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Cavalcante BRR, Freitas RD, Siquara da Rocha LO, Santos RSB, Souza BSDF, Ramos PIP, Rocha GV, Gurgel Rocha CA. In silico approaches for drug repurposing in oncology: a scoping review. Front Pharmacol 2024; 15:1400029. [PMID: 38919258 PMCID: PMC11196849 DOI: 10.3389/fphar.2024.1400029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
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Affiliation(s)
- Bruno Raphael Ribeiro Cavalcante
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | - Raíza Dias Freitas
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Social and Pediatric Dentistry of the School of Dentistry, Federal University of Bahia, Salvador, Brazil
| | - Leonardo de Oliveira Siquara da Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | | | - Bruno Solano de Freitas Souza
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Pablo Ivan Pereira Ramos
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil
| | - Gisele Vieira Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Clarissa Araújo Gurgel Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
- Department of Propaedeutics, School of Dentistry of the Federal University of Bahia, Salvador, Brazil
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Echeverría-Garcés G, Ramos-Medina MJ, Vargas R, Cabrera-Andrade A, Altamirano-Colina A, Freire MP, Montalvo-Guerrero J, Rivera-Orellana S, Echeverría-Espinoza P, Quiñones LA, López-Cortés A. Gastric cancer actionable genomic alterations across diverse populations worldwide and pharmacogenomics strategies based on precision oncology. Front Pharmacol 2024; 15:1373007. [PMID: 38756376 PMCID: PMC11096557 DOI: 10.3389/fphar.2024.1373007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/10/2024] [Indexed: 05/18/2024] Open
Abstract
Introduction: Gastric cancer is one of the most prevalent types of cancer worldwide. The World Health Organization (WHO), the International Agency for Research on Cancer (IARC), and the Global Cancer Statistics (GLOBOCAN) reported an age standardized global incidence rate of 9.2 per 100,000 individuals for gastric cancer in 2022, with a mortality rate of 6.1. Despite considerable progress in precision oncology through the efforts of international consortia, understanding the genomic features and their influence on the effectiveness of anti-cancer treatments across diverse ethnic groups remains essential. Methods: Our study aimed to address this need by conducting integrated in silico analyses to identify actionable genomic alterations in gastric cancer driver genes, assess their impact using deleteriousness scores, and determine allele frequencies across nine global populations: European Finnish, European non-Finnish, Latino, East Asian, South Asian, African, Middle Eastern, Ashkenazi Jewish, and Amish. Furthermore, our goal was to prioritize targeted therapeutic strategies based on pharmacogenomics clinical guidelines, in silico drug prescriptions, and clinical trial data. Results: Our comprehensive analysis examined 275,634 variants within 60 gastric cancer driver genes from 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, identifying 13,542 annotated and predicted oncogenic variants. We prioritized the most prevalent and deleterious oncogenic variants for subsequent pharmacogenomics testing. Additionally, we discovered actionable genomic alterations in the ARID1A, ATM, BCOR, ERBB2, ERBB3, CDKN2A, KIT, PIK3CA, PTEN, NTRK3, TP53, and CDKN2A genes that could enhance the efficacy of anti-cancer therapies, as suggested by in silico drug prescription analyses, reviews of current pharmacogenomics clinical guidelines, and evaluations of phase III and IV clinical trials targeting gastric cancer driver proteins. Discussion: These findings underline the urgency of consolidating efforts to devise effective prevention measures, invest in genomic profiling for underrepresented populations, and ensure the inclusion of ethnic minorities in future clinical trials and cancer research in developed countries.
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Affiliation(s)
- Gabriela Echeverría-Garcés
- Centro de Referencia Nacional de Genómica, Secuenciación y Bioinformática, Instituto Nacional de Investigación en Salud Pública “Leopoldo Izquieta Pérez”, Quito, Ecuador
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
| | - María José Ramos-Medina
- German Cancer Research Center (DKFZ), Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Rodrigo Vargas
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
- Department of Molecular Biology, Galileo University, Guatemala City, Guatemala
| | - Alejandro Cabrera-Andrade
- Escuela de Enfermería, Facultad de Ciencias de La Salud, Universidad de Las Américas, Quito, Ecuador
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito, Ecuador
| | | | - María Paula Freire
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
| | | | | | | | - Luis A. Quiñones
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Santiago, Chile
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic-Clinical Oncology (DOBC), Faculty of Medicine, University of Chile, Santiago, Chile
- Department of Pharmaceutical Sciences and Technology, Faculty of Chemical and Pharmaceutical Sciences, University of Chile, Santiago, Chile
| | - Andrés López-Cortés
- Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador
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Gu M, Ren B, Fang Y, Ren J, Liu X, Wang X, Zhou F, Xiao R, Luo X, You L, Zhao Y. Epigenetic regulation in cancer. MedComm (Beijing) 2024; 5:e495. [PMID: 38374872 PMCID: PMC10876210 DOI: 10.1002/mco2.495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 02/21/2024] Open
Abstract
Epigenetic modifications are defined as heritable changes in gene activity that do not involve changes in the underlying DNA sequence. The oncogenic process is driven by the accumulation of alterations that impact genome's structure and function. Genetic mutations, which directly disrupt the DNA sequence, are complemented by epigenetic modifications that modulate gene expression, thereby facilitating the acquisition of malignant characteristics. Principals among these epigenetic changes are shifts in DNA methylation and histone mark patterns, which promote tumor development and metastasis. Notably, the reversible nature of epigenetic alterations, as opposed to the permanence of genetic changes, positions the epigenetic machinery as a prime target in the discovery of novel therapeutics. Our review delves into the complexities of epigenetic regulation, exploring its profound effects on tumor initiation, metastatic behavior, metabolic pathways, and the tumor microenvironment. We place a particular emphasis on the dysregulation at each level of epigenetic modulation, including but not limited to, the aberrations in enzymes responsible for DNA methylation and histone modification, subunit loss or fusions in chromatin remodeling complexes, and the disturbances in higher-order chromatin structure. Finally, we also evaluate therapeutic approaches that leverage the growing understanding of chromatin dysregulation, offering new avenues for cancer treatment.
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Affiliation(s)
- Minzhi Gu
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Bo Ren
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Yuan Fang
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Jie Ren
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Xiaohong Liu
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Xing Wang
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Feihan Zhou
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Ruiling Xiao
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Xiyuan Luo
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Lei You
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
| | - Yupei Zhao
- Department of General SurgeryPeking Union Medical College HospitalPeking Union Medical CollegeChinese Academy of Medical SciencesBeijingP. R. China
- Key Laboratory of Research in Pancreatic TumorChinese Academy of Medical SciencesBeijingP. R. China
- National Science and Technology Key Infrastructure on Translational Medicine in Peking Union Medical College HospitalBeijingP. R. China
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Gayathiri E, Prakash P, Kumaravel P, Jayaprakash J, Ragunathan MG, Sankar S, Pandiaraj S, Thirumalaivasan N, Thiruvengadam M, Govindasamy R. Computational approaches for modeling and structural design of biological systems: A comprehensive review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 185:17-32. [PMID: 37821048 DOI: 10.1016/j.pbiomolbio.2023.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/14/2023] [Accepted: 08/27/2023] [Indexed: 10/13/2023]
Abstract
The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.
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Affiliation(s)
- Ekambaram Gayathiri
- Department of Plant Biology and Plant Biotechnology, Guru Nanak College (Autonomous), Chennai, 42, Tamil Nadu, India
| | - Palanisamy Prakash
- Department of Botany, Periyar University, Periyar Palkalai Nagar, Salem, 636011, Tamil Nadu, India
| | - Priya Kumaravel
- Department of Biotechnology, St. Joseph College (Arts & Science), Kovur, Chennai, Tamil Nadu, India
| | - Jayanthi Jayaprakash
- Department of Advanced Zoology and Biotechnology, Guru Nanak College, Chennai, Tamil Nadu, India
| | | | - Sharmila Sankar
- Department of Advanced Zoology and Biotechnology, Guru Nanak College, Chennai, Tamil Nadu, India
| | - Saravanan Pandiaraj
- Department of Self-Development Skills, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Natesan Thirumalaivasan
- Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMTAS), Chennai, 600077, Tamil Nadu, India
| | - Muthu Thiruvengadam
- Department of Applied Bioscience, College of Life and Environmental Sciences, Konkuk University, Seoul, 05029, South Korea
| | - Rajakumar Govindasamy
- Department of Orthodontics, Saveetha Dental College and Hospitals, Saveetha University, Chennai, India.
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5
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Abstract
The human genome is organized into multiple structural layers, ranging from chromosome territories to progressively smaller substructures, such as topologically associating domains (TADs) and chromatin loops. These substructures, collectively referred to as long-range chromatin interactions (LRIs), have a significant role in regulating gene expression. TADs are regions of the genome that harbour groups of genes and regulatory elements that frequently interact with each other and are insulated from other regions, thereby preventing widespread uncontrolled DNA contacts. Chromatin loops formed within TADs through enhancer and promoter interactions are elastic, allowing transcriptional heterogeneity and stochasticity. Over the past decade, it has become evident that the 3D genome structure, also referred to as the chromatin architecture, is central to many transcriptional cellular decisions. In this Review, we delve into the intricate relationship between steroid receptors and LRIs, discussing how steroid receptors interact with and modulate these chromatin interactions. Genetic alterations in the many processes involved in organizing the nuclear architecture are often associated with the development of hormone-dependent cancers. A better understanding of the interplay between architectural proteins and hormone regulatory networks can ultimately be exploited to develop improved approaches for cancer treatment.
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Affiliation(s)
- Theophilus T Tettey
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Lorenzo Rinaldi
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Gordon L Hager
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, Bethesda, MD, USA.
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Park MN. The Therapeutic Potential of a Strategy to Prevent Acute Myeloid Leukemia Stem Cell Reprogramming in Older Patients. Int J Mol Sci 2023; 24:12037. [PMID: 37569414 PMCID: PMC10418941 DOI: 10.3390/ijms241512037] [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: 06/26/2023] [Revised: 07/22/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Acute myeloid leukemia (AML) is the most common and incurable leukemia subtype. Despite extensive research into the disease's intricate molecular mechanisms, effective treatments or expanded diagnostic or prognostic markers for AML have not yet been identified. The morphological, immunophenotypic, cytogenetic, biomolecular, and clinical characteristics of AML patients are extensive and complex. Leukemia stem cells (LSCs) consist of hematopoietic stem cells (HSCs) and cancer cells transformed by a complex, finely-tuned interaction that causes the complexity of AML. Microenvironmental regulation of LSCs dormancy and the diagnostic and therapeutic implications for identifying and targeting LSCs due to their significance in the pathogenesis of AML are discussed in this review. It is essential to perceive the relationship between the niche for LSCs and HSCs, which together cause the progression of AML. Notably, methylation is a well-known epigenetic change that is significant in AML, and our data also reveal that microRNAs are a unique factor for LSCs. Multiple-targeted approaches to reduce the risk of epigenetic factors, such as the administration of natural compounds for the elimination of local LSCs, may prevent potentially fatal relapses. Furthermore, the survival analysis of overlapping genes revealed that specific targets had significant effects on the survival and prognosis of patients. We predict that the multiple-targeted effects of herbal products on epigenetic modification are governed by different mechanisms in AML and could prevent potentially fatal relapses. Thus, these strategies can facilitate the incorporation of herbal medicine and natural compounds into the advanced drug discovery and development processes achievable with Network Pharmacology research.
