1
|
Spasevska I, Sharma A, Steen CB, Josefsson SE, Blaker YN, Kolstad A, Rustad EH, Meyer S, Isaksen K, Chellappa S, Kushekhar K, Beiske K, Førsund MS, Spetalen S, Holte H, Østenstad B, Brodtkorb M, Kimby E, Olweus J, Taskén K, Newman AM, Lorenz S, Smeland EB, Alizadeh AA, Huse K, Myklebust JH. Diversity of intratumoral regulatory T cells in B-cell non-Hodgkin lymphoma. Blood Adv 2023; 7:7216-7230. [PMID: 37695745 PMCID: PMC10698546 DOI: 10.1182/bloodadvances.2023010158] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023] Open
Abstract
Tumor-infiltrating regulatory T cells (Tregs) contribute to an immunosuppressive tumor microenvironment. Despite extensive studies, the prognostic impact of tumor-infiltrating Tregs in B-cell non-Hodgkin lymphomas (B-NHLs) remains unclear. Emerging studies suggest substantial heterogeneity in the phenotypes and suppressive capacities of Tregs, emphasizing the importance of understanding Treg diversity and the need for additional markers to identify highly suppressive Tregs. Here, we applied single-cell RNA sequencing and T-cell receptor sequencing combined with high-dimensional cytometry to decipher the heterogeneity of intratumoral Tregs in diffuse large B-cell lymphoma and follicular lymphoma (FL), compared with that in nonmalignant tonsillar tissue. We identified 3 distinct transcriptional states of Tregs: resting, activated, and unconventional LAG3+FOXP3- Tregs. Activated Tregs were enriched in B-NHL tumors, coexpressed several checkpoint receptors, and had stronger immunosuppressive activity compared with resting Tregs. In FL, activated Tregs were found in closer proximity to CD4+ and CD8+ T cells than other cell types. Furthermore, we used a computational approach to develop unique gene signature matrices, which were used to enumerate each Treg subset in cohorts with bulk gene expression data. In 2 independent FL cohorts, activated Tregs was the major subset, and high abundance was associated with adverse outcome. This study demonstrates that Tregs infiltrating B-NHL tumors are transcriptionally and functionally diverse. Highly immunosuppressive activated Tregs were enriched in tumor tissue but absent in the peripheral blood. Our data suggest that a deeper understanding of Treg heterogeneity in B-NHL could open new paths for rational drug design, facilitating selective targeting to improve antitumor immunity.
Collapse
Affiliation(s)
- Ivana Spasevska
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Ankush Sharma
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Chloé B. Steen
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
- Division of Oncology, Stanford University School of Medicine, Stanford, CA
| | - Sarah E. Josefsson
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
| | - Yngvild N. Blaker
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
| | - Arne Kolstad
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Department of Oncology, Innlandet Hospital Trust, Lillehammer, Norway
- Division of Cancer Medicine, Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Even H. Rustad
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Saskia Meyer
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Kathrine Isaksen
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Stalin Chellappa
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Kushi Kushekhar
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
| | - Klaus Beiske
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Division of Cancer Medicine, Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Mette S. Førsund
- Division of Cancer Medicine, Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Signe Spetalen
- Division of Cancer Medicine, Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Harald Holte
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Division of Cancer Medicine, Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Bjørn Østenstad
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Division of Cancer Medicine, Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Marianne Brodtkorb
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Division of Cancer Medicine, Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Eva Kimby
- Department of Hematology, Karolinska Institute, Stockholm, Sweden
| | - Johanna Olweus
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Norway
| | - Kjetil Taskén
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
| | - Aaron M. Newman
- Division of Oncology, Stanford University School of Medicine, Stanford, CA
- Divisions of Hematology & Oncology, Department of Medicine, Stanford University, Stanford, CA
| | - Susanne Lorenz
- Department of Core Facilities, Geonomics Core Facility, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Erlend B. Smeland
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - Ash A. Alizadeh
- Division of Oncology, Stanford University School of Medicine, Stanford, CA
- Divisions of Hematology & Oncology, Department of Medicine, Stanford University, Stanford, CA
| | - Kanutte Huse
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| | - June H. Myklebust
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute of Clinical Medicine, University of Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
| |
Collapse
|
2
|
Vahid MR, Brown EL, Steen CB, Zhang W, Jeon HS, Kang M, Gentles AJ, Newman AM. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat Biotechnol 2023; 41:1543-1548. [PMID: 36879008 PMCID: PMC10635828 DOI: 10.1038/s41587-023-01697-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [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: 05/15/2022] [Accepted: 01/25/2023] [Indexed: 03/08/2023]
Abstract
Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.
