensively with bulk tumor DNA sequencing, which lacks the ability to integrate single cell resolution data, spatial architecture, and microenvironment composition. Therefore, we analyzed primary ccRCC tumors at multiple biopsy sites with CyTOF, multiplex immunofluorescence (MxIF), whole exome sequencing (WES), RNA sequencing (RNA-seq), single nuclei RNA-seq (snRNA-seq), and whole genome bisulfite sequencing (WGBS). Methods: Primary ccRCC tumors collected from 6 patients (pts) were biopsied at multiple locations and subjected to CyTOF (n = 21 sites, 6 pts), MxIF (20 markers, n = 8 sites, 3 pts), WES (n = 8 sites, 3 pts), RNA-seq (n = 8 sites, 3 pts), snRNA-seq (n = 8 sites, 3 pts), and WGBS (n = 8 sites, 3 pts), enabling integrated multi-omics analysis. MxIF, CyTOF, and genomic/transcriptomic analyses were performed by BostonGene. Results: Genomic intratumor (IT) evolution of ccRCC cells was tracked with WES, and subclonal distribution of SETD2, STAG2, TSC2 and PBRM1 mutations was observed in different IT regions. Different regions of the same tumor were similar, whereas individual patient tumors were distinct according to tumor microenvironment cellular composition measured by CyTOF or deconvoluted from RNA-seq. The cellular deconvolution of the ccRCC tumors reconstructed from RNA-seq correlated with CyTOF, snRNA-seq and WGBS, showing high concordance among the methods. The promoter CpG island methylation levels, averaged across all genes, positively correlated with ccRCC grade. MxIF revealed spatial IT heterogeneity in the distribution of immune infiltrate components. Macrophages and T cells dispersed among malignant cells; whereas, T cells formed clusters at unique tumor margins. Conclusions: The utilization of multi-omics methods produced a high-resolution portrait of the ccRCC tumor composition and identified differential ITH among regions within the primary tumors or among individual primary tumors. This study demonstrated strong concordance among the different technologies, suggesting that tumor deconvolution by bulk RNA-seq might be clinically applicable for ccRCC tumors. MxIF analysis enabled a fine elucidation of the spatial relationships among the tumor and the immune and stromal cells, missed by common omic platforms. Integrated single cell multi-omics could render specific pathobiological and therapeutic insights that impact treatment decisions.
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Boutet, Stephane C., Dagmar Walter, Michael J. T. Stubbington, Katherine A. Pfeiffer, Josephine Y. Lee, Sarah E. B. Taylor, Luz Montesclaros, et al. "Scalable and comprehensive characterization of antigen-specific CD8 T cells using multi-omics single cell analysis." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 131.4. http://dx.doi.org/10.4049/jimmunol.202.supp.131.4.
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Abstract Understanding the antigen binding specificities of lymphocytes is key to the development of effective therapeutics for cancers and infectious diseases. Recent technological advancements have enabled the integration of simultaneous cell-surface protein, transcriptome, immune repertoire and antigen specificity measurements at single cell resolution, providing comprehensive, scalable, high-throughput characterization of immune cells. Using the 10x Genomics Single Cell Immune Profiling Solution with Feature Barcoding technology with 14 oligo-conjugated antibodies and 50 Immudex peptide-MHC I Dextramer reagents (pMHC) panels spanning different CMV, EBV, Influenza, HIV and Cancer antigens, we performed multi-omic characterization of ~100,000 CD8+ T cells from four MHC-matched donors. The multi-omic combination of gene expression, paired alpha/beta T cell receptor (TCR) repertoire, cell surface proteins and pMHC binding specificity allowed the identification of CD8+ T cell subpopulations with specificity for pMHCs within our panel. We observed multiple TCRs that bound the same pMHC and identified enriched amino acid motifs within TCR sequences that shared specificities. We compared the CDR3 amino acid sequences of the pMHC-specific TCR clonotypes with previously reported sequences with the same binding specificities to show that we could identify new and known CDR3 sequences. This analytical framework provides a systematic and scalable method for deciphering TCR–pMHC specificity combined with cellular phenotype identity which is critical for developing a better understanding of the adaptive immune response to cancer and infectious diseases and will be key in the development of successful immunotherapies.
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Dhanasekaran, Renumathy. "Deciphering Tumor Heterogeneity in Hepatocellular Carcinoma (HCC)—Multi-Omic and Singulomic Approaches." Seminars in Liver Disease 41, no. 01 (January 2021): 009–18. http://dx.doi.org/10.1055/s-0040-1722261.
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AbstractTumor heterogeneity, a key hallmark of hepatocellular carcinomas (HCCs), poses a significant challenge to developing effective therapies or predicting clinical outcomes in HCC. Recent advances in next-generation sequencing-based multi-omic and single cell analysis technologies have enabled us to develop high-resolution atlases of tumors and pull back the curtain on tumor heterogeneity. By combining multiregion targeting sampling strategies with deep sequencing of the genome, transcriptome, epigenome, and proteome, several studies have revealed novel mechanistic insights into tumor initiation and progression in HCC. Advances in multiparametric immune cell profiling have facilitated a deeper dive into the biological complexity of HCC, which is crucial in this era of immunotherapy. Moreover, studies using liquid biopsy have demonstrated their potential to circumvent the need for tissue sampling to investigate heterogeneity. In this review, we discuss how multi-omic and single-cell sequencing technologies have advanced our understanding of tumor heterogeneity in HCC.
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Barber, Brittany, Martin Prlic, Florian Mair, and Jami Erickson. "Multi-omic single-cell analysis of antigen-presenting compartment reveals a unique functional signature for head and neck squamous cell carcinoma." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e15179-e15179. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e15179.
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e15179 Background: Immune studies in head and neck squamous cell carcinoma (HNSCC) are lacking. Studies in the past decade have highlighted the biological significance and the distinct functional properties of immune cell subsets that are resident in non-lymphoid tissues. While tissue-resident memory T cells (TRM) have been well characterized, much less is known about the human myeloid compartment in HNSCC, which includes professional antigen-presenting cells (APCs) such as dendritic cells (DCs) and macrophages, both of which are critical for shaping the local T cell response. This is particularly relevant in the context of anti-tumor immune responses, which are currently a major focus for therapeutic intervention in HNSCC by either checkpoint inhibitory blockade or adoptive T cell therapy. Methods: A combination of multi-omic single cell RNA sequencing (sc-RNAseq) and 30-parameter fluorescent flow cytometry were used to define the APC compartment in 7 human head and neck squamous cell carcinoma (HNSCC) samples and 4 normal gingival tissue samples as references. Importantly, we performed parallel profiling of the adaptive and innate T cell compartment to elucidate the relationship between APCs and local TRM cells that have been designated as critical players for immune responses in solid tumor tissue. Results: Several novel myeloid phenotypes and an altered composition of the APC compartment in HNSCC relative to normal gingival tissues. APCs expressing pro-inflammatory cytokines such as IL-1b and IL-6 showed reduced abundance, while previously unknown APC subsets defined by the expression of a unique chemokine profile were found to infiltrate HNSCC tissue. Furthermore, our multi-omic approach allowed for profiling of the protein surface phenotype in these transcript-defined clusters, opening up avenues for future therapeutic targeting of tumor-specific antigen-presenting cells. Conclusions: A novel myeloid phenotype and APC compartment were observed in HNSCC when compared to normal gingival tissues. A multi-omic assay identified tumor-specific APCs that may represent future therapeutic targets.
