1
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Launonen IM, Erkan EP, Niemiec I, Junquera A, Hincapié-Otero M, Afenteva D, Liang Z, Salko M, Szabo A, Perez-Villatoro F, Falco MM, Li Y, Micoli G, Nagaraj A, Haltia UM, Kahelin E, Oikkonen J, Hynninen J, Virtanen A, Nirmal AJ, Vallius T, Hautaniemi S, Sorger P, Vähärautio A, Färkkilä A. Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585657. [PMID: 38562799 PMCID: PMC10983974 DOI: 10.1101/2024.03.19.585657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed "Myelonets" and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.
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Affiliation(s)
- Inga-Maria Launonen
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | | | - Iga Niemiec
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Ada Junquera
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | | | - Daria Afenteva
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Zhihan Liang
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Matilda Salko
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Angela Szabo
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | | | - Matias M Falco
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Yilin Li
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Giulia Micoli
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Ashwini Nagaraj
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Ulla-Maija Haltia
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Department of Oncology, Clinical trials unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
| | - Essi Kahelin
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital
| | - Jaana Oikkonen
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Anni Virtanen
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital
| | - Ajit J Nirmal
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA
| | - Tuulia Vallius
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA
- Ludwig Center at Harvard
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Peter Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, USA
| | - Anna Vähärautio
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Foundation for the Finnish Cancer Institute, Finland
| | - Anniina Färkkilä
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Department of Oncology, Clinical trials unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute for Life Sciences, University of Helsinki, Finland
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2
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Jamalzadeh S, Dai J, Lavikka K, Li Y, Jiang J, Huhtinen K, Virtanen A, Oikkonen J, Hietanen S, Hynninen J, Vähärautio A, Häkkinen A, Hautaniemi S. Genome-wide quantification of copy-number aberration impact on gene expression in ovarian high-grade serous carcinoma. BMC Cancer 2024; 24:173. [PMID: 38317080 PMCID: PMC10840274 DOI: 10.1186/s12885-024-11895-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/17/2024] [Indexed: 02/07/2024] Open
Abstract
Copy-number alterations (CNAs) are a hallmark of cancer and can regulate cancer cell states via altered gene expression values. Herein, we have developed a copy-number impact (CNI) analysis method that quantifies the degree to which a gene expression value is impacted by CNAs and leveraged this analysis at the pathway level. Our results show that a high CNA is not necessarily reflected at the gene expression level, and our method is capable of detecting genes and pathways whose activity is strongly influenced by CNAs. Furthermore, the CNI analysis enables unbiased categorization of CNA categories, such as deletions and amplifications. We identified six CNI-driven pathways associated with poor treatment response in ovarian high-grade serous carcinoma (HGSC), which we found to be the most CNA-driven cancer across 14 cancer types. The key driver in most of these pathways was amplified wild-type KRAS, which we validated functionally using CRISPR modulation. Our results suggest that wild-type KRAS amplification is a driver of chemotherapy resistance in HGSC and may serve as a potential treatment target.
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Affiliation(s)
- Sanaz Jamalzadeh
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jun Dai
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kari Lavikka
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yilin Li
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jing Jiang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kaisa Huhtinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Institute of Biomedicine and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Anni Virtanen
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
| | - Jaana Oikkonen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sakari Hietanen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Anna Vähärautio
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Foundation for the Finnish Cancer Institute, Helsinki, Finland
| | - Antti Häkkinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
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3
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Lavikka K, Oikkonen J, Li Y, Muranen T, Micoli G, Marchi G, Lahtinen A, Huhtinen K, Lehtonen R, Hietanen S, Hynninen J, Virtanen A, Hautaniemi S. Deciphering cancer genomes with GenomeSpy: a grammar-based visualization toolkit. Gigascience 2024; 13:giae040. [PMID: 39101783 DOI: 10.1093/gigascience/giae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 05/13/2024] [Accepted: 06/19/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Visualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remains a distinct need for tailored solutions. However, their implementation typically requires extensive programming expertise from bioinformaticians and software developers, especially when building interactive applications. Toolkits based on visualization grammars offer a more accessible, declarative way to author new visualizations. Yet, current grammar-based solutions fall short in adequately supporting the interactive analysis of large datasets with extensive sample collections, a pivotal task often encountered in cancer research. FINDINGS We present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. By using combinatorial building blocks and a declarative language, users can implement new visualization designs easily and embed them in web pages or end-user-oriented applications. A distinctive element of GenomeSpy's architecture is its effective use of the graphics processing unit in all rendering, enabling a high frame rate and smoothly animated interactions, such as navigation within a genome. We demonstrate the utility of GenomeSpy by characterizing the genomic landscape of 753 ovarian cancer samples from patients in the DECIDER clinical trial. Our results expand the understanding of the genomic architecture in ovarian cancer, particularly the diversity of chromosomal instability. CONCLUSIONS GenomeSpy is a visualization toolkit applicable to a wide range of tasks pertinent to genome analysis. It offers high flexibility and exceptional performance in interactive analysis. The toolkit is open source with an MIT license, implemented in JavaScript, and available at https://genomespy.app/.
