1
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Jeong Y, Gerhäuser C, Sauter G, Schlomm T, Rohr K, Lutsik P. MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model. Nat Commun 2025; 16:788. [PMID: 39824848 PMCID: PMC11742067 DOI: 10.1038/s41467-025-55920-z] [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: 05/04/2024] [Accepted: 01/02/2025] [Indexed: 01/20/2025] Open
Abstract
DNA methylation (DNAm) is a key epigenetic mark that shows profound alterations in cancer. Read-level methylomes enable more in-depth analyses, due to their broad genomic coverage and preservation of rare cell-type signals, compared to summarized data such as 450K/EPIC microarrays. Here, we propose MethylBERT, a Transformer-based model for read-level methylation pattern classification. MethylBERT identifies tumour-derived sequence reads based on their methylation patterns and local genomic sequence, and estimates tumour cell fractions within bulk samples. In our evaluation, MethylBERT outperforms existing deconvolution methods and demonstrates high accuracy regardless of methylation pattern complexity, read length and read coverage. Moreover, we show its applicability to cell-type deconvolution as well as non-invasive early cancer diagnostics using liquid biopsy samples. MethylBERT represents a significant advancement in read-level methylome analysis and enables accurate tumour purity estimation. The broad applicability of MethylBERT will enhance studies on both tumour and non-cancerous bulk methylomes.
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Affiliation(s)
- Yunhee Jeong
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Clarissa Gerhäuser
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Guido Sauter
- Institute for Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thorsten Schlomm
- Department of Urology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Karl Rohr
- Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, Heidelberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Department of Oncology, KU Leuven, Leuven, Belgium.
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2
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Lu Q, Liu Z, Wang X. Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP. Brief Bioinform 2024; 26:bbaf056. [PMID: 39950745 PMCID: PMC11826339 DOI: 10.1093/bib/bbaf056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 12/24/2024] [Accepted: 01/27/2025] [Indexed: 02/17/2025] Open
Abstract
Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mutations, and DNA methylation. Here we proposed the machine learning Multi-omics Tumor Purity prediction (MoTP) algorithm to estimate tumor purity based on multiple types of omics data. MoTP utilizes the Bayesian Regularized Neural Networks as the prediction algorithm, and Consensus Tumor Purity Estimates as labels. We trained MoTP using multi-omics data (mRNA, microRNA, long non-coding RNA, and DNA methylation) across 21 TCGA solid cancer types. By testing MoTP in TCGA validation sets, TCGA test sets, and eight datasets outside the TCGA cancer cohorts, we showed that although MoTP could achieve excellent performance in predicting tumor purity based on a single omics data type, the integration of multiple single omics data-based predictions can enhance the prediction performance. Moreover, we demonstrated the robustness of MoTP by testing it in datasets with Gaussian noise and feature missing. Benchmark analysis showed that MoTP outperformed most established tumor purity prediction algorithms, and that it required less running time and computational resource to fulfill the predictive task. Thus, MoTP would be an attractive option for computational tumor purity inference.
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Affiliation(s)
- Qiqi Lu
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Big Data Research Institute, China Pharmaceutical University, Nanjing, China
| | - Zhixian Liu
- Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaosheng Wang
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Big Data Research Institute, China Pharmaceutical University, Nanjing, China
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3
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Sasiain I, Nacer D, Aine M, Veerla S, Staaf J. Tumor purity estimated from bulk DNA methylation can be used for adjusting beta values of individual samples to better reflect tumor biology. NAR Genom Bioinform 2024; 6:lqae146. [PMID: 39498434 PMCID: PMC11532792 DOI: 10.1093/nargab/lqae146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 10/01/2024] [Accepted: 10/18/2024] [Indexed: 11/07/2024] Open
Abstract
Epigenetic deregulation through altered DNA methylation is a fundamental feature of tumorigenesis, but tumor data from bulk tissue samples contain different proportions of malignant and non-malignant cells that may confound the interpretation of DNA methylation values. The adjustment of DNA methylation data based on tumor purity has been proposed to render both genome-wide and gene-specific analyses more precise, but it requires sample purity estimates. Here we present PureBeta, a single-sample statistical framework that uses genome-wide DNA methylation data to first estimate sample purity and then adjust methylation values of individual CpGs to correct for sample impurity. Purity values estimated with the algorithm have high correlation (>0.8) to reference values obtained from DNA sequencing when applied to samples from breast carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Methylation beta values adjusted based on purity estimates have a more binary distribution that better reflects theoretical methylation states, thus facilitating improved biological inference as shown for BRCA1 in breast cancer. PureBeta is a versatile tool that can be used for different Illumina DNA methylation arrays and can be applied to individual samples of different cancer types to enhance biological interpretability of methylation data.
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Affiliation(s)
- Iñaki Sasiain
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund 22381, Sweden
| | - Deborah F Nacer
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund 22381, Sweden
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund 22381, Sweden
| | - Mattias Aine
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund 22381, Sweden
| | - Srinivas Veerla
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund 22381, Sweden
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund 22381, Sweden
| | - Johan Staaf
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund 22381, Sweden
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund 22381, Sweden
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4
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Franceschini GM, Quaini O, Mizuno K, Orlando F, Ciani Y, Ku SY, Sigouros M, Rothmann E, Alonso A, Benelli M, Nardella C, Auh J, Freeman D, Hanratty B, Adil M, Elemento O, Tagawa ST, Feng FY, Caffo O, Buttigliero C, Basso U, Nelson PS, Corey E, Haffner MC, Attard G, Aparicio A, Demichelis F, Beltran H. Noninvasive Detection of Neuroendocrine Prostate Cancer through Targeted Cell-free DNA Methylation. Cancer Discov 2024; 14:424-445. [PMID: 38197680 PMCID: PMC10905672 DOI: 10.1158/2159-8290.cd-23-0754] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/15/2023] [Indexed: 01/11/2024]
Abstract
Castration-resistant prostate cancer (CRPC) is a heterogeneous disease associated with phenotypic subtypes that drive therapy response and outcome differences. Histologic transformation to castration-resistant neuroendocrine prostate cancer (CRPC-NE) is associated with distinct epigenetic alterations, including changes in DNA methylation. The current diagnosis of CRPC-NE is challenging and relies on metastatic biopsy. We developed a targeted DNA methylation assay to detect CRPC-NE using plasma cell-free DNA (cfDNA). The assay quantifies tumor content and provides a phenotype evidence score that captures diverse CRPC phenotypes, leveraging regions to inform transcriptional state. We tested the design in independent clinical cohorts (n = 222 plasma samples) and qualified it achieving an AUC > 0.93 for detecting pathology-confirmed CRPC-NE (n = 136). Methylation-defined cfDNA tumor content was associated with clinical outcomes in two prospective phase II clinical trials geared towards aggressive variant CRPC and CRPC-NE. These data support the application of targeted DNA methylation for CRPC-NE detection and patient stratification. SIGNIFICANCE Neuroendocrine prostate cancer is an aggressive subtype of treatment-resistant prostate cancer. Early detection is important, but the diagnosis currently relies on metastatic biopsy. We describe the development and validation of a plasma cell-free DNA targeted methylation panel that can quantify tumor fraction and identify patients with neuroendocrine prostate cancer noninvasively. This article is featured in Selected Articles from This Issue, p. 384.
