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Aney KJ, Jeong WJ, Vallejo AF, Burdziak C, Chen E, Wang A, Koak P, Wise K, Jensen K, Pe'er D, Dougan SK, Martelotto L, Nissim S. Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis. Gastroenterology 2024:S0016-5085(24)00130-6. [PMID: 38325760 DOI: 10.1053/j.gastro.2024.01.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
BACKGROUND & AIMS Acinar cells produce digestive enzymes that impede transcriptomic characterization of the exocrine pancreas. Thus, single-cell RNA-sequencing studies of the pancreas underrepresent acinar cells relative to histological expectations, and a robust approach to capture pancreatic cell responses in disease states is needed. We sought to innovate a method that overcomes these challenges to accelerate study of the pancreas in health and disease. METHODS We leverage FixNCut, a single-cell RNA-sequencing approach in which tissue is reversibly fixed with dithiobis(succinimidyl propionate) before dissociation and single-cell preparation. We apply FixNCut to an established mouse model of acute pancreatitis, validate findings using GeoMx whole transcriptome atlas profiling, and integrate our data with prior studies to compare our method in both mouse and human pancreas datasets. RESULTS FixNCut achieves unprecedented definition of challenging pancreatic cells, including acinar and immune populations in homeostasis and acute pancreatitis, and identifies changes in all major cell types during injury and recovery. We define the acinar transcriptome during homeostasis and acinar-to-ductal metaplasia and establish a unique gene set to measure deviation from normal acinar identity. We characterize pancreatic immune cells, and analysis of T-cell subsets reveals a polarization of the homeostatic pancreas toward type-2 immunity. We report immune responses during acute pancreatitis and recovery, including early neutrophil infiltration, expansion of dendritic cell subsets, and a substantial shift in the transcriptome of macrophages due to both resident macrophage activation and monocyte infiltration. CONCLUSIONS FixNCut preserves pancreatic transcriptomes to uncover novel cell states during homeostasis and following pancreatitis, establishing a broadly applicable approach and reference atlas for study of pancreas biology and disease.
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Affiliation(s)
- Katherine J Aney
- Biological and Biomedical Sciences Program, Harvard Medical School, Boston, Massachusetts; Health Sciences & Technology Program, Harvard-MIT, Boston, Massachusetts; Genetics Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Woo-Jeong Jeong
- Genetics Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | - Cassandra Burdziak
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ethan Chen
- Genetics Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Austin Wang
- Harvard University, Cambridge, Massachusetts
| | - Pal Koak
- Genetics Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Kellie Wise
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, South Australia, Australia; South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, South Australia, Australia
| | - Kirk Jensen
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, South Australia, Australia; South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, South Australia, Australia; Australian Genome Research Facility, Melbourne, Victoria, Australia
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York; Howard Hughes Medical Institute, Chevy Chase, Maryland
| | - Stephanie K Dougan
- Dana-Farber Cancer Institute, Boston, Massachusetts; Department of Immunology, Harvard Medical School, Boston, Massachusetts
| | - Luciano Martelotto
- Adelaide Centre for Epigenetics (ACE), University of Adelaide, South Australia, Australia; South Australian immunoGENomics Cancer Institute (SAiGENCI), University of Adelaide, South Australia, Australia.
| | - Sahar Nissim
- Biological and Biomedical Sciences Program, Harvard Medical School, Boston, Massachusetts; Health Sciences & Technology Program, Harvard-MIT, Boston, Massachusetts; Genetics Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Dana-Farber Cancer Institute, Boston, Massachusetts; Gastroenterology Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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Bachireddy P, Azizi E, Burdziak C, Nguyen VN, Ennis CS, Maurer K, Park CY, Choo ZN, Li S, Gohil SH, Ruthen NG, Ge Z, Keskin DB, Cieri N, Livak KJ, Kim HT, Neuberg DS, Soiffer RJ, Ritz J, Alyea EP, Peer D, Wu CJ. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy. Cell Rep 2023; 42:113011. [PMID: 37556329 PMCID: PMC10618917 DOI: 10.1016/j.celrep.2023.113011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023] Open
Affiliation(s)
| | - Elham Azizi
- Correspondence: (P.B.), (E.A.), (D.P.), (C.J.W.)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Dana Peer
- Correspondence: (P.B.), (E.A.), (D.P.), (C.J.W.)
