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Chung YS, Kang S, Kim J, Lee S, Kim S. CLEMENT: genomic decomposition and reconstruction of non-tumor subclones. Nucleic Acids Res 2024; 52:e62. [PMID: 38922688 DOI: 10.1093/nar/gkae527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 05/27/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Genome-level clonal decomposition of a single specimen has been widely studied; however, it is mostly limited to cancer research. In this study, we developed a new algorithm CLEMENT, which conducts accurate decomposition and reconstruction of multiple subclones in genome sequencing of non-tumor (normal) samples. CLEMENT employs the Expectation-Maximization (EM) algorithm with optimization strategies specific to non-tumor subclones, including false variant call identification, non-disparate clone fuzzy clustering, and clonal allele fraction confinement. In the simulation and in vitro cell line mixture data, CLEMENT outperformed current cancer decomposition algorithms in estimating the number of clones (root-mean-square-error = 0.58-0.78 versus 1.43-3.34) and in the variant-clone membership agreement (∼85.5% versus 70.1-76.7%). Additional testing on human multi-clonal normal tissue sequencing confirmed the accurate identification of subclones that originated from different cell types. Clone-level analysis, including mutational burden and signatures, provided a new understanding of normal-tissue composition. We expect that CLEMENT will serve as a crucial tool in the currently emerging field of non-tumor genome analysis.
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Affiliation(s)
- Young-Soo Chung
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Seungseok Kang
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jisu Kim
- DataShape team, Inria Saclay Île-De-France, Palaiseau 91120, France
- Department of Statistics, Seoul National University, Seoul 08826, Republic of Korea
| | - Sangbo Lee
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sangwoo Kim
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
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Bernstein N, Spencer Chapman M, Nyamondo K, Chen Z, Williams N, Mitchell E, Campbell PJ, Cohen RL, Nangalia J. Analysis of somatic mutations in whole blood from 200,618 individuals identifies pervasive positive selection and novel drivers of clonal hematopoiesis. Nat Genet 2024; 56:1147-1155. [PMID: 38744975 PMCID: PMC11176083 DOI: 10.1038/s41588-024-01755-1] [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: 07/21/2023] [Accepted: 04/17/2024] [Indexed: 05/16/2024]
Abstract
Human aging is marked by the emergence of a tapestry of clonal expansions in dividing tissues, particularly evident in blood as clonal hematopoiesis (CH). CH, linked to cancer risk and aging-related phenotypes, often stems from somatic mutations in a set of established genes. However, the majority of clones lack known drivers. Here we infer gene-level positive selection in whole blood exomes from 200,618 individuals in UK Biobank. We identify 17 additional genes, ZBTB33, ZNF318, ZNF234, SPRED2, SH2B3, SRCAP, SIK3, SRSF1, CHEK2, CCDC115, CCL22, BAX, YLPM1, MYD88, MTA2, MAGEC3 and IGLL5, under positive selection at a population level, and validate this selection pattern in 10,837 whole genomes from single-cell-derived hematopoietic colonies. Clones with mutations in these genes grow in frequency and size with age, comparable to classical CH drivers. They correlate with heightened risk of infection, death and hematological malignancy, highlighting the significance of these additional genes in the aging process.
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Affiliation(s)
| | - Michael Spencer Chapman
- Wellcome Sanger Institute, Hinxton, UK
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Kudzai Nyamondo
- Wellcome Sanger Institute, Hinxton, UK
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | - Zhenghao Chen
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | | | - Emily Mitchell
- Wellcome Sanger Institute, Hinxton, UK
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
| | | | | | - Jyoti Nangalia
- Wellcome Sanger Institute, Hinxton, UK.
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.
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3
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Vaisband M, Schubert M, Gassner FJ, Geisberger R, Greil R, Zaborsky N, Hasenauer J. Validation of genetic variants from NGS data using deep convolutional neural networks. BMC Bioinformatics 2023; 24:158. [PMID: 37081386 PMCID: PMC10116675 DOI: 10.1186/s12859-023-05255-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 03/27/2023] [Indexed: 04/22/2023] Open
Abstract
Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessary step in state-of-the-art processing pipelines. This limits reproducibility and introduces a bottleneck with respect to scalability. We demonstrate that the validation of genetic variants can be improved using a machine learning approach resting on a Convolutional Neural Network, trained using existing human annotation. In contrast to existing approaches, we introduce a way in which contextual data from sequencing tracks can be included into the automated assessment. A rigorous evaluation shows that the resulting model is robust and performs on par with trained researchers following published standard operating procedure.
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Affiliation(s)
- Marc Vaisband
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center; Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR); Cancer Cluster Salzburg, Paracelsus Medical University, Salzburg, Austria.