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Affiliation(s)
- Gian Marco Franceschini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Orsetta Quaini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Kei Mizuno
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Francesco Orlando
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Yari Ciani
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Sheng-Yu Ku
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Michael Sigouros
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | - Emily Rothmann
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Alicia Alonso
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | | | - Caterina Nardella
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Joonghoon Auh
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | - Dory Freeman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Brian Hanratty
- Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mohamed Adil
- Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Olivier Elemento
- Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York
| | - Scott T. Tagawa
- Department of Medicine, Division of Medical Oncology, Weill Cornell Medicine, New York, New York
| | - Felix Y. Feng
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
| | - Orazio Caffo
- Department of Medical Oncology, Santa Chiara Hospital, Trento, Italy
| | - Consuelo Buttigliero
- Department of Oncology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy
| | - Umberto Basso
- Department of Oncology, Istituto Oncologico Veneto IOV - IRCCS, Padua, Italy
| | | | - Eva Corey
- University of Washington, Seattle, Washington
| | - Michael C. Haffner
- Fred Hutchinson Cancer Research Center, Seattle, Washington
- University of Washington, Seattle, Washington
| | - Gerhardt Attard
- Cancer Institute and University College London Hospitals, University College London, London, United Kingdom
| | - Ana Aparicio
- Department of GU Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Himisha Beltran
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
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5
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Kim K, Kim H, Shin I, Noh SJ, Kim JY, Suh KJ, Kim YN, Lee JY, Cho DY, Kim SH, Kim JH, Lee SH, Choi JK. Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer. Sci Rep 2023; 13:22482. [PMID: 38110532 PMCID: PMC10728099 DOI: 10.1038/s41598-023-49639-4] [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: 08/08/2023] [Accepted: 12/10/2023] [Indexed: 12/20/2023] Open
Abstract
Genomic hypomethylation has recently been identified as a determinant of therapeutic responses to immune checkpoint blockade (ICB). However, it remains unclear whether this approach can be applied to cell-free DNA (cfDNA) and whether it can address the issue of low tumor purity encountered in tissue-based methylation profiling. In this study, we developed an assay named iMethyl, designed to estimate the genomic hypomethylation status from cfDNA. This was achieved through deep targeted sequencing of young LINE-1 elements with > 400,000 reads per sample. iMethyl was applied to a total of 653 ICB samples encompassing lung cancer (cfDNA n = 167; tissue n = 137; cfDNA early during treatment n = 40), breast cancer (cfDNA n = 91; tissue n = 50; PBMC n = 50; cfDNA at progression n = 44), and ovarian cancer (tissue n = 74). iMethyl-liquid predicted ICB responses accurately regardless of the tumor purity of tissue samples. iMethyl-liquid was also able to monitor therapeutic responses early during treatment (3 or 6 weeks after initiation of ICB) and detect progressive hypomethylation accompanying tumor progression. iMethyl-tissue had better predictive power than tumor mutation burden and PD-L1 expression. In conclusion, our iMethyl-liquid method allows for reliable noninvasive prediction, early evaluation, and monitoring of clinical responses to ICB therapy.
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Affiliation(s)
- Kyeonghui Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Hyemin Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Inkyung Shin
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Seung-Jae Noh
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Jeong Yeon Kim
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Koung Jin Suh
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Yoo-Na Kim
- Department of Obstetrics and Gynecology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jung-Yun Lee
- Department of Obstetrics and Gynecology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dae-Yeon Cho
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea
| | - Se Hyun Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeongi-do, Republic of Korea.
| | - Jee Hyun Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam-si, Gyeongi-do, Republic of Korea.
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul, Republic of Korea.
- Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
| | - Jung Kyoon Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
- Penta Medix Co., Ltd, Seongnam-si, Gyeongi-do, Republic of Korea.
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6
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Romagnoli D, Nardone A, Galardi F, Paoli M, De Luca F, Biagioni C, Franceschini GM, Pestrin M, Sanna G, Moretti E, Demichelis F, Migliaccio I, Biganzoli L, Malorni L, Benelli M. MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples. Brief Bioinform 2023; 24:6991124. [PMID: 36653909 DOI: 10.1093/bib/bbad015] [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: 11/16/2022] [Revised: 12/17/2022] [Accepted: 01/03/2023] [Indexed: 01/20/2023] Open
Abstract
DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.
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Affiliation(s)
| | - Agostina Nardone
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Francesca Galardi
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Marta Paoli
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Francesca De Luca
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Chiara Biagioni
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Gian Marco Franceschini
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
| | - Marta Pestrin
- Medical Oncology Unit, Azienda Sanitaria Universitaria Giuliano Isontina, 34170 Gorizia, Italy
| | - Giuseppina Sanna
- Medical Oncology, Ospedale Civile SS Annunziata, 07100 Sassari, Italy
| | - Erica Moretti
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology, University of Trento, 38123 Trento, Italy
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Ilenia Migliaccio
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
| | - Laura Biganzoli
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Luca Malorni
- "Sandro Pitigliani" Translational Research Unit, Hospital of Prato, 59100 Prato, Italy
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
| | - Matteo Benelli
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy
- "Sandro Pitigliani" Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy
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7
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Zhang Z, Wiencke JK, Kelsey KT, Koestler DC, Christensen BC, Salas LA. HiTIMED: hierarchical tumor immune microenvironment epigenetic deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data. J Transl Med 2022; 20:516. [DOI: 10.1186/s12967-022-03736-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 10/30/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.
Results
We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.
Conclusion
We developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.
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8
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Staaf J, Aine M. Tumor purity adjusted beta values improve biological interpretability of high-dimensional DNA methylation data. PLoS One 2022; 17:e0265557. [PMID: 36084090 PMCID: PMC9462735 DOI: 10.1371/journal.pone.0265557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 08/15/2022] [Indexed: 11/19/2022] Open
Abstract
A common issue affecting DNA methylation analysis in tumor tissue is the presence of a substantial amount of non-tumor methylation signal derived from the surrounding microenvironment. Although approaches for quantifying and correcting for the infiltration component have been proposed previously, we believe these have not fully addressed the issue in a comprehensive and universally applicable way. We present a multi-population framework for adjusting DNA methylation beta values on the Illumina 450/850K platform using generic purity estimates to account for non-tumor signal. Our approach also provides an indirect estimate of the aggregate methylation state of the surrounding normal tissue. Using whole exome sequencing derived purity estimates and Illumina 450K methylation array data generated by The Cancer Genome Atlas project (TCGA), we provide a demonstration of this framework in breast cancer illustrating the effect of beta correction on the aggregate methylation beta value distribution, clustering accuracy, and global methylation profiles.