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Forte, Elvira, Micheal A. McLellan, Daniel A. Skelly, and Nadia A. Rosenthal. "Ex uno, plures–From One Tissue to Many Cells: A Review of Single-Cell Transcriptomics in Cardiovascular Biology." International Journal of Molecular Sciences 22, no. 4 (February 19, 2021): 2071. http://dx.doi.org/10.3390/ijms22042071.
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Recent technological advances have revolutionized the study of tissue biology and garnered a greater appreciation for tissue complexity. In order to understand cardiac development, heart tissue homeostasis, and the effects of stress and injury on the cardiovascular system, it is essential to characterize the heart at high cellular resolution. Single-cell profiling provides a more precise definition of tissue composition, cell differentiation trajectories, and intercellular communication, compared to classical bulk approaches. Here, we aim to review how recent single-cell multi-omic studies have changed our understanding of cell dynamics during cardiac development, and in the healthy and diseased adult myocardium.
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Liu, Hailong, Xiaoguang Qiu, and Tao Jiang. "TAMI-74. SPATIOTEMPORAL MULTI-OMIC LANDSCAPE OF HUMAN MEDULLOBLASTOMA AT SINGLE CELL RESOLUTION." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi213—vi214. http://dx.doi.org/10.1093/neuonc/noab196.856.
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Abstract Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Here we use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Collectively, our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.
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Manickam, A., J. Peterson, W. Mei, D. Murdoch, D. Margolis, A. Oesterling, Z. Guo, C. Rudin, Y. Jiang, and E. Browne. "PP 1.33 – 00167 Integrated single-cell multi-omic profiling of HIV latency reversal." Journal of Virus Eradication 8 (December 2022): 100137. http://dx.doi.org/10.1016/j.jve.2022.100137.
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Islam, Mirazul, Bob Chen, Jeffrey M. Spraggins, Ryan T. Kelly, and Ken S. Lau. "Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features." Gastroenterology 159, no. 2 (August 2020): 453–66. http://dx.doi.org/10.1053/j.gastro.2020.04.073.
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Kiner, Evgeny, Hila Sharim, Antonino Montalbano, Tali Raveh-Sadka, Uri Yanover, Daniel K. Wells, and Ansuman Satpathy. "Curated, multi-omic, ML-driven single-cell atlas for characterizing the human immune system across disease states." Journal of Immunology 204, no. 1_Supplement (May 1, 2020): 159.11. http://dx.doi.org/10.4049/jimmunol.204.supp.159.11.
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Abstract The immune system plays a key role in various diseases such as cancer, autoimmune disorders and infections. It has been recently shown that cell surface protein markers on peripheral blood mononuclear cells (PBMCs) can be predictive of response to therapies1, but measurements of such markers is technically limited in the number of cells and markers that can be assessed. Single-cell RNA technology allows the measurement of the entire transcriptome in tens of thousands of cells, and therefore has the potential to survey the immune system in more depth and reveal rare cell states. However, no large single-cell datasets for immune cell states exist. Moreover, attempts to combine smaller distinct datasets are often hindered by batch effects stemming from differences in sample handling, single-cell technologies and computational analyses. We created a large curated multi-omic single-cell human PBMC atlas with clinical annotations from dozens of patients with several conditions across hundreds of thousands of cells. Our standardized end-to-end protocols and quality control processes provide a platform that allows production of large datasets with minimal batch effects. By combining single-cell RNA-seq (scRNA-seq) with surface marker identification by CITE-seq, we generate curated immune gene signatures and train a classifier to robustly identify many cell types and states across patients and different diseases, including rare cell populations. This curated, multi-omic clinically-annotated atlas is particularly suited for use by machine learning algorithms, and we are confident that as this data accumulates, it will be instrumental for inferring disease states and predict responses to various therapies.
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Carrillo-Perez, Francisco, Juan Carlos Morales, Daniel Castillo-Secilla, Olivier Gevaert, Ignacio Rojas, and Luis Javier Herrera. "Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis." Journal of Personalized Medicine 12, no. 4 (April 8, 2022): 601. http://dx.doi.org/10.3390/jpm12040601.
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Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of 96.81±1.07, an AUC of 0.993±0.004, and an AUPRC of 0.980±0.016, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient.
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Granja, Jeffrey M., M. Ryan Corces, Sarah E. Pierce, S. Tansu Bagdatli, Hani Choudhry, Howard Y. Chang, and William J. Greenleaf. "ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis." Nature Genetics 53, no. 3 (February 25, 2021): 403–11. http://dx.doi.org/10.1038/s41588-021-00790-6.
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AbstractThe advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.
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Nikolaki, Sofia, and George Tsiamis. "Microbial Diversity in the Era of Omic Technologies." BioMed Research International 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/958719.
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Human life and activity depends on microorganisms, as they are responsible for providing basic elements of life. Although microbes have such a key role in sustaining basic functions for all living organisms, very little is known about their biology since only a small fraction (average 1%) can be cultured under laboratory conditions. This is even more evident when considering that >88% of all bacterial isolates belong to four bacterial phyla, theProteobacteria,Firmicutes,Actinobacteria, andBacteroidetes. Advanced technologies, developed in the last years, promise to revolutionise the way that we characterize, identify, and study microbial communities. In this review, we present the most advanced tools that microbial ecologists can use for the study of microbial communities. Innovative microbial ecological DNA microarrays such as PhyloChip and GeoChip that have been developed for investigating the composition and function of microbial communities are presented, along with an overview of the next generation sequencing technologies. Finally, the Single Cell Genomics approach, which can be used for obtaining genomes from uncultured phyla, is outlined. This tool enables the amplification and sequencing of DNA from single cells obtained directly from environmental samples and is promising to revolutionise microbiology.
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Iglesia, Michael D., Reyka G. Jayasinghe, Daniel Cui Zhou, Nadezhda V. Terekhanova, John Herndon, Alla Karpova, Siqi Chen, et al. "Abstract 2936: Multi-omic characterization of transitional cell populations in breast cancer." Cancer Research 82, no. 12_Supplement (June 15, 2022): 2936. http://dx.doi.org/10.1158/1538-7445.am2022-2936.