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany.
| | - Maria Schubert
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center; Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR); Cancer Cluster Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Franz Josef Gassner
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center; Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR); Cancer Cluster Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Roland Geisberger
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center; Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR); Cancer Cluster Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Richard Greil
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center; Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR); Cancer Cluster Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Nadja Zaborsky
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center; Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR); Cancer Cluster Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Jan Hasenauer
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
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4
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Voorwerk L, Isaeva OI, Horlings HM, Balduzzi S, Chelushkin M, Bakker NAM, Champanhet E, Garner H, Sikorska K, Loo CE, Kemper I, Mandjes IAM, de Maaker M, van Geel JJL, Boers J, de Boer M, Salgado R, van Dongen MGJ, Sonke GS, de Visser KE, Schumacher TN, Blank CU, Wessels LFA, Jager A, Tjan-Heijnen VCG, Schröder CP, Linn SC, Kok M. PD-L1 blockade in combination with carboplatin as immune induction in metastatic lobular breast cancer: the GELATO trial. NATURE CANCER 2023; 4:535-549. [PMID: 37038006 PMCID: PMC10132987 DOI: 10.1038/s43018-023-00542-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 03/08/2023] [Indexed: 04/12/2023]
Abstract
Invasive lobular breast cancer (ILC) is the second most common histological breast cancer subtype, but ILC-specific trials are lacking. Translational research revealed an immune-related ILC subset, and in mouse ILC models, synergy between immune checkpoint blockade and platinum was observed. In the phase II GELATO trial ( NCT03147040 ), patients with metastatic ILC were treated with weekly carboplatin (area under the curve 1.5 mg ml-1 min-1) as immune induction for 12 weeks and atezolizumab (PD-L1 blockade; triweekly) from the third week until progression. Four of 23 evaluable patients had a partial response (17%), and 2 had stable disease, resulting in a clinical benefit rate of 26%. From these six patients, four had triple-negative ILC (TN-ILC). We observed higher CD8+ T cell infiltration, immune checkpoint expression and exhausted T cells after treatment. With this GELATO trial, we show that ILC-specific clinical trials are feasible and demonstrate promising antitumor activity of atezolizumab with carboplatin, particularly for TN-ILC, and provide insights for the design of highly needed ILC-specific trials.
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Affiliation(s)
- Leonie Voorwerk
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Olga I Isaeva
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hugo M Horlings
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sara Balduzzi
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Maksim Chelushkin
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Noor A M Bakker
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Elisa Champanhet
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hannah Garner
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Karolina Sikorska
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Claudette E Loo
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Inge Kemper
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ingrid A M Mandjes
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Michiel de Maaker
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Jasper J L van Geel
- Department of Medical Oncology, University Medical Center Groningen, Groningen, the Netherlands
| | - Jorianne Boers
- Department of Medical Oncology, University Medical Center Groningen, Groningen, the Netherlands
| | - Maaike de Boer
- Department of Medical Oncology, GROW, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Marloes G J van Dongen
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Karin E de Visser
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
- Department of Immunology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ton N Schumacher
- Oncode Institute, Utrecht, the Netherlands
- Division of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands
| | - Christian U Blank
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Agnes Jager
- Department of Medical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Vivianne C G Tjan-Heijnen
- Department of Medical Oncology, GROW, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Carolien P Schröder
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Oncology, University Medical Center Groningen, Groningen, the Netherlands
| | - Sabine C Linn
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marleen Kok
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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Reed MR, Lyle AG, De Loose A, Maddukuri L, Learned K, Beale HC, Kephart ET, Cheney A, van den Bout A, Lee MP, Hundley KN, Smith AM, DesRochers TM, Vibat CRT, Gokden M, Salama S, Wardell CP, Eoff RL, Vaske OM, Rodriguez A. A Functional Precision Medicine Pipeline Combines Comparative Transcriptomics and Tumor Organoid Modeling to Identify Bespoke Treatment Strategies for Glioblastoma. Cells 2021; 10:cells10123400. [PMID: 34943910 PMCID: PMC8699481 DOI: 10.3390/cells10123400] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 12/15/2022] Open
Abstract
Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer, occurring in 30-50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used a comparative transcriptomics approach to identify genes that are uniquely overexpressed in the LFS GBM patient relative to a cancer compendium of 12,747 tumor RNA sequencing data sets, including 200 GBMs. STAT1 and STAT2 were identified as being significantly overexpressed in the LFS patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitors, as a potential therapy. The LFS patient had the highest level of STAT1 and STAT2 expression in an institutional high-grade glioma cohort of 45 patients, further supporting the cancer compendium results. To empirically validate the comparative transcriptomics pipeline, we used a combination of adherent and organoid cell culture techniques, including ex vivo patient-derived organoids (PDOs) from four patient-derived cell lines, including the LFS patient. STAT1 and STAT2 expression levels in the four patient-derived cells correlated with levels identified in the respective parent tumors. In both adherent and organoid cultures, cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. A spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. Two targeted therapies were selected for the patient of interest and resulted in radiographic disease stability. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors.