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Affiliation(s)
- Johan Staaf
- Department of Clinical Sciences Lund, Division of Oncology, Lund University, Medicon Village, Lund, Sweden
| | - Mattias Aine
- Department of Clinical Sciences Lund, Division of Oncology, Lund University, Medicon Village, Lund, Sweden
- * E-mail:
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9
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Intratumor and informatic heterogeneity influence meningioma molecular classification. Acta Neuropathol 2022; 144:579-583. [PMID: 35759011 DOI: 10.1007/s00401-022-02455-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 11/01/2022]
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10
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Choudhury A, Magill ST, Eaton CD, Prager BC, Chen WC, Cady MA, Seo K, Lucas CHG, Casey-Clyde TJ, Vasudevan HN, Liu SJ, Villanueva-Meyer JE, Lam TC, Pu JKS, Li LF, Leung GKK, Swaney DL, Zhang MY, Chan JW, Qiu Z, Martin MV, Susko MS, Braunstein SE, Bush NAO, Schulte JD, Butowski N, Sneed PK, Berger MS, Krogan NJ, Perry A, Phillips JJ, Solomon DA, Costello JF, McDermott MW, Rich JN, Raleigh DR. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nat Genet 2022; 54:649-659. [PMID: 35534562 PMCID: PMC9374001 DOI: 10.1038/s41588-022-01061-8] [Citation(s) in RCA: 143] [Impact Index Per Article: 47.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 03/22/2022] [Indexed: 02/06/2023]
Abstract
Meningiomas are the most common primary intracranial tumors. There are no effective medical therapies for meningioma patients, and new treatments have been encumbered by limited understanding of meningioma biology. Here, we use DNA methylation profiling on 565 meningiomas integrated with genetic, transcriptomic, biochemical, proteomic and single-cell approaches to show meningiomas are composed of three DNA methylation groups with distinct clinical outcomes, biological drivers and therapeutic vulnerabilities. Merlin-intact meningiomas (34%) have the best outcomes and are distinguished by NF2/Merlin regulation of susceptibility to cytotoxic therapy. Immune-enriched meningiomas (38%) have intermediate outcomes and are distinguished by immune infiltration, HLA expression and lymphatic vessels. Hypermitotic meningiomas (28%) have the worst outcomes and are distinguished by convergent genetic and epigenetic mechanisms driving the cell cycle and resistance to cytotoxic therapy. To translate these findings into clinical practice, we show cytostatic cell cycle inhibitors attenuate meningioma growth in cell culture, organoids, xenografts and patients.
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Affiliation(s)
- Abrar Choudhury
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Stephen T Magill
- Department of Neurological Surgery, Northwestern University, Chicago, IL, USA.
| | - Charlotte D Eaton
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Briana C Prager
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - William C Chen
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Martha A Cady
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Kyounghee Seo
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Calixto-Hope G Lucas
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - Tim J Casey-Clyde
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Harish N Vasudevan
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - S John Liu
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Javier E Villanueva-Meyer
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Tai-Chung Lam
- Department of Clinical Oncology, The University of Hong Kong, Pokfulam, Hong Kong
| | - Jenny Kan-Suen Pu
- Division of Neurosurgery, Department of Surgery, The University of Hong Kong, Pokfulam, Hong Kong
| | - Lai-Fung Li
- Division of Neurosurgery, Department of Surgery, The University of Hong Kong, Pokfulam, Hong Kong
| | - Gilberto Ka-Kit Leung
- Division of Neurosurgery, Department of Surgery, The University of Hong Kong, Pokfulam, Hong Kong
| | - Danielle L Swaney
- J. David Gladstone Institutes, California Institute for Quantitative Biosciences, San Francisco, CA, USA
- California Institute for Quantitative Biosciences, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
| | - Michael Y Zhang
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Jason W Chan
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Zhixin Qiu
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Michael V Martin
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Matthew S Susko
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Steve E Braunstein
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Nancy Ann Oberheim Bush
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Jessica D Schulte
- Department of Neurosciences, University of California, San Diego, San Diego, CA, USA
| | - Nicholas Butowski
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Penny K Sneed
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Mitchel S Berger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Nevan J Krogan
- J. David Gladstone Institutes, California Institute for Quantitative Biosciences, San Francisco, CA, USA
- California Institute for Quantitative Biosciences, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
| | - Arie Perry
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - Joanna J Phillips
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - David A Solomon
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - Joseph F Costello
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Michael W McDermott
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Miami Neuroscience Institute, Baptist Health, Miami, FL, USA
| | - Jeremy N Rich
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - David R Raleigh
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA.
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
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11
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Ferreyra Vega S, Wenger A, Kling T, Olsson Bontell T, Jakola AS, Carén H. Spatial heterogeneity in DNA methylation and chromosomal alterations in diffuse gliomas and meningiomas. Mod Pathol 2022; 35:1551-1561. [PMID: 35701666 PMCID: PMC9596370 DOI: 10.1038/s41379-022-01113-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 02/07/2023]
Abstract
Adult-type diffuse gliomas and meningiomas are the most common primary intracranial tumors of the central nervous system. DNA methylation profiling is a novel diagnostic technique increasingly used also in the clinic. Although molecular heterogeneity is well described in these tumors, DNA methylation heterogeneity is less studied. We therefore investigated the intratumor genetic and epigenetic heterogeneity in diffuse gliomas and meningiomas, with focus on potential clinical implications. We further investigated tumor purity as a source for heterogeneity in the tumors. We analyzed genome-wide DNA methylation profiles generated from 126 spatially separated tumor biopsies from 39 diffuse gliomas and meningiomas. Moreover, we evaluated five methods for measurement of tumor purity and investigated intratumor heterogeneity by assessing DNA methylation-based classification, chromosomal copy number alterations and molecular markers. Our results demonstrated homogeneous methylation-based classification of IDH-mutant gliomas and further corroborates subtype heterogeneity in glioblastoma IDH-wildtype and high-grade meningioma patients after excluding samples with low tumor purity. We detected a large number of differentially methylated CpG sites within diffuse gliomas and meningiomas, particularly in tumors of higher grades. The presence of CDKN2A/B homozygous deletion differed in one out of two patients with IDH-mutant astrocytomas, CNS WHO grade 4. We conclude that diffuse gliomas and high-grade meningiomas are characterized by intratumor heterogeneity, which should be considered in clinical diagnostics and in the assessment of methylation-based and molecular markers.