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Affiliation(s)
- Moon Nyeo Park
- Department of Pathology, College of Korean Medicine, Kyung Hee University, Hoegidong Dongdaemungu, Seoul 05253, Republic of Korea
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7
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Esposito R, Lanzós A, Uroda T, Ramnarayanan S, Büchi I, Polidori T, Guillen-Ramirez H, Mihaljevic A, Merlin BM, Mela L, Zoni E, Hovhannisyan L, McCluggage F, Medo M, Basile G, Meise DF, Zwyssig S, Wenger C, Schwarz K, Vancura A, Bosch-Guiteras N, Andrades Á, Tham AM, Roemmele M, Medina PP, Ochsenbein AF, Riether C, Kruithof-de Julio M, Zimmer Y, Medová M, Stroka D, Fox A, Johnson R. Tumour mutations in long noncoding RNAs enhance cell fitness. Nat Commun 2023; 14:3342. [PMID: 37291246 PMCID: PMC10250536 DOI: 10.1038/s41467-023-39160-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/01/2023] [Indexed: 06/10/2023] Open
Abstract
Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.
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Affiliation(s)
- Roberta Esposito
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland.
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland.
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso", CNR, 80131, Naples, Italy.
| | - Andrés Lanzós
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Graduate School of Cellular and Biomedical Sciences, University of Bern, 3012, Bern, Switzerland
| | - Tina Uroda
- School of Biology and Environmental Science, University College Dublin, Dublin, D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Sunandini Ramnarayanan
- School of Biology and Environmental Science, University College Dublin, Dublin, D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland
- The SFI Centre for Research Training in Genomics Data Science, Dublin, Ireland
| | - Isabel Büchi
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Taisia Polidori
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Hugo Guillen-Ramirez
- School of Biology and Environmental Science, University College Dublin, Dublin, D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Ante Mihaljevic
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Bernard Mefi Merlin
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Lia Mela
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Eugenio Zoni
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Urology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Lusine Hovhannisyan
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Finn McCluggage
- School of Molecular Sciences, University of Western Australia, Crawley, WA, Australia
- School of Human Sciences, University of Western Australia, Crawley, WA, Australia
| | - Matúš Medo
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Giulia Basile
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Dominik F Meise
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Sandra Zwyssig
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Corina Wenger
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Kyriakos Schwarz
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Adrienne Vancura
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Núria Bosch-Guiteras
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Graduate School of Cellular and Biomedical Sciences, University of Bern, 3012, Bern, Switzerland
| | - Álvaro Andrades
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, Granada, 18016, Spain
- Instituto de Investigación Biosanitaria, Granada, 18014, Spain
- Department of Biochemistry and Molecular Biology I, University of Granada, Granada, 18071, Spain
| | - Ai Ming Tham
- School of Biology and Environmental Science, University College Dublin, Dublin, D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Michaela Roemmele
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Pedro P Medina
- GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, Granada, 18016, Spain
- Instituto de Investigación Biosanitaria, Granada, 18014, Spain
- Department of Biochemistry and Molecular Biology I, University of Granada, Granada, 18071, Spain
| | - Adrian F Ochsenbein
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Carsten Riether
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Marianna Kruithof-de Julio
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Urology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Yitzhak Zimmer
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Michaela Medová
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Deborah Stroka
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Archa Fox
- School of Molecular Sciences, University of Western Australia, Crawley, WA, Australia
- School of Human Sciences, University of Western Australia, Crawley, WA, Australia
| | - Rory Johnson
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, 3010, Bern, Switzerland.
- Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland.
- School of Biology and Environmental Science, University College Dublin, Dublin, D04 V1W8, Ireland.
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, D04 V1W8, Ireland.
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8
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Jiménez-Santos MJ, Nogueira-Rodríguez A, Piñeiro-Yáñez E, López-Fernández H, García-Martín S, Gómez-Plana P, Reboiro-Jato M, Gómez-López G, Glez-Peña D, Al-Shahrour F. PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data. Nucleic Acids Res 2023:7173696. [PMID: 37207338 DOI: 10.1093/nar/gkad412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/27/2023] [Accepted: 05/05/2023] [Indexed: 05/21/2023] Open
Abstract
Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug-gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.
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Affiliation(s)
| | - Alba Nogueira-Rodríguez
- CINBIO, Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, 32004 Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Elena Piñeiro-Yáñez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Hugo López-Fernández
- CINBIO, Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, 32004 Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Santiago García-Martín
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Paula Gómez-Plana
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Miguel Reboiro-Jato
- CINBIO, Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, 32004 Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Gonzalo Gómez-López
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Daniel Glez-Peña
- CINBIO, Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática, 32004 Ourense, Spain
- SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Fátima Al-Shahrour
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
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9
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Ulgen E, Ozisik O, Sezerman OU. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics 2023; 39:btad022. [PMID: 36689556 PMCID: PMC9869653 DOI: 10.1093/bioinformatics/btad022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/09/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023] Open
Abstract
MOTIVATION Identifying appropriate pharmacotherapy options from genomics results is a significant challenge in personalized oncology. However, computational methods for prioritizing drugs are underdeveloped. With the hypothesis that network-based approaches can improve the performance by extending the use of potential drug targets beyond direct interactions, we devised two network-based methods for personalized pharmacotherapy prioritization in cancer. RESULTS We developed novel personalized drug prioritization approaches, PANACEA: PersonAlized Network-based Anti-Cancer therapy EvaluAtion. In PANACEA, initially, the protein interaction network is extended with drugs, and a driverness score is assigned to each altered gene. For scoring drugs, either (i) the 'distance-based' method, incorporating the shortest distance between drugs and altered genes, and driverness scores, or (ii) the 'propagation' method involving the propagation of driverness scores via a random walk with restart framework is performed. We evaluated PANACEA using multiple datasets, and demonstrated that (i) the top-ranking drugs are relevant for cancer pharmacotherapy using TCGA data; (ii) drugs that cancer cell lines are sensitive to are identified using GDSC data; and (iii) PANACEA can perform adequately in the clinical setting using cases with known drug responses. We also illustrate that the proposed methods outperform iCAGES and PanDrugs, two previous personalized drug prioritization approaches. AVAILABILITY AND IMPLEMENTATION The corresponding R package is available on GitHub. (https://github.com/egeulgen/PANACEA.git). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ege Ulgen
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul 34752, Turkey
| | - Ozan Ozisik
- Aix Marseille University, Inserm, MMG, Marseille 13385, France
| | - Osman Ugur Sezerman
- Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul 34752, Turkey
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10
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Inference of epigenetic subnetworks by Bayesian regression with the incorporation of prior information. Sci Rep 2022; 12:20224. [PMID: 36418365 PMCID: PMC9684215 DOI: 10.1038/s41598-022-19879-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 09/06/2022] [Indexed: 11/25/2022] Open
Abstract
Changes in gene expression have been thought to play a crucial role in various types of cancer. With the advance of high-throughput experimental techniques, many genome-wide studies are underway to analyze underlying mechanisms that may drive the changes in gene expression. It has been observed that the change could arise from altered DNA methylation. However, the knowledge about the degree to which epigenetic changes might cause differences in gene expression in cancer is currently lacking. By considering the change of gene expression as the response of altered DNA methylation, we introduce a novel analytical framework to identify epigenetic subnetworks in which the methylation status of a set of highly correlated genes is predictive of a set of gene expression. By detecting highly correlated modules as representatives of the regulatory scenario underling the gene expression and DNA methylation, the dependency between DNA methylation and gene expression is explored by a Bayesian regression model with the incorporation of g-prior followed by a strategy of an optimal predictor subset selection. The subsequent network analysis indicates that the detected epigenetic subnetworks are highly biologically relevant and contain many verified epigenetic causal mechanisms. Moreover, a survival analysis indicates that they might be effective prognostic factors associated with patient survival time.
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11
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Nussinov R, Tsai CJ, Jang H. A New View of Activating Mutations in Cancer. Cancer Res 2022; 82:4114-4123. [PMID: 36069825 PMCID: PMC9664134 DOI: 10.1158/0008-5472.can-22-2125] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/16/2022] [Accepted: 09/01/2022] [Indexed: 12/14/2022]
Abstract
A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, NCI, Frederick, Maryland
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12
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Jiménez‐Santos MJ, García‐Martín S, Fustero‐Torre C, Di Domenico T, Gómez‐López G, Al‐Shahrour F. Bioinformatics roadmap for therapy selection in cancer genomics. Mol Oncol 2022; 16:3881-3908. [PMID: 35811332 PMCID: PMC9627786 DOI: 10.1002/1878-0261.13286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/22/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.
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Affiliation(s)
| | | | - Coral Fustero‐Torre
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Tomás Di Domenico
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Gonzalo Gómez‐López
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Fátima Al‐Shahrour
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
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13
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Liu Z, Cai C, Ma X, Liu J, Chen L, Lui VWY, Cooper GF, Lu X. A Novel Bayesian Framework Infers Driver Activation States and Reveals Pathway-Oriented Molecular Subtypes in Head and Neck Cancer. Cancers (Basel) 2022; 14:cancers14194825. [PMID: 36230748 PMCID: PMC9563147 DOI: 10.3390/cancers14194825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/28/2022] [Accepted: 09/30/2022] [Indexed: 02/08/2023] Open
Abstract
Head and neck squamous cell cancer (HNSCC) is an aggressive cancer resulting from heterogeneous causes. To reveal the underlying drivers and signaling mechanisms of different HNSCC tumors, we developed a novel Bayesian framework to identify drivers of individual tumors and infer the states of driver proteins in cellular signaling system in HNSCC tumors. First, we systematically identify causal relationships between somatic genome alterations (SGAs) and differentially expressed genes (DEGs) for each TCGA HNSCC tumor using the tumor-specific causal inference (TCI) model. Then, we generalize the most statistically significant driver SGAs and their regulated DEGs in TCGA HNSCC cohort. Finally, we develop machine learning models that combine genomic and transcriptomic data to infer the protein functional activation states of driver SGAs in tumors, which enable us to represent a tumor in the space of cellular signaling systems. We discovered four mechanism-oriented subtypes of HNSCC, which show distinguished patterns of activation state of HNSCC driver proteins, and importantly, this subtyping is orthogonal to previously reported transcriptomic-based molecular subtyping of HNSCC. Further, our analysis revealed driver proteins that are likely involved in oncogenic processes induced by HPV infection, even though they are not perturbed by genomic alterations in HPV+ tumors.