Collapse
Affiliation(s)
- Milad R Vahid
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Erin L Brown
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Chloé B Steen
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Wubing Zhang
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Hyun Soo Jeon
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Minji Kang
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Andrew J Gentles
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
| |
Collapse
|
3
|
Steen CB, Luca BA, Alizadeh AA, Gentles AJ, Newman AM. Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper. Methods Mol Biol 2023; 2629:43-71. [PMID: 36929073 DOI: 10.1007/978-1-0716-2986-4_4] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.
Collapse
Affiliation(s)
- Chloé B Steen
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Bogdan A Luca
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Ash A Alizadeh
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Andrew J Gentles
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
| |
Collapse
|
4
|
Sikandar SS, Gulati GS, Antony J, Fetter I, Kuo AH, Ho WHD, Haro-Acosta V, Das S, Steen CB, Pereira TA, Qian D, Beachy PA, Dirbas FM, Red-Horse K, Rabbitts TH, Thiery JP, Newman AM, Clarke MF. Identification of a minority population of LMO2 + breast cancer cells that integrate into the vasculature and initiate metastasis. Sci Adv 2022; 8:eabm3548. [PMID: 36351009 PMCID: PMC10939096 DOI: 10.1126/sciadv.abm3548] [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] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Metastasis is responsible for most breast cancer-related deaths; however, identifying the cellular determinants of metastasis has remained challenging. Here, we identified a minority population of immature THY1+/VEGFA+ tumor epithelial cells in human breast tumor biopsies that display angiogenic features and are marked by the expression of the oncogene, LMO2. Higher abundance of LMO2+ basal cells correlated with tumor endothelial content and predicted poor distant recurrence-free survival in patients. Using MMTV-PyMT/Lmo2CreERT2 mice, we demonstrated that Lmo2 lineage-traced cells integrate into the vasculature and have a higher propensity to metastasize. LMO2 knockdown in human breast tumors reduced lung metastasis by impairing intravasation, leading to a reduced frequency of circulating tumor cells. Mechanistically, we find that LMO2 binds to STAT3 and is required for STAT3 activation by tumor necrosis factor-α and interleukin-6. Collectively, our study identifies a population of metastasis-initiating cells with angiogenic features and establishes the LMO2-STAT3 signaling axis as a therapeutic target in breast cancer metastasis.