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Affiliation(s)
- Megan R. Reed
- Department of Biochemistry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (M.R.R.); (L.M.); (R.L.E.)
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (A.D.L.); (M.P.L.); (K.N.H.)
| | - A. Geoffrey Lyle
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (A.G.L.); (H.C.B.); (A.C.); (A.v.d.B.); (S.S.); (O.M.V.)
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (K.L.); (E.T.K.)
| | - Annick De Loose
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (A.D.L.); (M.P.L.); (K.N.H.)
| | - Leena Maddukuri
- Department of Biochemistry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (M.R.R.); (L.M.); (R.L.E.)
| | - Katrina Learned
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (K.L.); (E.T.K.)
| | - Holly C. Beale
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (A.G.L.); (H.C.B.); (A.C.); (A.v.d.B.); (S.S.); (O.M.V.)
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (K.L.); (E.T.K.)
| | - Ellen T. Kephart
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (K.L.); (E.T.K.)
| | - Allison Cheney
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (A.G.L.); (H.C.B.); (A.C.); (A.v.d.B.); (S.S.); (O.M.V.)
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (K.L.); (E.T.K.)
| | - Anouk van den Bout
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (A.G.L.); (H.C.B.); (A.C.); (A.v.d.B.); (S.S.); (O.M.V.)
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (K.L.); (E.T.K.)
| | - Madison P. Lee
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (A.D.L.); (M.P.L.); (K.N.H.)
| | - Kelsey N. Hundley
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (A.D.L.); (M.P.L.); (K.N.H.)
| | - Ashley M. Smith
- KIYATEC Inc., Greenville, SC 29605, USA; (A.M.S.); (T.M.D.); (C.R.T.V.)
| | | | | | - Murat Gokden
- Department of Pathology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Sofie Salama
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (A.G.L.); (H.C.B.); (A.C.); (A.v.d.B.); (S.S.); (O.M.V.)
- Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Christopher P. Wardell
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Robert L. Eoff
- Department of Biochemistry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (M.R.R.); (L.M.); (R.L.E.)
| | - Olena M. Vaske
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95064, USA; (A.G.L.); (H.C.B.); (A.C.); (A.v.d.B.); (S.S.); (O.M.V.)
| | - Analiz Rodriguez
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; (A.D.L.); (M.P.L.); (K.N.H.)
- Correspondence: ; Tel.: +1-501-686-8078; Fax: +1-501-686-8767
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Genomic and Transcriptomic Profiling of Brain Metastases. Cancers (Basel) 2021; 13:cancers13225598. [PMID: 34830758 PMCID: PMC8615723 DOI: 10.3390/cancers13225598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 10/31/2021] [Accepted: 11/05/2021] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Brain metastases (BM) are the most common brain tumors in adults and are the main cause of cancer-associated death. Omics analysis of BM will allow for a better understanding of metastatic progression, prognosis and therapeutic targeting. In this study, BM samples underwent comprehensive molecular profiling with genomics and transcriptomics. Mutational signatures suggested that most mutations were gained prior to metastasis. A novel copy number event centered around the MCL1 gene was found in 75% of all samples. Transcriptomics revealed that melanoma BM formed a distinct cluster in comparison to other subtypes. Poor survival correlated to self-identified black race and absence of radiation treatment but not molecular profiles. These data identify potential new drivers of brain metastatic progression, implicate that melanoma BM are distinctive and likely responsive to unique therapies, and further investigation of sociodemographic and clinical features are needed in BM cohorts. Abstract Brain metastases (BM) are the most common brain tumors in adults occurring in up to 40% of all cancer patients. Multi-omics approaches allow for understanding molecular mechanisms and identification of markers with prognostic significance. In this study, we profile 130 BM using genomics and transcriptomics and correlate molecular characteristics to clinical parameters. The most common tumor origins for BM were lung (40%) followed by melanoma (21%) and breast (15%). Melanoma and lung BMs contained more deleterious mutations than other subtypes (p < 0.001). Mutational signatures suggested that the bulk of the mutations were gained before metastasis. A novel copy number event centered around the MCL1 gene was found in 75% of all samples, suggesting a broader role in promoting metastasis. Unsupervised hierarchical cluster analysis of transcriptional signatures available in 65 samples based on the hallmarks of cancer revealed four distinct clusters. Melanoma samples formed a distinctive cluster in comparison to other BM subtypes. Characteristics of molecular profiles did not correlate with survival. However, patients with self-identified black race or those who did not receive radiation correlated with poor survival. These data identify potential new drivers of brain metastatic progression. Our data also suggest further investigation of sociodemographic and clinical features is needed in BM cohorts.
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