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Abstract Breast cancer is a heterogeneous collection of diseases grouped by hormone receptor status or by expression of key subtype-determining genes. Breast cancer subtypes, in particular basal-like breast cancer and the luminal breast cancer subtypes, differ by hormonal receptor status, proliferation, genomic instability and mutational signatures, treatment response, and prognosis. The highly distinct signatures of basal-like and luminal breast cancer suggest that they may have different cells of origin within the breast duct. As part of the Washington University Human Tumor Atlas Network (WU-HTAN) program, we generated multi-omic data for 53 samples from 37 breast cancer tumors and 4 normal adjacent tissues. Genomic subtyping was applied to both bulk and single-nucleus RNA sequencing. Analysis of single-nucleus gene expression and chromatin accessibility in epithelial cells underscores similarities between basal-like breast cancer and luminal progenitor cells within the breast duct, and between luminal breast cancer and mature luminal ductal cells. This study links distinct breast cancer subtypes to normal breast cell populations and suggests distinct cells of origin for these cancer types. Citation Format: Michael D. Iglesia, Reyka G. Jayasinghe, Daniel Cui Zhou, Nadezhda V. Terekhanova, John Herndon, Alla Karpova, Siqi Chen, Nataly Naser Al Deen, Kazuhito Sato, Feng Chen, Deborah J. Veis, Ryan C. Fields, William E. Gillanders, Li Ding. Multi-omic characterization of transitional cell populations in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2936.
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Nourbakhsh-Rey, Mehrnoush, and Marc Libault. "Decipher the Molecular Response of Plant Single Cell Types to Environmental Stresses." BioMed Research International 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4182071.
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The analysis of the molecular response of entire plants or organs to environmental stresses suffers from the cellular complexity of the samples used. Specifically, this cellular complexity masks cell-specific responses to environmental stresses and logically leads to the dilution of the molecular changes occurring in each cell type composing the tissue/organ/plant in response to the stress. Therefore, to generate a more accurate picture of these responses, scientists are focusing on plant single cell type approaches. Several cell types are now considered as models such as the pollen, the trichomes, the cotton fiber, various root cell types including the root hair cell, and the guard cell of stomata. Among them, several have been used to characterize plant response to abiotic and biotic stresses. In this review, we are describing the various -omic studies performed on these different plant single cell type models to better understand plant cell response to biotic and abiotic stresses.
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deMel, Sanjay, Jonahtan Scolnick, Xiaojing Huo, Stacy Xu, Cinnie Soekojo, Fangfang Song, Melissa Ooi, and Wee Joo Chng. "MM-253: Single Cell Multi-Omic Analysis And Immune Cell Type Profiling Of Multiple Myeloma With t(4;14)." Clinical Lymphoma Myeloma and Leukemia 21 (September 2021): S432. http://dx.doi.org/10.1016/s2152-2650(21)01963-7.
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Rodriguez de la Fuente, Laura, Andrew M. K. Law, David Gallego-Ortega, and Fatima Valdes-Mora. "Tumor dissociation of highly viable cell suspensions for single-cell omic analyses in mouse models of breast cancer." STAR Protocols 2, no. 4 (December 2021): 100841. http://dx.doi.org/10.1016/j.xpro.2021.100841.
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Rodriguez-Meira, Alba, Jennifer O'Sullivan, Haseeb Rahman, Guanlin Wang, Wei Wen, Bethan Psaila, Ileana Antony-Debre, Supat Thongjuea, and Adam Mead. "3033 – SINGLE-CELL MULTI-OMIC ANALYSIS UNRAVELS GENETIC AND MOLECULAR HETEROGENEITY IN TRANSFORMED MYELOPROLIFERATIVE NEOPLASMS." Experimental Hematology 88 (August 2020): S48. http://dx.doi.org/10.1016/j.exphem.2020.09.053.
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Kaminska, Bozena, and Anna M. Lenkiewicz. "Dissecting the Immune Microenvironment of Malignant Gliomas with Single-Cell Omic Reveals New Treatment Opportunities." Onco Therapeutics 8, no. 2 (2021): 1–28. http://dx.doi.org/10.1615/forumimmundisther.2021041708.
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Liu, Ilon, Jiang Li, Daeun Jeong, Olivia A. Hack, McKenzie Shaw, Bernhard Englinger, Byron Avihai, et al. "EPCO-06. AGE- AND REGION-SPECIFIC MULTI-OMIC CHARACTERIZATION OF H3-K27M MUTANT DIFFUSE MIDLINE GLIOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi2. http://dx.doi.org/10.1093/neuonc/noab196.005.
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Abstract Diffuse midline gliomas driven by lysine27-to-methionine mutations in histone 3 (H3-K27M DMGs) are among the most fatal brain tumors. Molecular studies including single cell RNA-sequencing (scRNA-seq) of pediatric and predominantly pontine H3-K27M DMGs have shown that the H3-K27M oncohistone keeps glioma cells locked in a stem-like oligodendrocyte precursor cell (OPC) state that is capable of self-renewal and tumor-initiation. However, a comprehensive dissection of the cellular architecture of H3-K27M DMGs across different midline regions and age groups is required to better understand the cell-intrinsic and contextual regulation of H3-K27M DMG cell identities. In particular, the more recently described group of adult H3-K27M DMGs remains understudied. Here, we have collected and characterized 45 H3-K27M mutant patient tumors, spanning pontine (n=26), thalamic (n=17), and spinal (n=2) locations. Median age at surgery was 12 (2-68) years, encompassing 21 early childhood (0-10 years), 12 adolescent (11-20 years), and 12 adult (≥ 21 years) tumors. The majority of samples were obtained pre-treatment (n=28), as opposed to post-treatment or at autopsy (n=17). We profiled all 45 tumors by single cell/single nucleus RNA-seq and selected tumors were further characterized by the single cell assay for transposase-accessible chromatin (scATAC-seq). Our integrated analyses highlight the predominance of transcriptionally and epigenetically defined OPC-like tumor cells as the main cell population of H3-K27M DMGs across all age groups and locations. We further identify distinct age- and location-specific OPC-like cell subpopulations. Comparison of pediatric and adult tumors further demonstrates a significant increase of mesenchymal cell states in adult H3-K27M DMGs, which we link to differences in glioma-associated immune cell compartments between age groups. Together, this study sheds light on the effects of age- and region-dependent microenvironments in shaping cellular identities in H3-K27M DMGs.
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Johnson, Kevin, Kevin Anderson, Elise Courtois, Floris Barthel, Frederick Varn, Diane Luo, Eunhee Yi, et al. "EPCO-27. GLIOMA SINGLE CELL MULTI-OMIC ANALYSES REVEALS REGULATORS OF PLASTICITY AND ADAPTIVE STRESS RESPONSE." Neuro-Oncology 22, Supplement_2 (November 2020): ii75. http://dx.doi.org/10.1093/neuonc/noaa215.306.
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Abstract Extensive intra- and intertumoral heterogeneity in glioma contributes to therapeutic resistance and poor patient outcomes. Alterations to DNA methylation (DNAme) modulate epigenetic homeostasis, allowing tumor cells to sample alternative cell states to promote tumorigenesis. However, the epigenetic mechanisms that promote cellular plasticity and regulate cell states are still poorly understood. To characterize the epigenetic mechanisms underlying glioma heterogeneity we profiled 914 single-cell methylomes, 55,284 single-cell transcriptomes, and bulk whole genomes across 11 patient samples spanning initial and recurrent time points and 3 molecular subtypes delineated by IDH mutation status. Local DNAme disorder, defined as epimutation burden, was increased in tumor cells relative to nontumor cells, higher in IDH wild-type than in IDH mutant glioma and was positively associated with copy number alteration (CNA). Epimutation was positively associated with transcriptional variability and enriched at genes involved in cellular differentiation. Epimutation was also increased in the binding sites of transcription factors (TFs) associated with response to extracellular stimuli, suggesting that stochastic DNAme alterations enable cellular plasticity and diverse responses to microenvironmental stressors. Integrative clustering of DNAme and scRNAseq profiles defined stem-like and differentiated-like cell states which exhibited differences in TF activity. Stem-like cells were enriched for differentially methylated binding sites of TFs associated with hypoxia response. scDNAme and scRNAseq-derived copy number profiles were compared with bulk copy number profiles and inferred tumor phylogenies to assess how the timing of CNAs impact epigenetic instability, with results suggesting that early CNA events propagate both genetic and epigenetic heterogeneity. Bulk longitudinal data was used to validate the relationship of epigenetic instability with CNA burden as well as differentially methylated binding sites of cell stress response TFs. Our work suggests that local DNAme disorder promotes cellular plasticity and enables adaptive response to cellular stress such as hypoxia.