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Affiliation(s)
- Zhengping Liu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- School of Medicine, Tsinghua University, Beijing 100190, China
| | - Chunhui Cai
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- Correspondence:
| | - Xiaojun Ma
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
| | - Jinling Liu
- Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
- Department of Biological Sciences, Missouri University of Science and Technology, Rolla, MO 65409, USA
| | - Lujia Chen
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
| | - Vivian Wai Yan Lui
- Georgia Cancer Center, and Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA
| | - Gregory F. Cooper
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh 15206, PA, USA
- UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15232, USA
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14
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Dehingia B, Milewska M, Janowski M, Pękowska A. CTCF
shapes chromatin structure and gene expression in health and disease. EMBO Rep 2022; 23:e55146. [PMID: 35993175 PMCID: PMC9442299 DOI: 10.15252/embr.202255146] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/31/2022] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Bondita Dehingia
- Dioscuri Centre for Chromatin Biology and Epigenomics, Nencki Institute of Experimental Biology Polish Academy of Sciences Warsaw Poland
| | - Małgorzata Milewska
- Dioscuri Centre for Chromatin Biology and Epigenomics, Nencki Institute of Experimental Biology Polish Academy of Sciences Warsaw Poland
| | - Marcin Janowski
- Dioscuri Centre for Chromatin Biology and Epigenomics, Nencki Institute of Experimental Biology Polish Academy of Sciences Warsaw Poland
| | - Aleksandra Pękowska
- Dioscuri Centre for Chromatin Biology and Epigenomics, Nencki Institute of Experimental Biology Polish Academy of Sciences Warsaw Poland
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15
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Olbrich T, Ruiz S. Genome architecture and totipotency: An intertwined relation during early embryonic development. Bioessays 2022; 44:e2200029. [PMID: 35560026 DOI: 10.1002/bies.202200029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/19/2022] [Accepted: 05/03/2022] [Indexed: 11/09/2022]
Abstract
Chromosomes are not randomly packed and positioned into the nucleus but folded in higher-order chromatin structures with defined functions. However, the genome of a fertilized embryo undergoes a dramatic epigenetic reprogramming characterized by extensive chromatin relaxation and the lack of a defined three-dimensional structure. This reprogramming is followed by a slow genome refolding that gradually strengthens the chromatin architecture during preimplantation development. Interestingly, genome refolding during early development coincides with a progressive loss of developmental potential suggesting a link between chromatin organization and cell plasticity. In agreement, loss of chromatin architecture upon depletion of the insulator transcription factor CTCF in embryonic stem cells led to the upregulation of the transcriptional program found in totipotent cells of the embryo, those with the highest developmental potential. This essay will discuss the impact of genome folding in controlling the expression of transcriptional programs involved in early development and their plastic-associated features.
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Affiliation(s)
- Teresa Olbrich
- Laboratory of Genome Integrity, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sergio Ruiz
- Laboratory of Genome Integrity, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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16
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Gurova K. Can aggressive cancers be identified by the "aggressiveness" of their chromatin? Bioessays 2022; 44:e2100212. [PMID: 35452144 DOI: 10.1002/bies.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/15/2022]
Abstract
Phenotypic plasticity is a crucial feature of aggressive cancer, providing the means for cancer progression. Stochastic changes in tumor cell transcriptional programs increase the chances of survival under any condition. I hypothesize that unstable chromatin permits stochastic transitions between transcriptional programs in aggressive cancers and supports non-genetic heterogeneity of tumor cells as a basis for their adaptability. I present a mechanistic model for unstable chromatin which includes destabilized nucleosomes, mobile chromatin fibers and random enhancer-promoter contacts, resulting in stochastic transcription. I suggest potential markers for "unsettled" chromatin in tumors associated with poor prognosis. Although many of the characteristics of unstable chromatin have been described, they were mostly used to explain changes in the transcription of individual genes. I discuss approaches to evaluate the role of unstable chromatin in non-genetic tumor cell heterogeneity and suggest using the degree of chromatin instability and transcriptional noise in tumor cells to predict cancer aggressiveness.
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Affiliation(s)
- Katerina Gurova
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
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17
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Wu P, Liu Y, Zhou R, Liu L, Zeng H, Xiong F, Zhang S, Gong Z, Zhang W, Guo C, Wang F, Zhou M, Zu X, Zeng Z, Li Y, Li G, Huang H, Xiong W. Extrachromosomal Circular DNA: A New Target in Cancer. Front Oncol 2022; 12:814504. [PMID: 35494014 PMCID: PMC9046939 DOI: 10.3389/fonc.2022.814504] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/17/2022] [Indexed: 12/12/2022] Open
Abstract
Genomic instability and amplification are intrinsically important traits determining the development and heterogeneity of tumors. The role of extrachromosomal circular DNA (eccDNA) in tumors has recently been highlighted. EccDNAs are unique genetic materials located off the chromosomal DNA. They have been detected in a variety of tumors. This review analyzes the mechanisms involved in the formation of eccDNAs and their genetic characteristics. In addition, the high-copy number and transcriptional levels of oncogenes located in eccDNA molecules contribute to the acceleration of tumor evolution and drug resistance and drive the development of genetic heterogeneity. Understanding the specific genomic forms of eccDNAs and characterizing their potential functions will provide new strategies for tumor therapy. Further research may yield new targets and molecular markers for the early diagnosis and treatment of human cancer.
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Affiliation(s)
- Pan Wu
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Translational Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yuhang Liu
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Translational Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Ruijia Zhou
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Translational Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Lingyun Liu
- Cancer Research Institute, The First Affiliated Hospital, University of South China, Hengyang, China
| | - Hongli Zeng
- Cancer Research Institute, The First Affiliated Hospital, University of South China, Hengyang, China
| | - Fang Xiong
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Shanshan Zhang
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Wenling Zhang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Can Guo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Ming Zhou
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Xuyu Zu
- Cancer Research Institute, The First Affiliated Hospital, University of South China, Hengyang, China
| | - Zhaoyang Zeng
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Yong Li
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, United States
| | - Guiyuan Li
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - He Huang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Translational Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: He Huang, ; Wei Xiong,
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Translational Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: He Huang, ; Wei Xiong,
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18
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Meng F, Zhang K, Yang C, Zhang K, Xu Q, Ren R, Zhou Y, Sun Y, Peng Y, Li Y, Guo H, Ren Y, Zhao Z. Prognostic Pathways Guide Drug Indications in Pan-Cancers. Front Oncol 2022; 12:849552. [PMID: 35372084 PMCID: PMC8964428 DOI: 10.3389/fonc.2022.849552] [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: 01/06/2022] [Accepted: 02/07/2022] [Indexed: 11/18/2022] Open
Abstract
Pathway-level analysis is a powerful approach enabling the interpretation of post-genomic data at a higher level than that of individual molecules. Molecular-targeted therapy focusing on cascade signaling pathways has become a new paradigm in anticancer therapy, instead of a single protein. However, the approaches to narrowing down the long list of biological pathways are limited. Here, we proposed a strategy for in silico Drug Prescription on biological pathways across pan-Cancers (CDP), by connecting drugs to candidate pathways. Applying on a list of 120 traditional Chinese medicines (TCM), we especially identified the “TCM–pathways–cancers” triplet and constructed it into a heterogeneous network across pan-cancers. Applying them into TCMs, the computational prescribing methods deepened the understanding of the efficacy of TCM at the molecular level. Further applying them into Western medicines, CDP could promote drug reposition avoiding time-consuming developments of new drugs.
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Affiliation(s)
- Fanlin Meng
- Marketing and Management Department, CapitalBio Technology, Beijing, China.,National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Kenan Zhang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Changlin Yang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Ke Zhang
- National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Quan Xu
- National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Ruifang Ren
- National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Yiming Zhou
- National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Yimin Sun
- Marketing and Management Department, CapitalBio Technology, Beijing, China.,National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Yan Peng
- Marketing and Management Department, CapitalBio Technology, Beijing, China
| | - Yanze Li
- Marketing and Management Department, CapitalBio Technology, Beijing, China
| | - Hongyan Guo
- National Engineering Research Center for Beijing Biochip Technology, Beijing, China
| | - Yonghong Ren
- Marketing and Management Department, CapitalBio Technology, Beijing, China
| | - Zheng Zhao
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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19
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Systematic illumination of druggable genes in cancer genomes. Cell Rep 2022; 38:110400. [PMID: 35196490 PMCID: PMC8919705 DOI: 10.1016/j.celrep.2022.110400] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 09/12/2021] [Accepted: 01/26/2022] [Indexed: 01/15/2023] Open
Abstract
By combining 6 druggable genome resources, we identify 6,083 genes as potential druggable genes (PDGs). We characterize their expression, recurrent genomic alterations, cancer dependencies, and therapeutic potentials by integrating genome, functionome, and druggome profiles across cancers. 81.5% of PDGs are reliably expressed in major adult cancers, 46.9% show selective expression patterns, and 39.1% exhibit at least one recurrent genomic alteration. We annotate a total of 784 PDGs as dependent genes for cancer cell growth. We further quantify 16 cancer-related features and estimate a PDG cancer drug target score (PCDT score). PDGs with higher PCDT scores are significantly enriched for genes encoding kinases and histone modification enzymes. Importantly, we find that a considerable portion of high PCDT score PDGs are understudied genes, providing unexplored opportunities for drug development in oncology. By integrating the druggable genome and the cancer genome, our study thus generates a comprehensive blueprint of potential druggable genes across cancers. Jiang et al. generate a comprehensive blueprint of potential druggable genes (PDGs) across cancers by a systematic integration of the druggable genome and the cancer genome. This resource is publicly available to the cancer research community in The Cancer Druggable Gene Atlas (TCDA) through the Functional Cancer Genome data portal.
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Ben-Cohen G, Doffe F, Devir M, Leroy B, Soussi T, Rosenberg S. TP53_PROF: a machine learning model to predict impact of missense mutations in TP53. Brief Bioinform 2022; 23:6510957. [PMID: 35043155 PMCID: PMC8921628 DOI: 10.1093/bib/bbab524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/04/2021] [Accepted: 11/15/2021] [Indexed: 11/27/2022] Open
Abstract
Correctly identifying the true driver mutations in a patient’s tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model’s predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.
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Affiliation(s)
- Gil Ben-Cohen
- Corresponding authors: Gil Ben Cohen, Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, The Wohl Institute for Translational Medicine. Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel. Tel.: +972549410946. E-mail: ; Shai Rosenberg, Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, The Wohl Institute for Translational Medicine. Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel. Tel.: 972-2-6776289. E-mail:
| | - Flora Doffe
- INSERM UMR 1186, Integrative Tumor Immunology and Immunotherapy, Gustave Roussy, Faculté de Médecine, Université Paris-Sud, Université Paris-Saclay, 94805 Villejuif, France
| | - Michal Devir
- Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel
- The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel
| | - Bernard Leroy
- Sorbonne Université, UPMC Univ Paris 06, F- 75005 Paris, France
| | | | - Shai Rosenberg
- Corresponding authors: Gil Ben Cohen, Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, The Wohl Institute for Translational Medicine. Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel. Tel.: +972549410946. E-mail: ; Shai Rosenberg, Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, The Wohl Institute for Translational Medicine. Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Israel. Tel.: 972-2-6776289. E-mail:
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21
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Fernàndez-Castillo N, Cabana-Domínguez J, Corominas R, Cormand B. Molecular genetics of cocaine use disorders in humans. Mol Psychiatry 2022; 27:624-639. [PMID: 34453125 PMCID: PMC8960411 DOI: 10.1038/s41380-021-01256-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 07/01/2021] [Accepted: 07/30/2021] [Indexed: 12/11/2022]
Abstract
Drug addiction, one of the major health problems worldwide, is characterized by the loss of control in drug intake, craving, and withdrawal. At the individual level, drugs of abuse produce serious consequences on health and have a negative impact on the family environment and on interpersonal and work relationships. At a wider scale, they have significant socio-economic and public health consequences and they cause delinquency and citizen insecurity. Cocaine, a psychostimulant substance, is one of the most used illicit drugs, especially in America, Western Europe, and Australia. Cocaine use disorders (CUD) are complex multifactorial conditions driven by both genetic and environmental influences. Importantly, not all people who use cocaine develop CUD, and this is due, at least in part, to biological factors that are encoded in the genome of individuals. Acute and repeated use of cocaine induces epigenetic and gene expression changes responsible for the neuronal adaptations and the remodeling of brain circuits that lead to the transition from use to abuse or dependence. The purpose of this review is to delineate such factors, which should eventually help to understand the inter-individual variability in the susceptibility to cocaine addiction. Heritability estimates for CUD are high and genetic risk factors for cocaine addiction have been investigated by candidate gene association studies (CGAS) and genome-wide association studies (GWAS), reviewed here. Also, the high comorbidity that exists between CUD and several other psychiatric disorders is well known and includes phenotypes like schizophrenia, aggression, antisocial or risk-taking behaviors. Such comorbidities are associated with a worse lifetime trajectory, and here we report shared genetic factors that may contribute to them. Gene expression changes and epigenetic modifications induced by cocaine use and chronic abuse in humans are addressed by reviewing transcriptomic studies performed on neuronal cells and on postmortem brains. We report some genes which expression is altered by cocaine that also bear genetic risk variants for the disorder. Finally, we have a glance to the pharmacogenetics of CUD treatments, still in early stages. A better understanding of the genetic underpinnings of CUD will foster the search of effective treatments and help to move forward to personalized medicine.