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Affiliation(s)
- Sandra Ferreyra Vega
- grid.8761.80000 0000 9919 9582Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ,grid.8761.80000 0000 9919 9582Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Wenger
- grid.8761.80000 0000 9919 9582Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Teresia Kling
- grid.8761.80000 0000 9919 9582Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Thomas Olsson Bontell
- grid.8761.80000 0000 9919 9582Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ,grid.1649.a000000009445082XDepartment of Clinical Pathology and Cytology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Asgeir Store Jakola
- grid.8761.80000 0000 9919 9582Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ,grid.1649.a000000009445082XDepartment of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden ,grid.52522.320000 0004 0627 3560Department of Neurosurgery, St.Olavs University Hospital, Trondheim, Norway
| | - Helena Carén
- Sahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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12
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Charting differentially methylated regions in cancer with Rocker-meth. Commun Biol 2021; 4:1249. [PMID: 34728774 PMCID: PMC8563962 DOI: 10.1038/s42003-021-02761-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 10/04/2021] [Indexed: 12/15/2022] Open
Abstract
Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a new computational method exploiting a heterogeneous hidden Markov model to detect DMRs across multiple experimental platforms. Through an extensive comparative study, we first demonstrate Rocker-meth excellent performance on synthetic data. Its application to more than 6,000 methylation profiles across 14 tumor types provides a comprehensive catalog of tumor type-specific and shared DMRs, and agnostically identifies cancer-related partially methylated domains (PMD). In depth integrative analysis including orthogonal omics shows the enhanced ability of Rocker-meth in recapitulating known associations, further uncovering the pan-cancer relationship between DNA hypermethylation and transcription factor deregulation depending on the baseline chromatin state. Finally, we demonstrate the utility of the catalog for the study of colorectal cancer single-cell DNA-methylation data. Matteo Benelli et al. present Rocker-meth, a new Hidden Markov Model (HMM)-based method, to robustly identify differentially methylated regions (DMRs). They use Rocker-meth to analyse more than 6000 methylation profiles across 14 cancer types, providing a catalog of tumor-specific and shared DMRs.
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13
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Verburg N, Barthel FP, Anderson KJ, Johnson KC, Koopman T, Yaqub MM, Hoekstra OS, Lammertsma AA, Barkhof F, Pouwels PJW, Reijneveld JC, Rozemuller AJM, Beliën JAM, Boellaard R, Taylor MD, Das S, Costello JF, Vandertop WP, Wesseling P, de Witt Hamer PC, Verhaak RGW. Spatial concordance of DNA methylation classification in diffuse glioma. Neuro Oncol 2021; 23:2054-2065. [PMID: 34049406 PMCID: PMC8643482 DOI: 10.1093/neuonc/noab134] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence. Methods We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites. Results Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account. Conclusion Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists.
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Affiliation(s)
- Niels Verburg
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, and Brain Tumor Centre, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.,Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Hill Rd, Cambridge CB2 0QQ, UK
| | - Floris P Barthel
- The Jackson Laboratory For Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Kevin J Anderson
- The Jackson Laboratory For Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Kevin C Johnson
- The Jackson Laboratory For Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Thomas Koopman
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Maqsood M Yaqub
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Otto S Hoekstra
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.,UCL institutes of Neurology & Healthcare Engineering, Gower St, Bloomsbury, London WC1E 6BT, United Kingdom
| | - Petra J W Pouwels
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.,Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede, The Netherlands
| | - Annemieke J M Rozemuller
- Department of Pathology, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jeroen A M Beliën
- Department of Pathology, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Michael D Taylor
- Department of Neurosurgery, The Hospital for Sick Children, 555 University Ave, Toronto, ON M5G 1X8, Canada.,Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Kids, Toronto, Ontario Canada
| | - Sunit Das
- Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Kids, Toronto, Ontario Canada.,Division of Neurosurgery, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, Ontario Canada
| | - Joseph F Costello
- Department of Neurological Surgery, UCSF, 505 Parnassus Ave, San Francisco, CA 94143, USA
| | - W Pieter Vandertop
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, and Brain Tumor Centre, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.,Princess Máxima Centre for Paediatric Oncology, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Philip C de Witt Hamer
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, and Brain Tumor Centre, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Roel G W Verhaak
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit, and Brain Tumor Centre, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.,The Jackson Laboratory For Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
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14
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Koo B, Rhee JK. Prediction of tumor purity from gene expression data using machine learning. Brief Bioinform 2021; 22:6265216. [PMID: 33954576 DOI: 10.1093/bib/bbab163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 01/11/2023] Open
Abstract
MOTIVATION Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity. RESULTS We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system. AVAILABILITY The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity.
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Affiliation(s)
- Bonil Koo
- School of Systems Biomedical Science, Soongsil University, Seoul, Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Je-Keun Rhee
- School of Systems Biomedical Science, Soongsil University, Seoul, Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
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15
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Nagaraju GP, Kasa P, Dariya B, Surepalli N, Peela S, Ahmad S. Epigenetics and therapeutic targets in gastrointestinal malignancies. Drug Discov Today 2021; 26:2303-2314. [PMID: 33895313 DOI: 10.1016/j.drudis.2021.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/07/2021] [Accepted: 04/11/2021] [Indexed: 12/12/2022]
Abstract
Gastrointestinal (GI) malignancies account for substantial mortality and morbidity worldwide. They are generally promoted by dysregulated signal transduction and epigenetic pathways, which are controlled by specific enzymes. Recent studies demonstrated that histone deacetylases (HDACs) together with DNA methyltransferases (DNMTs) have crucial roles in the signal transduction/epigenetic pathways in GI regulation. In this review, we discuss various enzyme targets and their functional mechanisms responsible for the regulatory processes of GI malignancies. We also discuss the epigenetic therapeutic targets that are mainly facilitated by DNMT and HDAC inhibitors, which have functional consequences and clinical outcomes for GI malignancies.
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Affiliation(s)
- Ganji Purnachandra Nagaraju
- Department of Hematology & Medical Oncology, Winship Cancer Institute, Emory University, Atlanta, GA 30332, USA
| | - Prameswari Kasa
- Dr L.V. Prasad Diagnostics and Research Laboratory, Khairtabad, Hyderabad 500004, India
| | - Begum Dariya
- Department of Biosciences and Biotechnology, Banasthali University, Banasthali 304022, Rajasthan, India
| | | | - Sujatha Peela
- Department of Biotechnology, Dr B.R. Ambedkar University, Srikakulam 532410, AP, India
| | - Sarfraz Ahmad
- AdventHealth Cancer Institute, FSU and UCF Colleges of Medicine, Orlando, FL 32804, USA.
