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Yellen BB, Zawistowski JS, Czech EA, Sanford CI, SoRelle ED, Luftig MA, Forbes ZG, Wood KC, Hammerbacher J. Massively parallel quantification of phenotypic heterogeneity in single-cell drug responses. Sci Adv 2021; 7:eabf9840. [PMID: 34533995 PMCID: PMC8448449 DOI: 10.1126/sciadv.abf9840] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
Single-cell analysis tools have made substantial advances in characterizing genomic heterogeneity; however, tools for measuring phenotypic heterogeneity have lagged due to the increased difficulty of handling live biology. Here, we report a single-cell phenotyping tool capable of measuring image-based clonal properties at scales approaching 100,000 clones per experiment. These advances are achieved by exploiting a previously unidentified flow regime in ladder microfluidic networks that, under appropriate conditions, yield a mathematically perfect cell trap. Machine learning and computer vision tools are used to control the imaging hardware and analyze the cellular phenotypic parameters within these images. Using this platform, we quantified the responses of tens of thousands of single cell–derived acute myeloid leukemia (AML) clones to targeted therapy, identifying rare resistance and morphological phenotypes at frequencies down to 0.05%. This approach can be extended to higher-level cellular architectures such as cell pairs and organoids and on-chip live-cell fluorescence assays.
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Affiliation(s)
- Benjamin B. Yellen
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA
- Celldom Inc., San Carlos, CA 94070, USA
| | | | - Eric A. Czech
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Caleb I. Sanford
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA
| | - Elliott D. SoRelle
- Department of Molecular Genetics and Microbiology, Center for Virology, Duke University, Durham, NC 27708, USA
| | - Micah A. Luftig
- Department of Molecular Genetics and Microbiology, Center for Virology, Duke University, Durham, NC 27708, USA
| | | | - Kris C. Wood
- Celldom Inc., San Carlos, CA 94070, USA
- Department of Pharmacology and Cancer Biology, Duke University, Durham, NC 27708, USA
| | - Jeff Hammerbacher
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC 29425, USA
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2
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Harjanto D, Abelin JG, Malloy M, Suri P, Colson T, Goulding SP, Creech AL, Serrano LR, Nasir G, Nasrullah Y, McGann CD, Velez D, Ting YS, Poran A, Rothenberg DA, Chhangawala S, Rubinsteyn A, Hammerbacher J, Gaynor RB, Fritsch EF, Oslund RC, Barthelme D, Addona TA, Arieta CM, Rooney MS. Abstract B23: Enhanced HLA-II epitope prediction for immunotherapy with novel proteomics and genomics approaches. Cancer Immunol Res 2020. [DOI: 10.1158/2326-6074.tumimm19-b23] [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
While tumor-reactive CD4+ T cells have been associated with effective immunotherapy responses, accurate prediction of MHC class II-restricted ligands remains a challenge, limiting our ability to harness CD4+ immunity for cancer therapy. To this end, we have developed a state-of-the-art MHC class II binding predictor, neonmhc2, trained on HLA-II ligands identified from mass spectrometry (MS) of cell lines engineered to express a single affinity-tagged HLA-II allele. Over 60 HLA-II alleles have been characterized to date. We demonstrate that neonmhc2 outperforms NetMHCIIpan, the current benchmark for class II prediction, in distinguishing 1) HLA-II ligands presented in cell lines and tissues, and critically, 2) immunogenic CD4+ epitopes identified with tetramer-guided epitope mapping. Furthermore, we show that numerous neoantigen peptides that were ranked highly by neonmhc2 but poorly by NetMHCIIpan were immunogenic in an ex vivo induction. We next studied HLA-II antigen processing in order to further boost our ability to predict CD4+ epitopes. We determined a gene-level bias by comparing transcript expression and gene length to the frequency of observations in MS, finding that secreted genes are over-represented in HLA-II ligandomes from tissues. We built numerous primary sequence-based processability predictors trained on MS data, but only achieved significant prediction improvement when using the simple feature of determining if a candidate peptide contained sequence that overlapped a previously observed HLA-II ligand. By combining neonmhc2 binding prediction, transcript expression, gene bias, and the overlap feature in an integrated presentation predictor, we were able to achieve up to a 61-fold increase in ability to predict HLA-II peptides presented from tissue MS data over NetMHCIIpan alone. We also sought to understand which cells are presenting HLA-II ligands in the tumor microenvironment to elucidate the presentation pathway most relevant to immunotherapy. By leveraging publicly available RNA-seq (bulk and single cell), we found that professional antigen-presenting cells rather than tumor cells are primarily responsible for HLA-II presentation. We developed a novel SILAC-based MS workflow to directly interrogate peptides derived from phagocytosed tumor cells that are presented by dendritic cells. The experiment revealed that mitochondrial genes are preferentially presented from phagocytosed cells. In conclusion, by integrating proteomics and genomics data at large scale, we have defined new rules for understanding HLA-II processing and presentation, particularly in the context of the tumor microenvironment. This work should enhance our ability to predict CD4+ epitopes for immunotherapy.