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Affiliation(s)
- Noèlia Fernàndez-Castillo
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain. .,Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain. .,Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia, Spain.
| | - Judit Cabana-Domínguez
- grid.5841.80000 0004 1937 0247Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia Spain ,grid.452372.50000 0004 1791 1185Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain ,grid.5841.80000 0004 1937 0247Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia Spain ,grid.411160.30000 0001 0663 8628Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia Spain
| | - Roser Corominas
- grid.5841.80000 0004 1937 0247Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia Spain ,grid.452372.50000 0004 1791 1185Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain ,grid.5841.80000 0004 1937 0247Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia Spain ,grid.411160.30000 0001 0663 8628Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia Spain
| | - Bru Cormand
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain. .,Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain. .,Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia, Spain.
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22
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Zhang Z, Tan Z, Lv Q, Wang L, Yu K, Yang H, Liang H, Lu T, Ji Y, Chen J, He W, Chen Z, Chen S, Shen X. High Expression of C1ORF112 Predicts a Poor Outcome: A Potential Target for the Treatment of Low-Grade Gliomas. Front Genet 2021; 12:710944. [PMID: 34880897 PMCID: PMC8645850 DOI: 10.3389/fgene.2021.710944] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/01/2021] [Indexed: 12/30/2022] Open
Abstract
Background: Glioma is the most common primary tumor of the central nervous system and is associated with poor overall survival, creating an urgent need to identify survival-associated biomarkers. C1ORF112, an alpha-helical protein, is overexpressed in some cancers; however, its prognostic role has not yet been explored in gliomas. Thus, in this study, we attempted to address this by determining the prognostic value and potential function of C1ORF112 in low-grade gliomas (LGGs). Methods: The expression of C1ORF112 in normal and tumor tissues was analyzed using data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Oncomine, and Rembrandt databases. The genetic changes of C1ORF112 in LGG were analyzed using cBioPortal. Survival analysis was used to evaluate the relationship between C1ORF112 expression and survival in patients with LGG. Correlation between immune infiltration and C1ORF112 expression was determined using Timer software. Additionally, data from three online platforms were integrated to identify the co-expressed genes of C1ORF112. The potential biological functions of C1ORF112 were investigated by enrichment analysis. Results: C1ORF112 mRNA was highly expressed in LGGs (p < 0.01). Area under the ROC curve (AUC) showed that the expression of C1ORF112 in LGG was 0.673 (95% confidence interval [CI] = 0.618–0.728). Kaplan-Meier survival analysis showed that patients with high C1ORF112 expression had lower OS than patients with low C1ORF112 expression (p < 0.05). Multivariate analysis showed that high expression of C1ORF112 was an independent prognostic factor for the overall survival in patients from TCGA and CGGA databases. C1ORF112 expression was positively correlated with six immunoinfiltrating cells (all p < 0.001). The enrichment analysis suggested the enrichment of C1ORF112 and co-expressed genes in cell cycle and DNA replication. Conclusion: This study suggested that C1ORF112 may be a prognostic biomarker and a potential immunotherapeutic target for LGG.
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Affiliation(s)
- Zhe Zhang
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
| | - Zilong Tan
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
| | - Qiaoli Lv
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, China
| | - Lichong Wang
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
| | - Kai Yu
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
| | - Huan Yang
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
| | - Huaizhen Liang
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tianzhu Lu
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
| | - Yulong Ji
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, China
| | - Junjun Chen
- Jiangxi Key Laboratory of Translational Cancer Research, Jiangxi Cancer Hospital, Nanchang, China
| | - Wei He
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
| | - Zhen Chen
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
| | - Shuhui Chen
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, China
| | - Xiaoli Shen
- Department of Neurosurgery, The Second Affifiliated Hospital of Nanchang University, Nanchang, China
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Nussinov R, Tsai CJ, Jang H. Anticancer drug resistance: An update and perspective. Drug Resist Updat 2021; 59:100796. [PMID: 34953682 PMCID: PMC8810687 DOI: 10.1016/j.drup.2021.100796] [Citation(s) in RCA: 145] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/08/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA
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24
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Elmas A, Tharakan S, Jaladanki S, Galsky MD, Liu T, Huang KL. Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets. Commun Biol 2021; 4:1112. [PMID: 34552204 PMCID: PMC8458405 DOI: 10.1038/s42003-021-02636-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 09/03/2021] [Indexed: 12/19/2022] Open
Abstract
Identifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations-sometimes not observed by genomics-represent cancer vulnerabilities that may be targeted in precision oncology.
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Affiliation(s)
- Abdulkadir Elmas
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Serena Tharakan
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Suraj Jaladanki
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Matthew D Galsky
- Division of Hematology and Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Kuan-Lin Huang
- Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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25
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Tokutomi N, Nakai K, Sugano S. Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes. PLoS One 2021; 16:e0243595. [PMID: 34424899 PMCID: PMC8382180 DOI: 10.1371/journal.pone.0243595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 08/10/2021] [Indexed: 12/31/2022] Open
Abstract
Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.
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Affiliation(s)
- Natsuki Tokutomi
- Department of Computational Biology and Medical Science, Graduate School of Frontier Science, University of Tokyo, Kashiwa, Chiba, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Science, Graduate School of Frontier Science, University of Tokyo, Kashiwa, Chiba, Japan
- Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan
| | - Sumio Sugano
- Medical Research Institute, Tokyo Medical and Dental University, Bunkyou-ku, Tokyo, Japan
- Future Medicine Education and Research Organization, Chiba University, Chiba, Chiba, Japan
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26
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Olbrich T, Vega-Sendino M, Tillo D, Wu W, Zolnerowich N, Pavani R, Tran AD, Domingo CN, Franco M, Markiewicz-Potoczny M, Pegoraro G, FitzGerald PC, Kruhlak MJ, Lazzerini-Denchi E, Nora EP, Nussenzweig A, Ruiz S. CTCF is a barrier for 2C-like reprogramming. Nat Commun 2021; 12:4856. [PMID: 34381034 PMCID: PMC8358036 DOI: 10.1038/s41467-021-25072-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 07/17/2021] [Indexed: 01/28/2023] Open
Abstract
Totipotent cells have the ability to generate embryonic and extra-embryonic tissues. Interestingly, a rare population of cells with totipotent-like potential, known as 2 cell (2C)-like cells, has been identified within ESC cultures. They arise from ESC and display similar features to those found in the 2C embryo. However, the molecular determinants of 2C-like conversion have not been completely elucidated. Here, we show that the CCCTC-binding factor (CTCF) is a barrier for 2C-like reprogramming. Indeed, forced conversion to a 2C-like state by the transcription factor DUX is associated with DNA damage at a subset of CTCF binding sites. Depletion of CTCF in ESC efficiently promotes spontaneous and asynchronous conversion to a 2C-like state and is reversible upon restoration of CTCF levels. This phenotypic reprogramming is specific to pluripotent cells as neural progenitor cells do not show 2C-like conversion upon CTCF-depletion. Furthermore, we show that transcriptional activation of the ZSCAN4 cluster is necessary for successful 2C-like reprogramming. In summary, we reveal an unexpected relationship between CTCF and 2C-like reprogramming.
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Affiliation(s)
- Teresa Olbrich
- Laboratory of Genome Integrity, CCR, NCI, NIH, Bethesda, MD, USA
| | | | | | - Wei Wu
- Laboratory of Genome Integrity, CCR, NCI, NIH, Bethesda, MD, USA
| | | | - Raphael Pavani
- Laboratory of Genome Integrity, CCR, NCI, NIH, Bethesda, MD, USA
| | - Andy D Tran
- Laboratory of Cancer Biology and Genetics, CCR, NCI, NIH, Bethesda, MD, USA
| | | | - Mariajose Franco
- Laboratory of Genome Integrity, CCR, NCI, NIH, Bethesda, MD, USA
| | | | - Gianluca Pegoraro
- Laboratory of Receptor Biology and Gene Expression, CCR, NCI, NIH, Bethesda, MD, USA
| | | | - Michael J Kruhlak
- Laboratory of Cancer Biology and Genetics, CCR, NCI, NIH, Bethesda, MD, USA
| | | | - Elphege P Nora
- Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA
| | | | - Sergio Ruiz
- Laboratory of Genome Integrity, CCR, NCI, NIH, Bethesda, MD, USA.
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27
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Rosenbaum JN, Berry AB, Church AJ, Crooks K, Gagan JR, López-Terrada D, Pfeifer JD, Rennert H, Schrijver I, Snow AN, Wu D, Ewalt MD. A Curriculum for Genomic Education of Molecular Genetic Pathology Fellows: A Report of the Association for Molecular Pathology Training and Education Committee. J Mol Diagn 2021; 23:1218-1240. [PMID: 34245921 DOI: 10.1016/j.jmoldx.2021.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 06/16/2021] [Accepted: 07/01/2021] [Indexed: 12/19/2022] Open
Abstract
Molecular genetic pathology (MGP) is a subspecialty of pathology and medical genetics and genomics. Genomic testing, which we define as that which generates large data sets and interrogates large segments of the genome in a single assay, is increasingly recognized as essential for optimal patient care through precision medicine. The most common genomic testing technologies in clinical laboratories are next-generation sequencing and microarray. It is essential to train in these methods and to consider the data generated in the context of the diagnosis, medical history, and other clinical findings of individual patients. Accordingly, updating the MGP fellowship curriculum to include genomics is timely, important, and challenging. At the completion of training, an MGP fellow should be capable of independently interpreting and signing out results of a wide range of genomic assays and, given the appropriate context and institutional support, of developing and validating new assays in compliance with applicable regulations. The Genomics Task Force of the MGP Program Directors, a working group of the Association for Molecular Pathology Training and Education Committee, has developed a genomics curriculum framework and recommendations specific to the MGP fellowship. These recommendations are presented for consideration and implementation by MGP fellowship programs with the understanding that MGP programs exist in a diversity of clinical practice environments with a spectrum of available resources.