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Affiliation(s)
- Kari Lavikka
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Jaana Oikkonen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Yilin Li
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Taru Muranen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Giulia Micoli
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Giovanni Marchi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Alexandra Lahtinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Kaisa Huhtinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
- Cancer Research Unit, Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, 20521 Turku, Finland
| | - Rainer Lehtonen
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Sakari Hietanen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Anni Virtanen
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, 00260 Helsinki, Finland
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
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4
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Marchi G, Rajavuori A, Nguyen MTN, Huhtinen K, Oksa S, Hietanen S, Hautaniemi S, Hynninen J, Oikkonen J. Extensive mutational ctDNA profiles reflect High-grade serous cancer tumors and reveal emerging mutations at recurrence. Transl Oncol 2024; 39:101814. [PMID: 37924564 PMCID: PMC10641709 DOI: 10.1016/j.tranon.2023.101814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/06/2023] Open
Abstract
OBJECTIVE Circulating tumor DNA (ctDNA) offers a minimally-invasive alternative to study genomic changes in recurrent malignancies. With a high recurrence rate, the overall survival in high-grade serous ovarian carcinoma (HGSC) has remained low. Our objectives were to determine whether ctDNA from plasma adequately represents HGSC, and to find mutational changes at relapse suggesting therapy options that could alter patient outcome. METHODS We collected 152 longitudinal plasma and 92 fresh tissue samples from 29 HGSC patients, sequencing and detecting mutations with a gene panel of more than 700 cancer-related genes. Tumor content was measured using TP53 VAF. We analyzed the concordance between the mutations in tissue and plasma samples and calculated correlations to patient outcomes. We also searched for novel mutations appearing at relapse. RESULTS The concordance rate between mutations in plasma compared to tumor tissue was 83 % at diagnosis and 90 % at relapse. CtDNA was released similarly from the tubo-ovarian tumors, intra-abdominal metastases and ascites. CtDNA release was high when CA-125 level was elevated. The TP53 VAF in ctDNA from plasma samples before the third cycle of primary chemotherapy showed a negative correlation to patient outcome. At relapse, 19 novel, pathogenic DNA mutations appeared, suggesting possible actionable alterations and biological mechanisms related to chemoresistance. CONCLUSION Relapse ctDNA samples reflect tissue samples well and longitudinal sampling provides a timely source for mutational profiling. The emerging genetic mutations at recurrence propose that ctDNA accurately represents the widespread disease and provides possibilities for personalized therapy options.
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Affiliation(s)
- Giovanni Marchi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki 00291, Finland
| | - Anna Rajavuori
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Mai T N Nguyen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki 00291, Finland
| | - Kaisa Huhtinen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki 00291, Finland
| | - Sinikka Oksa
- Satasairaala Central Hospital, Department of Obstetrics and Gynecology, 28500 Pori, Finland
| | - Sakari Hietanen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki 00291, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Jaana Oikkonen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki 00291, Finland.