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Liu, Vincent, Katalin Sandor, Bence Daniel, Lionel Berthoin, Shan Sabri, Sofia Panagiotopoulou, Yajie Yin, et al. "Abstract 1701: Single-cell multi-omic profiling and clonal tracing of the human gynecological tumor microenvironment." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1701. http://dx.doi.org/10.1158/1538-7445.am2022-1701.
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Abstract Tumor initiation and progression are the result of a close interplay between malignant cells and immune cells. While single-cell genomics has enabled the molecular dissection of the cellular heterogeneity underlying complex cancers, many facets of tumor microenvironments remain unresolved. In particular, the ontogeny of myeloid cells have not been fully understood in human tumors, although the source and expansion of macrophages have been suggested to underlie tumor development and metastasis in mice. In an effort to comprehensively characterize these tumor microenvironments, we profiled endometrial, ovarian, and metastatic tumors from 16 patients using CITE-seq (an atlas of 258,810 cells) and mitochondrial single-cell ATAC-seq (mtscATAC-seq; consisting of 173,820 cells). Our multi-omic analyses reveal the heterogeneity of tumor-infiltrating immune cells at clonal, epigenetic, transcriptomic, and proteomic levels. Notably, all tumor types harbored diverse exhausted T cell subsets, including terminal and precursor exhausted T cells, and gamma/delta T cells. Clonal somatic variant analysis between tumor tissues and peripheral blood mononuclear cells identified blood-derived monocytes, rather than embryonically derived cells, as the origin of macrophages within gynecological tumors. Further, our analyses show that macrophages expand minimally within the tumor microenvironment, suggesting that they may be continuously replenished by blood, an observation that may facilitate new approaches for cancer immunotherapy. In total, our dataset represents the first multi-modal single cell atlas of gynecologic tumors and reveals distinct features of T cell heterogeneity and myeloid expansions in complex tumor microenvironments. Citation Format: Vincent Liu, Katalin Sandor, Bence Daniel, Lionel Berthoin, Shan Sabri, Sofia Panagiotopoulou, Yajie Yin, Kamir Hiam-Galvez, Rene Sit, Zi Fan, Brendan Galvin, Omar Khan, Natalie Bezman, Jane Grogan, Brooke Howitt, Grace Zheng, Caleb Lareau, Ansuman Satpathy. Single-cell multi-omic profiling and clonal tracing of the human gynecological tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1701.
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deMel, Sanjay, Jonahtan Scolnick, Xiaojing Huo, Stacy Xu, Cinnie Soekojo, Fangfang Song, Melissa Ooi, and Wee Joo Chng. "Poster: MM-253: Single Cell Multi-Omic Analysis And Immune Cell Type Profiling Of Multiple Myeloma With t(4;14)." Clinical Lymphoma Myeloma and Leukemia 21 (September 2021): S255. http://dx.doi.org/10.1016/s2152-2650(21)01602-5.
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Gertz, Monica L., Christopher R. Chin, Delia Tomoiaga, Matthew MacKay, Christina Chang, Daniel Butler, Ebrahim Afshinnekoo, et al. "Multi-omic, Single-Cell, and Biochemical Profiles of Astronauts Guide Pharmacological Strategies for Returning to Gravity." Cell Reports 33, no. 10 (December 2020): 108429. http://dx.doi.org/10.1016/j.celrep.2020.108429.
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Zhou, Jianbiao, Jonathan Adam Scolnick, Stacy Xu, Melissa Ooi, Priscella Shirley Chia, Sabrina Hui-Min Toh, Kalpnaa Balan, et al. "Single-Cell Multi-Omic Analysis Uncovers Comprised Immune Function and Primary Resistance Mechanism in Acute Myeloid Leukemia." Blood 138, Supplement 1 (November 5, 2021): 378. http://dx.doi.org/10.1182/blood-2021-149022.
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Abstract Background: Approximately 20% of AML patients do not respond to induction chemotherapy (primary resistance) and 40-60% of patients develop secondary resistance, eventually leading to relapse followed by refractory disease (RR-AML). Diversified molecular mechanisms have been proposed for drug resistance and RR phenotype. However, we still cannot predict when relapse will occur, nor which patients will become resistant to therapy. Single-cell multi-omic (ScMo) profiling may provide new insights into our understanding of hematopoietic stem cell (HSC) differentiation trajectories, tumor heterogeneity and clonal evolution. Here we applied ScMo to profile bone marrow (BM) from AML patients and healthy controls. Methods: AML samples were collected at diagnosis with institutional IRB approval. Cells were stained with a panel of 62 DNA barcoded antibodies and 10x Genomics Single Cell 3' Library Kit v3 was used to generate ScMo data. After normalization, clusters were identified using Uniform Manifold Approximation and Projection (UMAP) and annotated using MapCell (Koh and Hoon, 2019). We analyzed 23,933 cells from 4 adult AML BM samples, and 39,522 cells from 2 healthy adults and 3 sorted CD34+ normal BM samples. Gene set enrichment analysis (GSEA) and Enrichr program were used to examine underlying pathways among differentially expressed genes between healthy and AML samples. Results: We identified 16 cell types between the AML and normal samples (Fig 1a) amongst 45 clusters in the UMAP projection (Fig 1b). Comparative analysis of the T cell clusters in AML samples with healthy BM cells identified an "AML T-cell signature" with over-expression of genes such as granzymes, NK/T cell markers, chemokine and cytokine, proteinase and proteinase inhibitor (Fig 2a). Among them, IL32 is known to be involved in activation-induced cell death in T cells and has immunosuppressive role, while CD8+ GZMB+ and CD8+ GZMK+ cells are considered as dysfunctional or pre-dysfunctional T cells. Indeed, Enrichr analysis showed the top rank of phenotype term - "decreased cytotoxic T cell cytolysis". We next examined whether NK cells, are similarly dysfunctional in the AML ecosystem. The "AML NK cell signature" includes Fc Fragment family, IFN-stimulated genes (ISGs), the effector protein-encoding genes and other genes when compared to normal NK cells (Fig 2b). GSEA analysis revealed "PD-1 signalling" among the top 5 ranked pathways in AML-NK cells, though no increase in PD-1 protein nor PDCD1 gene were identified in these cells. Inhibitory receptor CD160 was expressed higher in AML samples along with exhaustion (dysfunction) associated genes TIGIT, PRF1 and GZMB (Fig 2c). Enrichr analysis uncovered enrichment of "abnormal NK cell physiology and "impaired natural killer cell mediated cytotoxicity". Similarly, the "AML monocyte signature" was significantly enriched with genes in "Tumor Infiltrating Macrophages in Cancer Progression and Immune Escape" and "Myeloid Derived Suppressor Cells in Cancer Immune Escape". We also analyzed HSPC component in one pair of cytogenetically matched, untreated complete remission (CR) /RR AML pair (Fig 2d). Notably, half of the 10 genes overexpressed in RR-AML, CXCR4, LGALS1, S100A8, S100A9, SRGN (Serglycin), regulate