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Affiliation(s)
- Jason N Rosenbaum
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anna B Berry
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Swedish Cancer Institute and Institute of Systems Biology, Seattle, Washington
| | - Alanna J Church
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Boston Children's Hospital, Boston, Massachusetts
| | - Kristy Crooks
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Jeffrey R Gagan
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Dolores López-Terrada
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Baylor College of Medicine, Houston, Texas
| | - John D Pfeifer
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Washington University School of Medicine, St. Louis, Missouri
| | - Hanna Rennert
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Iris Schrijver
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - Anthony N Snow
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - David Wu
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington
| | - Mark D Ewalt
- Molecular Genetic Pathology Fellow Training in Genomics Task Force of the Training and Education Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.
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Gregorc V, Mazzarella L, Lazzari C, Graziano P, Vigneri P, Genova C, Toschi L, Ciliberto G, Bonanno L, Delmonte A, Bucci G, Rossi A, Motta G, Coco S, Marinello A, Buglioni S, Cangi MG, Di Micco C, Bandiera A, Bonfiglio S, Pecciarini L, Guida A, Ceol A, Frige' G, De Maria R, Pelicci PG. Prospective Validation of the Italian Alliance Against Cancer Lung Panel in Patients With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer 2021; 22:e637-e641. [PMID: 33642178 DOI: 10.1016/j.cllc.2020.12.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 11/25/2020] [Accepted: 12/12/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND The deeper knowledge of non-small-cell lung cancer (NSCLC) biology and the discovery of driver molecular alterations have opened the era of precision medicine in lung oncology, thus significantly revolutionizing the diagnostic and therapeutic approach to NSCLC. In Italy, however, molecular assessment remains heterogeneous across the country, and numbers of patients accessing personalized treatments remain relatively low. Nationwide programs have demonstrated that the creation of consortia represent a successful strategy to increase the number of patients with a molecular classification. PATIENTS AND METHODS The Alliance Against Cancer (ACC), a network of 25 Italian Research Institutes, has developed a targeted sequencing panel for the detection of genomic alterations in 182 genes in patients with a diagnosis of NSCLC (ACC lung panel). One thousand metastatic NSCLC patients will be enrolled onto a prospective trial designed to measure the sensitivity and specificity of the ACC lung panel as a tool for molecular screening compared to standard methods. RESULTS AND CONCLUSION The ongoing trial is part of a nationwide strategy of ACC to develop infrastructures and improve competences to make the Italian research institutes independent for genomic profiling of cancer patients.
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Affiliation(s)
- Vanesa Gregorc
- Department of Oncology, IRCCS San Raffaele Scientific Institute, Milano.
| | | | - Chiara Lazzari
- Department of Oncology, IRCCS San Raffaele Scientific Institute, Milano
| | - Paolo Graziano
- Unit of Pathology, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia)
| | - Paolo Vigneri
- A.O.U. Policlinico "G. Rodolico - S. Marco", Catania
| | - Carlo Genova
- UOC Clinica di Oncologia Medica; IRCCS Ospedale Policlinico San Martino, Genova; Dipartimento di Medicina Interna e Specialità Mediche (DiMI); Università degli Studi di Genova
| | - Luca Toschi
- Humanitas Clinical and Research Center - IRCCS, Humanitas Cancer Center, Rozzano, Milano
| | - Gennaro Ciliberto
- Direzione Scientifica, IRCCS Istituto Nazionale Tumori Regina Elena, Roma
| | - Laura Bonanno
- Oncologia Medica 2, IstitutoOncologico Veneto IOV IRCCS, Padova
| | - Angelo Delmonte
- Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola
| | - Gabriele Bucci
- Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milano
| | - Antonio Rossi
- Medical Oncology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia)
| | | | - Simona Coco
- UOS Tumori Polmonari; IRCCS Ospedale Policlinico San Martino, Genova
| | - Arianna Marinello
- Humanitas Clinical and Research Center - IRCCS, Humanitas Cancer Center, Rozzano, Milano
| | | | - Maria Giulia Cangi
- Department of Pathology, IRCCS San Raffaele Scientific Institute, Milano
| | - Concetta Di Micco
- Medical Oncology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia)
| | - Alessandro Bandiera
- Department of Thoracic Surgery, IRCCS San Raffaele Scientific Institute, Milano
| | - Silvia Bonfiglio
- Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milano
| | - Lorenza Pecciarini
- Department of Pathology, IRCCS San Raffaele Scientific Institute, Milano
| | | | - Arnaud Ceol
- IRCCS European Institute of Oncology, Milano
| | - Gianmaria Frige'
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milano
| | - Ruggero De Maria
- Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, RomeFondazione Policlinico A. Gemelli IRCCS, Roma; Universitá Cattolica del Sacro Cuore, Roma
| | - Pier Giuseppe Pelicci
- IRCCS European Institute of Oncology, Milano; Department of Oncology and Hemato-Oncology, Università Degli Studi di Milano
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29
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Ding RB, Chen P, Rajendran BK, Lyu X, Wang H, Bao J, Zeng J, Hao W, Sun H, Wong AHH, Valecha MV, Yang EJ, Su SM, Choi TK, Liu S, Chan KI, Yang LL, Wu J, Miao K, Chen Q, Shim JS, Xu X, Deng CX. Molecular landscape and subtype-specific therapeutic response of nasopharyngeal carcinoma revealed by integrative pharmacogenomics. Nat Commun 2021; 12:3046. [PMID: 34031426 PMCID: PMC8144567 DOI: 10.1038/s41467-021-23379-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 04/16/2021] [Indexed: 02/04/2023] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a malignant head and neck cancer type with high morbidity in Southeast Asia, however the pathogenic mechanism of this disease is poorly understood. Using integrative pharmacogenomics, we find that NPC subtypes maintain distinct molecular features, drug responsiveness, and graded radiation sensitivity. The epithelial carcinoma (EC) subtype is characterized by activations of microtubule polymerization and defective mitotic spindle checkpoint related genes, whereas sarcomatoid carcinoma (SC) and mixed sarcomatoid-epithelial carcinoma (MSEC) subtypes exhibit enriched epithelial-mesenchymal transition (EMT) and invasion promoting genes, which are well correlated with their morphological features. Furthermore, patient-derived organoid (PDO)-based drug test identifies potential subtype-specific treatment regimens, in that SC and MSEC subtypes are sensitive to microtubule inhibitors, whereas EC subtype is more responsive to EGFR inhibitors, which is synergistically enhanced by combining with radiotherapy. Through combinational chemoradiotherapy (CRT) screening, effective CRT regimens are also suggested for patients showing less sensitivity to radiation. Altogether, our study provides an example of applying integrative pharmacogenomics to establish a personalized precision oncology for NPC subtype-guided therapies.
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Affiliation(s)
- Ren-Bo Ding
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Ping Chen
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.488387.8Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan China
| | - Barani Kumar Rajendran
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Xueying Lyu
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Haitao Wang
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Jiaolin Bao
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Jianming Zeng
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Wenhui Hao
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Heng Sun
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Ada Hang-Heng Wong
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Monica Vishnu Valecha
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Eun Ju Yang
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Sek Man Su
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Tak Kan Choi
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Shuiming Liu
- grid.507998.a0000 0004 0639 5728Kiang Wu Hospital, Macau SAR, China
| | - Kin Iong Chan
- grid.507998.a0000 0004 0639 5728Kiang Wu Hospital, Macau SAR, China
| | - Ling-Lin Yang
- grid.488387.8Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan China
| | - Jingbo Wu
- grid.488387.8Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan China
| | - Kai Miao
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR, China
| | - Qiang Chen
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR, China
| | - Joong Sup Shim
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR, China
| | - Xiaoling Xu
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR, China
| | - Chu-Xia Deng
- grid.437123.00000 0004 1794 8068Cancer Centre, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068Centre for Precision Medicine Research and Training, Faculty of Health Sciences, University of Macau, Macau SAR, China ,grid.437123.00000 0004 1794 8068MOE Frontier Science Centre for Precision Oncology, University of Macau, Macau SAR, China
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Edogbanya J, Tejada-Martinez D, Jones NJ, Jaiswal A, Bell S, Cordeiro R, van Dam S, Rigden DJ, de Magalhães JP. Evolution, structure and emerging roles of C1ORF112 in DNA replication, DNA damage responses, and cancer. Cell Mol Life Sci 2021; 78:4365-4376. [PMID: 33625522 PMCID: PMC8164572 DOI: 10.1007/s00018-021-03789-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/28/2021] [Accepted: 02/09/2021] [Indexed: 02/08/2023]
Abstract
The C1ORF112 gene initially drew attention when it was found to be strongly co-expressed with several genes previously associated with cancer and implicated in DNA repair and cell cycle regulation, such as RAD51 and the BRCA genes. The molecular functions of C1ORF112 remain poorly understood, yet several studies have uncovered clues as to its potential functions. Here, we review the current knowledge on C1ORF112 biology, its evolutionary history, possible functions, and its potential relevance to cancer. C1ORF112 is conserved throughout eukaryotes, from plants to humans, and is very highly conserved in primates. Protein models suggest that C1ORF112 is an alpha-helical protein. Interestingly, homozygous knockout mice are not viable, suggesting an essential role for C1ORF112 in mammalian development. Gene expression data show that, among human tissues, C1ORF112 is highly expressed in the testes and overexpressed in various cancers when compared to healthy tissues. C1ORF112 has also been shown to have altered levels of expression in some tumours with mutant TP53. Recent screens associate C1ORF112 with DNA replication and reveal possible links to DNA damage repair pathways, including the Fanconi anaemia pathway and homologous recombination. These insights provide important avenues for future research in our efforts to understand the functions and potential disease relevance of C1ORF112.
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Affiliation(s)
- Jacob Edogbanya
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Daniela Tejada-Martinez
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
- Programa de Doctorado en Ciencias mención Ecología Y Evolución, Facultad de Ciencias, Instituto de Ciencias Ambientales Y Evolutivas, Universidad Austral de Chile, Valdivia, 5090000, Chile
- Department of Biochemistry and Molecular Biology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Nigel J Jones
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Amit Jaiswal
- Institute of Aging Research, School of Medicine, Hangzhou Normal University, Hangzhou, China
- Faculty of Biological Sciences, Friedrich Schiller University, Jena, Germany
| | - Sarah Bell
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Rui Cordeiro
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK
| | - Sipko van Dam
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
- Ancora Health, Herestraat 106, 9711 LM, Groningen, The Netherlands
| | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - João Pedro de Magalhães
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, L7 8TX, UK.