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5
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Koskela H, Li Y, Joutsiniemi T, Muranen T, Isoviita VM, Huhtinen K, Micoli G, Lavikka K, Marchi G, Hietanen S, Virtanen A, Hautaniemi S, Oikkonen J, Hynninen J. HRD related signature 3 predicts clinical outcome in advanced tubo-ovarian high-grade serous carcinoma. Gynecol Oncol 2024; 180:91-98. [PMID: 38061276 DOI: 10.1016/j.ygyno.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 11/14/2023] [Accepted: 11/25/2023] [Indexed: 02/18/2024]
Abstract
OBJECTIVES We evaluated usability of single base substitution signature 3 (Sig3) as a biomarker for homologous recombination deficiency (HRD) in tubo-ovarian high-grade serous carcinoma (HGSC). MATERIALS AND METHODS This prospective observational trial includes 165 patients with advanced HGSC. Fresh tissue samples (n = 456) from multiple intra-abdominal areas at diagnosis and after neoadjuvant chemotherapy (NACT) were collected for whole-genome sequencing. Sig3 was assessed by fitting samples independently with COSMIC v3.2 reference signatures. An HR scar assay was applied for comparison. Progression-free survival (PFS) and overall survival (OS) were studied using Kaplan-Meier and Cox regression analysis. RESULTS Sig3 has a bimodal distribution, eliminating the need for an arbitrary cutoff typical in HR scar tests. Sig3 could be assessed from samples with low (10%) cancer cell proportion and was consistent between multiple samples and stable during NACT. At diagnosis, 74 (45%) patients were HRD (Sig3+), while 91 (55%) were HR proficient (HRP, Sig3-). Sig3+ patients had longer PFS and OS than Sig3- patients (22 vs. 13 months and 51 vs. 34 months respectively, both p < 0.001). Sig3 successfully distinguished the poor prognostic HRP group among BRCAwt patients (PFS 19 months for Sig3+ and 13 months for Sig3- patients, p < 0.001). However, Sig3 at diagnosis did not predict chemoresponse anymore in the first relapse. The patient-level concordance between Sig3 and HR scar assay was 87%, and patients with HRD according to both tests had the longest median PFS. CONCLUSIONS Sig3 is a prognostic marker in advanced HGSC and useful tool in patient stratification for HRD.
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Affiliation(s)
- Heidi Koskela
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Yilin Li
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Titta Joutsiniemi
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Taru Muranen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Veli-Matti Isoviita
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kaisa Huhtinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Institute of Biomedicine and FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Giulia Micoli
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Kari Lavikka
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Giovanni Marchi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sakari Hietanen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
| | - Anni Virtanen
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jaana Oikkonen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland.
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6
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Lahtinen A, Lavikka K, Virtanen A, Li Y, Jamalzadeh S, Skorda A, Lauridsen AR, Zhang K, Marchi G, Isoviita VM, Ariotta V, Lehtonen O, Muranen TA, Huhtinen K, Carpén O, Hietanen S, Senkowski W, Kallunki T, Häkkinen A, Hynninen J, Oikkonen J, Hautaniemi S. Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma. Cancer Cell 2023:S1535-6108(23)00143-5. [PMID: 37207655 DOI: 10.1016/j.ccell.2023.04.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 02/15/2023] [Accepted: 04/25/2023] [Indexed: 05/21/2023]
Abstract
Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we integrate clonal composition and topology using whole-genome sequencing data from 510 samples of 148 patients with HGSC in the prospective, longitudinal, multiregion DECIDER study. Our results reveal three evolutionary states, which have distinct features in genomics, pathways, and morphological phenotypes, and significant association with treatment response. Nested pathway analysis suggests two evolutionary trajectories between the states. Experiments with five tumor organoids and three PI3K inhibitors support targeting tumors with enriched PI3K/AKT pathway with alpelisib. Heterogeneity analysis of samples from multiple anatomical sites shows that site-of-origin samples have 70% more unique clones than metastatic tumors or ascites. In conclusion, these analysis and visualization methods enable integrative tumor evolution analysis to identify patient subtypes using data from longitudinal, multiregion cohorts.