Citation Format: Dewi Harjanto, Jennifer G. Abelin, Matthew Malloy, Prerna Suri, Tyler Colson, Scott P. Goulding, Amanda L. Creech, Lia R. Serrano, Gibbs Nasir, Yusuf Nasrullah, Christopher D. McGann, Diana Velez, Ying S. Ting, Asaf Poran, Daniel A. Rothenberg, Sagar Chhangawala, Alex Rubinsteyn, Jeff Hammerbacher, Richard B. Gaynor, Edward F. Fritsch, Rob C. Oslund, Dominik Barthelme, Terri A. Addona, Christina M. Arieta, Michael S. Rooney. Enhanced HLA-II epitope prediction for immunotherapy with novel proteomics and genomics approaches [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr B23.
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3
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Olcina MM, Balanis NG, Kim RK, Aksoy BA, Kodysh J, Thompson MJ, Hammerbacher J, Graeber TG, Giaccia AJ. Mutations in an Innate Immunity Pathway Are Associated with Poor Overall Survival Outcomes and Hypoxic Signaling in Cancer. Cell Rep 2019; 25:3721-3732.e6. [PMID: 30590044 PMCID: PMC6405289 DOI: 10.1016/j.celrep.2018.11.093] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 10/01/2018] [Accepted: 11/27/2018] [Indexed: 12/18/2022] Open
Abstract
Complement-mediated cytotoxicity may act as a selective pressure for tumor overexpression of complement regulators. We hypothesize that the same selective pressure could lead to complement alterations at the genetic level. We find that, when analyzed as a pathway, mutations in complement genes occur at a relatively high frequency and are associated with changes in overall survival across a number of cancer types. Analysis of pathways expressed in patients with complement mutations that are associated with poor overall survival reveals crosstalk between complement and hypoxia in colorectal cancer. The importance of this crosstalk is highlighted by two key findings: hypoxic signaling is increased in tumors harboring complement mutations, and hypoxic tumor cells are resistant to complement-mediated cytotoxicity due, in part, to hypoxia-induced expression of complement regulator CD55. The range of strategies employed by tumors to dysregulate the complement system testifies to the importance of this pathway in tumor progression.
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Affiliation(s)
- Monica M Olcina
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
| | - Nikolas G Balanis
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Crump Institute for Molecular Imaging, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ryan K Kim
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA
| | - B Arman Aksoy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Julia Kodysh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael J Thompson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeff Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Thomas G Graeber
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Crump Institute for Molecular Imaging, University of California, Los Angeles, Los Angeles, CA, USA
| | - Amato J Giaccia
- Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
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4
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Abelin JG, Harjanto D, Malloy M, Suri P, Colson T, Goulding SP, Creech AL, Serrano LR, Nasir G, Nasrullah Y, McGann CD, Velez D, Ting YS, Poran A, Rothenberg DA, Chhangawala S, Rubinsteyn A, Hammerbacher J, Gaynor RB, Fritsch EF, Greshock J, Oslund RC, Barthelme D, Addona TA, Arieta CM, Rooney MS. Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction. Immunity 2019; 51:766-779.e17. [PMID: 31495665 DOI: 10.1016/j.immuni.2019.08.012] [Citation(s) in RCA: 149] [Impact Index Per Article: 29.8] [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: 03/13/2019] [Revised: 06/19/2019] [Accepted: 08/15/2019] [Indexed: 12/30/2022]
Abstract
Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Asaf Poran
- Neon Therapeutics, Cambridge, MA 02139, USA
| | | | | | - Alex Rubinsteyn
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jeff Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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5
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Czech E, Aksoy BA, Aksoy P, Hammerbacher J. Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging. BMC Bioinformatics 2019; 20:448. [PMID: 31477013 PMCID: PMC6720861 DOI: 10.1186/s12859-019-3055-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 08/26/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind. RESULTS Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset. CONCLUSION Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit .