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Varela NM, Guevara-Ramírez P, Acevedo C, Zambrano T, Armendáriz-Castillo I, Guerrero S, Quiñones LA, López-Cortés A. A New Insight for the Identification of Oncogenic Variants in Breast and Prostate Cancers in Diverse Human Populations, With a Focus on Latinos. Front Pharmacol 2021; 12:630658. [PMID: 33912047 PMCID: PMC8072346 DOI: 10.3389/fphar.2021.630658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Breast cancer (BRCA) and prostate cancer (PRCA) are the most commonly diagnosed cancer types in Latin American women and men, respectively. Although in recent years large-scale efforts from international consortia have focused on improving precision oncology, a better understanding of genomic features of BRCA and PRCA in developing regions and racial/ethnic minority populations is still required. Methods: To fill in this gap, we performed integrated in silico analyses to elucidate oncogenic variants from BRCA and PRCA driver genes; to calculate their deleteriousness scores and allele frequencies from seven human populations worldwide, including Latinos; and to propose the most effective therapeutic strategies based on precision oncology. Results: We analyzed 339,100 variants belonging to 99 BRCA and 82 PRCA driver genes and identified 18,512 and 15,648 known/predicted oncogenic variants, respectively. Regarding known oncogenic variants, we prioritized the most frequent and deleterious variants of BRCA (n = 230) and PRCA (n = 167) from Latino, African, Ashkenazi Jewish, East Asian, South Asian, European Finnish, and European non-Finnish populations, to incorporate them into pharmacogenomics testing. Lastly, we identified which oncogenic variants may shape the response to anti-cancer therapies, detailing the current status of pharmacogenomics guidelines and clinical trials involved in BRCA and PRCA cancer driver proteins. Conclusion: It is imperative to unify efforts where developing countries might invest in obtaining databases of genomic profiles of their populations, and developed countries might incorporate racial/ethnic minority populations in future clinical trials and cancer researches with the overall objective of fomenting pharmacogenomics in clinical practice and public health policies.
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Affiliation(s)
- Nelson M Varela
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago, Chile.,Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
| | - Patricia Guevara-Ramírez
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
| | - Cristian Acevedo
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago, Chile.,Department of Basic and Clinical Oncology, Clinical Hospital University of Chile, Santiago, Chile
| | - Tomás Zambrano
- Department of Medical Technology, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Isaac Armendáriz-Castillo
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Santiago Guerrero
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Luis A Quiñones
- Laboratory of Chemical Carcinogenesis and Pharmacogenetics, Department of Basic and Clinical Oncology, Faculty of Medicine, University of Chile, Santiago, Chile.,Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain
| | - Andrés López-Cortés
- Latin American Network for the Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), Madrid, Spain.,Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador.,Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruna, A Coruña, Spain
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Zhu X, Tian X, Ji L, Zhang X, Cao Y, Shen C, Hu Y, Wong JWH, Fang JY, Hong J, Chen H. A tumor microenvironment-specific gene expression signature predicts chemotherapy resistance in colorectal cancer patients. NPJ Precis Oncol 2021; 5:7. [PMID: 33580207 PMCID: PMC7881244 DOI: 10.1038/s41698-021-00142-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 10/21/2020] [Indexed: 12/14/2022] Open
Abstract
Studies have shown that tumor microenvironment (TME) might affect drug sensitivity and the classification of colorectal cancer (CRC). Using TME-specific gene signature to identify CRC subtypes with distinctive clinical relevance has not yet been tested. A total of 18 "bulk" RNA-seq datasets (total n = 2269) and four single-cell RNA-seq datasets were included in this study. We constructed a "Signature associated with FOLFIRI resistant and Microenvironment" (SFM) that could discriminate both TME and drug sensitivity. Further, SFM subtypes were identified using K-means clustering and verified in three independent cohorts. Nearest template prediction algorithm was used to predict drug response. TME estimation was performed by CIBERSORT and microenvironment cell populations-counter (MCP-counter) methods. We identified six SFM subtypes based on SFM signature that discriminated both TME and drug sensitivity. The SFM subtypes were associated with distinct clinicopathological, molecular and phenotypic characteristics, specific enrichments of gene signatures, signaling pathways, prognosis, gut microbiome patterns, and tumor lymphocytes infiltration. Among them, SFM-C and -F were immune suppressive. SFM-F had higher stromal fraction with epithelial-to-mesenchymal transition phenotype, while SFM-C was characterized as microsatellite instability phenotype which was responsive to immunotherapy. SFM-D, -E, and -F were sensitive to FOLFIRI and FOLFOX, while SFM-A, -B, and -C were responsive to EGFR inhibitors. Finally, SFM subtypes had strong prognostic value in which SFM-E and -F had worse survival than other subtypes. SFM subtypes enable the stratification of CRC with potential chemotherapy response thereby providing more precise therapeutic options for these patients.
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Affiliation(s)
- Xiaoqiang Zhu
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xianglong Tian
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Gastroenterology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linhua Ji
- Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xinyu Zhang
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yingying Cao
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chaoqin Shen
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ye Hu
- Department of Gastroenterology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason W H Wong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jing-Yuan Fang
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Jie Hong
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Haoyan Chen
- State Key Laboratory for Oncogenes and Related Genes, Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Emad A, Sinha S. Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study. NPJ Syst Biol Appl 2021; 7:9. [PMID: 33558504 PMCID: PMC7870953 DOI: 10.1038/s41540-021-00169-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 01/05/2021] [Indexed: 01/30/2023] Open
Abstract
Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.
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Affiliation(s)
- Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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Wang T, Ruan S, Zhao X, Shi X, Teng H, Zhong J, You M, Xia K, Sun Z, Mao F. OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers. Nucleic Acids Res 2021; 49:D1289-D1301. [PMID: 33179738 PMCID: PMC7778899 DOI: 10.1093/nar/gkaa1033] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/15/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, 'Mutation', 'Gene', 'Pathway' and 'Cancer', to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.
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Affiliation(s)
- Tao Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Shasha Ruan
- Department of Clinical Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430072, China
| | - Xiaolu Zhao
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
| | - Xiaohui Shi
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Huajing Teng
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Jianing Zhong
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases of Ministry of Education, Gannan Medical University, Ganzhou 341000, China
| | | | - Kun Xia
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410083, China
- CAS Center for Excellence in Brain Science and Intelligences Technology (CEBSIT), Shanghai 200031, China
- School of Basic Medical Science, Central South University, Changsha, Hunan 410078, China
| | - Zhongsheng Sun
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
- CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Chinese Academy of Sciences, Beijing 100101, China
| | - Fengbiao Mao
- Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China
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Lazzari C, Bulotta A, Cangi MG, Bucci G, Pecciarini L, Bonfiglio S, Lorusso V, Ippati S, Arrigoni G, Grassini G, Doglioni C, Gregorc V. Next Generation Sequencing in Non-Small Cell Lung Cancer: Pitfalls and Opportunities. Diagnostics (Basel) 2020; 10:E1092. [PMID: 33333743 PMCID: PMC7765222 DOI: 10.3390/diagnostics10121092] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/07/2020] [Accepted: 12/10/2020] [Indexed: 12/19/2022] Open
Abstract
Lung cancer remains the first cause of cancer-related deaths worldwide. Thanks to the improvement in the knowledge of the biology of non-small cell lung cancer (NSCLC), patients' survival has significantly improved. A growing number of targetable molecular alterations have been identified. Next-generation sequencing (NGS) has become one of the methodologies entered in clinical practice and was recently recommended by the European society for medical oncology (ESMO) to perform a comprehensive molecular characterization in patients with cancer. The current review provides an overview of the clinical trials that have explored the impact of NGS in patients with cancer, its limits, and advantages.
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Affiliation(s)
- Chiara Lazzari
- Department of Oncology, IRCCS San Raffaele, 20132 Milan, Italy; (A.B.); (V.L.); (S.I.); (V.G.)
| | - Alessandra Bulotta
- Department of Oncology, IRCCS San Raffaele, 20132 Milan, Italy; (A.B.); (V.L.); (S.I.); (V.G.)
| | - Maria Giulia Cangi
- Department of Pathology, IRCCS San Raffaele, 20132 Milan, Italy; (M.G.C.); (L.P.); (G.A.); (G.G.); (C.D.)
| | - Gabriele Bucci
- Center for Omics Science, IRCCS San Raffaele, 20132 Milan, Italy; (G.B.); (S.B.)
| | - Lorenza Pecciarini
- Department of Pathology, IRCCS San Raffaele, 20132 Milan, Italy; (M.G.C.); (L.P.); (G.A.); (G.G.); (C.D.)
| | - Silvia Bonfiglio
- Center for Omics Science, IRCCS San Raffaele, 20132 Milan, Italy; (G.B.); (S.B.)
| | - Vincenza Lorusso
- Department of Oncology, IRCCS San Raffaele, 20132 Milan, Italy; (A.B.); (V.L.); (S.I.); (V.G.)
| | - Stefania Ippati
- Department of Oncology, IRCCS San Raffaele, 20132 Milan, Italy; (A.B.); (V.L.); (S.I.); (V.G.)
| | - Gianluigi Arrigoni
- Department of Pathology, IRCCS San Raffaele, 20132 Milan, Italy; (M.G.C.); (L.P.); (G.A.); (G.G.); (C.D.)
| | - Greta Grassini
- Department of Pathology, IRCCS San Raffaele, 20132 Milan, Italy; (M.G.C.); (L.P.); (G.A.); (G.G.); (C.D.)
| | - Claudio Doglioni
- Department of Pathology, IRCCS San Raffaele, 20132 Milan, Italy; (M.G.C.); (L.P.); (G.A.); (G.G.); (C.D.)
| | - Vanesa Gregorc
- Department of Oncology, IRCCS San Raffaele, 20132 Milan, Italy; (A.B.); (V.L.); (S.I.); (V.G.)
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36
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Zhou C, Wei Z, Zhang L, Yang Z, Liu Q. Systematically Characterizing A-to-I RNA Editing Neoantigens in Cancer. Front Oncol 2020; 10:593989. [PMID: 33363023 PMCID: PMC7758481 DOI: 10.3389/fonc.2020.593989] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/09/2020] [Indexed: 11/16/2022] Open
Abstract
A-to-I RNA editing can contribute to the transcriptomic and proteomic diversity of many diseases including cancer. It has been reported that peptides generated from RNA editing could be naturally presented by human leukocyte antigen (HLA) molecules and elicit CD8+ T cell activation. However, a systematical characterization of A-to-I RNA editing neoantigens in cancer is still lacking. Here, an integrated RNA-editing based neoantigen identification pipeline PREP (Prioritizing of RNA Editing-based Peptides) was presented. A comprehensive RNA editing neoantigen profile analysis on 12 cancer types from The Cancer Genome Atlas (TCGA) cohorts was performed. PREP was also applied to 14 ovarian tumor samples and two clinical melanoma cohorts treated with immunotherapy. We finally proposed an RNA editing neoantigen immunogenicity score scheme, i.e. REscore, which takes RNA editing level and infiltrating immune cell population into consideration. We reported variant peptide from protein IFI30 in breast cancer which was confirmed expressed and presented in two samples with mass spectrometry data support. We showed that RNA editing neoantigen could be identified from RNA-seq data and could be validated with mass spectrometry data in ovarian tumor samples. Furthermore, we characterized the RNA editing neoantigen profile of clinical melanoma cohorts treated with immunotherapy. Finally, REscore showed significant associations with improved overall survival in melanoma cohorts treated with immunotherapy. These findings provided novel insights of cancer biomarker and enhance our understanding of neoantigen derived from A-to-I RNA editing as well as more types of candidates for personalized cancer vaccines design in the context of cancer immunotherapy.
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Affiliation(s)
- Chi Zhou
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Zhiting Wei
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Liye Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhaoyi Yang
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Qi Liu
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
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37
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Yao H, Liang Q, Qian X, Wang J, Sham PC, Li MJ. Methods and resources to access mutation-dependent effects on cancer drug treatment. Brief Bioinform 2020; 21:1886-1903. [PMID: 31750520 DOI: 10.1093/bib/bbz109] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 07/31/2019] [Accepted: 08/01/2019] [Indexed: 12/13/2022] Open
Abstract
In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.