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Affiliation(s)
- Alexandra Lahtinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Kari Lavikka
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Anni Virtanen
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, 00029 Helsinki, Finland
| | - Yilin Li
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Sanaz Jamalzadeh
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Aikaterini Skorda
- Cancer Invasion and Resistance Group, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Anna Røssberg Lauridsen
- Cancer Invasion and Resistance Group, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Kaiyang Zhang
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Giovanni Marchi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Veli-Matti Isoviita
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Valeria Ariotta
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Oskari Lehtonen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Taru A Muranen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Kaisa Huhtinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland; Cancer Research Unit, Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, 20014 Turku, Finland
| | - Olli Carpén
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland; Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, 00029 Helsinki, Finland
| | - Sakari Hietanen
- Department of Obstetrics and Gynaecology, University of Turku and Turku University Hospital, 200521 Turku, Finland
| | - Wojciech Senkowski
- Biotech Research and Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Tuula Kallunki
- Cancer Invasion and Resistance Group, Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark; Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Antti Häkkinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynaecology, University of Turku and Turku University Hospital, 200521 Turku, Finland
| | - Jaana Oikkonen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland.
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland.
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7
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Senkowski W, Gall-Mas L, Falco MM, Li Y, Lavikka K, Kriegbaum MC, Oikkonen J, Bulanova D, Pietras EJ, Voßgröne K, Chen YJ, Erkan EP, Dai J, Lundgren A, Grønning Høg MK, Larsen IM, Lamminen T, Kaipio K, Huvila J, Virtanen A, Engelholm L, Christiansen P, Santoni-Rugiu E, Huhtinen K, Carpén O, Hynninen J, Hautaniemi S, Vähärautio A, Wennerberg K. A platform for efficient establishment and drug-response profiling of high-grade serous ovarian cancer organoids. Dev Cell 2023:S1534-5807(23)00182-X. [PMID: 37148882 DOI: 10.1016/j.devcel.2023.04.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 02/24/2023] [Accepted: 04/14/2023] [Indexed: 05/08/2023]
Abstract
The broad research use of organoids from high-grade serous ovarian cancer (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here, we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (53% vs. 23%-38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic, and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in a culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data are explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.
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Affiliation(s)
- Wojciech Senkowski
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, 2200 Copenhagen, Denmark.
| | - Laura Gall-Mas
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, 2200 Copenhagen, Denmark
| | - Matías Marín Falco
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Yilin Li
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Kari Lavikka
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Mette C Kriegbaum
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, 2200 Copenhagen, Denmark
| | - Jaana Oikkonen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Daria Bulanova
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, 2200 Copenhagen, Denmark
| | - Elin J Pietras
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Karolin Voßgröne
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Yan-Jun Chen
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Erdogan Pekcan Erkan
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland; Applied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Jun Dai
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Anastasia Lundgren
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Mia Kristine Grønning Høg
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Finsen Laboratory, Rigshospitalet, Copenhagen University Hospital, 2200 Copenhagen, Denmark
| | - Ida Marie Larsen
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Finsen Laboratory, Rigshospitalet, Copenhagen University Hospital, 2200 Copenhagen, Denmark
| | - Tarja Lamminen
- Institute of Biomedicine and FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Katja Kaipio
- Institute of Biomedicine and FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Jutta Huvila
- Department of Pathology, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Anni Virtanen
- Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, 00260 Helsinki, Finland
| | - Lars Engelholm
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Finsen Laboratory, Rigshospitalet, Copenhagen University Hospital, 2200 Copenhagen, Denmark
| | - Pernille Christiansen
- Department of Pathology, Rigshospitalet, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Eric Santoni-Rugiu
- Department of Pathology, Rigshospitalet, Copenhagen University Hospital, 2100 Copenhagen, Denmark
| | - Kaisa Huhtinen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland; Institute of Biomedicine and FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Olli Carpén
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland; Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital, 00260 Helsinki, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, 20521 Turku, Finland
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Anna Vähärautio
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland
| | - Krister Wennerberg
- Biotech Research & Innovation Centre, University of Copenhagen, 2200 Copenhagen, Denmark; Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, 2200 Copenhagen, Denmark.