cell-matrix interaction and play pivotal roles in leukemic cells homing bone marrow niche. The first 4 of these genes have been demonstrated as prognostic indicators of poor survival and associated with chemo-resistance and anti-apoptotic function. Furthermore, single-cell trajectory analysis of this CR/RR pair illustrated a change in differentiation pattern of HSPCs in CR-AML to monocytes in RR-AML. We are currently analyzing more AML samples to validate these findings. Conclusions: Our ScMo analysis demonstrates that the immune cells are systematically reprogrammed and functionally comprised in the AML ecosystem. Upregulation of BM niche factors could be the underlying mechanism for RR-AML. Thus, reversing the inhibited immune system is an important strategy for AML therapy and targeting leukemic cell-BM niche interaction should be considered for cases with high expression of these molecules on AML HSPCs. Note: J.Z. and J.A.S. share co-first authorship. Figure 1 Figure 1. Disclosures Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Xu: Proteona Pte Ltd: Current Employment. Ooi: Jansen: Honoraria; Teva Pharmaceuticals: Honoraria; GSK: Honoraria; Abbvie: Honoraria; Amgen: Honoraria. Lovci: Proteona Pte Ltd: Current Employment. Chng: Aslan: Research Funding; Takeda: Honoraria; Johnson & Johnson: Honoraria, Research Funding; BMS/Celgene: Honoraria, Research Funding; Amgen: Honoraria; Novartis: Honoraria, Research Funding; Antengene: Honoraria; Pfizer: Honoraria; Sanofi: Honoraria; AbbVie: Honoraria.
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Silverbush, Dana, Mario Suva, and Volker Hovestadt. "LTBK-08. Inferring cell type and cell state composition in glioblastoma from bulk DNA methylation profiles using multi-omic single-cell analyses." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii300. http://dx.doi.org/10.1093/neuonc/noac209.1172.
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Abstract OBJECTIVE Cellular heterogeneity determines tumor phenotype and response to therapy. This is particularly pronounced in glioblastoma (GBM), which is characterized by multiple malignant cell states with distinct proliferation potentials, and different cell types of the microenvironment. Ideally, cellular heterogeneity is characterized using single cell genomic profiling techniques. However, these techniques remain challenging to apply in a diagnostic setting and to large retrospective patient cohorts (TCGA, GLASS, DKFZ and clinical trials). Instead, clinicians routinely support their diagnosis with bulk DNA methylation profiling, which generates robust results from low quality material but does not inform on cellular heterogeneity. We have developed a powerful new computational method to deconvolute bulk DNA methylation data and infer cellular heterogeneity within individual tumors, to support prognostic accuracy and personalized treatment decisions. METHODS Using both bulk and single-cell multi-omic datasets, we created a DNA methylation-based reference of cell types (malignant, glial, neuronal, and immune) within GBM tumors, and the state of malignant cells therein (stem-like vs. differentiated-like). Using this reference, our computational approach accurately deconvolutes bulk DNA methylation profiles of individual query samples. RESULTS High deconvolution accuracy of GBM heterogeneity was achieved from frozen and FFPE tissue samples, including those of low quality or purity (Jensen Shannon divergence for composition similarity < 0.05). Our approach eliminates bias derived from the microenvironment, and results in patient stratification that harmonizes the DNA methylation- and RNA-based classifications of GBM. It also reveals the inter- and intra-tumoral links between the genetic, DNA methylation, and transcriptomic components of GBM pathology, and suggests their specific impacts on treatment efficacy. To facilitate clinical translation, we created a public website that allows clinicians to infer the relative abundance of different cell states within a tumor at the click of a button.
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De Visser, Yanti, Dena Panovska, Basiel Cole, Lotte Hermans, Asier Antoranz Martinez, Pouya Nazari, Maaike Van Trimpont, et al. "BIOM-16. A MULTI-OMIC, FUNCTIONAL PRECISION ONCOLOGY METHOD TO IDENTIFY RESPONSIVE GLIOBLASTOMA TUMOR CELLS AT SINGLE CELL RESOLUTION." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii7. http://dx.doi.org/10.1093/neuonc/noac209.026.
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Abstract Glioblastoma remains a highly malignant and intrinsically resistant brain tumor. Despite intensive research through which numerous potential druggable targets were identified, virtually all clinical trials of the past 20 years failed to improve the outcome for the vast majority of GBM patients. However, the identification of small subgroups of patients that showed an exceptional response across several trials, implies that, when selected more carefully, some GBM patients could probably still benefit from these therapies. Identifying these patients requires that suitable biomarkers are identified. In this project, we reassessed the molecular mechanisms of ten actionable compounds (selected from previously failed trials but for which exceptional responders had been observed) in a set of carefully selected patient-derived cell lines that were sensitive/resistant to the selected therapies. Moreover, to deal with tumor heterogeneity, we used a multi-omic functional precision oncology approach, combining scRNA-seq and CyTOF, to identify drug-specific biomarkers by comparing control and treated samples at single-cell resolution. By subsequently correlating the molecular signatures to eventual cytotoxicity profiles, we could identify intrinsically responsive tumor cells at the single-cell level within hours following drug exposure. Overall, this work lays the foundation for an actionable functional diagnostic assay that could help to identify eligible GBM patients in future clinical trials.
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Liu, Hailong, Tao Jiang, and Xiaoguang Qiu. "Spatiotemporal multiomic landscape of human medulloblastoma at single cell resolution." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 2069. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.2069.
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2069 Background: Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Methods: We use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. Results: This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Conclusions: Our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.
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De Mel, Sanjay, Jonathan Adam Scolnick, Chern Han Yong, Xiaojing Huo, Stacy Xu, Cinnie Yentia Soekojo, Fangfang Song, Melissa Ooi, and Wee Joo Chng. "Single Cell Multi-Omic Profiling of Multiple Myeloma with t(4;14) Finds an Immune Microenvironment Gene Signature That Correlates with Clinical Outcomes." Blood 138, Supplement 1 (November 5, 2021): 2653. http://dx.doi.org/10.1182/blood-2021-149107.