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3
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Burdziak C, Zhao CJ, Haviv D, Alonso-Curbelo D, Lowe SW, Pe’er D. scKINETICS: inference of regulatory velocity with single-cell transcriptomics data. Bioinformatics 2023; 39:i394-i403. [PMID: 37387147 PMCID: PMC10311321 DOI: 10.1093/bioinformatics/btad267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Transcriptional dynamics are governed by the action of regulatory proteins and are fundamental to systems ranging from normal development to disease. RNA velocity methods for tracking phenotypic dynamics ignore information on the regulatory drivers of gene expression variability through time. RESULTS We introduce scKINETICS (Key regulatory Interaction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit with the simultaneous learning of per-cell transcriptional velocities and a governing gene regulatory network. Fitting is accomplished through an expectation-maximization approach designed to learn the impact of each regulator on its target genes, leveraging biologically motivated priors from epigenetic data, gene-gene coexpression, and constraints on cells' future states imposed by the phenotypic manifold. Applying this approach to an acute pancreatitis dataset recapitulates a well-studied axis of acinar-to-ductal transdifferentiation whilst proposing novel regulators of this process, including factors with previously appreciated roles in driving pancreatic tumorigenesis. In benchmarking experiments, we show that scKINETICS successfully extends and improves existing velocity approaches to generate interpretable, mechanistic models of gene regulatory dynamics. AVAILABILITY AND IMPLEMENTATION All python code and an accompanying Jupyter notebook with demonstrations are available at http://github.com/dpeerlab/scKINETICS.
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Affiliation(s)
- Cassandra Burdziak
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, 408 E 69th Street, New York, NY 10021, United States
| | - Chujun Julia Zhao
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, 408 E 69th Street, New York, NY 10021, United States
- Department of Biomedical Engineering, Columbia University, 1210 Amsterdam Ave, New York, NY 10027, United States
| | - Doron Haviv
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, 408 E 69th Street, New York, NY 10021, United States
| | - Direna Alonso-Curbelo
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Carrer de Baldiri Reixac, 10, Barcelona 08028, Spain
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, 408 E 69th Street, New York, NY 10021, United States
| | - Scott W Lowe
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, 408 E 69th Street, New York, NY 10021, United States
- Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, Maryland 20815, United States
| | - Dana Pe’er
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, 408 E 69th Street, New York, NY 10021, United States
- Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, Maryland 20815, United States
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4
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Burdziak C, Alonso-Curbelo D, Walle T, Reyes J, Barriga FM, Haviv D, Xie Y, Zhao Z, Zhao CJ, Chen HA, Chaudhary O, Masilionis I, Choo ZN, Gao V, Luan W, Wuest A, Ho YJ, Wei Y, Quail DF, Koche R, Mazutis L, Chaligné R, Nawy T, Lowe SW, Pe’er D. Epigenetic plasticity cooperates with cell-cell interactions to direct pancreatic tumorigenesis. Science 2023; 380:eadd5327. [PMID: 37167403 PMCID: PMC10316746 DOI: 10.1126/science.add5327] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 03/31/2023] [Indexed: 05/13/2023]
Abstract
The response to tumor-initiating inflammatory and genetic insults can vary among morphologically indistinguishable cells, suggesting as yet uncharacterized roles for epigenetic plasticity during early neoplasia. To investigate the origins and impact of such plasticity, we performed single-cell analyses on normal, inflamed, premalignant, and malignant tissues in autochthonous models of pancreatic cancer. We reproducibly identified heterogeneous cell states that are primed for diverse, late-emerging neoplastic fates and linked these to chromatin remodeling at cell-cell communication loci. Using an inference approach, we revealed signaling gene modules and tissue-level cross-talk, including a neoplasia-driving feedback loop between discrete epithelial and immune cell populations that was functionally validated in mice. Our results uncover a neoplasia-specific tissue-remodeling program that may be exploited for pancreatic cancer interception.