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8
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Casado J, Lehtonen O, Rantanen V, Kaipio K, Pasquini L, Häkkinen A, Petrucci E, Hynninen J, Hietanen S, Carpén O, Biffoni M, Färkkilä A, Hautaniemi S. Agile workflow for interactive analysis of mass cytometry data. Bioinformatics 2021; 37:1263-1268. [PMID: 33135052 PMCID: PMC8189671 DOI: 10.1093/bioinformatics/btaa946] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/22/2020] [Accepted: 10/27/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Single-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge. RESULTS We present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AVAILABILITYAND IMPLEMENTATION The method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Julia Casado
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Oskari Lehtonen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ville Rantanen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Katja Kaipio
- Department of Pathology, University of Turku, Turku, Finland
| | - Luca Pasquini
- Istituto Superiore di Sanità, Core Facilities, Rome, Italy
| | - Antti Häkkinen
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Elenora Petrucci
- Istituto Superiore di Sanità, Department of Haematology, Rome, Italy
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, Turku University Hospital, University of Turku, Turku, Finland
| | - Sakari Hietanen
- Department of Obstetrics and Gynecology, Turku University Hospital, University of Turku, Turku, Finland
| | - Olli Carpén
- Department of Pathology, University of Turku, Turku, Finland
| | - Mauro Biffoni
- Istituto Superiore di Sanità, Department of Haematology, Rome, Italy
| | - Anniina Färkkilä
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Department of Obstetrics and Gynecology, Helsinki University Hospital, Helsinki, Finland.,Laboratory of Systems Pharmacology, Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
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9
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Kivikoski M, Rastas P, Löytynoja A, Merilä J. Automated improvement of stickleback reference genome assemblies with Lep-Anchor software. Mol Ecol Resour 2021; 21:2166-2176. [PMID: 33955177 DOI: 10.1111/1755-0998.13404] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/12/2021] [Accepted: 04/13/2021] [Indexed: 01/06/2023]
Abstract
We describe an integrative approach to improve contiguity and haploidy of a reference genome assembly and demonstrate its impact with practical examples. With two novel features of Lep-Anchor software and a combination of dense linkage maps, overlap detection and bridging long reads, we generated an improved assembly of the nine-spined stickleback (Pungitius pungitius) reference genome. We were able to remove a significant number of haplotypic contigs, detect more genetic variation and improve the contiguity of the genome, especially that of X chromosome. However, improved scaffolding cannot correct for mosaicism of erroneously assembled contigs, demonstrated by a de novo assembly of a 1.6-Mbp inversion. Qualitatively similar gains were obtained with the genome of three-spined stickleback (Gasterosteus aculeatus). Since the utility of genome-wide sequencing data in biological research depends heavily on the quality of the reference genome, the improved and fully automated approach described here should be helpful in refining reference genome assemblies.
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Affiliation(s)
- Mikko Kivikoski
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
| | - Pasi Rastas
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Ari Löytynoja
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Juha Merilä
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.,Division of Ecology and Biodiversity, The University of Hong Kong, Hong Kong, Hong Kong, SAR
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10
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Lysophosphatidylcholine in phospholipase A 2-modified LDL triggers secretion of angiopoietin 2. Atherosclerosis 2021; 327:87-99. [PMID: 34020784 DOI: 10.1016/j.atherosclerosis.2021.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND AIMS Secretory phospholipase A2 (PLA2) hydrolyzes LDL phospholipids generating modified LDL particles (PLA2-LDL) with increased atherogenic properties. Exocytosis of Weibel-Palade bodies (WPB) releases angiopoietin 2 (Ang2) and externalizes P-selectin, which both play important roles in vascular inflammation. Here, we investigated the effects of PLA2-LDL on exocytosis of WPBs. METHODS Human coronary artery endothelial cells (HCAECs) were stimulated with PLA2- LDL, and its uptake and effect on Ang2 release, leukocyte adhesion, and intracellular calcium levels were measured. The effects of PLA2-LDL on Ang2 release and WPB exocytosis were measured in and ex vivo in mice. RESULTS Exposure of HCAECs to PLA2-LDL triggered Ang2 secretion and promoted leukocyte-HCAEC interaction. Lysophosphatidylcholine was identified as a critical component of PLA2-LDL regulating the WPB exocytosis, which was mediated by cell-surface proteoglycans, phospholipase C, intracellular calcium, and cytoskeletal remodeling. PLA2-LDL also induced murine endothelial WPB exocytosis in blood vessels in and ex vivo, as evidenced by secretion of Ang2 in vivo, P-selectin translocation to plasma membrane in intact endothelial cells in thoracic artery and tracheal vessels, and reduced Ang2 staining in tracheal endothelial cells. Finally, in contrast to normal human coronary arteries, in which Ang2 was present only in the endothelial layer, at sites of advanced atherosclerotic lesions, Ang2 was detected also in the intima, media, and adventitia. CONCLUSIONS Our studies reveal PLA2-LDL as a potent agonist of endothelial WPB exocytosis, resulting in increased secretion of Ang2 and translocation of P-selectin. The results provide mechanistic insight into PLA2-LDL-dependent promotion of vascular inflammation and atherosclerosis.