Collapse
Affiliation(s)
- Shaheen S. Sikandar
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Gunsagar S. Gulati
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
| | - Jane Antony
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
| | - Isobel Fetter
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Angera H. Kuo
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
| | - William Hai Dang Ho
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
| | - Veronica Haro-Acosta
- Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Soumyashree Das
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Chloé B. Steen
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Thiago Almeida Pereira
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
| | - Dalong Qian
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
| | - Philip A. Beachy
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
| | - Frederick M. Dirbas
- Department of Surgery, Stanford Cancer Institute, Stanford University School of Medicine, 875 Blake Wilbur Drive, Rm CC2235, Stanford, CA 94305, USA
| | - Kristy Red-Horse
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford, CA 94305, USA
| | - Terence H. Rabbitts
- Division of Cancer Therapeutics, Institute of Cancer Research, London SM2 5NG, UK
| | - Jean Paul Thiery
- Guangzhou Laboratory, International Biological Island, Guangzhou, Guangdong 510005, China
| | - Aaron M. Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Michael F. Clarke
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, 265 Campus Drive, School of Medicine, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
5
|
Esfahani MS, Hamilton EG, Mehrmohamadi M, Nabet BY, Alig SK, King DA, Steen CB, Macaulay CW, Schultz A, Nesselbush MC, Soo J, Schroers-Martin JG, Chen B, Binkley MS, Stehr H, Chabon JJ, Sworder BJ, Hui ABY, Frank MJ, Moding EJ, Liu CL, Newman AM, Isbell JM, Rudin CM, Li BT, Kurtz DM, Diehn M, Alizadeh AA. Inferring gene expression from cell-free DNA fragmentation profiles. Nat Biotechnol 2022; 40:585-597. [PMID: 35361996 PMCID: PMC9337986 DOI: 10.1038/s41587-022-01222-4] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 01/14/2022] [Indexed: 02/07/2023]
Abstract
Profiling of circulating tumor DNA (ctDNA) in the bloodstream shows promise for noninvasive cancer detection. Chromatin fragmentation features have previously been explored to infer gene expression profiles from cell-free DNA (cfDNA), but current fragmentomic methods require high concentrations of tumor-derived DNA and provide limited resolution. Here we describe promoter fragmentation entropy as an epigenomic cfDNA feature that predicts RNA expression levels at individual genes. We developed 'epigenetic expression inference from cell-free DNA-sequencing' (EPIC-seq), a method that uses targeted sequencing of promoters of genes of interest. Profiling 329 blood samples from 201 patients with cancer and 87 healthy adults, we demonstrate classification of subtypes of lung carcinoma and diffuse large B cell lymphoma. Applying EPIC-seq to serial blood samples from patients treated with PD-(L)1 immune-checkpoint inhibitors, we show that gene expression profiles inferred by EPIC-seq are correlated with clinical response. Our results indicate that EPIC-seq could enable noninvasive, high-throughput tissue-of-origin characterization with diagnostic, prognostic and therapeutic potential.
Collapse
Affiliation(s)
- Mohammad Shahrokh Esfahani
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.,Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA
| | - Emily G. Hamilton
- Program in Cancer Biology, Stanford School of Medicine, Stanford, CA, USA
| | - Mahya Mehrmohamadi
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.,Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA
| | - Barzin Y. Nabet
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA
| | - Stefan K. Alig
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Daniel A. King
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Chloé B. Steen
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.,Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA.,Department of Biomedical Informatics, Stanford School of Medicine, Stanford, CA, USA
| | - Charles W. Macaulay
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Andre Schultz
- Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA
| | | | - Joanne Soo
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Joseph G. Schroers-Martin
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA
| | - Binbin Chen
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Michael S. Binkley
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA
| | - Henning Stehr
- Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA
| | - Jacob J. Chabon
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA
| | - Brian J. Sworder
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Angela B-Y Hui
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA
| | - Matthew J. Frank
- Division of Blood and Marrow Transplantation and Cellular Therapy, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Everett J. Moding
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA
| | - Chih Long Liu
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Aaron M. Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA.,Department of Biomedical Informatics, Stanford School of Medicine, Stanford, CA, USA
| | - James M. Isbell
- Thoracic Surgery Service, Memorial Sloan Kettering Cancer Center and Weill Cornell Medicine, New York, NY, USA
| | - Charles M. Rudin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bob T. Li
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David M. Kurtz
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA.,Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA.,Correspondence and requests for materials should be addressed to Maximilian Diehn or Ash A. Alizadeh, ;