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Affiliation(s)
- Cassandra Burdziak
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine; New York, NY 10065, USA
| | - Direna Alonso-Curbelo
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology; Barcelona 08028, Spain
| | - Thomas Walle
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Clinical Cooperation Unit Virotherapy, German Cancer Research Center (DKFZ); Heidelberg 69120, Germany
- Department of Medical Oncology, National Center for Tumor Diseases; Heidelberg University Hospital, Heidelberg 69120, Germany
- German Cancer Consortium (DKTK); Heidelberg 69120, Germany
| | - José Reyes
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Francisco M. Barriga
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Doron Haviv
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine; New York, NY 10065, USA
| | - Yubin Xie
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine; New York, NY 10065, USA
| | - Zhen Zhao
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai; New York, NY 10029, USA
| | - Chujun Julia Zhao
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Department of Biomedical Engineering, Columbia University; New York, NY 10027, USA
| | - Hsuan-An Chen
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Ojasvi Chaudhary
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center; Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ignas Masilionis
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center; Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Zi-Ning Choo
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Vianne Gao
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine; New York, NY 10065, USA
| | - Wei Luan
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Alexandra Wuest
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Yu-Jui Ho
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Yuhong Wei
- Rosalind and Morris Goodman Cancer Institute, McGill University; Montreal, QC H3A 1A3, Canada
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University; Montreal, QC H3A 1A3, Canada
| | - Richard Koche
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Linas Mazutis
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Department of Biomedical Engineering, Columbia University; New York, NY 10027, USA
- Institute of Biotechnology, Life Sciences Centre; Vilnius University, Vilnius LT 02158, Lithuania
| | - Ronan Chaligné
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center; Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
| | - Scott W. Lowe
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Howard Hughes Medical Institute; Chevy Chase, MD 20815, USA
| | - Dana Pe’er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center; New York, NY 10065, USA
- Howard Hughes Medical Institute; Chevy Chase, MD 20815, USA
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5
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Mehta A, Bi L, Al'Khafaji A, Jankowiak M, Parikh M, Babadi M, Bloemendal A, Schwartz M, Munson G, Chan J, Burdziak C, Donnard E, Park R, Lu C, Rigollet P, Aguirre A, Subramanian V, Jones R, Lander ES, Ting DT, Pe'er D, Hacohen N. Abstract B016: Quantifying and dissecting pancreatic cancer cell phenotypic plasticity using lineage tracing, single-cell multiomics and CRISPR perturbations reveals novel regulators of plastic states. Cancer Res 2022. [DOI: 10.1158/1538-7445.panca22-b016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Abstract
Pancreatic cancer is a lethal disease in part because tumor cells exist in distinct transcriptional phenotypes (e.g. basal and classical states), each with a selective ability to evade current chemotherapy regimens. Two major mechanisms have been suggested for treatment evasion: 1) intrinsic resistance of certain phenotypes to particular chemotherapy regimens and 2) plasticity of treatment sensitive phenotypes to adopt more resistant phenotypes. However, the relative contribution of these mechanisms to treatment resistance is still poorly understood. Whereas previous work has described the redistribution of tumor cell states under selective treatment pressure, there is no direct evidence that tumor cells exhibit phenotypic plasticity at steady state or with treatment. By leveraging technological advancements in single-cell methods, lineage tracing and functional genomics, we have now shown direct evidence of phenotypic state switching in human pancreatic cancer cell lines. By performing single-cell RNA-seq on 5 barcoded PDAC cell lines over a steady state timecourse and under chemotherapy selective pressure (>600k cells total), we identify unique plasticity phenotypes within these cell lines and infer regulators of these plastic states. We validate the role of several of these regulators using bulk phenotypic CRISPRi screens in these cell lines. We next perform CRISPRi perturbations along with lineage tracing and single-cell multiomics (>300k cells) to dissect the regulatory relationships that underlie these cell states. We identify several novel epithelial and mesenchymal biasing factors, including those with unique roles in the most plastic clones. Collectively, we nominate several regulators that bias PDAC cell states thus posing a paradigm whereby perturbations may be used to homogenize tumor populations towards treatment-sensitive phenotypes. We believe this approach combined with current chemotherapy regimens could benefit pancreatic cancer patients by targeting residual, resistant tumor cells in the localized and metastatic disease settings to improve patient survival.