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11
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Li Y, Ma L, Wu D, Chen G. Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine. Brief Bioinform 2021; 22:6189773. [PMID: 33778867 DOI: 10.1093/bib/bbab024] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 12/31/2020] [Accepted: 01/20/2021] [Indexed: 12/13/2022] Open
Abstract
Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.
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Affiliation(s)
| | - Lu Ma
- China Normal University, China
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12
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Cervera A, Rausio H, Kähkönen T, Andersson N, Partel G, Rantanen V, Paciello G, Ficarra E, Hynninen J, Hietanen S, Carpén O, Lehtonen R, Hautaniemi S, Huhtinen K. FUNGI: Fusion Gene Integration Toolset. Bioinformatics 2021; 37:3353-3355. [PMID: 33772596 PMCID: PMC8504624 DOI: 10.1093/bioinformatics/btab206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 02/27/2021] [Accepted: 03/25/2021] [Indexed: 11/17/2022] Open
Abstract
Motivation Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. Results We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. Availabilityand implementation FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alejandra Cervera
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, 00014
| | - Heidi Rausio
- Cancer Research Unit, Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, Turku, 20014
| | - Tiia Kähkönen
- Cancer Research Unit, Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, Turku, 20014
| | - Noora Andersson
- Department of Pathology, University of Helsinki and HUS-Diagnostics, Helsinki University Hospital, Helsinki, 00014
| | - Gabriele Partel
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, 00014
| | - Ville Rantanen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, 00014
| | | | - Elisa Ficarra
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia (UNIMORE), Reggio Emilia, 42121
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, 20521
| | - Sakari Hietanen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, 20521
| | - Olli Carpén
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, 00014.,Cancer Research Unit, Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, Turku, 20014.,Department of Pathology, University of Helsinki and HUS-Diagnostics, Helsinki University Hospital, Helsinki, 00014
| | - Rainer Lehtonen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, 00014
| | - Sampsa Hautaniemi
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, 00014
| | - Kaisa Huhtinen
- Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, 00014.,Cancer Research Unit, Institute of Biomedicine and FICAN West Cancer Centre, University of Turku, Turku, 20014
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13
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Häkkinen A, Zhang K, Alkodsi A, Andersson N, Erkan EP, Dai J, Kaipio K, Lamminen T, Mansuri N, Huhtinen K, Vähärautio A, Carpén O, Hynninen J, Hietanen S, Lehtonen R, Hautaniemi S. PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples. Bioinformatics 2021; 37:2882-2888. [PMID: 33720334 PMCID: PMC8479664 DOI: 10.1093/bioinformatics/btab178] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/11/2021] [Accepted: 03/11/2021] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Antti Häkkinen
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland,To whom correspondence should be addressed. or
| | - Kaiyang Zhang
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland
| | - Amjad Alkodsi
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland
| | - Noora Andersson
- Department of Pathology, University of Helsinki and HUSLAB, Helsinki University Hospital, FI-00014 Helsinki, Finland
| | - Erdogan Pekcan Erkan
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland
| | - Jun Dai
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland
| | - Katja Kaipio
- Research Center for Cancer, Infections and Immunity, Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland
| | - Tarja Lamminen
- Research Center for Cancer, Infections and Immunity, Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland
| | - Naziha Mansuri
- Research Center for Cancer, Infections and Immunity, Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland
| | - Kaisa Huhtinen
- Research Center for Cancer, Infections and Immunity, Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland
| | - Anna Vähärautio
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland
| | - Olli Carpén
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland,Department of Pathology, University of Helsinki and HUSLAB, Helsinki University Hospital, FI-00014 Helsinki, Finland,Research Center for Cancer, Infections and Immunity, Institute of Biomedicine, University of Turku, FI-20014 Turku, Finland
| | - Johanna Hynninen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, FI-20521 Turku, Finland
| | - Sakari Hietanen
- Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, FI-20521 Turku, Finland
| | - Rainer Lehtonen
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland
| | - Sampsa Hautaniemi
- Research Programs Unit, Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, FI-00014 Helsinki, Finland,To whom correspondence should be addressed. or
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14
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Mölder F, Jablonski KP, Letcher B, Hall MB, Tomkins-Tinch CH, Sochat V, Forster J, Lee S, Twardziok SO, Kanitz A, Wilm A, Holtgrewe M, Rahmann S, Nahnsen S, Köster J. Sustainable data analysis with Snakemake. F1000Res 2021; 10:33. [PMID: 34035898 PMCID: PMC8114187 DOI: 10.12688/f1000research.29032.2] [Citation(s) in RCA: 467] [Impact Index Per Article: 155.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/08/2021] [Indexed: 01/22/2023] Open
Abstract
Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.