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Affiliation(s)
- Eric Czech
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA.
| | - Bulent Arman Aksoy
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA
| | - Pinar Aksoy
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA
| | - Jeff Hammerbacher
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA
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6
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Sanseviero E, O'Brien EM, Karras JR, Shabaneh TB, Aksoy BA, Xu W, Zheng C, Yin X, Xu X, Karakousis GC, Amaravadi RK, Nam B, Turk MJ, Hammerbacher J, Rubinstein MP, Schuchter LM, Mitchell TC, Liu Q, Stone EL. Anti-CTLA-4 Activates Intratumoral NK Cells and Combined with IL15/IL15Rα Complexes Enhances Tumor Control. Cancer Immunol Res 2019; 7:1371-1380. [PMID: 31239316 DOI: 10.1158/2326-6066.cir-18-0386] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 02/02/2019] [Accepted: 06/18/2019] [Indexed: 11/16/2022]
Abstract
Antibodies targeting CTLA-4 induce durable responses in some patients with melanoma and are being tested in a variety of human cancers. However, these therapies are ineffective for a majority of patients across tumor types. Further understanding the immune alterations induced by these therapies may enable the development of novel strategies to enhance tumor control and biomarkers to identify patients most likely to respond. In several murine models, including colon26, MC38, CT26, and B16 tumors cotreated with GVAX, anti-CTLA-4 efficacy depends on interactions between the Fc region of CTLA-4 antibodies and Fc receptors (FcR). Anti-CTLA-4 binding to FcRs has been linked to depletion of intratumoral T regulatory cells (Treg). In agreement with previous studies, we found that Tregs infiltrating CT26, B16-F1, and autochthonous Braf V600E Pten -/- melanoma tumors had higher expression of surface CTLA-4 (sCTLA-4) than other T-cell subsets, and anti-CTLA-4 treatment led to FcR-dependent depletion of Tregs infiltrating CT26 tumors. This Treg depletion coincided with activation and degranulation of intratumoral natural killer cells. Similarly, in non-small cell lung cancer (NSCLC) and melanoma patient-derived tumor tissue, Tregs had higher sCTLA-4 expression than other intratumoral T-cell subsets, and Tregs infiltrating NSCLC expressed more sCTLA-4 than circulating Tregs. Patients with cutaneous melanoma who benefited from ipilimumab, a mAb targeting CTLA-4, had higher intratumoral CD56 expression, compared with patients who received little to no benefit from this therapy. Furthermore, using the murine CT26 model we found that combination therapy with anti-CTLA-4 plus IL15/IL15Rα complexes enhanced tumor control compared with either monotherapy.
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Affiliation(s)
- Emilio Sanseviero
- Immunology, Microenvironment and Metastasis Program, Wistar Cancer Center, The Wistar Institute, Philadelphia, Pennsylvania
| | - Erin M O'Brien
- Immunology, Microenvironment and Metastasis Program, Wistar Cancer Center, The Wistar Institute, Philadelphia, Pennsylvania
| | - Jenna R Karras
- Immunology, Microenvironment and Metastasis Program, Wistar Cancer Center, The Wistar Institute, Philadelphia, Pennsylvania
| | - Tamer B Shabaneh
- Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.,Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Bulent Arman Aksoy
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, South Carolina
| | - Wei Xu
- Melanoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Cathy Zheng
- Melanoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Xiangfan Yin
- Molecular and Cellular Oncogenesis Program, Wistar Cancer Center, The Wistar Institute, Philadelphia, Pennsylvania
| | - Xiaowei Xu
- Melanoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Giorgos C Karakousis
- Melanoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ravi K Amaravadi
- Melanoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Brian Nam
- The Helen F. Graham Cancer Center & Research Institute, Christiana Care Health System, Newark, Delaware
| | - Mary Jo Turk
- Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.,Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Jeff Hammerbacher
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, South Carolina
| | - Mark P Rubinstein
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, South Carolina.,Department of Surgery, Medical University of South Carolina, Charleston, South Carolina
| | - Lynn M Schuchter
- Melanoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tara C Mitchell
- Melanoma Program, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Qin Liu
- Molecular and Cellular Oncogenesis Program, Wistar Cancer Center, The Wistar Institute, Philadelphia, Pennsylvania
| | - Erica L Stone
- Immunology, Microenvironment and Metastasis Program, Wistar Cancer Center, The Wistar Institute, Philadelphia, Pennsylvania.