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Abstract Background Multiple Myeloma (MM) is an incurable plasma cell (PC) malignancy and high risk MM remains an unmet clinical need. Translocation 4;14 occurs in 15% of MM and is associated with an adverse prognosis. A deeper understanding of the biology and immune micro-environment of t(4;14) MM is necessary for the development of effective targeted therapies. Single Cell multi-omics provides a new tool for phenotypic characterization of MM. Here we used Proteona's ESCAPE™ single cell multi-omics platform to study a cohort of patients with t(4;14) MM. Methods Diagnostic bone marrow (BM) samples from 13 patients with t(4;14) MM (one of whom had samples at diagnosis and relapse) were analysed using the ESCAPE™ platform from Proteona which simultaneously measures gene and cell surface protein expression of 65 proteins in single cells. Cryopreserved BM samples were stained with antibodies and subsequently sorted on CD138 expression. The CD138 positive and negative fractions were recombined at a 1:1 ratio for analysis using the 10x Genomics 3' RNAseq kit. Resulting data were analyzed with Proteona's MapSuite™ single cell analytics platform. In particular, Mapcell was used to annotate the cells and MapBatch was used for batch normalization in order to preserve rare cell populations. Results Patients had a median age of 63 years and received novel agent-based induction. Median progression free and overall survival (PFS and OS) were 22 and 34 months respectively. We first analyzed serial BM samples from an individual patient that were taken at diagnosis and relapse following bortezomib based treatment. The PCs in this patient showed variations in gene expression between diagnosis and relapse (Fig 1A), including the reduction of HIST1H2BG expression, which has previously been correlated with resistance to bortezomib. Subsequent analysis of the immune cells identified a shift in the ratio of T cells to CD14 monocytes from 5.7 at diagnosis to 0.6 at relapse suggesting a major change in the BM immune micro-environment in response to therapy. Next, we analyzed the malignant PCs of the diagnostic samples. As expected, MMSET (NSD2) was overexpressed in all PCs compared to normal PCs, while FGFR3 expression could be categorized into no expression of FGFR3, low expression (<10% of cells expressing FGFR3) or high expression (>80% of cells expressing FGFR3) (Fig 1B). No gene or protein expression patterns within the PCs were identified that correlated with PFS or OS in this cohort. Finally, we analyzed the immune micro-environment in the diagnostic samples (Fig 1C). While there was no overall discernable pattern of cell types present, one cluster of cells, annotated as 'unknown' cell type, suggested a small population of cells that had not been previously annotated in published single cell RNA-seq data. The cells were CD45+ and CD138 - both at the protein and RNA level, suggesting they are not plasma cells. We tested if the number of the 'unknown' cells in each sample correlated with PFS, but there was no significant correlation. We then used these cells to derive a gene signature profile which was expressed in most of the cells in the 'unknown' cluster as well as a minor fraction of cells in other clusters including some PCs. The number of cells expressing the gene signature negatively correlated with PFS, with samples containing more cells expressing the signature having a lower PFS than samples with fewer signature positive cells (Fig 2). The correlation remained significant whether we included PCs in the analysis or not, but was not significant amongst only the PC population, suggesting that the cells responsible for the correlation are from the immune micro-environment. Conclusions We present the first application of single cell multi-omic immune profiling in high-risk MM and demonstrate that t(4;14) is a phenotypically heterogenous disease. While no consistent gene or protein expression patterns were identified within the malignant cell population, we did identify gene expression changes in a relapsed patient sample that may reflect key alterations in the PCs responsible for therapy resistance. In addition, we identified a gene signature expressed in a rare population of non-plasma cells that significantly correlated with PFS in this patient cohort. These data highlight the potential of single cell multi-omic analysis to identify immune micro-environmental signatures that correlate with response to therapy in t(4;14) MM. Figure 1 Figure 1. Disclosures Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Huo: Proteona Pte Ltd: Ended employment in the past 24 months. Xu: Proteona Pte Ltd: Current Employment. Chng: Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria; Abbvie: Honoraria.
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de Mel, Sanjay, Jonathan Adam Scolnick, Chern Han Yong, Stacy Xu, Cinnie Yentia Soekojo, Fangfang Song, Melissa Ooi, and Wee Joo Chng. "Single Cell Multi-Omic Profiling of Multiple Myeloma with t(4;14) Identifies a T-Cell Population That Correlates with Clinical Outcomes." Blood 140, Supplement 1 (November 15, 2022): 4336–37. http://dx.doi.org/10.1182/blood-2022-164465.
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Liu, Hanqing, Jingtian Zhou, Wei Tian, Chongyuan Luo, Anna Bartlett, Andrew Aldridge, Jacinta Lucero, et al. "DNA methylation atlas of the mouse brain at single-cell resolution." Nature 598, no. 7879 (October 6, 2021): 120–28. http://dx.doi.org/10.1038/s41586-020-03182-8.
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AbstractMammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer–gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.
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Lin, Jian-Da, Tzu-Yin Chou, Chen-Hsuan Yang, Pei-An Chao, Yen-Ting Chen, and P’ng Loke. "Single-cell multi-omic analysis identify heterogeneity and distinct features in fate-mapped tissue-resident alternatively activated macrophages." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 172.11. http://dx.doi.org/10.4049/jimmunol.208.supp.172.11.
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Abstract Peritoneal cavity cells play pivotal roles in inflammation, repair, and maintaining homeostasis in response to pathogenic infections and tissue injury. Two major tissue-resident macrophages (TRMs) are large peritoneal macrophages (LPMs) and small peritoneal macrophages (SPMs). These are the major macrophage subsets in the peritoneal cavity and originate from embryogenic (LPMs) or bone-marrow-derived myeloid precursors (SPMs). CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) provides simultaneous information for single cells in both cell-surface protein and gene expression levels. Here, we used CITE-seq to profile peritoneal cells by an oligonucleotide-labeled antibody panel designed to react with 189 unique mouse cell surface antigens. We identify 14 markers exclusively expressed on TRMs but not other immune cell types. These markers can classify phenotype differences between LPMs and SPMs during IL-4 stimulation. We further profile fate-mapped TRMs by scRNA-seq and identified more heterogenous phenotypes of TRMs that originated from embryogenic rather than bone-marrow-derived myeloid precursors. Notably, serum amyloid A-3 (Saa3) and platelet factor 4 (Pf4), can distinguish TRM clusters from alternative activation to IL-4 stimulation and Heligmosomoides polygyrus infection. Hence, we identify distinct markers that can be used to distinguish the different origins and heterogenous TRM phenotypes under steady state and type 2 immune responses.
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Chai, Shoujie, Nicholas Matsumoto, Ryan Storgard, Chen-Ching Peng, Ana Aparicio, Benjamin Ormseth, Kate Rappard, et al. "Abstract 1957: Dissecting CTC phenotypic heterogeneity for predictive biomarker identification and its association with clonal lineage through single-cell multi-omic profiling." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1957. http://dx.doi.org/10.1158/1538-7445.am2022-1957.