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Affiliation(s)
- Hongcheng Yao
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Qian Liang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xinyi Qian
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Junwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, USA
| | - Pak Chung Sham
- Center for Genomic Sciences, The University of Hong Kong, Hong Kong SAR, China.,Departments of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mulin Jun Li
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.,Department of Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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38
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Baek B, Lee H. Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data. Sci Rep 2020; 10:18951. [PMID: 33144687 PMCID: PMC7609582 DOI: 10.1038/s41598-020-76025-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/20/2020] [Indexed: 01/08/2023] Open
Abstract
Predicting the prognosis of pancreatic cancer is important because of the very low survival rates of patients with this particular cancer. Although several studies have used microRNA and gene expression profiles and clinical data, as well as images of tissues and cells, to predict cancer survival and recurrence, the accuracies of these approaches in the prediction of high-risk pancreatic adenocarcinoma (PAAD) still need to be improved. Accordingly, in this study, we proposed two biological features based on multi-omics datasets to predict survival and recurrence among patients with PAAD. First, the clonal expansion of cancer cells with somatic mutations was used to predict prognosis. Using whole-exome sequencing data from 134 patients with PAAD from The Cancer Genome Atlas (TCGA), we found five candidate genes that were mutated in the early stages of tumorigenesis with high cellular prevalence (CP). CDKN2A, TP53, TTN, KCNJ18, and KRAS had the highest CP values among the patients with PAAD, and survival and recurrence rates were significantly different between the patients harboring mutations in these candidate genes and those harboring mutations in other genes (p = 2.39E-03, p = 8.47E-04, respectively). Second, we generated an autoencoder to integrate the RNA sequencing, microRNA sequencing, and DNA methylation data from 134 patients with PAAD from TCGA. The autoencoder robustly reduced the dimensions of these multi-omics data, and the K-means clustering method was then used to cluster the patients into two subgroups. The subgroups of patients had significant differences in survival and recurrence (p = 1.41E-03, p = 4.43E-04, respectively). Finally, we developed a prediction model for prognosis using these two biological features and clinical data. When support vector machines, random forest, logistic regression, and L2 regularized logistic regression were used as prediction models, logistic regression analysis generally revealed the best performance for both disease-free survival (DFS) and overall survival (OS) (accuracy [ACC] = 0.762 and area under the curve [AUC] = 0.795 for DFS; ACC = 0.776 and AUC = 0.769 for OS). Thus, we could classify patients with a high probability of recurrence and at a high risk of poor outcomes. Our study provides insights into new personalized therapies on the basis of mutation status and multi-omics data.
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Affiliation(s)
- Bin Baek
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea.
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea.
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39
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Ghinda DC, Yang Y, Wu S, Lu J, Su L, Damiani S, Tumati S, Jansen G, Duffau H, Wu JS, Northoff G. Personalized Multimodal Demarcation of Peritumoral Tissue in Glioma. JCO Precis Oncol 2020; 4:1128-1140. [PMID: 35050774 DOI: 10.1200/po.20.00115] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Gliomas are life-threatening brain tumors, and the extent of surgical resection is one of the strongest influences on survival rate. However, the proper distinction of infiltrated tissue remains elusive. The aim of this study was to use multimodal analyses to demarcate peritumoral tissue (PT) from tumoral (TT) and healthy tissue (HT). METHODS A total of 40 patients with histologically confirmed glioma were recruited. We analyzed resting-state functional magnetic resonance imaging (rs-fMRI) using the voxel-based mean blood-oxygen-level-dependent (BOLD) signal and the corresponding structural MRI (s-MRI) alongside RNA sequencing, whole-exome sequencing, and histology results of biopsy samples obtained from PT, HT, and TT. RESULTS We demarcated a functionally defined PT area where the mean BOLD signal gradually decreased near the edge of the tumor and extended beyond the TT borders (as defined by s-MRI), which was confirmed on a case-by-case basis. Correspondingly, genetic analyses showed a gene expression pattern and mutational landscape of the PT that were distinct from that seen in HT and TT. The genetic characterization of PT relative to HT and TT converged with the MRI-defined PT zones. This was confirmed in three individual cases after additional histologic analysis. A wider PT was associated with a longer progression-free survival, which suggests PT might act as an intermediate area between TT and HT. CONCLUSION Combined multimodal imaging and genetic analyses can allow for an objective demarcation of the PT in glioma and a robust classification of the degree of infiltration of the PT. These findings could help improve both neurosurgical resection and radio-oncologic therapy.
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Affiliation(s)
- Diana C Ghinda
- Department of Neurosurgery, The Ottawa Hospital, University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.,Glioma Surgery Division, Neurologic Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.,Mind, Brain Imaging, and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
| | - Yufei Yang
- Genetron Health (Beijing) Co Ltd, Beijing, China
| | - Shuai Wu
- Glioma Surgery Division, Neurologic Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Junfeng Lu
- Glioma Surgery Division, Neurologic Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lan Su
- Genetron Health (Beijing) Co Ltd, Beijing, China
| | - Stefano Damiani
- Department of Brain and Behavioral Science, University of Pavia, Pavia, Italy
| | - Shankar Tumati
- Mind, Brain Imaging, and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
| | - Gerard Jansen
- Department of Neuropathology, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Hugues Duffau
- Department of Neurosurgery, Hôpital Gui de Chauliac, Montpellier University Medical Center, Montpellier, France.,Brain Plasticity, Stem Cells, and Glial Tumors Team, National Institute for Health and Medical Research, Montpellier, France
| | - Jin-Song Wu
- Glioma Surgery Division, Neurologic Surgery Department, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Georg Northoff
- Mind, Brain Imaging, and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
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40
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Tsirvouli E, Touré V, Niederdorfer B, Vázquez M, Flobak Å, Kuiper M. A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines. Front Mol Biosci 2020; 7:502573. [PMID: 33195403 PMCID: PMC7581946 DOI: 10.3389/fmolb.2020.502573] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 09/22/2020] [Indexed: 11/23/2022] Open
Abstract
Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.
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Affiliation(s)
- Eirini Tsirvouli
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Barbara Niederdorfer
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Miguel Vázquez
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Åsmund Flobak
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- The Cancer Clinic, St. Olav’s University Hospital, Trondheim, Norway
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
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41
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Martínez-Jiménez F, Muiños F, Sentís I, Deu-Pons J, Reyes-Salazar I, Arnedo-Pac C, Mularoni L, Pich O, Bonet J, Kranas H, Gonzalez-Perez A, Lopez-Bigas N. A compendium of mutational cancer driver genes. Nat Rev Cancer 2020; 20:555-572. [PMID: 32778778 DOI: 10.1038/s41568-020-0290-x] [Citation(s) in RCA: 529] [Impact Index Per Article: 132.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/02/2020] [Indexed: 12/11/2022]
Abstract
A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.
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Affiliation(s)
- Francisco Martínez-Jiménez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ferran Muiños
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Inés Sentís
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jordi Deu-Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Iker Reyes-Salazar
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Claudia Arnedo-Pac
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Loris Mularoni
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Oriol Pich
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jose Bonet
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Hanna Kranas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Abel Gonzalez-Perez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.
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42
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Mateo L, Duran-Frigola M, Gris-Oliver A, Palafox M, Scaltriti M, Razavi P, Chandarlapaty S, Arribas J, Bellet M, Serra V, Aloy P. Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns. Genome Med 2020; 12:78. [PMID: 32907621 PMCID: PMC7488324 DOI: 10.1186/s13073-020-00774-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/11/2020] [Indexed: 12/19/2022] Open
Abstract
Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.
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Affiliation(s)
- Lidia Mateo
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Albert Gris-Oliver
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Marta Palafox
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain
| | - Maurizio Scaltriti
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Department of Pathology, MSKCC, New York, NY, 10065, USA
| | - Pedram Razavi
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Sarat Chandarlapaty
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, NY, 10065, USA.,Breast Medicine Service, Department of Medicine, MSKCC and Weill-Cornell Medical College, New York, NY, 10065, USA
| | - Joaquin Arribas
- Growth Factors Laboratory, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Biochemistry and Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Meritxell Bellet
- Breast Cancer Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,Department of Medical Oncology, Hospital Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
| | - Violeta Serra
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology, Barcelona, Catalonia, Spain.,CIBERONC, Barcelona, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain. .,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer. Cell Rep 2020; 28:938-948.e6. [PMID: 31340155 DOI: 10.1016/j.celrep.2019.06.067] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/01/2019] [Accepted: 06/18/2019] [Indexed: 12/14/2022] Open
Abstract
The phenotypic effect of perturbing a gene's activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional relationship between two genes-synthetic lethality (SL). Here, we define the more general concept of "survival-associated pairwise gene expression states" (SPAGEs) as gene pairs whose joint expression levels are associated with survival. We describe a data-driven approach called SPAGE-finder that when applied to The Cancer Genome Atlas (TCGA) data identified 71,946 SPAGEs spanning 12 distinct types, only a minority of which are SLs. The detected SPAGEs explain cancer driver genes' tissue specificity and differences in patients' response to drugs and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer SPAGEs and lay a conceptual basis for future studies of SPAGEs and their translational applications.
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Patel SK, George B, Rai V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Front Pharmacol 2020; 11:1177. [PMID: 32903628 PMCID: PMC7438594 DOI: 10.3389/fphar.2020.01177] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 07/20/2020] [Indexed: 12/13/2022] Open
Abstract
The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.
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Affiliation(s)
- Sandip Kumar Patel
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
- Buck Institute for Research on Aging, Novato, CA, United States
| | - Bhawana George
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vineeta Rai
- Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, NC, United States
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Zhang Z, Zhou L, Xie N, Nice EC, Zhang T, Cui Y, Huang C. Overcoming cancer therapeutic bottleneck by drug repurposing. Signal Transduct Target Ther 2020; 5:113. [PMID: 32616710 PMCID: PMC7331117 DOI: 10.1038/s41392-020-00213-8] [Citation(s) in RCA: 262] [Impact Index Per Article: 65.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/03/2020] [Accepted: 06/04/2020] [Indexed: 02/06/2023] Open
Abstract
Ever present hurdles for the discovery of new drugs for cancer therapy have necessitated the development of the alternative strategy of drug repurposing, the development of old drugs for new therapeutic purposes. This strategy with a cost-effective way offers a rare opportunity for the treatment of human neoplastic disease, facilitating rapid clinical translation. With an increased understanding of the hallmarks of cancer and the development of various data-driven approaches, drug repurposing further promotes the holistic productivity of drug discovery and reasonably focuses on target-defined antineoplastic compounds. The "treasure trove" of non-oncology drugs should not be ignored since they could target not only known but also hitherto unknown vulnerabilities of cancer. Indeed, different from targeted drugs, these old generic drugs, usually used in a multi-target strategy may bring benefit to patients. In this review, aiming to demonstrate the full potential of drug repurposing, we present various promising repurposed non-oncology drugs for clinical cancer management and classify these candidates into their proposed administration for either mono- or drug combination therapy. We also summarize approaches used for drug repurposing and discuss the main barriers to its uptake.