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7
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Brilleman SL, Crowther MJ, Moreno-Betancur M, Buros Novik J, Dunyak J, Al-Huniti N, Fox R, Hammerbacher J, Wolfe R. Joint longitudinal and time-to-event models for multilevel hierarchical data. Stat Methods Med Res 2018; 28:3502-3515. [PMID: 30378472 DOI: 10.1177/0962280218808821] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.
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Affiliation(s)
- Samuel L Brilleman
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.,Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Australia
| | - Michael J Crowther
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
| | - Margarita Moreno-Betancur
- Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Australia.,Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia.,Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Jacqueline Buros Novik
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James Dunyak
- Quantitative Clinical Pharmacology, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA
| | - Nidal Al-Huniti
- Quantitative Clinical Pharmacology, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA
| | - Robert Fox
- Quantitative Clinical Pharmacology, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA
| | - Jeff Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, USA
| | - Rory Wolfe
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.,Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Australia
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8
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Metz KS, Deoudes EM, Berginski ME, Jimenez-Ruiz I, Aksoy BA, Hammerbacher J, Gomez SM, Phanstiel DH. Coral: Clear and Customizable Visualization of Human Kinome Data. Cell Syst 2018; 7:347-350.e1. [PMID: 30172842 DOI: 10.1016/j.cels.2018.07.001] [Citation(s) in RCA: 92] [Impact Index Per Article: 15.3] [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: 05/09/2018] [Revised: 06/22/2018] [Accepted: 07/02/2018] [Indexed: 12/15/2022]
Abstract
Protein kinases represent one of the largest gene families in eukaryotes and play roles in a wide range of cell signaling processes and human diseases. Current tools for visualizing kinase data in the context of the human kinome superfamily are limited to encoding data through the addition of nodes to a low-resolution image of the kinome tree. We present Coral, a user-friendly interactive web application for visualizing both quantitative and qualitative data. Unlike previous tools, Coral can encode data in three features (node color, node size, and branch color), allows three modes of kinome visualization (the traditional kinome tree as well as radial and dynamic force networks), and generates high-resolution scalable vector graphics files suitable for publication without the need for refinement using graphics editing software. Due to its user-friendly, interactive, and highly customizable design, Coral is broadly applicable to high-throughput studies of the human kinome. The source code and web application are available at github.com/dphansti/CORAL and phanstiel-lab.med.unc.edu/Coral, respectively.
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Affiliation(s)
- Kathleen S Metz
- Curriculum in Genetics & Molecular Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Erika M Deoudes
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Matthew E Berginski
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27514, USA; Department of Pharmacology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ivan Jimenez-Ruiz
- Curriculum in Bioinformatics & Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Bulent Arman Aksoy
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jeff Hammerbacher
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Shawn M Gomez
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27514, USA; Department of Pharmacology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Douglas H Phanstiel
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Cell Biology & Physiology, University of North Carolina, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.
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9
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Hellmann MD, Nathanson T, Rizvi H, Creelan BC, Sanchez-Vega F, Ahuja A, Ni A, Novik JB, Mangarin LMB, Abu-Akeel M, Liu C, Sauter JL, Rekhtman N, Chang E, Callahan MK, Chaft JE, Voss MH, Tenet M, Li XM, Covello K, Renninger A, Vitazka P, Geese WJ, Borghaei H, Rudin CM, Antonia SJ, Swanton C, Hammerbacher J, Merghoub T, McGranahan N, Snyder A, Wolchok JD. Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer. Cancer Cell 2018; 33:843-852.e4. [PMID: 29657128 PMCID: PMC5953836 DOI: 10.1016/j.ccell.2018.03.018] [Citation(s) in RCA: 711] [Impact Index Per Article: 118.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 02/09/2018] [Accepted: 03/16/2018] [Indexed: 12/29/2022]
Abstract
Combination immune checkpoint blockade has demonstrated promising benefit in lung cancer, but predictors of response to combination therapy are unknown. Using whole-exome sequencing to examine non-small-cell lung cancer (NSCLC) treated with PD-1 plus CTLA-4 blockade, we found that high tumor mutation burden (TMB) predicted improved objective response, durable benefit, and progression-free survival. TMB was independent of PD-L1 expression and the strongest feature associated with efficacy in multivariable analysis. The low response rate in TMB low NSCLCs demonstrates that combination immunotherapy does not overcome the negative predictive impact of low TMB. This study demonstrates the association between TMB and benefit to combination immunotherapy in NSCLC. TMB should be incorporated in future trials examining PD-(L)1 with CTLA-4 blockade in NSCLC.