| | - Ash A. Alizadeh
- Divisions of Oncology and of Hematology, Department of Medicine, Stanford School of Medicine, Stanford, CA, USA.,Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA.,Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA.,Correspondence and requests for materials should be addressed to Maximilian Diehn or Ash A. Alizadeh, ;
| |
Collapse
|
6
|
Lozano AX, Chaudhuri AA, Nene A, Bacchiocchi A, Earland N, Vesely MD, Usmani A, Turner BE, Steen CB, Luca BA, Badri T, Gulati GS, Vahid MR, Khameneh F, Harris PK, Chen DY, Dhodapkar K, Sznol M, Halaban R, Newman AM. T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma. Nat Med 2022; 28:353-362. [PMID: 35027754 DOI: 10.1038/s41591-021-01623-z] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 11/09/2021] [Indexed: 12/15/2022]
Abstract
Severe immune-related adverse events (irAEs) occur in up to 60% of patients with melanoma treated with immune checkpoint inhibitors (ICIs). However, it is unknown whether a common baseline immunological state precedes irAE development. Here we applied mass cytometry by time of flight, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing and bulk T cell receptor (TCR) sequencing to study peripheral blood samples from patients with melanoma treated with anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination ICIs. By analyzing 93 pre- and early on-ICI blood samples and 3 patient cohorts (n = 27, 26 and 18), we found that 2 pretreatment factors in circulation-activated CD4 memory T cell abundance and TCR diversity-are associated with severe irAE development regardless of organ system involvement. We also explored on-treatment changes in TCR clonality among patients receiving combination therapy and linked our findings to the severity and timing of irAE onset. These results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.
Collapse
Affiliation(s)
- Alexander X Lozano
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.,Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Aadel A Chaudhuri
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Computer Science & Engineering, Washington University, St. Louis, MO, USA. .,Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.
| | - Aishwarya Nene
- Yale School of Medicine, Yale University, New Haven, CT, USA
| | | | - Noah Earland
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Matthew D Vesely
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Abul Usmani
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Brandon E Turner
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Chloé B Steen
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.,Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Bogdan A Luca
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
| | - Ti Badri
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Gunsagar S Gulati
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Milad R Vahid
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Farnaz Khameneh
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Peter K Harris
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - David Y Chen
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA.,Division of Dermatology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kavita Dhodapkar
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Emory University, Atlanta, GA, USA
| | - Mario Sznol
- Department of Medicine, Division of Medical Oncology, Yale University School of Medicine, New Haven, CT, USA.,Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Ruth Halaban
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA.,Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. .,Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| |
Collapse
|
7
|
Luca BA, Steen CB, Matusiak M, Azizi A, Varma S, Zhu C, Przybyl J, Espín-Pérez A, Diehn M, Alizadeh AA, van de Rijn M, Gentles AJ, Newman AM. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 2021; 184:5482-5496.e28. [PMID: 34597583 DOI: 10.1016/j.cell.2021.09.014] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [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: 11/19/2020] [Revised: 06/21/2021] [Accepted: 09/08/2021] [Indexed: 12/31/2022]
Abstract
Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.
Collapse
Affiliation(s)
- Bogdan A Luca
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Chloé B Steen
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | | | - Armon Azizi
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Sushama Varma
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Chunfang Zhu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Joanna Przybyl
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Almudena Espín-Pérez
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Maximilian Diehn
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Ash A Alizadeh
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Division of Hematology, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Matt van de Rijn
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Andrew J Gentles
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
| |
Collapse
|
8
|
Steen CB, Luca BA, Esfahani MS, Azizi A, Sworder BJ, Nabet BY, Kurtz DM, Liu CL, Khameneh F, Advani RH, Natkunam Y, Myklebust JH, Diehn M, Gentles AJ, Newman AM, Alizadeh AA. The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell 2021; 39:1422-1437.e10. [PMID: 34597589 PMCID: PMC9205168 DOI: 10.1016/j.ccell.2021.08.011] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 06/24/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022]
Abstract
Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine-learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.edu/lymphoma).