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Affiliation(s)
- Zhe Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, 610041, Chengdu, China
| | - Li Zhou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, 610041, Chengdu, China
| | - Na Xie
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, 610041, Chengdu, China
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia
| | - Tao Zhang
- The School of Biological Science and Technology, Chengdu Medical College, 610083, Chengdu, China.
- Department of Oncology, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, 610051, Sichuan, China.
| | - Yongping Cui
- Cancer Institute, Peking University Shenzhen Hospital, Shenzhen Peking University-the Hong Kong University of Science and Technology (PKU-HKUST) Medical Center, and Cancer Institute, Shenzhen Bay Laboratory Shenzhen, 518035, Shenzhen, China.
- Department of Pathology & Shanxi Key Laboratory of Carcinogenesis and Translational Research on Esophageal Cancer, Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
| | - Canhua Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, 610041, Chengdu, China.
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, Sichuan, China.
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46
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Perales-Patón J, Di Domenico T, Fustero-Torre C, Piñeiro-Yáñez E, Carretero-Puche C, Tejero H, Valencia A, Gómez-López G, Al-Shahrour F. vulcanSpot: a tool to prioritize therapeutic vulnerabilities in cancer. Bioinformatics 2020; 35:4846-4848. [PMID: 31173067 PMCID: PMC6853644 DOI: 10.1093/bioinformatics/btz465] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/21/2019] [Accepted: 06/04/2019] [Indexed: 01/29/2023] Open
Abstract
Motivation Genetic alterations lead to tumor progression and cell survival but also uncover cancer-specific vulnerabilities on gene dependencies that can be therapeutically exploited. Results vulcanSpot is a novel computational approach implemented to expand the therapeutic options in cancer beyond known-driver genes unlocking alternative ways to target undruggable genes. The method integrates genome-wide information provided by massive screening experiments to detect genetic vulnerabilities associated to tumors. Then, vulcanSpot prioritizes drugs to target cancer-specific gene dependencies using a weighted scoring system based on well known drug-gene relationships and drug repositioning strategies. Availability and implementation http://www.vulcanspot.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Javier Perales-Patón
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Tomás Di Domenico
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Coral Fustero-Torre
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Elena Piñeiro-Yáñez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Carlos Carretero-Puche
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Héctor Tejero
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Alfonso Valencia
- Computational Biology Life Sciences Group, Barcelona Supercomputing Centre, Barcelona, Spain
| | - Gonzalo Gómez-López
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
| | - Fátima Al-Shahrour
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid 28029, Spain
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47
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Dinger TF, Chen O, Dittfeld C, Hetze L, Hüther M, Wondrak M, Löck S, Eicheler W, Breier G, Kunz-Schughart LA. Microenvironmentally-driven Plasticity of CD44 isoform expression determines Engraftment and Stem-like Phenotype in CRC cell lines. Am J Cancer Res 2020; 10:7599-7621. [PMID: 32685007 PMCID: PMC7359088 DOI: 10.7150/thno.39893] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 04/14/2020] [Indexed: 02/07/2023] Open
Abstract
Theranostic biomarkers for putative cancer stem-like cells (CSC) in colorectal cancer (CRC) are of particular interest in translational research to develop patient-individualized treatment strategies. Surface proteins still under debate are CD44 and CD133. The structural and functional diversity of these antigens, as well as their plasticity, has only just begun to be understood. Our study aimed to gain novel insight into the plasticity of CD133/CD44, thereby proving the hypothesis of marker-associated tumorigenic and non-tumorigenic phenotypes to be environmentally driven. Methods: CD133/CD44 profiles of 20 CRC cell lines were monitored; three models with distinct surface patterns in vitro were systematically examined. CD133/CD44 subpopulations were isolated by FACS and analyzed upon in vitro growth and/or in limiting dilution engraftment studies. The experimental setup included biomarker analyses on the protein (flow cytometry, Western blotting, immunofluorescence) and mRNA levels (RT-/qPCR) as well as CD44 gene sequencing. Results: In general, we found that (i) the in vitro CD133/CD44 pattern never determined engraftment and (ii) the CD133/CD44 population distributions harmonized under in vivo conditions. The LS1034 cell line appeared as a unique model due to its de novo in vivo presentation of CD44. CD44v8-10 was identified as main transcript, which was stronger expressed in primary human CRC than in normal colon tissues. Biomarker pattern of LS1034 cells in vivo reflected secondary engraftment: the tumorigenic potential was highest in CD133+/CD44+, intermediate in CD133+/CD44- and entirely lost in CD133-/CD44- subfractions. Both CD44+ and CD44- LS1034 cells gave rise to tumorigenic and non-tumorigenic progeny and were convertible - but only as long as they expressed CD133 in vivo. The highly tumorigenic CD133+/CD44(v8-10)+ LS1034 cells were localized in well-oxygenated perivascular but not hypoxic regions. From a multitude of putative modulators, only the direct interaction with stromal fibroblasts triggered an essential, in vivo-like enhancement of CD44v8-10 presentation in vitro. Conclusion: Environmental conditions modulate CD133/CD44 phenotypes and tumorigenic potential of CRC subpopulations. The identification of fibroblasts as drivers of cancer-specific CD44 expression profile and plasticity sheds light on the limitation of per se dynamic surface antigens as biomarkers. It can also explain the location of putative CD133/CD44-positive CRC CSC in the perivascular niche, which is likely to comprise cancer-associated fibroblasts. The LS1034 in vitro/in vivo model is a valuable tool to unravel the mechanism of stromal-induced CD44v8-10 expression and identify further therapeutically relevant, mutual interrelations between microenvironment and tumorigenic phenotype.
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48
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Chen LH, Pan C, Diplas BH, Xu C, Hansen LJ, Wu Y, Chen X, Geng Y, Sun T, Sun Y, Zhang P, Wu Z, Zhang J, Li D, Zhang Y, Wu W, Wang Y, Li G, Yang J, Wang X, Xu C, Wang S, Waitkus MS, He Y, McLendon RE, Ashley DM, Yan H, Zhang L. The integrated genomic and epigenomic landscape of brainstem glioma. Nat Commun 2020; 11:3077. [PMID: 32555164 PMCID: PMC7299931 DOI: 10.1038/s41467-020-16682-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 05/14/2020] [Indexed: 12/15/2022] Open
Abstract
Brainstem gliomas are a heterogeneous group of tumors that encompass both benign tumors cured with surgical resection and highly lethal cancers with no efficacious therapies. We perform a comprehensive study incorporating epigenetic and genomic analyses on a large cohort of brainstem gliomas, including Diffuse Intrinsic Pontine Gliomas. Here we report, from DNA methylation data, distinct clusters termed H3-Pons, H3-Medulla, IDH, and PA-like, each associated with unique genomic and clinical profiles. The majority of tumors within H3-Pons and-H3-Medulla harbors H3F3A mutations but shows distinct methylation patterns that correlate with anatomical localization within the pons or medulla, respectively. Clinical data show significantly different overall survival between these clusters, and pathway analysis demonstrates different oncogenic mechanisms in these samples. Our findings indicate that the integration of genetic and epigenetic data can facilitate better understanding of brainstem gliomagenesis and classification, and guide future studies for the development of novel treatments for this disease.
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Affiliation(s)
- Lee H Chen
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA.,Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA
| | - Changcun Pan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China.,Beijing Key Laboratory of Brain Tumor, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Bill H Diplas
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA.,Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA
| | - Cheng Xu
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA.,Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Landon J Hansen
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA.,Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA
| | - Yuliang Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Xin Chen
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Yibo Geng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Tao Sun
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Yu Sun
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Zhen Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Junting Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Deling Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Yang Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Wenhao Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Yu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China
| | - Guangyu Li
- Genetron Health (Beijing) Co. Ltd, 102208, Beijing, China
| | - Jie Yang
- Genetron Health (Beijing) Co. Ltd, 102208, Beijing, China
| | - Xiaoyue Wang
- State Key Laboratory of Medical Molecular Biology, Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, 100005, Beijing, China
| | - Ce Xu
- Genetron Health (Beijing) Co. Ltd, 102208, Beijing, China
| | - Sizhen Wang
- Genetron Health (Beijing) Co. Ltd, 102208, Beijing, China
| | - Matthew S Waitkus
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA.,Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA
| | - Yiping He
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA.,Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA
| | - Roger E McLendon
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA
| | - David M Ashley
- Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA
| | - Hai Yan
- Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA. .,Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA.
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China. .,China National Clinical Research Center for Neurological Diseases, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China. .,Beijing Key Laboratory of Brain Tumor, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China.
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49
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Molecular matching and treatment strategies for advanced stage lung cancer at Dartmouth-Hitchcock Medical Center: A three-year review of a Molecular Tumor Board. Pract Lab Med 2020; 21:e00174. [PMID: 32613070 PMCID: PMC7322356 DOI: 10.1016/j.plabm.2020.e00174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 05/20/2020] [Accepted: 06/08/2020] [Indexed: 11/21/2022] Open
Abstract
Matching of actionable tumor mutations with targeted therapy increases response rates and prolongs survival in lung cancer patients. Drug development and trials targeting genetic alterations are expanding rapidly. We describe the role of a Molecular Tumor Board (MTB) in the design of molecularly informed treatment strategies in our lung cancer patient population. Tumor DNA was sequenced using a 50-gene targeted next-generation sequencing panel. Cases were evaluated by a multidisciplinary MTB who suggested a course of treatment based on each patient's molecular findings. During a three-year period, 21 lung cancer patients were presented at the MTB. All patients lacked common activating EGFR mutations and ALK rearrangements. One patient had Stage IIIb disease; all others were Stage IV; 18 patients had received ≥1 prior line of therapy (range 0-5). Suggestions for treatment with a targeted therapy were made for 19/21 (90.5%) patients, and four patients (21%) underwent treatment with a targeted agent, two as part of a clinical trial. Identified barriers to treatment with targeted therapy included: ineligibility for clinical trials (n = 2), lack of interest in study/distance to travel (n = 2), lack of disease progression (n = 2), poor performance status (n = 5), decision to treat next with immunotherapy (n = 3), and unknown (n = 1). For the majority of lung cancer patients, the MTB provided recommendations based on tumor genetic profiles. Identified barriers to treatment suggest that presentation to the MTB at earlier stages of disease may increase the number of patients eligible for treatment with a genetically informed targeted agent.
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50
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Campos-Laborie FJ, Risueño A, Ortiz-Estévez M, Rosón-Burgo B, Droste C, Fontanillo C, Loos R, Sánchez-Santos JM, Trotter MW, De Las Rivas J. DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling. Bioinformatics 2020; 35:3651-3662. [PMID: 30824909 PMCID: PMC6761977 DOI: 10.1093/bioinformatics/btz148] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 02/09/2019] [Accepted: 02/28/2019] [Indexed: 02/07/2023] Open
Abstract
Motivation Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation. Results DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification. Availability and implementation DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- F J Campos-Laborie
- Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain
| | - A Risueño
- Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain
| | - M Ortiz-Estévez
- Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain
| | - B Rosón-Burgo
- Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain
| | - C Droste
- Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain
| | - C Fontanillo
- Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain
| | - R Loos
- Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain
| | - J M Sánchez-Santos
- Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain
| | - M W Trotter
- Celgene Institute for Translational Research Europe (CITRE), Parque Científico y Tecnológico Cartuja 93, Sevilla, Spain
| | - J De Las Rivas
- Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno s/n, Salamanca, Spain
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