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Affiliation(s)
- Matthew D Hellmann
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA; Weill Cornell School of Medicine, New York, NY, USA; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Tavi Nathanson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hira Rizvi
- Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Benjamin C Creelan
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Francisco Sanchez-Vega
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Marie-Josèe and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arun Ahuja
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ai Ni
- Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jacki B Novik
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Levi M B Mangarin
- Ludwig Collaborative Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohsen Abu-Akeel
- Ludwig Collaborative Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Cailian Liu
- Ludwig Collaborative Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer L Sauter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Natasha Rekhtman
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eliza Chang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Margaret K Callahan
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA; Weill Cornell School of Medicine, New York, NY, USA; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jamie E Chaft
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA; Weill Cornell School of Medicine, New York, NY, USA; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Martin H Voss
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA; Weill Cornell School of Medicine, New York, NY, USA
| | - Megan Tenet
- Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Xue-Mei Li
- Bristol Myers Squibb, Princeton, NJ, USA
| | | | | | | | | | | | - Charles M Rudin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA; Weill Cornell School of Medicine, New York, NY, USA; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Scott J Antonia
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Translational Cancer Therapeutics Laboratory, Francis Crick Institute, London, UK
| | - Jeff Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, USA
| | - Taha Merghoub
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA; Weill Cornell School of Medicine, New York, NY, USA; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Ludwig Collaborative Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Alexandra Snyder
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA
| | - Jedd D Wolchok
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 885 2(nd) Avenue, New York, NY 10017, USA; Weill Cornell School of Medicine, New York, NY, USA; Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Ludwig Collaborative Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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10
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O'Donnell T, Christie EL, Ahuja A, Buros J, Aksoy BA, Bowtell DDL, Snyder A, Hammerbacher J. Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer. BMC Cancer 2018; 18:87. [PMID: 29357823 PMCID: PMC5778667 DOI: 10.1186/s12885-017-3825-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Accepted: 11/23/2017] [Indexed: 12/31/2022] Open
Abstract
Background Patients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown. Methods We sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for high grade serous ovarian carcinoma patients enrolled in the Australian Ovarian Cancer Study. In this series, 35 of 114 samples were collected after exposure to chemotherapy; 14 are matched with an untreated sample from the same patient. Our approach integrates whole genome and RNA sequencing of bulk tumor samples with class I MHC binding prediction and mutational signatures extracted from studies of chemotherapy-exposed Caenorhabditis elegans and Gallus gallus cells. We additionally investigated the relationship between neoantigens, tumor infiltrating immune cells estimated from RNA-seq with CIBERSORT, and patient survival. Results Greater neoantigen burden and CD8+ T cell infiltration in primary, pre-treatment samples were independently associated with improved survival. Relapse samples collected after chemotherapy harbored a median of 78% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other processes operative during relapse. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0–16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology. Conclusion Relapsed ovarian cancers harbor more predicted neoantigens than primary tumors, but the increase is due to pre-existing mutational processes, not mutagenesis from chemotherapy. Electronic supplementary material The online version of this article (doi:10.1186/s12885-017-3825-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | - Arun Ahuja
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - B Arman Aksoy
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David D L Bowtell
- Peter MacCallum Cancer Centre, East Melbourne, Victoria, 3002, Australia
| | - Alexandra Snyder
- Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.,Adaptive Biotechnologies, Seattle, WA, USA
| | - Jeff Hammerbacher
- Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Microbiology and Immunology, Medical University of South Carolina, Charleston, SC, USA.
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11
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Abstract
The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution, and T-cell receptor sequencing. Attempts to integrate these data are in early stages of development and are discussed in this review. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies.
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Affiliation(s)
- J Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston
| | - A Snyder
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York
- Adaptive Biotechnologies, Seattle, USA
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12
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Snyder A, Nathanson T, Funt SA, Ahuja A, Buros Novik J, Hellmann MD, Chang E, Aksoy BA, Al-Ahmadie H, Yusko E, Vignali M, Benzeno S, Boyd M, Moran M, Iyer G, Robins HS, Mardis ER, Merghoub T, Hammerbacher J, Rosenberg JE, Bajorin DF. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis. PLoS Med 2017; 14:e1002309. [PMID: 28552987 PMCID: PMC5446110 DOI: 10.1371/journal.pmed.1002309] [Citation(s) in RCA: 218] [Impact Index Per Article: 31.1] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 04/26/2017] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Inhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance. METHODS AND FINDINGS The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (n = 25, p = 0.044). A limitation of our study is its small sample size (n = 29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating. CONCLUSIONS These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.