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Affiliation(s)
- Felix Mölder
- Algorithms for reproducible bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Kim Philipp Jablonski
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics (SIB), Basel, Switzerland
| | | | | | - Christopher H Tomkins-Tinch
- Broad Institute of MIT and Harvard, Cambridge, USA.,Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, USA
| | - Vanessa Sochat
- Stanford University Research Computing Center, Stanford University, Stanford, USA
| | - Jan Forster
- Algorithms for reproducible bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,German Cancer Consortium (DKTK, partner site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - Soohyun Lee
- Biomedical Informatics, Harvard Medical School, Harvard University, Boston, USA
| | - Sven O Twardziok
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Center for Digital Health, Berlin, Germany
| | - Alexander Kanitz
- Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics / ELIXIR Switzerland, Lausanne, Switzerland
| | | | - Manuel Holtgrewe
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Center for Digital Health, Berlin, Germany.,CUBI - Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany
| | - Sven Rahmann
- Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Sven Nahnsen
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
| | - Johannes Köster
- Algorithms for reproducible bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,Medical Oncology, Harvard Medical School, Harvard University, Boston, USA
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15
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Mölder F, Jablonski KP, Letcher B, Hall MB, Tomkins-Tinch CH, Sochat V, Forster J, Lee S, Twardziok SO, Kanitz A, Wilm A, Holtgrewe M, Rahmann S, Nahnsen S, Köster J. Sustainable data analysis with Snakemake. F1000Res 2021; 10:33. [PMID: 34035898 DOI: 10.12688/f1000research.29032.1] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/07/2021] [Indexed: 01/22/2023] Open
Abstract
Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.
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Affiliation(s)
- Felix Mölder
- Algorithms for reproducible bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Kim Philipp Jablonski
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics (SIB), Basel, Switzerland
| | | | | | - Christopher H Tomkins-Tinch
- Broad Institute of MIT and Harvard, Cambridge, USA.,Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, USA
| | - Vanessa Sochat
- Stanford University Research Computing Center, Stanford University, Stanford, USA
| | - Jan Forster
- Algorithms for reproducible bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,German Cancer Consortium (DKTK, partner site Essen) and German Cancer Research Center, DKFZ, Heidelberg, Germany
| | - Soohyun Lee
- Biomedical Informatics, Harvard Medical School, Harvard University, Boston, USA
| | - Sven O Twardziok
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Center for Digital Health, Berlin, Germany
| | - Alexander Kanitz
- Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics / ELIXIR Switzerland, Lausanne, Switzerland
| | | | - Manuel Holtgrewe
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Center for Digital Health, Berlin, Germany.,CUBI - Core Unit Bioinformatics, Berlin Institute of Health, Berlin, Germany
| | - Sven Rahmann
- Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Sven Nahnsen
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
| | - Johannes Köster
- Algorithms for reproducible bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,Medical Oncology, Harvard Medical School, Harvard University, Boston, USA
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16
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Partel G, Hilscher MM, Milli G, Solorzano L, Klemm AH, Nilsson M, Wählby C. Automated identification of the mouse brain's spatial compartments from in situ sequencing data. BMC Biol 2020; 18:144. [PMID: 33076915 PMCID: PMC7574211 DOI: 10.1186/s12915-020-00874-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 09/18/2020] [Indexed: 01/03/2023] Open
Abstract
Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles. Conclusion We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.