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16
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Beltran H, Romanel A, Conteduca V, Casiraghi N, Sigouros M, Franceschini GM, Orlando F, Fedrizzi T, Ku SY, Dann E, Alonso A, Mosquera JM, Sboner A, Xiang J, Elemento O, Nanus DM, Tagawa ST, Benelli M, Demichelis F. Circulating tumor DNA profile recognizes transformation to castration-resistant neuroendocrine prostate cancer. J Clin Invest 2020; 130:1653-1668. [PMID: 32091413 DOI: 10.1172/jci131041] [Citation(s) in RCA: 132] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
Loss of androgen receptor (AR) signaling dependence occurs in approximately 15%-20% of advanced treatment-resistant prostate cancers, and this may manifest clinically as transformation from a prostate adenocarcinoma histology to a castration-resistant neuroendocrine prostate cancer (CRPC-NE). The diagnosis of CRPC-NE currently relies on a metastatic tumor biopsy, which is invasive for patients and sometimes challenging to diagnose due to morphologic heterogeneity. By studying whole-exome sequencing and whole-genome bisulfite sequencing of cell free DNA (cfDNA) and of matched metastatic tumor biopsies from patients with metastatic prostate adenocarcinoma and CRPC-NE, we identified CRPC-NE features detectable in the circulation. Overall, there was markedly higher concordance between cfDNA and biopsy tissue genomic alterations in patients with CRPC-NE compared with castration-resistant adenocarcinoma, supporting greater intraindividual genomic consistency across metastases. Allele-specific copy number and serial sampling analyses allowed for the detection and tracking of clonal and subclonal tumor cell populations. cfDNA methylation was indicative of circulating tumor content fraction, reflective of methylation patterns observed in biopsy tissues, and was capable of detecting CRPC-NE-associated epigenetic changes (e.g., hypermethylation of ASXL3 and SPDEF; hypomethylation of INSM1 and CDH2). A targeted set combining genomic (TP53, RB1, CYLD, AR) and epigenomic (hypo- and hypermethylation of 20 differential sites) alterations applied to ctDNA was capable of identifying patients with CRPC-NE.
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Affiliation(s)
- Himisha Beltran
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA.,Department of Medicine, Division of Medical Oncology, Weill Cornell Medicine, New York, New York, USA
| | - Alessandro Romanel
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Vincenza Conteduca
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA.,Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Nicola Casiraghi
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Michael Sigouros
- Department of Medicine, Division of Medical Oncology, Weill Cornell Medicine, New York, New York, USA
| | - Gian Marco Franceschini
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Francesco Orlando
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Tarcisio Fedrizzi
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Sheng-Yu Ku
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts, USA
| | - Emma Dann
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Alicia Alonso
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA
| | - Juan Miguel Mosquera
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA.,Department of Pathology and Laboratory Medicine, and
| | - Andrea Sboner
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
| | - Jenny Xiang
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
| | - David M Nanus
- Department of Medicine, Division of Medical Oncology, Weill Cornell Medicine, New York, New York, USA.,Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA
| | - Scott T Tagawa
- Department of Medicine, Division of Medical Oncology, Weill Cornell Medicine, New York, New York, USA.,Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA
| | - Matteo Benelli
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.,Bioinformatics Unit, Hospital of Prato, Prato, Italy
| | - Francesca Demichelis
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.,Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York, USA.,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
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17
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Galardi F, De Luca F, Romagnoli D, Biagioni C, Moretti E, Biganzoli L, Di Leo A, Migliaccio I, Malorni L, Benelli M. Cell-Free DNA-Methylation-Based Methods and Applications in Oncology. Biomolecules 2020; 10:E1677. [PMID: 33334040 PMCID: PMC7765488 DOI: 10.3390/biom10121677] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/07/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022] Open
Abstract
Liquid biopsy based on cell-free DNA (cfDNA) enables non-invasive dynamic assessment of disease status in patients with cancer, both in the early and advanced settings. The analysis of DNA-methylation (DNAm) from cfDNA samples holds great promise due to the intrinsic characteristics of DNAm being more prevalent, pervasive, and cell- and tumor-type specific than genomics, for which established cfDNA assays already exist. Herein, we report on recent advances on experimental strategies for the analysis of DNAm in cfDNA samples. We describe the main steps of DNAm-based analysis workflows, including pre-analytics of cfDNA samples, DNA treatment, assays for DNAm evaluation, and methods for data analysis. We report on protocols, biomolecular techniques, and computational strategies enabling DNAm evaluation in the context of cfDNA analysis, along with practical considerations on input sample requirements and costs. We provide an overview on existing studies exploiting cell-free DNAm biomarkers for the detection and monitoring of cancer in early and advanced settings, for the evaluation of drug resistance, and for the identification of the cell-of-origin of tumors. Finally, we report on DNAm-based tests approved for clinical use and summarize their performance in the context of liquid biopsy.
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Affiliation(s)
- Francesca Galardi
- «Sandro Pitigliani» Translational Research Unit, Hospital of Prato, 59100 Prato, Italy; (F.G.); (F.D.L.); (I.M.); (L.M.)
| | - Francesca De Luca
- «Sandro Pitigliani» Translational Research Unit, Hospital of Prato, 59100 Prato, Italy; (F.G.); (F.D.L.); (I.M.); (L.M.)
| | - Dario Romagnoli
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy; (D.R.); (C.B.)
| | - Chiara Biagioni
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy; (D.R.); (C.B.)
- «Sandro Pitigliani» Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy; (E.M.); (L.B.); (A.D.L.)
| | - Erica Moretti
- «Sandro Pitigliani» Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy; (E.M.); (L.B.); (A.D.L.)
| | - Laura Biganzoli
- «Sandro Pitigliani» Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy; (E.M.); (L.B.); (A.D.L.)
| | - Angelo Di Leo
- «Sandro Pitigliani» Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy; (E.M.); (L.B.); (A.D.L.)
| | - Ilenia Migliaccio
- «Sandro Pitigliani» Translational Research Unit, Hospital of Prato, 59100 Prato, Italy; (F.G.); (F.D.L.); (I.M.); (L.M.)
| | - Luca Malorni
- «Sandro Pitigliani» Translational Research Unit, Hospital of Prato, 59100 Prato, Italy; (F.G.); (F.D.L.); (I.M.); (L.M.)
- «Sandro Pitigliani» Medical Oncology Department, Hospital of Prato, 59100 Prato, Italy; (E.M.); (L.B.); (A.D.L.)
| | - Matteo Benelli
- Bioinformatics Unit, Hospital of Prato, 59100 Prato, Italy; (D.R.); (C.B.)
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18
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Lee D, Park Y, Kim S. Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches. Brief Bioinform 2020; 22:5896573. [PMID: 34020548 DOI: 10.1093/bib/bbaa188] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/29/2020] [Accepted: 07/21/2020] [Indexed: 12/19/2022] Open
Abstract
The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.