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Abstract Background: The mutation, selection, and adaptation of tumor cells along disease progression exhibits a spectrum of phenotypic and genotypic heterogeneity. The importance of distinguishing phenotypic states of CTC in addition to genomic alterations has been addressed for identifying predictive biomarkers and understanding CTC biology. Current liquid biopsy usually relies on only one phenotypic state of CTCs without further genomic validation of cancer cell identity. Here, we developed HDSCA3.0, a multi-omic platform, which could distinguish various phenotypic states of CTCs followed by genomic characterization. Methods: Paired peripheral blood (PB) and bone marrow aspirate (BMA) samples were collected prospectively from 80 metastatic castrate resistant prostate cancer (mCRPC) patients for retrospective analysis. Seventy-nine of them were part of Cabazitaxel With or Without Carboplatin Trial (NCT01505868) and one independent index patient was included with aggressive disease and unfavorable prognosis. CTCs were detected, classified, enumerated through a four-channel immunostaining assay (DAPI|Cytokeratin|Vimentin|CD45/CD31) and a computational pipeline followed by manual curation, and subjected to single-cell copy-number profiling for clonality analysis and aggressive variant prostate cancer molecular signature (AVPC-MS) detection i.e. 2+ defects in PTEN, RB1, and TP53 genes. Results: CTC subtypes were categorized from Cytokeratin-positive rare cell groups based on the presence of mesenchymal features and platelet attachment. Of 79 trial cases, 77 (97.5%) had CTCs, 24 (30.4%) were positive for platelet-coated CTCs (pc.CTCs) and 25 (38.5%) of 65 sequenced patients exhibited AVPC-MS in CTCs. Survival analysis indicated that the presence of pc.CTCs identified the subset of patients who were AVPC-MS-positive with the worst prognosis. In AVPC-MS-negative patients, its presence showed significant survival improvement from combination therapy. In index patient, we uniquely identified genetically clonal mesenchymal-like CTCs (mes.CTCs) and their presence was significantly associated with one subclone emerged along clonal lineage. Meanwhile, differences of CTC abundance and phenotypic diversity were observed between paired PB and BMA as well as genomic variations. Conclusion: Our findings suggest pc.CTCs and AVPC-MS in CTCs as a multi-omic predictive biomarker to stratify mCRPC subpopulations with the worst prognosis and the most significant benefit of additional platinum therapy and illustrate a robust approach to analyze intra-patient CTC genotypic and phenotypic heterogeneity and association. Citation Format: Shoujie Chai, Nicholas Matsumoto, Ryan Storgard, Chen-Ching Peng, Ana Aparicio, Benjamin Ormseth, Kate Rappard, Cunningham Kate, Anand Kolatkar, Rafael Nevarez, Kai Han Tu, Ching-Ju Hsu, Amin Naghdloo, Paymaneh Malihi, Liya Xu, Paul Corn, Amado Zurita-Saavedra, James Hicks, Carmen Ruiz-Velasco, Peter Kuhn. Dissecting CTC phenotypic heterogeneity for predictive biomarker identification and its association with clonal lineage through single-cell multi-omic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1957.
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Prazich, Jack. "High-dimensional single-cell analysis of the immune response in multiple myeloma and profiling of the T cell repertoire in response to immunomodulatory treatment." Journal of Immunology 208, no. 1_Supplement (May 1, 2022): 118.13. http://dx.doi.org/10.4049/jimmunol.208.supp.118.13.
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Abstract Multiple myeloma is a largely incurable malignancy of plasma cells in the bone marrow. The standard of care for this disease has increasingly included the use of immunomodulatory drugs such as thalidomide and its derivatives. However, high-dimensional analysis of the immune response in multiple myeloma tumors and the resultant response to immunomodulatory treatment is lacking. Here, we present a single-cell multi-omic analysis of the immune response in refractory multiple myeloma patient tumors including profiling of CD8+ T cell antigen specificities. We also analyze changes in the T cell repertoire and gene expression following treatment of pomalidomide. We observe a contraction of the repertoire that includes the expansion of select clones within the repertoire. These results provide a clearer map of the immune response in multiple myeloma and how patients respond to immunomodulatory treatment. Supported by grants from CRI Lloyd J. Old STAR Program and NIH R33 IMAT 2018
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Mair, Florian, Jami R. Erickson, Valentin Voillet, Yannick Simoni, Timothy Bi, Aaron J. Tyznik, Jody Martin, Raphael Gottardo, Evan W. Newell, and Martin Prlic. "A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level." Cell Reports 31, no. 1 (April 2020): 107499. http://dx.doi.org/10.1016/j.celrep.2020.03.063.
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Orchard, Peter, Nandini Manickam, Christa Ventresca, Swarooparani Vadlamudi, Arushi Varshney, Vivek Rai, Jeremy Kaplan, et al. "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits." Genome Research 31, no. 12 (November 23, 2021): 2258–75. http://dx.doi.org/10.1101/gr.268482.120.
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Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.
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Becker, Pamela S., Vivian G. Oehler, Carl Anthony Blau, Timothy S. Martins, Niall Curley, Sylvia Chien, Jin Dai, et al. "A Multi-Omic Precision Medicine Clinical Trial in Acute Leukemia." Blood 134, Supplement_1 (November 13, 2019): 1269. http://dx.doi.org/10.1182/blood-2019-130996.
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Background: Conventional precision medicine for cancer targets specific gene mutations, but single agent inhibitors rarely result in remission or improve survival in acute leukemia. Current functional screening strategies assay individual drugs, thus missing potential synergistic combinations. We therefore developed a multi-omic approach that integrates mutation data, gene expression data (transcriptome), and in vitro drug sensitivity (functional) data to select drugs including drug combinations for individual patients. Herein we report results of the clinical trial testing this concept [NCT02551718]. This study contributed to validation of 44 genes identified for which expression correlates with drug sensitivity (Lee S-I et al. Nat Commun 2018). Patients and Methods: Eligible patients failed at least 2 prior regimens, or if adverse risk, one multi-drug intensive regimen, had ECOG PS 0-3 and at least 1 million blasts in blood, marrow, tissue or fluid for analysis. The original enrollment was 25 patients to establish feasibility, and the study was later expanded to provide an option for refractory patients. Samples were obtained from the 54 consented patients (44 AML, 8 ALL, 2 acute leukemias of ambiguous lineage). Median age is 58 (range 23-82) and 31.5% of patients had an antecedent hematologic disorder. Of the AML patients (n=44), 7 were favorable, 13 intermediate, and 24 adverse risk (ELN 2017). Patients had a median of 3 prior treatments (range 1-6); 39 patients (72%) had relapsed after prior complete remission (CR) with median duration of 6 months (m) (range: 0-52 m), and 20 had relapsed after allogeneic transplant, 8 within the first 100 days, and 3 never achieved a CR after transplant. Enriched blasts were assayed in a custom CLIA-approved high throughput sensitivity (HTS) screen with 153 drugs and combinations, both conventional and targeted inhibitors, both FDA approved and investigational. Results were obtained within a mean of 5.2 (range 4-7) days. Mutation testing was performed by Invivoscribe using MyAML® targeted NGS panel. Clinical outcomes examined were peripheral blast reduction, response and survival. Results: Twenty-nine patients (53.7%; 25 AML, 2 ALL, 2 acute leukemias of ambiguous lineage) received therapy based