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Affiliation(s)
- Alexandra Snyder
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America
- * E-mail:
| | - Tavi Nathanson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Samuel A. Funt
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Arun Ahuja
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jacqueline Buros Novik
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Matthew D. Hellmann
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Eliza Chang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Bulent Arman Aksoy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Hikmat Al-Ahmadie
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Erik Yusko
- Adaptive Biotechnologies, Seattle, Washington, United States of America
| | - Marissa Vignali
- Adaptive Biotechnologies, Seattle, Washington, United States of America
| | - Sharon Benzeno
- Adaptive Biotechnologies, Seattle, Washington, United States of America
| | - Mariel Boyd
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Meredith Moran
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Gopa Iyer
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Harlan S. Robins
- Adaptive Biotechnologies, Seattle, Washington, United States of America
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Elaine R. Mardis
- Department of Pediatrics, Institute for Genomic Medicine, The Research Institute at Nationwide Children's Hospital, The Ohio State University College of Medicine, Columbus, Ohio, United States of America
| | - Taha Merghoub
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Jeff Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jonathan E. Rosenberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Dean F. Bajorin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- Department of Medicine, Weill Cornell Medical College, New York, New York, United States of America
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13
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Nathanson T, Ahuja A, Rubinsteyn A, Aksoy BA, Hellmann MD, Miao D, Van Allen E, Merghoub T, Wolchok JD, Snyder A, Hammerbacher J. Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade. Cancer Immunol Res 2016; 5:84-91. [PMID: 27956380 DOI: 10.1158/2326-6066.cir-16-0019] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 11/21/2016] [Accepted: 11/23/2016] [Indexed: 01/08/2023]
Abstract
Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that the presence of somatic mutations is associated with benefit from checkpoint inhibition. A hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from our previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of these patients. We found that the ability to accurately predict patient benefit did not increase as the analysis narrowed from somatic mutation burden, to inclusion of only those mutations predicted to be MHC class I neoantigens, to only including those neoantigens that were expressed or that had homology to pathogens. The only association between somatic mutation burden and response was found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden were also associated with response, but neither was more predictive than somatic mutation burden. Neither the previously described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens was more predictive than mutation burden. Cancer Immunol Res; 5(1); 84-91. ©2016 AACR.
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Affiliation(s)
- Tavi Nathanson
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Arun Ahuja
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander Rubinsteyn
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Bulent Arman Aksoy
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew D Hellmann
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Diana Miao
- Department of Medical Oncology, Dana-Farber Cancer Institute, Broad Institute of MIT and Harvard, Boston, Massachusetts.,Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Eliezer Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Broad Institute of MIT and Harvard, Boston, Massachusetts.,Center for Cancer Precision Medicine, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Taha Merghoub
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Medicine, Weill Cornell Medical College, New York, New York.,Swim Across America-Ludwig Collaborative Research Laboratory, Immunology Program, Ludwig Center for Cancer Immunotherapy, New York, New York
| | - Jedd D Wolchok
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Medicine, Weill Cornell Medical College, New York, New York.,Swim Across America-Ludwig Collaborative Research Laboratory, Immunology Program, Ludwig Center for Cancer Immunotherapy, New York, New York
| | - Alexandra Snyder
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York. .,Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Jeff Hammerbacher
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
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14
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Vanderkam D, Aksoy BA, Hodes I, Perrone J, Hammerbacher J. pileup.js: a JavaScript library for interactive and in-browser visualization of genomic data. ACTA ACUST UNITED AC 2016; 32:2378-9. [PMID: 27153605 PMCID: PMC4965634 DOI: 10.1093/bioinformatics/btw167] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 03/23/2016] [Indexed: 11/13/2022]
Abstract
pileup.js is a new browser-based genome viewer. It is designed to facilitate the investigation of evidence for genomic variants within larger web applications. It takes advantage of recent developments in the JavaScript ecosystem to provide a modular, reliable and easily embedded library. Availability and implementation: The code and documentation for pileup.js is publicly available at https://github.com/hammerlab/pileup.js under the Apache 2.0 license. Contact: correspondence@hammerlab.org
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Affiliation(s)
- Dan Vanderkam
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - B Arman Aksoy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Isaac Hodes
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Jaclyn Perrone
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Jeff Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
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