Collapse
Affiliation(s)
- Chloé B Steen
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Bogdan A Luca
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Mohammad S Esfahani
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA
| | - Armon Azizi
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Brian J Sworder
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA
| | - Barzin Y Nabet
- Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA 94305, USA
| | - David M Kurtz
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA
| | - Chih Long Liu
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA
| | - Farnaz Khameneh
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Ranjana H Advani
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA
| | - Yasodha Natkunam
- Department of Pathology, Stanford University Medical Center, Stanford, CA 94305, USA
| | - June H Myklebust
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA 94305, USA
| | - Andrew J Gentles
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
| | - Ash A Alizadeh
- Department of Medicine, Division of Oncology, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
| |
Collapse
|
9
|
Luca BA, Steen CB, Azizi A, Matusiak M, Przybyl J, Neishaboori N, Pérez AE, Diehn M, Alizadeh AA, van de Rijn M, Gentles AJ, Newman AM. Abstract 3443: Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors. Cancer Res 2020. [DOI: 10.1158/1538-7445.am2020-3443] [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
Tumors are complex ecosystems consisting of malignant, immune, and stromal elements whose dynamic interactions drive patient survival and response to therapy. A comprehensive understanding of the diversity of cellular states within the tumor microenvironment (TME), and their patterns of co-occurrence, could provide new diagnostic tools for improved disease management and novel targets for therapeutic intervention. To address this challenge, we developed EcoTyper, a novel machine learning framework for large-scale identification of TME cell states and their co-association patterns from bulk, single-cell, and spatially resolved tumor expression data. EcoTyper starts by “purifying” cell type-specific gene expression profiles of epithelial cells, immune, and stromal cell types from bulk tissue transcriptomes using CIBERSORTx (Newman et al., Nat Biotechnol 2019). It then identifies transcriptional states for each cell type, validates them in scRNA-seq data, and uncovers co-occurrence patterns between cell states in order to define tumor cellular ecosystems. Applied to 6,475 tumor and adjacent normal samples from solid tumor types profiled by The Cancer Genome Atlas (TCGA), EcoTyper identified robust transcriptional states across 12 major cell types, including epithelial, fibroblast, endothelial, and 9 immune subsets. These states included both known and novel cellular phenotypes, nearly all of which could be validated in a compendium of scRNA-seq tumor atlases spanning ~140,000 cells. Most cell states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly associated with overall survival, both in TCGA and in 9,062 held-out tumor specimens (Gentles/Newman et al., Nat Medicine 2015). We found that specific cell states co-occur in distinct cellular communities with characteristic patterns of ligand-receptor interactions, genomic features, clinical outcomes, and spatial organization. One such ecosystem defined a normal-like state that was strongly enriched in non-malignant samples. Others delineated novel pro- and anti-tumor inflammatory environments involving specific fibroblast, endothelial, and immune cell transcriptional programs. In summary, large-scale deconvolution of cell type-specific transcriptomes across thousands of solid tumors revealed a comprehensive atlas of TME cell states and cellular ecosystems. Our results provide a high-resolution portrait of cellular heterogeneity in the TME across multiple solid tumor types, with implications for novel diagnostics and immunotherapeutic targets.
References:
1. Newman, A.M., et al., Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol, 2019. 37(7): p. 773-782.
2. Gentles, A.J., et al., The prognostic landscape of genes and infiltrating immune cells across human cancers. Nature medicine, 2015. 21(8): p. 938.
Citation Format: Bogdan A. Luca, Chloé B. Steen, Armon Azizi, Magdalena Matusiak, Joanna Przybyl, Nastaran Neishaboori, Almudena Espín Pérez, Maximilian Diehn, Ash A. Alizadeh, Matt van de Rijn, Andrew J. Gentles, Aaron M. Newman. Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3443.