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Affiliation(s)
- Gabriele Partel
- Centre for Image Analysis, Department of Information Technology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Markus M Hilscher
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Giorgia Milli
- Centre for Image Analysis, Department of Information Technology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Leslie Solorzano
- Centre for Image Analysis, Department of Information Technology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Anna H Klemm
- Centre for Image Analysis, Department of Information Technology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.,BioImage Informatics Facility of SciLifeLab, Uppsala, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Carolina Wählby
- Centre for Image Analysis, Department of Information Technology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden. .,BioImage Informatics Facility of SciLifeLab, Uppsala, Sweden.
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An O, Tan KT, Li Y, Li J, Wu CS, Zhang B, Chen L, Yang H. CSI NGS Portal: An Online Platform for Automated NGS Data Analysis and Sharing. Int J Mol Sci 2020; 21:ijms21113828. [PMID: 32481589 PMCID: PMC7312552 DOI: 10.3390/ijms21113828] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/24/2020] [Accepted: 05/26/2020] [Indexed: 12/18/2022] Open
Abstract
Next-generation sequencing (NGS) has been a widely-used technology in biomedical research for understanding the role of molecular genetics of cells in health and disease. A variety of computational tools have been developed to analyse the vastly growing NGS data, which often require bioinformatics skills, tedious work and a significant amount of time. To facilitate data processing steps minding the gap between biologists and bioinformaticians, we developed CSI NGS Portal, an online platform which gathers established bioinformatics pipelines to provide fully automated NGS data analysis and sharing in a user-friendly website. The portal currently provides 16 standard pipelines for analysing data from DNA, RNA, smallRNA, ChIP, RIP, 4C, SHAPE, circRNA, eCLIP, Bisulfite and scRNA sequencing, and is flexible to expand with new pipelines. The users can upload raw data in FASTQ format and submit jobs in a few clicks, and the results will be self-accessible via the portal to view/download/share in real-time. The output can be readily used as the final report or as input for other tools depending on the pipeline. Overall, CSI NGS Portal helps researchers rapidly analyse their NGS data and share results with colleagues without the aid of a bioinformatician. The portal is freely available at: https://csibioinfo.nus.edu.sg/csingsportal.
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Affiliation(s)
- Omer An
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
- Correspondence: (O.A.); (H.Y.); Tel.: +65-8452-1766 (O.A.); +65-6601-1533 (H.Y.)
| | - Kar-Tong Tan
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
| | - Ying Li
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
| | - Jia Li
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
| | - Chan-Shuo Wu
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
| | - Bin Zhang
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
| | - Leilei Chen
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
- Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore
| | - Henry Yang
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore; (K.-T.T.); (Y.L.); (J.L.); (C.-S.W.); (B.Z.); (L.C.)
- Correspondence: (O.A.); (H.Y.); Tel.: +65-8452-1766 (O.A.); +65-6601-1533 (H.Y.)
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Distinct subtypes of diffuse large B-cell lymphoma defined by hypermutated genes. Leukemia 2019; 33:2662-2672. [PMID: 31186494 DOI: 10.1038/s41375-019-0509-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 04/26/2019] [Accepted: 05/01/2019] [Indexed: 12/24/2022]
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease whose personalized clinical management requires robust molecular stratification. Here, we show that somatic hypermutation (SHM) patterns constitute a marker for DLBCL molecular classification. The activity of SHM mutational processes delineated the cell of origin (COO) in DLBCL. Expression of the herein identified 36 SHM target genes stratified DLBCL into four novel SHM subtypes. In a meta-analysis of patients with DLBCL treated with immunochemotherapy, the SHM subtypes were significantly associated with overall survival (1642 patients) and progression-free survival (795 patients). Multivariate analysis of survival indicated that the prognostic impact of the SHM subtypes is independent from the COO classification and the International Prognostic Index. Furthermore, the SHM subtypes had a distinct clinical outcome within each of the COO subtypes, and strikingly, even within unclassified DLBCL. The genetic landscape of the four SHM subtypes indicated unique associations with driver alterations and oncogenic signaling in DLBCL, which suggests a possibility for therapeutic exploitation. These findings provide a biologically driven classification system in DLBCL with potential clinical applications.
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