Citation Format: Arnav Mehta, Lynn Bi, Aziz Al'Khafaji, Martin Jankowiak, Milan Parikh, Mehrtash Babadi, Alex Bloemendal, Marc Schwartz, Glen Munson, Joeseph Chan, Cassandra Burdziak, Elisa Donnard, Ryan Park, Chen Lu, Philippe Rigollet, Andrew Aguirre, Vidya Subramanian, Ray Jones, Eric S. Lander, David T. Ting, Dana Pe'er, Nir Hacohen. Quantifying and dissecting pancreatic cancer cell phenotypic plasticity using lineage tracing, single-cell multiomics and CRISPR perturbations reveals novel regulators of plastic states [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B016.
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Affiliation(s)
- Arnav Mehta
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Lynn Bi
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | | | - Milan Parikh
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | | | | | - Glen Munson
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Joeseph Chan
- 2Memorial Sloan Kettering Cancer Center, New York, NY,
| | | | | | - Ryan Park
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | - Chen Lu
- 3Massachusetts Institute of Technology, Cambridge, MA,
| | | | | | | | - Ray Jones
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
| | | | | | - Dana Pe'er
- 2Memorial Sloan Kettering Cancer Center, New York, NY,
| | - Nir Hacohen
- 1Broad Institute of MIT and Harvard, Cambridge, MA,
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6
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Bachireddy P, Azizi E, Burdziak C, Nguyen VN, Ennis CS, Maurer K, Park CY, Choo ZN, Li S, Gohil SH, Ruthen NG, Ge Z, Keskin DB, Cieri N, Livak KJ, Kim HT, Neuberg DS, Soiffer RJ, Ritz J, Alyea EP, Pe'er D, Wu CJ. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy. Cell Rep 2021; 37:109992. [PMID: 34758319 PMCID: PMC9035342 DOI: 10.1016/j.celrep.2021.109992] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 06/23/2021] [Accepted: 10/21/2021] [Indexed: 01/06/2023] Open
Abstract
To elucidate mechanisms by which T cells eliminate leukemia, we study donor lymphocyte infusion (DLI), an established immunotherapy for relapsed leukemia. We model T cell dynamics by integrating longitudinal, multimodal data from 94,517 bone marrow-derived single T cell transcriptomes in addition to chromatin accessibility and single T cell receptor sequencing from patients undergoing DLI. We find that responsive tumors are defined by enrichment of late-differentiated T cells before DLI and rapid, durable expansion of early differentiated T cells after treatment, highly similar to "terminal" and "precursor" exhausted subsets, respectively. Resistance, in contrast, is defined by heterogeneous T cell dysfunction. Surprisingly, early differentiated T cells in responders mainly originate from pre-existing and novel clonotypes recruited to the leukemic microenvironment, rather than the infusion. Our work provides a paradigm for analyzing longitudinal single-cell profiling of scenarios beyond adoptive cell therapy and introduces Symphony, a Bayesian approach to infer regulatory circuitry underlying T cell subsets, with broad relevance to exhaustion antagonists across cancers.