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Affiliation(s)
- Dohoon Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Youngjune Park
- Department of Computer Science and Engineering, Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul 08826, Korea
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19
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Pang S, Wang L, Wang S, Zhang Y, Wang X. PESM: A novel approach of tumor purity estimation based on sample specific methylation sites. J Bioinform Comput Biol 2020; 18:2050027. [PMID: 32757807 DOI: 10.1142/s0219720020500274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background: Tumor purity is of great significance for the study of tumor genotyping and the prediction of recurrence, which is significantly affected by tumor heterogeneity. Tumor heterogeneity is the basis of drug resistance in various cancer treatments, and DNA methylation plays a core role in the generation of tumor heterogeneity. Almost all types of cancer cells are associated with abnormal DNA methylation in certain regions of the genome. The selection of tumor-related differential methylation sites, which can be used as an indicator of tumor purity, has important implications for purity assessment. At present, the selection of information sites mostly focuses on inter-tumor heterogeneity and ignores the heterogeneity of tumor growth space that is sample specificity. Results: Considering the specificity of tumor samples and the information gain of individual tumor sample relative to the normal samples, we present an approach, PESM, to evaluate the tumor purity through the specificity difference methylation sites of tumor samples. Applied to more than 200 tumor samples of Prostate adenocarcinoma (PRAD) and Kidney renal clear cell carcinoma (KIRC), it shows that the tumor purity estimated by PESM is highly consistent with other existing methods. In addition, PESM performs better than the method that uses the integrated signal of methylation sites to estimate purity. Therefore, different information sites selection methods have an important impact on the estimation of tumor purity, and the selection of sample specific information sites has a certain significance for accurate identification of tumor purity of samples.
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Affiliation(s)
- Shanchen Pang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, P. R. China
| | - Lihua Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, P. R. China
| | - Shudong Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, P. R. China
| | - Yuanyuan Zhang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, P. R. China.,School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, P. R. China
| | - Xinzeng Wang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, P. R. China
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20
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Azim R, Wang S, Zhou S, Zhong X. Purity estimation from differentially methylated sites using Illumina Infinium methylation microarray data. Cell Cycle 2020; 19:2028-2039. [PMID: 32627651 PMCID: PMC7469651 DOI: 10.1080/15384101.2020.1789315] [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: 01/22/2020] [Revised: 06/11/2020] [Accepted: 06/23/2020] [Indexed: 10/23/2022] Open
Abstract
Solid tissues collected from patient-driven clinical settings are composed of both normal and cancer cells, which often precede complications in data analysis and epigenetic findings. The Purity estimation of samples is crucial for reliable genomic aberration identification and uniform inter-sample and inter-patient comparisons as well. Here, an effective and flexible method has been developed and designed to estimate the level of methylation, which infers tumor purity without prior knowledge from the other datasets. The comprehensive analysis of our approach on Illumina Infinium 450 k methylation microarray explains that TCGA Breast Cancer data exhibits improved performance for purity assessment. This assessment has a strong correlation with other advanced methods.
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Affiliation(s)
- Riasat Azim
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, P.R. China
| | - Shulin Wang
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, P.R. China
| | - Su Zhou
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, P.R. China
| | - Xing Zhong
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, P.R. China
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21
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Liu B, Yang X, Wang T, Lin J, Kang Y, Jia P, Ye K. MEpurity: estimating tumor purity using DNA methylation data. Bioinformatics 2020; 35:5298-5300. [PMID: 31297508 DOI: 10.1093/bioinformatics/btz555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 07/04/2019] [Accepted: 07/10/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Tumor purity is a fundamental property of each cancer sample and affects downstream investigations. Current tumor purity estimation methods either require matched normal sample or report moderately high tumor purity even on normal samples. It is critical to develop a novel computational approach to estimate tumor purity with sufficient precision based on tumor-only sample. RESULTS In this study, we developed MEpurity, a beta mixture model-based algorithm, to estimate the tumor purity based on tumor-only Illumina Infinium 450k methylation microarray data. We applied MEpurity to both The Cancer Genome Atlas (TCGA) cancer data and cancer cell line data, demonstrating that MEpurity reports low tumor purity on normal samples and comparable results on tumor samples with other state-of-art methods. AVAILABILITY AND IMPLEMENTATION MEpurity is a C++ program which is available at https://github.com/xjtu-omics/MEpurity. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bowen Liu
- MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xi'an Jiaotong University
| | - Xiaofei Yang
- MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xi'an Jiaotong University.,The Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Tingjie Wang
- MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xi'an Jiaotong University
| | - Jiadong Lin
- MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xi'an Jiaotong University
| | - Yongyong Kang
- MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xi'an Jiaotong University
| | - Peng Jia
- MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xi'an Jiaotong University
| | - Kai Ye
- MOE Key Lab for Intelligent Networks & Networks Security, School of Electronics and Information Engineering, Xi'an Jiaotong University.,Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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22
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Locallo A, Prandi D, Fedrizzi T, Demichelis F. TPES: tumor purity estimation from SNVs. Bioinformatics 2020; 35:4433-4435. [PMID: 31099386 DOI: 10.1093/bioinformatics/btz406] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 04/08/2019] [Accepted: 05/08/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Tumor purity (TP) is the proportion of cancer cells in a tumor sample. TP impacts on the accurate assessment of molecular and genomics features as assayed with NGS approaches. State-of-the-art tools mainly rely on somatic copy-number alterations (SCNA) to quantify TP and therefore fail when a tumor genome is nearly euploid, i.e. 'non-aberrant' in terms of identifiable SCNAs. RESULTS We introduce a computational method, tumor purity estimation from single-nucleotide variants (SNVs), which derives TP from the allelic fraction distribution of SNVs. On more than 7800 whole-exome sequencing data of TCGA tumor samples, it showed high concordance with a range of TP tools (Spearman's correlation between 0.68 and 0.82; >9 SNVs) and rescued TP estimates of 1, 194 samples (15%) pan-cancer. AVAILABILITY AND IMPLEMENTATION TPES is available as an R package on CRAN and at https://bitbucket.org/l0ka/tpes.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alessio Locallo
- Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento, Trento, Italy
| | - Davide Prandi
- Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento, Trento, Italy
| | - Tarcisio Fedrizzi
- Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento, Trento, Italy
| | - Francesca Demichelis
- Laboratory of Computational and Functional Oncology, CIBIO Department, University of Trento, Trento, Italy.,Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian, New York, US.,Department of BioMedical Research, University of Bern, Bern 3008, Switzerland
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23
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Fan F, Chen D, Zhao Y, Wang H, Sun H, Sun K. Rapid preliminary purity evaluation of tumor biopsies using deep learning approach. Comput Struct Biotechnol J 2020; 18:1746-1753. [PMID: 32695267 PMCID: PMC7352054 DOI: 10.1016/j.csbj.2020.06.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/18/2020] [Accepted: 06/05/2020] [Indexed: 12/29/2022] Open
Abstract
Tumor biopsy is one of the most widely used materials in cancer diagnoses and molecular studies, where the purity of the biopsies (i.e., proportion of cells that are cancerous) is crucial for both applications. However, conventional approaches for tumor biopsy purity evaluation require experienced pathologists and/or various materials/experiments therefore were time-consuming and error prone. Rapid, easy-to-perform and cost-effective methods are thus still of demand. Recent studies had demonstrated that molecular signatures were informative to this task. Previously, we had developed GeneCT, a deep learning-based cancerous status and tissue-of-origin classifier for pan-tumor/tissue biopsies. In the current work, we applied GeneCT on datasets collected from various groups, where the experimental protocols and cancer types differed from each other. We found that GeneCT showed high accuracies on most datasets; for samples with unexpected results, in-depth investigations suggested that they might suffer from imperfect purity. In silico mixture experiments further showed that GeneCT classification was highly indicative in predicting the purity of the tumor biopsies. Considering that transcriptome profiling is a common and inexpensive experiment in molecular cancer studies, our deep learning-based GeneCT could thus serve as a valuable tool for rapid, preliminary tumor biopsy purity assessment.