Collapse
|
10
|
Abstract
CIBERSORTx is a suite of machine learning tools for the assessment of cellular abundance and cell type-specific gene expression patterns from bulk tissue transcriptome profiles. With this framework, single-cell or bulk-sorted RNA sequencing data can be used to learn molecular signatures of distinct cell types from a small collection of biospecimens. These signatures can then be repeatedly applied to characterize cellular heterogeneity from bulk tissue transcriptomes without physical cell isolation. In this chapter, we provide a detailed primer on CIBERSORTx and demonstrate its capabilities for high-throughput profiling of cell types and cellular states in normal and neoplastic tissues.
Collapse
Affiliation(s)
- Chloé B Steen
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Chih Long Liu
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.,Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Ash A Alizadeh
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. .,Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. .,Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA. .,Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. .,Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| |
Collapse
|
11
|
Liu CC, Steen CB, Newman AM. Computational approaches for characterizing the tumor immune microenvironment. Immunology 2019; 158:70-84. [PMID: 31347163 PMCID: PMC6742767 DOI: 10.1111/imm.13101] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [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: 06/08/2019] [Revised: 07/16/2019] [Accepted: 07/18/2019] [Indexed: 12/13/2022] Open
Abstract
Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.
Collapse
Affiliation(s)
- Candace C. Liu
- Immunology Graduate ProgramSchool of MedicineStanford UniversityStanfordCAUSA
| | - Chloé B. Steen
- Division of OncologyDepartment of MedicineStanford Cancer InstituteStanford UniversityStanfordCAUSA
| | - Aaron M. Newman
- Institute for Stem Cell Biology and Regenerative MedicineStanford UniversityStanfordCAUSA
- Department of Biomedical Data ScienceStanford UniversityStanfordCAUSA
| |
Collapse
|
12
|
Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, Diehn M, Alizadeh AA. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol 2019; 37:773-782. [PMID: 31061481 PMCID: PMC6610714 DOI: 10.1038/s41587-019-0114-2] [Citation(s) in RCA: 1912] [Impact Index Per Article: 382.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 03/26/2019] [Indexed: 02/07/2023]
Abstract
Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.
Collapse
Affiliation(s)
- Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. .,Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
| | - Chloé B Steen
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.,Department of Informatics, University of Oslo, Oslo, Norway
| | - Chih Long Liu
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.,Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Andrew J Gentles
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.,Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.,Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA.,Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Aadel A Chaudhuri
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA.,Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Florian Scherer
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.,Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Michael S Khodadoust
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Mohammad S Esfahani
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.,Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA.,Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Bogdan A Luca
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - David Steiner
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Maximilian Diehn
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.,Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA.,Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Ash A Alizadeh
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. .,Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA. .,Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA. .,Stanford Cancer Institute, Stanford University, Stanford, CA, USA. .,Division of Hematology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
| |
Collapse
|
13
|