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Affiliation(s)
- Pavan Bachireddy
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA; Department of Hematopoietic Biology & Malignancy, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Cancer Prevention and Research Institute of Texas (CPRIT) Scholar in Cancer Research, Austin, TX 78701, USA.
| | - Elham Azizi
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Biomedical Engineering and Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA.
| | - Cassandra Burdziak
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Vinhkhang N Nguyen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Christina S Ennis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Katie Maurer
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Cameron Y Park
- Department of Biomedical Engineering and Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
| | - Zi-Ning Choo
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Shuqiang Li
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Satyen H Gohil
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Neil G Ruthen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Zhongqi Ge
- Department of Hematopoietic Biology & Malignancy, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Derin B Keskin
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nicoletta Cieri
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Kenneth J Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Haesook T Kim
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Donna S Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Robert J Soiffer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Jerome Ritz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Edwin P Alyea
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Parker Institute of Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| | - Catherine J Wu
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA.
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7
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Bachireddy P, Azizi E, Burdziak C, Nguyen V, Ennis C, Choo ZN, Li S, Livak K, Neuberg D, Soiffer R, Ritz J, Alyea E, Pe'er D, Wu C. Abstract LT008: Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy. Cancer Res 2021. [DOI: 10.1158/1538-7445.tme21-lt008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Immune therapies have transformed the cancer therapeutic landscape but fail to benefit most patients. To elucidate the underlying mechanisms by which T cells mediate elimination of leukemia, we generated a high-resolution map of longitudinal T cell dynamics within the same tumor microenvironment (TME; bone marrow) during response or resistance to donor lymphocyte infusion (DLI), a widely used immunotherapy for relapsed leukemia. We analyzed 87,939 bone marrow-derived single T cell transcriptomes, along with chromatin accessibility and single T cell receptor clonality profiles, by developing novel machine learning tools for integrating longitudinal and multimodal data. We found that pre-treatment enrichment and post-treatment rapid, durable expansion of ‘terminal’ (TEX) and ‘precursor’ (TPEX) exhausted subsets, respectively, defined DLI response. In contrast to the common, shared pathways marking DLI response, a heterogeneous pattern of T cell dysfunction marked DLI resistance. Unexpectedly, TPEX cells that expanded in responders did not arise from the infusion product but instead from both pre-existing and novel clonotypes recruited to the TME. Further, we introduce a Bayesian method, Symphony, to define the T cell regulatory circuitry and master regulators underlying TEX and TPEX subsets that may be broadly relevant to other exhaustion antagonists across cancers. In conclusion, our data implicate the hierarchy of both TEX and TPEX subsets for immunotherapeutic responses in leukemia, extending the scope of their relevance beyond checkpoint blockade to adoptive cellular therapy. Moreover, our results provocatively suggest that immunologic ‘help’ from DLI, rather than direct transfer of anti-leukemic T cells, drove leukemic remission. Finally, we provide a general analysis paradigm for exploiting temporal single-cell genomic profiling for deep understanding of how immune therapies differentially shape the evolutionary trajectories of the TME in accordance with clinical outcome.
Citation Format: Pavan Bachireddy, Elham Azizi, Cassandra Burdziak, Vinhkhang Nguyen, Christina Ennis, Zi- Ning Choo, Shuqiang Li, Kenneth Livak, Donna Neuberg, Robert Soiffer, Jerome Ritz, Edwin Alyea, Dana Pe'er, Catherine Wu. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT008.