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Affiliation(s)
- Fei Fan
- Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Dan Chen
- The Third Affiliated Hospital (Provisional) of The Chinese University of Hong, Shenzhen, Shenzhen 518172, China
| | - Yu Zhao
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
| | - Huating Wang
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China.,Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
| | - Hao Sun
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR 999077, China.,Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong SAR 999077, China
| | - Kun Sun
- Shenzhen Bay Laboratory, Shenzhen 518132, China
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24
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McCartney A, Benelli M, Di Leo A. Estimating the magnitude of clinical benefit from (neo)adjuvant chemotherapy in patients with ER-positive/HER2-negative breast cancer. Breast 2020; 48 Suppl 1:S81-S84. [PMID: 31839168 DOI: 10.1016/s0960-9776(19)31130-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Abstract
Gene-expression assays were originally validated retrospectively as tools of prognostication, with evidence emerging from more recent prospectively-conducted studies such as MINDACT and TAILORx supporting their clinical validity and utility as biomarkers in identifying patients with luminal breast cancer who might be spared chemotherapy. However, these assays still do not have the ability to identify all patients who may safely avoid chemotherapy, and may over-estimate the risk of relapse in some cases. Future studies should aim to prospectively integrate contemporary approaches that assume a theoretical risk of relapse (based on pathological and/or genomic evaluation of the primary tumour), with new tools that can detect signals of active micro-metastatic disease. Until current methods of estimating prognosis and predicting benefit from adjuvant chemotherapy are significantly refined, estimating and improving the true magnitude of benefit derived from chemotherapy remains a challenge for clinicians.
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Affiliation(s)
- Amelia McCartney
- "Sandro Pitigliani" Department of Medical Oncology, Hospital of Prato, Prato, Italy
| | | | - Angelo Di Leo
- "Sandro Pitigliani" Department of Medical Oncology, Hospital of Prato, Prato, Italy.
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25
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Wu A, Cremaschi P, Wetterskog D, Conteduca V, Franceschini GM, Kleftogiannis D, Jayaram A, Sandhu S, Wong SQ, Benelli M, Salvi S, Gurioli G, Feber A, Pereira MB, Wingate AM, Gonzalez-Billalebeitia E, De Giorgi U, Demichelis F, Lise S, Attard G. Genome-wide plasma DNA methylation features of metastatic prostate cancer. J Clin Invest 2020; 130:1991-2000. [PMID: 32149736 PMCID: PMC7108919 DOI: 10.1172/jci130887] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 01/08/2020] [Indexed: 12/25/2022] Open
Abstract
Tumor DNA circulates in the plasma of cancer patients admixed with DNA from noncancerous cells. The genomic landscape of plasma DNA has been characterized in metastatic castration-resistant prostate cancer (mCRPC) but the plasma methylome has not been extensively explored. Here, we performed next-generation sequencing (NGS) on plasma DNA with and without bisulfite treatment from mCRPC patients receiving either abiraterone or enzalutamide in the pre- or post-chemotherapy setting. Principal component analysis on the mCRPC plasma methylome indicated that the main contributor to methylation variance (principal component one, or PC1) was strongly correlated with genomically determined tumor fraction (r = -0.96; P < 10-8) and characterized by hypermethylation of targets of the polycomb repressor complex 2 components. Further deconvolution of the PC1 top-correlated segments revealed that these segments are comprised of methylation patterns specific to either prostate cancer or prostate normal epithelium. To extract information specific to an individual's cancer, we then focused on an orthogonal methylation signature, which revealed enrichment for androgen receptor binding sequences and hypomethylation of these segments associated with AR copy number gain. Individuals harboring this methylation pattern had a more aggressive clinical course. Plasma methylome analysis can accurately quantitate tumor fraction and identify distinct biologically relevant mCRPC phenotypes.
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Affiliation(s)
- Anjui Wu
- University College London Cancer Institute, London, United Kingdom
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Paolo Cremaschi
- University College London Cancer Institute, London, United Kingdom
| | | | - Vincenza Conteduca
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | | | | | - Anuradha Jayaram
- University College London Cancer Institute, London, United Kingdom
| | - Shahneen Sandhu
- Peter MacCallum Cancer Centre and the University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen Q. Wong
- Peter MacCallum Cancer Centre and the University of Melbourne, Melbourne, Victoria, Australia
| | - Matteo Benelli
- Centre for Integrative Biology, University of Trento, Trento, Italy
| | - Samanta Salvi
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Giorgia Gurioli
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Andrew Feber
- University College London Cancer Institute, London, United Kingdom
| | | | | | - Enrique Gonzalez-Billalebeitia
- Servicio de Hematología y Oncología Médica, Hospital Universitario Morales Meseguer, IMIB-Universidad de Murcia, Murcia, Spain
| | - Ugo De Giorgi
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Francesca Demichelis
- Centre for Integrative Biology, University of Trento, Trento, Italy
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Gerhardt Attard
- University College London Cancer Institute, London, United Kingdom
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26
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Wang S, Wang L, Zhang Y, Pang S, Wang X. PEIS: a novel approach of tumor purity estimation by identifying information sites through integrating signal based on DNA methylation data. BMC Bioinformatics 2019; 20:714. [PMID: 31888435 PMCID: PMC6936156 DOI: 10.1186/s12859-019-3227-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Tumor purity plays an important role in understanding the pathogenic mechanism of tumors. The purity of tumor samples is highly sensitive to tumor heterogeneity. Due to Intratumoral heterogeneity of genetic and epigenetic data, it is suitable to study the purity of tumors. Among them, there are many purity estimation methods based on copy number variation, gene expression and other data, while few use DNA methylation data and often based on selected information sites. Consequently, how to choose methylation sites as information sites has an important influence on the purity estimation results. At present, the selection of information sites was often based on the differentially methylated sites that only consider the mean signal, without considering other possible signals and the strong correlation among adjacent sites. RESULTS Considering integrating multi-signals and strong correlation among adjacent sites, we propose an approach, PEIS, to estimate the purity of tumor samples by selecting informative differential methylation sites. Application to 12 publicly available tumor datasets, it is shown that PEIS provides accurate results in the estimation of tumor purity which has a high consistency with other existing methods. Also, through comparing the results of different information sites selection methods in the evaluation of tumor purity, it shows the PEIS is superior to other methods. CONCLUSIONS A new method to estimate the purity of tumor samples is proposed. This approach integrates multi-signals of the CpG sites and the correlation between the sites. Experimental analysis shows that this method is in good agreement with other existing methods for estimating tumor purity.