Steen CB, Leich E, Myklebust JH, Lockmer S, Wise JF, Wahlin BE, Østenstad B, Liestøl K, Kimby E, Rosenwald A, Smeland EB, Holte H, Lingjærde OC, Brodtkorb M. A clinico-molecular predictor identifies follicular lymphoma patients at risk of early transformation after first-line immunotherapy. Haematologica 2019; 104:e460-e464. [PMID: 30846496 DOI: 10.3324/haematol.2018.209080] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Chloé B Steen
- Department of Informatics, University of Oslo, Oslo, Norway.,Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Ellen Leich
- Institute of Pathology, University of Würzburg and Comprehensive Cancer Centre Mainfranken, Germany
| | - June H Myklebust
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,KG Jebsen Centre for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sandra Lockmer
- Division of Haematology, Department of Medicine at Huddinge, Karolinska Institute and University Hospital, Stockholm, Sweden
| | - Jillian F Wise
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,KG Jebsen Centre for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Björn E Wahlin
- Division of Haematology, Department of Medicine at Huddinge, Karolinska Institute and University Hospital, Stockholm, Sweden
| | - Bjørn Østenstad
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Knut Liestøl
- Department of Informatics, University of Oslo, Oslo, Norway.,Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
| | - Eva Kimby
- Division of Haematology, Department of Medicine at Huddinge, Karolinska Institute and University Hospital, Stockholm, Sweden
| | - Andreas Rosenwald
- Institute of Pathology, University of Würzburg and Comprehensive Cancer Centre Mainfranken, Germany
| | - Erlend B Smeland
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,KG Jebsen Centre for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - Harald Holte
- KG Jebsen Centre for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Oncology, Oslo University Hospital, Oslo, Norway
| | - Ole Christian Lingjærde
- Department of Informatics, University of Oslo, Oslo, Norway .,KG Jebsen Centre for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Marianne Brodtkorb
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway .,KG Jebsen Centre for B-Cell Malignancies, Institute for Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Oncology, Oslo University Hospital, Oslo, Norway
| |
Collapse
|
14
|
Josefsson SE, Huse K, Kolstad A, Beiske K, Pende D, Steen CB, Inderberg EM, Lingjærde OC, Østenstad B, Smeland EB, Levy R, Irish JM, Myklebust JH. T Cells Expressing Checkpoint Receptor TIGIT Are Enriched in Follicular Lymphoma Tumors and Characterized by Reversible Suppression of T-cell Receptor Signaling. Clin Cancer Res 2017; 24:870-881. [PMID: 29217528 DOI: 10.1158/1078-0432.ccr-17-2337] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 10/10/2017] [Accepted: 11/30/2017] [Indexed: 11/16/2022]
Abstract
Purpose: T cells infiltrating follicular lymphoma (FL) tumors are considered dysfunctional, yet the optimal target for immune checkpoint blockade is unknown. Characterizing coinhibitory receptor expression patterns and signaling responses in FL T-cell subsets might reveal new therapeutic targets.Experimental Design: Surface expression of 9 coinhibitory receptors governing T-cell function was characterized in T-cell subsets from FL lymph node tumors and from healthy donor tonsils and peripheral blood samples, using high-dimensional flow cytometry. The results were integrated with T-cell receptor (TCR)-induced signaling and cytokine production. Expression of T-cell immunoglobulin and ITIM domain (TIGIT) ligands was detected by immunohistochemistry.Results: TIGIT was a frequently expressed coinhibitory receptor in FL, expressed by the majority of CD8 T effector memory cells, which commonly coexpressed exhaustion markers such as PD-1 and CD244. CD8 FL T cells demonstrated highly reduced TCR-induced phosphorylation (p) of ERK and reduced production of IFNγ, while TCR proximal signaling (p-CD3ζ, p-SLP76) was not affected. The TIGIT ligands CD112 and CD155 were expressed by follicular dendritic cells in the tumor microenvironment. Dysfunctional TCR signaling correlated with TIGIT expression in FL CD8 T cells and could be fully restored upon in vitro culture. The costimulatory receptor CD226 was downregulated in TIGIT+ compared with TIGIT- CD8 FL T cells, further skewing the balance toward immunosuppression.Conclusions: TIGIT blockade is a relevant strategy for improved immunotherapy in FL. A deeper understanding of the interplay between coinhibitory receptors and key T-cell signaling events can further assist in engineering immunotherapeutic regimens to improve clinical outcomes of cancer patients. Clin Cancer Res; 24(4); 870-81. ©2017 AACR.