on the HTS and mutation analysis. The remaining patients did not receive protocol treatment for a variety of reasons, including insufficient marrow blasts for testing, opting for palliative care, returning to their local area, denial by insurance , lack of access to investigational drugs, or medical complications making them ineligible. Of the 22 patients who had circulating peripheral blasts, 21 (95%) had a reduction, including 7 eradication, of circulating peripheral blasts following therapy. The median number of assay-directed regimens patients received was 2 (range 1-10, mean 2.5). Therapies administered are shown in the Table. Mutation testing on 47 patients revealed that 32 samples (68%) harbored mutations for which an approved or investigational targeted inhibitor could be considered (Figure 1). Figure 2 shows the heatmaps for individual patient mutations and drug sensitivity. Median survival following initiation of protocol therapy was 70 days (range 19-811). Of these often heavily pre-treated patients, 2 achieved CR, 1 CRi, and 6 had partial remissions (PRs), overall response rate (ORR) 31%. It was not always possible to obtain the top drugs if the use was off label or they were investigational, so drugs were selected that were lower in the ranked list of IC50s. When we observed 1-3 log reduction in IC50s for a combination compared to the single agents, the combination regimen would be recommended. For the 9 patients who received intensive combination regimens, there were 1 CR and 2 PRs, (ORR 33%), and for the 13 who received low intensity regimens, there was 1 CR, 1 CRi and 1 PR (ORR 23%). Enrolled allogeneic transplant recipients who received study treatment had a median survival of 476 days post-transplant compared to 340 days for those who did not receive assay guided therapy. Conclusions: This study demonstrates the feasibility of simultaneous collection of genomics, gene expression, and functional drug sensitivity data with the intent to guide choice of therapy. Responses were observed after study guided treatment. Future trials will incorporate new algorithms based on the correlative analyses obtained by this study to optimize treatment choices. Disclosures Becker: The France Foundation: Honoraria; Accordant Health Services/Caremark: Consultancy; AbbVie, Amgen, Bristol-Myers Squibb, Glycomimetics, Invivoscribe, JW Pharmaceuticals, Novartis, Trovagene: Research Funding. Oehler:NCCN: Consultancy. Blau:All4Cure: Equity Ownership. Hammer:Glycomimetics: Consultancy. Cassaday:Kite/Gilead: Research Funding; Amgen: Consultancy, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Incyte: Research Funding; Merck: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Other: Spouse's disclosure: employment, stock and other ownership interests. Scott:Agios: Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Research Funding; Alexion: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Walter:Covagen: Consultancy; Daiichi Sankyo: Consultancy; Jazz Pharmaceuticals: Consultancy; Kite Pharma: Consultancy; Seattle Genetics: Research Funding; Race Oncology: Consultancy; Pfizer: Consultancy, Research Funding; New Link Genetics: Consultancy; Boston Biomedical: Consultancy; Boehringer Ingelheim: Consultancy; Agios: Consultancy; Amgen: Consultancy; Amphivena Therapeutics: Consultancy, Equity Ownership; Aptevo Therapeutics: Consultancy, Research Funding; Argenx BVBA: Consultancy; Astellas: Consultancy; BioLineRx: Consultancy; BiVictriX: Consultancy. Gardner:Abbvie: Speakers Bureau. Carson:Invivoscribe, Inc: Employment. Patay:Invivoscribe, Inc: Employment. OffLabel Disclosure: Bortezomib is approved for multiple myeloma. Cladribine is approved for hairy cell leukemia. Etoposide is approved for small cell lung and testicular cancer. Sorafenib is approved for hepatocellular and renal cell carcinoma, thyroid cancer. Romidepsin is approved for T cell lymphoma. Decitabine is approved for myelodysplastic syndrome. Gemcitabine is approved for non small cell lung cancer and ovarian cancer. Vinblastine is approved for breast cancer, choriocarcinoma, Hodgkin lymphoma, Kaposi sarcoma, mycosis fungoides, NHL, and testicular cancer. Trametinib is approved for melanoma, anaplastic thyroid and non small cell lung cancer. Omacetaxine is approved for CML.
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Meseguer, Marcos, Lorena Bori Arnal, Jose Ramon Hernandez Mora, Claudia Buhigas, Stephen Clark, and David Monk. "SINGLE-CELL MULTI-OMIC ANALYSIS REVEALS DEFECTIVE GENE EXPRESSION AND DNA METHILATION TOGETHER WITH CELL ANEULOIDY ASSOCIATED WITH CLEAVAGE-STAGE EMBRYO ARREST." Fertility and Sterility 118, no. 4 (October 2022): e365-e366. http://dx.doi.org/10.1016/j.fertnstert.2022.09.187.
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Curtis, Christina. "Abstract ED7-4: Beyond the lab: Clinical implications." Cancer Research 83, no. 5_Supplement (March 1, 2023): ED7–4—ED7–4. http://dx.doi.org/10.1158/1538-7445.sabcs22-ed7-4.
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Abstract During this talk, I will review the ever expanding repertoire of single cell and spatially resolved profiling techniques which enable the interrogation breast cancer pathlogy, immuno-biology and treatment response at unprecedented resolution. I will outline considerations for throughput, plex and resolution across different methods before providing several case studies in their application. As one example, I will outline the use of multi-omic single cell profiling and spatial proteomic profiling to characterize changes throughout the course of neoadjuvant Her2-targeted therapy and leading to the identification of candidate predictive biomarkers. I will go on to discuss translational and potential clinical applications of these techniques. Citation Format: Christina Curtis. Beyond the lab: Clinical implications [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr ED7-4.
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Luo, Zaili, and Richard Lu. "STEM-24. SINGLE-CELL ATLAS OF HUMAN FETAL CEREBELLUM DECODING MEDULLOBLASTOMA ORIGIN AND ONCOGENESIS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii36. http://dx.doi.org/10.1093/neuonc/noac209.141.
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Abstract Medulloblastomas (MBs) are the most common malignant childhood brain tumors, yet the origin of the most aggressive subgroup-3 form remains elusive, impeding development of effective targeted treatment strategies. Previous cell-type analyses of mouse cerebella or human counterparts from frozen tissue nuclei have not fully defined the compositional heterogeneity of MBs. Here, we undertook an unprecedented single-cell profiling of freshly-isolated human fetal cerebella at different developmental stages to establish a reference map for delineating the hierarchical cellular states in MBs. We identified a unique transitional cerebellar progenitor connecting neural stem cells to neuronal lineages in human fetal cerebella. Intersectional analysis revealed that the transitional progenitors were enriched in aggressive MB subgroups, including group-3 and metastatic tumors. Integrated single-cell multi-omic profiling revealed unique regulatory networks in the transitional progenitor populations, including transcriptional determinants HNRNPH1 and SOX11, which are correlated with clinical prognosis in aggressive group-3 MBs. Genomic profiling and Hi-C analyses identified de novo long-range chromatin loops juxtaposing HNRNPH1/SOX11-targeted super-enhancers to cis-regulatory elements of MYC, an oncogenic driver for group-3 MBs. Targeting the transitional progenitor regulators inhibited MYC expression and MYC-driven group-3 MB growth. Together, our integrated single-cell atlases of human fetal cerebella and MBs reveal important cell populations predisposed to transformation and regulatory circuitries underlying tumor cell state evolution and oncogenesis, highlighting hitherto unrecognized transitional progenitor intermediates predictive of disease prognosis and potential therapeutic vulnerabilities.