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Affiliation(s)
- Shudong Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China
| | - Lihua Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China
| | - Yuanyuan Zhang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China. .,School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China.
| | - Shanchen Pang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China
| | - Xinzeng Wang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, Shandong, China.
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27
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Petralia F, Wang L, Peng J, Yan A, Zhu J, Wang P. A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity. Bioinformatics 2019; 34:i528-i536. [PMID: 29949994 PMCID: PMC6022554 DOI: 10.1093/bioinformatics/bty280] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. Results Using extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells. Availability and implementation R codes can be found at https://github.com/petraf01/TSNet. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Francesca Petralia
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Li Wang
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Sema4, a Mount Sinai Venture, Stamford, CT, USA
| | - Jie Peng
- Department of Statistics, University of California, Davis, Davis, CA, USA
| | - Arthur Yan
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jun Zhu
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Sema4, a Mount Sinai Venture, Stamford, CT, USA
| | - Pei Wang
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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28
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Johann PD, Jäger N, Pfister SM, Sill M. RF_Purify: a novel tool for comprehensive analysis of tumor-purity in methylation array data based on random forest regression. BMC Bioinformatics 2019; 20:428. [PMID: 31419933 PMCID: PMC6697926 DOI: 10.1186/s12859-019-3014-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 07/30/2019] [Indexed: 11/17/2022] Open
Abstract
Background With the advent of array-based techniques to measure methylation levels in primary tumor samples, systematic investigations of methylomes have widely been performed on a large number of tumor entities. Most of these approaches are not based on measuring individual cell methylation but rather the bulk tumor sample DNA, which contains a mixture of tumor cells, infiltrating immune cells and other stromal components. This raises questions about the purity of a certain tumor sample, given the varying degrees of stromal infiltration in different entities. Previous methods to infer tumor purity require or are based on the use of matching control samples which are rarely available. Here we present a novel, reference free method to quantify tumor purity, based on two Random Forest classifiers, which were trained on ABSOLUTE as well as ESTIMATE purity values from TCGA tumor samples. We subsequently apply this method to a previously published, large dataset of brain tumors, proving that these models perform well in datasets that have not been characterized with respect to tumor purity . Results Using two gold standard methods to infer purity – the ABSOLUTE score based on whole genome sequencing data and the ESTIMATE score based on gene expression data- we have optimized Random Forest classifiers to predict tumor purity in entities that were contained in the TCGA project. We validated these classifiers using an independent test data set and cross-compared it to other methods which have been applied to the TCGA datasets (such as ESTIMATE and LUMP). Using Illumina methylation array data of brain tumor entities (as published in Capper et al. (Nature 555:469-474,2018)) we applied this model to estimate tumor purity and find that subgroups of brain tumors display substantial differences in tumor purity. Conclusions Random forest- based tumor purity prediction is a well suited tool to extrapolate gold standard measures of purity to novel methylation array datasets. In contrast to other available methylation based tumor purity estimation methods, our classifiers do not need a priori knowledge about the tumor entity or matching control tissue to predict tumor purity. Electronic supplementary material The online version of this article (10.1186/s12859-019-3014-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pascal David Johann
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany. .,Department of Pediatric Hematology and Oncology, University Children's Hospital Heidelberg, Heidelberg, Germany. .,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Natalie Jäger
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan M Pfister
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.,Department of Pediatric Hematology and Oncology, University Children's Hospital Heidelberg, Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Sill
- Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
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29
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Morigi C. Highlights of the 16th St Gallen International Breast Cancer Conference, Vienna, Austria, 20-23 March 2019: personalised treatments for patients with early breast cancer. Ecancermedicalscience 2019; 13:924. [PMID: 31281421 PMCID: PMC6546258 DOI: 10.3332/ecancer.2019.924] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Indexed: 12/15/2022] Open
Abstract
The 16th St Gallen International Breast Cancer Conference took place in Vienna for the third time, from 20–23 March 2019. More than 3000 people from all over the world were invited to take part in this important bi-annual critical review of the ‘state of the art’ in the primary care of breast cancer (BC), independent of political and industrial pressure, with the aim to integrate the most recent research data and most important developments in BC therapies since St Gallen International Breast Cancer Conference 2017, with the ultimate goal of drawing up a consensus for the current optimal treatment and prevention of BC. This year, the St Gallen Breast Cancer Award was won by Monica Morrow (Memorial Sloan Kettering Cancer Center, USA) for her extraordinary contribution in research and practise development in the treatment of BC. She opened the session with the lecture ‘Will surgery be a part of BC treatment in the future?’ Improved systemic therapy has decreased BC mortality and increased pathologic complete response (pCR) rates after neoadjuvant chemotherapy (NACT). Improved imaging and increased screening uptake have led to detect smaller cancers. These factors have highlighted two possible scenarios to omit surgery: for patients with small low-grade ductal carcinoma in situ (DCIS) and for those who have received NACT and had a clinical and radiological complete response. However, considering that 7%–20% of `low-risk’ DCIS patients have co-existing invasive cancer at diagnosis, that surgery has become progressively less morbid and less toxic than some systemic therapies with a lower cost-effectiveness ratio, and that identification of pathologic complete response (pCR) without surgery requires more intensive imaging follow-up (more biopsies, higher cost and more anxiety for the patient), surgery still appears to be an essential treatment for BC. The Umberto Veronesi Memorial Award went to Lesley Fallowfield (Brighton and Sussex Medical School, UK) for her important research and activity in the field of the development of patient outcome, of better communication skills and quality of life for women. In her lecture, she remarked on the importance of improving BC personalised treatments, especially through co-operation between scientists, always considering the whole woman and not just her breast disease. This award was given by Paolo Veronesi, after a moving introduction which culminated with the following words of Professor Umberto Veronesi: ‘It is not possible to take care of the people’s bodies without taking care of their mind. My duty, the duty of all doctors, is to listen and be part of the emotions of those we treat every day’.
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Affiliation(s)
- Consuelo Morigi
- Division of Senology, IRCCS European Institute of Oncology, 20141 Milan, Italy
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