Collapse
Affiliation(s)
- Sarah E Josefsson
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway.,Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Kanutte Huse
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway.,Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Arne Kolstad
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Klaus Beiske
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Daniela Pende
- Immunology Laboratory, Ospedale Policlinico San Martino, Genova, Italy
| | - Chloé B Steen
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway.,Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Department of Computer Science, University of Oslo, Oslo, Norway
| | | | - Ole Christian Lingjærde
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway.,Department of Computer Science, University of Oslo, Oslo, Norway
| | - Bjørn Østenstad
- Department of Oncology, Division of Cancer Medicine, Oslo University Hospital, Oslo, Norway
| | - Erlend B Smeland
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway.,Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Ronald Levy
- Division of Oncology, Stanford School of Medicine, Stanford, California
| | - Jonathan M Irish
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee.,Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.,Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - June H Myklebust
- Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway. .,Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| |
Collapse
|
15
|
Eikenes ÅH, Malerød L, Christensen AL, Steen CB, Mathieu J, Nezis IP, Liestøl K, Huynh JR, Stenmark H, Haglund K. ALIX and ESCRT-III coordinately control cytokinetic abscission during germline stem cell division in vivo. PLoS Genet 2015; 11:e1004904. [PMID: 25635693 PMCID: PMC4312039 DOI: 10.1371/journal.pgen.1004904] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [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: 06/12/2014] [Accepted: 11/18/2014] [Indexed: 12/21/2022] Open
Abstract
Abscission is the final step of cytokinesis that involves the cleavage of the intercellular bridge connecting the two daughter cells. Recent studies have given novel insight into the spatiotemporal regulation and molecular mechanisms controlling abscission in cultured yeast and human cells. The mechanisms of abscission in living metazoan tissues are however not well understood. Here we show that ALIX and the ESCRT-III component Shrub are required for completion of abscission during Drosophila female germline stem cell (fGSC) division. Loss of ALIX or Shrub function in fGSCs leads to delayed abscission and the consequent formation of stem cysts in which chains of daughter cells remain interconnected to the fGSC via midbody rings and fusome. We demonstrate that ALIX and Shrub interact and that they co-localize at midbody rings and midbodies during cytokinetic abscission in fGSCs. Mechanistically, we show that the direct interaction between ALIX and Shrub is required to ensure cytokinesis completion with normal kinetics in fGSCs. We conclude that ALIX and ESCRT-III coordinately control abscission in Drosophila fGSCs and that their complex formation is required for accurate abscission timing in GSCs in vivo. Cytokinesis, the final step of cell division, concludes with a process termed abscission, during which the two daughter cells physically separate. In spite of their importance, the molecular machineries controlling abscission are poorly characterized especially in the context of living metazoan tissues. Here we provide molecular insight into the mechanism of abscission using the fruit fly Drosophila melanogaster as a model organism. We show that the scaffold protein ALIX and the ESCRT-III component Shrub are required for completion of abscission in Drosophila female germline stem cells (fGSCs). ESCRT-III has been implicated in topologically similar membrane scission events as abscission, namely intraluminal vesicle formation at endosomes and virus budding. Here we demonstrate that ALIX and Shrub co-localize and interact to promote abscission with correct timing in Drosophila fGSCs. We thus show that ALIX and ESCRT-III coordinately control abscission in Drosophila fGSCs cells and report an evolutionarily conserved function of the ALIX/ESCRT-III pathway during cytokinesis in a multi-cellular organism.
Collapse
Affiliation(s)
- Åsmund H. Eikenes
- Department of Biochemistry, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Lene Malerød
- Department of Biochemistry, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Anette Lie Christensen
- Department of Biochemistry, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Chloé B. Steen
- Department of Biochemistry, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Juliette Mathieu
- Department of Genetics and Developmental Biology, Institut Curie, Paris, France
- CNRS UMR3215, Inserm U934 F-75248, Paris, France
| | - Ioannis P. Nezis
- School of Life Sciences, University of Warwick, Coventry, United Kingdom
| | - Knut Liestøl
- Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Jean-René Huynh
- Department of Genetics and Developmental Biology, Institut Curie, Paris, France
- CNRS UMR3215, Inserm U934 F-75248, Paris, France
| | - Harald Stenmark
- Department of Biochemistry, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Kaisa Haglund
- Department of Biochemistry, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Centre for Cancer Biomedicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- * E-mail:
| |
Collapse
|