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Affiliation(s)
| | | | | | | | | | - Zi- Ning Choo
- 3Memorial Sloan Kettering Cancer Center, New York City, NY,
| | | | | | | | | | | | | | - Dana Pe'er
- 3Memorial Sloan Kettering Cancer Center, New York City, NY,
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Alonso-Curbelo D, Ho YJ, Burdziak C, Maag JLV, Morris JP, Chandwani R, Chen HA, Tsanov KM, Barriga FM, Luan W, Tasdemir N, Livshits G, Azizi E, Chun J, Wilkinson JE, Mazutis L, Leach SD, Koche R, Pe'er D, Lowe SW. A gene-environment-induced epigenetic program initiates tumorigenesis. Nature 2021; 590:642-648. [PMID: 33536616 PMCID: PMC8482641 DOI: 10.1038/s41586-020-03147-x] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 12/18/2020] [Indexed: 02/07/2023]
Abstract
Tissue damage increases the risk of cancer through poorly understood mechanisms1. In mouse models of pancreatic cancer, pancreatitis associated with tissue injury collaborates with activating mutations in the Kras oncogene to markedly accelerate the formation of early neoplastic lesions and, ultimately, adenocarcinoma2,3. Here, by integrating genomics, single-cell chromatin assays and spatiotemporally controlled functional perturbations in autochthonous mouse models, we show that the combination of Kras mutation and tissue damage promotes a unique chromatin state in the pancreatic epithelium that distinguishes neoplastic transformation from normal regeneration and is selected for throughout malignant evolution. This cancer-associated epigenetic state emerges within 48 hours of pancreatic injury, and involves an 'acinar-to-neoplasia' chromatin switch that contributes to the early dysregulation of genes that define human pancreatic cancer. Among the factors that are most rapidly activated after tissue damage in the pre-malignant pancreatic epithelium is the alarmin cytokine interleukin 33, which recapitulates the effects of injury in cooperating with mutant Kras to unleash the epigenetic remodelling program of early neoplasia and neoplastic transformation. Collectively, our study demonstrates how gene-environment interactions can rapidly produce gene-regulatory programs that dictate early neoplastic commitment, and provides a molecular framework for understanding the interplay between genetic and environmental cues in the initiation of cancer.
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Affiliation(s)
- Direna Alonso-Curbelo
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yu-Jui Ho
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Cassandra Burdziak
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jesper L V Maag
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John P Morris
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rohit Chandwani
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Surgery, Weill Cornell Medical College, New York, NY, USA
- Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, NY, USA
| | - Hsuan-An Chen
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Louis V. Gerstner Jr Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kaloyan M Tsanov
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Francisco M Barriga
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wei Luan
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nilgun Tasdemir
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Geulah Livshits
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elham Azizi
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jaeyoung Chun
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John E Wilkinson
- Department of Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Linas Mazutis
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Steven D Leach
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Dartmouth Norris Cotton Cancer Center, Hanover, NH, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard Koche
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Scott W Lowe
- Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe'er D. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell 2018; 174:716-729.e27. [PMID: 29961576 DOI: 10.1016/j.cell.2018.05.061] [Citation(s) in RCA: 810] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 02/19/2018] [Accepted: 05/30/2018] [Indexed: 01/06/2023]
Abstract
Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.
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Affiliation(s)
- David van Dijk
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Roshan Sharma
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Applied Physics and Applied Math, Columbia University, New York, NY, USA
| | - Juozas Nainys
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Institute of Biotechnology, Vilnius University, Vilnius, Lithuania
| | - Kristina Yim
- Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA
| | - Pooja Kathail
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Ambrose J Carr
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Cassandra Burdziak
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kevin R Moon
- Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA; Applied Mathematics Program, Yale University, New Haven, CT, USA
| | | | | | - Brian Bierie
- Whitehead Institute for Biomedical Research, MIT, Cambridge, MA, USA
| | - Linas Mazutis
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guy Wolf
- Applied Mathematics Program, Yale University, New Haven, CT, USA
| | - Smita Krishnaswamy
- Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, USA; Applied Mathematics Program, Yale University, New Haven, CT, USA.
| | - Dana Pe'er
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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